diff --git a/trunk/PQP/Makefile b/trunk/PQP/Makefile
deleted file mode 100644
index 2790aafa650c30eda371dbdbdaed8d2906b64d4f..0000000000000000000000000000000000000000
--- a/trunk/PQP/Makefile
+++ /dev/null
@@ -1,36 +0,0 @@
-all: installed
-
-#
-# Download, extract and compile from a released tarball:
-#
-TARBALL = build/pqp-1.3.tar.gz
-TARBALL_URL = http://gamma.cs.unc.edu/software/downloads/SSV/pqp-1.3.tar.gz
-TARBALL_PATCH = pqp.diff
-INITIAL_DIR = build/pqp-1.3
-SOURCE_DIR = build/pqp-tar
-include $(shell rospack find mk)/download_unpack_build.mk
-
-INSTALL_DIR = PQP
-CMAKE = cmake
-CMAKE_ARGS = -D CMAKE_BUILD_TYPE="Release" -D CMAKE_INSTALL_PREFIX=`rospack find PQP`/$(INSTALL_DIR)
-MAKE = make
-
-installed: wiped $(SOURCE_DIR)/unpacked
- cd $(SOURCE_DIR)/PQP_v1.3 && make $(ROS_PARALLEL_JOBS)
- mkdir -p $(INSTALL_DIR)/lib
- mkdir -p $(INSTALL_DIR)/include
- mkdir -p $(INSTALL_DIR)/include/PQP
- cp -r $(SOURCE_DIR)/PQP_v1.3/include/*.h $(INSTALL_DIR)/include
- cp -r $(SOURCE_DIR)/PQP_v1.3/lib/*.a $(INSTALL_DIR)/lib
- touch installed
-
-clean:
- rm -rf build
- rm -rf $(INSTALL_DIR) installed
-
-wiped: Makefile
- make wipe
- touch wiped
-
-wipe: clean
- rm -rf build patched
diff --git a/trunk/PQP/PQP/include/BV.h b/trunk/PQP/PQP/include/BV.h
deleted file mode 100644
index cfe42c73785d1ebad055dfca953870eeb32d5a19..0000000000000000000000000000000000000000
--- a/trunk/PQP/PQP/include/BV.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_BV_H
-#define PQP_BV_H
-
-#include <math.h>
-#include "Tri.h"
-#include "PQP_Compile.h"
-
-struct BV
-{
- PQP_REAL R[3][3]; // orientation of RSS & OBB
-
-#if PQP_BV_TYPE & RSS_TYPE
- PQP_REAL Tr[3]; // position of rectangle
- PQP_REAL l[2]; // side lengths of rectangle
- PQP_REAL r; // radius of sphere summed with rectangle to form RSS
-#endif
-
-#if PQP_BV_TYPE & OBB_TYPE
- PQP_REAL To[3]; // position of obb
- PQP_REAL d[3]; // (half) dimensions of obb
-#endif
-
- int first_child; // positive value is index of first_child bv
- // negative value is -(index + 1) of triangle
-
- BV();
- ~BV();
- int Leaf() { return first_child < 0; }
- PQP_REAL GetSize();
- void FitToTris(PQP_REAL O[3][3], Tri *tris, int num_tris);
-};
-
-inline
-PQP_REAL
-BV::GetSize()
-{
-#if PQP_BV_TYPE & RSS_TYPE
- return (sqrt(l[0]*l[0] + l[1]*l[1]) + 2*r);
-#else
- return (d[0]*d[0] + d[1]*d[1] + d[2]*d[2]);
-#endif
-}
-
-int
-BV_Overlap(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2);
-
-#if PQP_BV_TYPE & RSS_TYPE
-PQP_REAL
-BV_Distance(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2);
-#endif
-
-#endif
-
-
diff --git a/trunk/PQP/PQP/include/PQP.h b/trunk/PQP/PQP/include/PQP.h
deleted file mode 100644
index f6f3e539581c4e118d2afecace8e8a35d10c3db2..0000000000000000000000000000000000000000
--- a/trunk/PQP/PQP/include/PQP.h
+++ /dev/null
@@ -1,338 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_H
-#define PQP_H
-
-#include "PQP_Compile.h"
-#include "PQP_Internal.h"
-
-//----------------------------------------------------------------------------
-//
-// PQP API Return Values
-//
-//----------------------------------------------------------------------------
-
-const int PQP_OK = 0;
- // Used by all API routines upon successful completion except
- // constructors and destructors
-
-const int PQP_ERR_MODEL_OUT_OF_MEMORY = -1;
- // Returned when an API function cannot obtain enough memory to
- // store or process a PQP_Model object.
-
-const int PQP_ERR_OUT_OF_MEMORY = -2;
- // Returned when a PQP query cannot allocate enough storage to
- // compute or hold query information. In this case, the returned
- // data should not be trusted.
-
-const int PQP_ERR_UNPROCESSED_MODEL = -3;
- // Returned when an unprocessed model is passed to a function which
- // expects only processed models, such as PQP_Collide() or
- // PQP_Distance().
-
-const int PQP_ERR_BUILD_OUT_OF_SEQUENCE = -4;
- // Returned when:
- // 1. AddTri() is called before BeginModel().
- // 2. BeginModel() is called immediately after AddTri().
- // This error code is something like a warning: the invoked
- // operation takes place anyway, and PQP does what makes "most
- // sense", but the returned error code may tip off the client that
- // something out of the ordinary is happenning.
-
-const int PQP_ERR_BUILD_EMPTY_MODEL = -5;
- // Returned when EndModel() is called on a model to which no
- // triangles have been added. This is similar in spirit to the
- // OUT_OF_SEQUENCE return code, except that the requested operation
- // has FAILED -- the model remains "unprocessed", and the client may
- // NOT use it in queries.
-
-//----------------------------------------------------------------------------
-//
-// PQP_REAL
-//
-// The floating point type used throughout the package. The type is defined
-// in PQP_Compile.h, and by default is "double"
-//
-//----------------------------------------------------------------------------
-
-//----------------------------------------------------------------------------
-//
-// PQP_Model
-//
-// A PQP_Model stores geometry to be used in a proximity query.
-// The geometry is loaded with a call to BeginModel(), at least one call to
-// AddTri(), and then a call to EndModel().
-//
-// // create a two triangle model, m
-//
-// PQP_Model m;
-//
-// PQP_REAL p1[3],p2[3],p3[3]; // 3 points will make triangle p
-// PQP_REAL q1[3],q2[3],q3[3]; // another 3 points for triangle q
-//
-// // some initialization of these vertices not shown
-//
-// m.BeginModel(); // begin the model
-// m.AddTri(p1,p2,p3,0); // add triangle p
-// m.AddTri(q1,q2,q3,1); // add triangle q
-// m.EndModel(); // end (build) the model
-//
-// The last parameter of AddTri() is the number to be associated with the
-// triangle. These numbers are used to identify the triangles that overlap.
-//
-// AddTri() copies into the PQP_Model the data pointed to by the three vertex
-// pointers, so that it is safe to delete vertex data after you have
-// passed it to AddTri().
-//
-//----------------------------------------------------------------------------
-//
-// class PQP_Model - declaration contained in PQP_Internal.h
-// {
-//
-// public:
-// PQP_Model();
-// ~PQP_Model();
-//
-// int BeginModel(int num_tris = 8); // preallocate for num_tris triangles;
-// // the parameter is optional, since
-// // arrays are reallocated as needed
-//
-// int AddTri(const PQP_REAL *p1, const PQP_REAL *p2, const PQP_REAL *p3,
-// int id);
-//
-// int EndModel();
-// int MemUsage(int msg); // returns model mem usage in bytes
-// // prints message to stderr if msg == TRUE
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_CollideResult
-//
-// This saves and reports results from a collision query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_CollideResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // free the list of contact pairs; ordinarily this list is reused
-// // for each query, and only deleted in the destructor.
-//
-// void FreePairsList();
-//
-// // query results
-//
-// int Colliding();
-// int NumPairs();
-// int Id1(int k);
-// int Id2(int k);
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Collide() - detects collision between two PQP_Models
-//
-//
-// Declare a PQP_CollideResult struct and pass its pointer to collect
-// collision data.
-//
-// [R1, T1] is the placement of model 1 in the world &
-// [R2, T2] is the placement of model 2 in the world.
-// The columns of each 3x3 matrix are the basis vectors for the model
-// in world coordinates, and the matrices are in row-major order:
-// R(row r, col c) = R[r][c].
-//
-// If PQP_ALL_CONTACTS is the flag value, after calling PQP_Collide(),
-// the PQP_CollideResult object will contain an array with all
-// colliding triangle pairs. Suppose CR is a pointer to the
-// PQP_CollideResult object. The number of pairs is gotten from
-// CR->NumPairs(), and the ids of the 15'th pair of colliding
-// triangles is gotten from CR->Id1(14) and CR->Id2(14).
-//
-// If PQP_FIRST_CONTACT is the flag value, the PQP_CollideResult array
-// will only get the first colliding triangle pair found. Thus
-// CR->NumPairs() will be at most 1, and if 1, CR->Id1(0) and
-// CR->Id2(0) give the ids of the colliding triangle pair.
-//
-//----------------------------------------------------------------------------
-
-const int PQP_ALL_CONTACTS = 1; // find all pairwise intersecting triangles
-const int PQP_FIRST_CONTACT = 2; // report first intersecting tri pair found
-
-int
-PQP_Collide(PQP_CollideResult *result,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- int flag = PQP_ALL_CONTACTS);
-
-
-#if PQP_BV_TYPE & RSS_TYPE // this is true by default,
- // and explained in PQP_Compile.h
-
-//----------------------------------------------------------------------------
-//
-// PQP_DistanceResult
-//
-// This saves and reports results from a distance query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_DistanceResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // The following distance and points established the minimum distance
-// // for the models, within the relative and absolute error bounds
-// // specified.
-//
-// PQP_REAL Distance();
-// const PQP_REAL *P1(); // pointers to three PQP_REALs
-// const PQP_REAL *P2();
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Distance() - computes the distance between two PQP_Models
-//
-//
-// Declare a PQP_DistanceResult struct and pass its pointer to collect
-// distance information.
-//
-// "rel_err" is the relative error margin from actual distance.
-// "abs_err" is the absolute error margin from actual distance. The
-// smaller of the two will be satisfied, so set one large to nullify
-// its effect.
-//
-// "qsize" is an optional parameter controlling the size of a priority
-// queue used to direct the search for closest points. A larger queue
-// can help the algorithm discover the minimum with fewer steps, but
-// will increase the cost of each step. It is not beneficial to increase
-// qsize if the application has frame-to-frame coherence, i.e., the
-// pair of models take small steps between each call, since another
-// speedup trick already accelerates this situation with no overhead.
-//
-// However, a queue size of 100 to 200 has been seen to save time in a
-// planning application with "non-coherent" placements of models.
-//
-//----------------------------------------------------------------------------
-
-int
-PQP_Distance(PQP_DistanceResult *result,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL rel_err, PQP_REAL abs_err,
- int qsize = 2);
-
-//----------------------------------------------------------------------------
-//
-// PQP_ToleranceResult
-//
-// This saves and reports results from a tolerance query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_ToleranceResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // If the models are closer than ( <= ) tolerance, these points
-// // and distance were what established this. Otherwise,
-// // distance and point values are not meaningful.
-//
-// PQP_REAL Distance();
-// const PQP_REAL *P1();
-// const PQP_REAL *P2();
-//
-// // boolean says whether models are closer than tolerance distance
-//
-// int CloserThanTolerance();
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Tolerance() - checks if distance between PQP_Models is <= tolerance
-//
-//
-// Declare a PQP_ToleranceResult and pass its pointer to collect
-// tolerance information.
-//
-// The algorithm returns whether the true distance is <= or >
-// "tolerance". This routine does not simply compute true distance
-// and compare to the tolerance - models can often be shown closer or
-// farther than the tolerance more trivially. In most cases this
-// query should run faster than a distance query would on the same
-// models and configurations.
-//
-// "qsize" again controls the size of a priority queue used for
-// searching. Not setting qsize is the current recommendation, since
-// increasing it has only slowed down our applications.
-//
-//----------------------------------------------------------------------------
-
-int
-PQP_Tolerance(PQP_ToleranceResult *res,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL tolerance,
- int qsize = 2);
-
-#endif
-#endif
-
-
-
-
-
-
diff --git a/trunk/PQP/PQP/include/PQP_Compile.h b/trunk/PQP/PQP/include/PQP_Compile.h
deleted file mode 100644
index f76c981370ecd5833f257fa7e14564d5704ce2e6..0000000000000000000000000000000000000000
--- a/trunk/PQP/PQP/include/PQP_Compile.h
+++ /dev/null
@@ -1,101 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_COMPILE_H
-#define PQP_COMPILE_H
-
-// prevents compiler warnings when PQP_REAL is float
-
-#include <math.h>
-inline float sqrt(float x) { return (float)sqrt((double)x); }
-inline float cos(float x) { return (float)cos((double)x); }
-inline float sin(float x) { return (float)sin((double)x); }
-inline float fabs(float x) { return (float)fabs((double)x); }
-
-//-------------------------------------------------------------------------
-//
-// PQP_REAL
-//
-// This is the floating point type used throughout PQP. doubles are
-// recommended, both for their precision and because the software has
-// mainly been tested using them. However, floats appear to be faster
-// (by 60% on some machines).
-//
-//-------------------------------------------------------------------------
-
-typedef double PQP_REAL;
-
-//-------------------------------------------------------------------------
-//
-// PQP_BV_TYPE
-//
-// PQP introduces a bounding volume (BV) type known as the "rectangle
-// swept sphere" (RSS) - the volume created by sweeping a sphere so
-// that its center visits every point on a rectangle; it looks
-// something like a rounded box.
-//
-// In our experiments, the RSS type is comparable to the oriented
-// bounding box (OBB) in terms of the number of BV-pair and triangle-pair
-// tests incurred. However, with our present implementations, overlap
-// tests are cheaper for OBBs, while distance tests are cheaper for the
-// RSS type (we used a public gjk implementation for the OBB distance test).
-//
-// Consequently, PQP is configured to use the RSS type in distance and
-// tolerance queries (which use BV distance tests) and to use OBBs for
-// collision queries (which use BV overlap tests). Using both requires six
-// more PQP_REALs per BV node than using just one type.
-//
-// To save space, you can configure PQP to use only one type, however,
-// with RSS alone, collision queries will typically be slower. With OBB's
-// alone, distance and tolerance queries are currently not supported, since
-// we have not developed our own OBB distance test. The three options are:
-//
-// #define PQP_BV_TYPE RSS_TYPE
-// #define PQP_BV_TYPE OBB_TYPE
-// #define PQP_BV_TYPE RSS_TYPE | OBB_TYPE
-//
-//-------------------------------------------------------------------------
-
-#define RSS_TYPE 1
-#define OBB_TYPE 2
-
-#define PQP_BV_TYPE RSS_TYPE | OBB_TYPE
-
-#endif
diff --git a/trunk/PQP/PQP/include/PQP_Internal.h b/trunk/PQP/PQP/include/PQP_Internal.h
deleted file mode 100644
index 90cedcfa2d7f3ec96c3d378774c48f9e9a5a6d29..0000000000000000000000000000000000000000
--- a/trunk/PQP/PQP/include/PQP_Internal.h
+++ /dev/null
@@ -1,203 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include "Tri.h"
-#include "BV.h"
-
-class PQP_Model
-{
-
-public:
-
- int build_state;
-
- Tri *tris;
- int num_tris;
- int num_tris_alloced;
-
- BV *b;
- int num_bvs;
- int num_bvs_alloced;
-
- Tri *last_tri; // closest tri on this model in last distance test
-
- BV *child(int n) { return &b[n]; }
-
- PQP_Model();
- ~PQP_Model();
-
- int BeginModel(int num_tris = 8); // preallocate for num_tris triangles;
- // the parameter is optional, since
- // arrays are reallocated as needed
- int AddTri(const PQP_REAL *p1, const PQP_REAL *p2, const PQP_REAL *p3,
- int id);
- int EndModel();
- int MemUsage(int msg); // returns model mem usage.
- // prints message to stderr if msg == TRUE
-};
-
-struct CollisionPair
-{
- int id1;
- int id2;
-};
-
-struct PQP_CollideResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- int num_pairs_alloced;
- int num_pairs;
- CollisionPair *pairs;
-
- void SizeTo(int n);
- void Add(int i1, int i2);
-
- PQP_CollideResult();
- ~PQP_CollideResult();
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // free the list of contact pairs; ordinarily this list is reused
- // for each query, and only deleted in the destructor.
-
- void FreePairsList();
-
- // query results
-
- int Colliding() { return (num_pairs > 0); }
- int NumPairs() { return num_pairs; }
- int Id1(int k) { return pairs[k].id1; }
- int Id2(int k) { return pairs[k].id2; }
-};
-
-#if PQP_BV_TYPE & RSS_TYPE // distance/tolerance are only available with RSS
-
-struct PQP_DistanceResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- PQP_REAL rel_err;
- PQP_REAL abs_err;
-
- PQP_REAL distance;
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- int qsize;
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // The following distance and points established the minimum distance
- // for the models, within the relative and absolute error bounds
- // specified.
- // Points are defined: PQP_REAL p1[3], p2[3];
-
- PQP_REAL Distance() { return distance; }
- const PQP_REAL *P1() { return p1; }
- const PQP_REAL *P2() { return p2; }
-};
-
-struct PQP_ToleranceResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- int closer_than_tolerance;
- PQP_REAL tolerance;
-
- PQP_REAL distance;
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- int qsize;
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // If the models are closer than ( <= ) tolerance, these points
- // and distance were what established this. Otherwise,
- // distance and point values are not meaningful.
-
- PQP_REAL Distance() { return distance; }
- const PQP_REAL *P1() { return p1; }
- const PQP_REAL *P2() { return p2; }
-
- // boolean says whether models are closer than tolerance distance
-
- int CloserThanTolerance() { return closer_than_tolerance; }
-};
-
-#endif
diff --git a/trunk/PQP/PQP/include/Tri.h b/trunk/PQP/PQP/include/Tri.h
deleted file mode 100644
index 496cddd928e5ab4fe8c5b1046f55f2a6079f4c71..0000000000000000000000000000000000000000
--- a/trunk/PQP/PQP/include/Tri.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_TRI_H
-#define PQP_TRI_H
-
-#include "PQP_Compile.h"
-
-struct Tri
-{
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- PQP_REAL p3[3];
- int id;
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-1.3.tar.gz b/trunk/PQP/build/pqp-1.3.tar.gz
deleted file mode 100644
index 5d6a386167dbc1fdc61ae05638d30ae37f8b2e69..0000000000000000000000000000000000000000
Binary files a/trunk/PQP/build/pqp-1.3.tar.gz and /dev/null differ
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/Makefile b/trunk/PQP/build/pqp-tar/PQP_v1.3/Makefile
deleted file mode 100644
index 5f1f4397f3c328541c3769faf9239a83caa85fd5..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/Makefile
+++ /dev/null
@@ -1,33 +0,0 @@
-CC = g++
-
-CFLAGS = -O2 -fPIC -I.
-
-.SUFFIXES: .C .cpp
-
-OBJECTS = lib/PQP.o \
- lib/BV.o \
- lib/Build.o \
- lib/TriDist.o
-
-CLEAN = $(OBJECTS) lib/libPQP.a include/*.h
-
-library: $(OBJECTS)
- /bin/rm -f lib/libPQP.a
- ar ruv lib/libPQP.a $(OBJECTS)
- cp src/PQP.h include/
- cp src/PQP_Compile.h include/
- cp src/PQP_Internal.h include/
- cp src/BV.h include/
- cp src/Tri.h include/
-
-lib/BV.o: src/BV.cpp
- $(CC) $(CFLAGS) -c src/BV.cpp -o lib/BV.o
-lib/PQP.o: src/PQP.cpp
- $(CC) $(CFLAGS) -c src/PQP.cpp -o lib/PQP.o
-lib/Build.o: src/Build.cpp
- $(CC) $(CFLAGS) -c src/Build.cpp -o lib/Build.o
-lib/TriDist.o: src/TriDist.cpp
- $(CC) $(CFLAGS) -c src/TriDist.cpp -o lib/TriDist.o
-
-clean:
- /bin/rm -f $(CLEAN)
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.DSP b/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.DSP
deleted file mode 100644
index ddd11ad2f90227222eb51daa2a3bc9ed39ab1867..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.DSP
+++ /dev/null
@@ -1,154 +0,0 @@
-# Microsoft Developer Studio Project File - Name="PQP" - Package Owner=<4>
-# Microsoft Developer Studio Generated Build File, Format Version 5.00
-# ** DO NOT EDIT **
-
-# TARGTYPE "Win32 (x86) Static Library" 0x0104
-
-CFG=PQP - Win32 Debug
-!MESSAGE This is not a valid makefile. To build this project using NMAKE,
-!MESSAGE use the Export Makefile command and run
-!MESSAGE
-!MESSAGE NMAKE /f "PQP.MAK".
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-!MESSAGE You can specify a configuration when running NMAKE
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-!MESSAGE
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-
-# Begin Project
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-CPP=cl.exe
-
-!IF "$(CFG)" == "PQP - Win32 Release"
-
-# PROP BASE Use_MFC 0
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-# ADD CPP /nologo /W3 /GX /Ot /Ob2 /D "WIN32" /D "NDEBUG" /D "_WINDOWS" /YX /FD /c
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LIB32=link.exe -lib
-# ADD BASE LIB32 /nologo
-# ADD LIB32 /nologo
-# Begin Special Build Tool
-SOURCE=$(InputPath)
-PostBuild_Cmds=copy src\PQP.h include copy src\PQP_Internal.h include\
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-# End Special Build Tool
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-# ADD CPP /nologo /W3 /GX /Z7 /Od /D "WIN32" /D "_DEBUG" /D "_WINDOWS" /YX /FD /c
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LIB32=link.exe -lib
-# ADD BASE LIB32 /nologo
-# ADD LIB32 /nologo
-# Begin Special Build Tool
-SOURCE=$(InputPath)
-PostBuild_Cmds=copy src\PQP.h include copy src\PQP_Internal.h include\
- copy src\PQP_Compile.h include copy src\Tri.h include copy src\BV.h\
- include
-# End Special Build Tool
-
-!ENDIF
-
-# Begin Target
-
-# Name "PQP - Win32 Release"
-# Name "PQP - Win32 Debug"
-# Begin Source File
-
-SOURCE=.\src\Build.cpp
-# End Source File
-# Begin Source File
-
-SOURCE=.\src\Build.h
-# End Source File
-# Begin Source File
-
-SOURCE=.\src\BV.cpp
-# End Source File
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-SOURCE=.\src\BV.h
-# End Source File
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-SOURCE=.\src\BVTQ.h
-# End Source File
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-SOURCE=.\src\GetTime.h
-# End Source File
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-# End Source File
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-SOURCE=.\src\OBB_Disjoint.h
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-# End Source File
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-# End Source File
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-# End Source File
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-# End Source File
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-SOURCE=.\src\TriDist.cpp
-# End Source File
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-SOURCE=.\src\TriDist.h
-# End Source File
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.PLG b/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.PLG
deleted file mode 100644
index f2175cfed602dd1f4d6d8bfc196259f81c531eb8..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.PLG
+++ /dev/null
@@ -1,43 +0,0 @@
---------------------Configuration: PQP - Win32 Release--------------------
-Begining build with project "C:\WIN95\DESKTOP\PQP_v1.2.1\PQP.DSP", at root.
-Active configuration is Win32 (x86) Static Library (based on Win32 (x86) Static Library)
-
-Project's tools are:
- "32-bit C/C++ Compiler for 80x86" with flags "/nologo /ML /W3 /GX /Ot /Ob2 /D "WIN32" /D "NDEBUG" /D "_WINDOWS" /Fp"lib/PQP.pch" /YX /Fo"lib/" /Fd"lib/" /FD /c "
- "Browser Database Maker" with flags "/nologo /o"lib/PQP.bsc" "
- "Library Manager" with flags "/nologo /out:"lib\PQP.lib" "
- "Custom Build" with flags ""
- "<Component 0xa>" with flags ""
-
-Creating temp file "C:\WIN95\TEMP\RSP4244.TMP" with contents </nologo /ML /W3 /GX /Ot /Ob2 /D "WIN32" /D "NDEBUG" /D "_WINDOWS" /Fp"lib/PQP.pch" /YX /Fo"lib/" /Fd"lib/" /FD /c
-"C:\WIN95\DESKTOP\PQP_v1.2.1\src\Build.cpp"
-"C:\WIN95\DESKTOP\PQP_v1.2.1\src\BV.cpp"
-"C:\WIN95\DESKTOP\PQP_v1.2.1\src\PQP.cpp"
-"C:\WIN95\DESKTOP\PQP_v1.2.1\src\TriDist.cpp"
->
-Creating command line "cl.exe @C:\WIN95\TEMP\RSP4244.TMP"
-Creating command line "link.exe -lib /nologo /out:"lib\PQP.lib" .\lib\Build.obj .\lib\BV.obj .\lib\PQP.obj .\lib\TriDist.obj"
-Compiling...
-Build.cpp
-BV.cpp
-PQP.cpp
-TriDist.cpp
-Creating library...
-Creating temp file "C:\WIN95\TEMP\RSP4280.BAT" with contents <@echo off
-copy src\PQP.h include
-copy src\PQP_Internal.h include
-copy src\PQP_Compile.h include
-copy src\Tri.h include
-copy src\BV.h include
->
-Creating command line "C:\WIN95\TEMP\RSP4280.BAT"
-
- 1 file(s) copied
- 1 file(s) copied
- 1 file(s) copied
- 1 file(s) copied
- 1 file(s) copied
-
-
-
-PQP.lib - 0 error(s), 0 warning(s)
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.dsw b/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.dsw
deleted file mode 100644
index a1af0d1b450795b4ff9a7635668cdf7ce7f03fbe..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.dsw
+++ /dev/null
@@ -1,29 +0,0 @@
-Microsoft Developer Studio Workspace File, Format Version 5.00
-# WARNING: DO NOT EDIT OR DELETE THIS WORKSPACE FILE!
-
-###############################################################################
-
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-
-Package=<5>
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.ncb b/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.ncb
deleted file mode 100644
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Binary files a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.ncb and /dev/null differ
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.opt b/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.opt
deleted file mode 100644
index 3c0d0a739a15af438cfb023f06a35fed161d9c35..0000000000000000000000000000000000000000
Binary files a/trunk/PQP/build/pqp-tar/PQP_v1.3/PQP.opt and /dev/null differ
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/README.txt b/trunk/PQP/build/pqp-tar/PQP_v1.3/README.txt
deleted file mode 100644
index aeca6bbff93ca6f1697ec932ceda7551de9512c9..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/README.txt
+++ /dev/null
@@ -1,206 +0,0 @@
----------------------------------------------------------------------------
-
-
- PQP v. 1.3
-
- Eric Larsen, Stefan Gottschalk
- UNC - Chapel Hill Computer Science
- geom@cs.unc.edu
-
----------------------------------------------------------------------------
-Changes:
-
-1.0 - first release of library
-1.1 - fixed a bug in calculating query times on Win32 machines.
- added a demo 'falling' which can demonstrate all of the proximity
- query types.
-1.2 - altered the triangle distance routine due to a degeneracy problem
- when edges of two triangles nearly intersect.
-1.3 - now use isnan() to test for NaN, instead of a comparison that was
- sometimes optimized away.
----------------------------------------------------------------------------
-
-
-I. Introduction
-
- PQP, which stands for Proximity Query Package, is a library for three
- types of proximity queries performed on geometric models composed of
- triangles:
-
- * collision detection - detect whether two models overlap, and
- optionally, which triangles of the models
- overlap.
-
- * distance computation - compute the distance between two models,
- i. e., the length of the shortest translation
- that makes the models overlap
-
- * tolerance verification - detect whether two models are closer or
- farther than a tolerance value.
-
- By default, the library uses "RSS" bounding volumes for distance and
- tolerance queries, and OBBs for collision detection (see PQP_Compile.h).
- Descriptions of the bounding volumes and algorithms used in this package
- are contained in:
-
- Eric Larsen, Stefan Gottschalk, Ming Lin, Dinesh Manocha,
- "Fast Proximity Queries with Swept Sphere Volumes",
- Technical report TR99-018, Department of Computer Science,
- UNC Chapel Hill
-
- S. Gottschalk, M. C. Lin and D. Manocha,
- "OBB-Tree: A Hierarchical Structure for Rapid Interference Detection",
- Technical report TR96-013, Department of Computer Science, University
- of N. Carolina, Chapel Hill.
- Proc. of ACM Siggraph'96.
-
-II. Layout of Files
-
- PQP_v1.3/
- Makefile Unix makefile to build PQP library
- PQP.dsw PQP.dsp MS VC++ 5.0 workspace and project files for PQP
-
- src/
- PQP source
-
- lib/
- libPQP.a after Unix compilation
- PQP.lib after Win32 compilation
-
- include/
- PQP.h include this file to use PQP classes and functions.
- PQP_Internal.h
- PQP_Compile.h *WARNING* you should only modify PQP_Compile.h in
- Tri.h the src directory, not here, because these files
- BV.h are copied here from src when you perform a build
-
- demos/
- Makefile Unix makefile for both demos
- demos.dsw MS VC++ 5.0 workspace for demos
-
- falling/ source and project files
- sample/ " " " "
- spinning/ " " " "
-
-III. Building the PQP Library
-
- In the top level directory, there is a Unix Makefile for building the PQP
- library. Type 'make' to create a 'libPQP.a' in the lib directory.
- The compiler is currently set to g++ with -O2 level optimization.
-
- In Visual C++ 5.0 or higher, open PQP.dsw to build the library.
-
- Building on either platform has a side effect of copying the include
- files needed for a client application to the include/ directory.
-
-IV. Building the Demos
-
- In the demos directory is a Unix Makefile. Typing 'make' will perform a
- 'make' in the 'sample' and 'spinning' directories. For VC++5.0
- users, the demos directory contains a demos.dsw file which contains
- projects for both demos.
-
- sample
-
- This demo is adapted from the sample client included with RAPID. Two
- tori are created, and proximity queries are performed on them at
- several configurations
-
- spinning
-
- The spinning demo is a GLUT application, so paths to the GLUT & OpenGL
- libraries and includes must be set in spinning/Makefile, or in the
- VC++ project settings. When run, a bunny and a torus should appear in
- the GLUT window, with a line drawn between their closest points.
- Pressing a key alternately starts and stops them spinning.
-
- falling
-
- This demo is also a GLUT application, showing a bent torus
- falling through the center of a knobby torus. Each of the three
- proximity query types can be demonstrated.
-
-V. Creating a PQP Client Application
-
- "PQP.h" contains the most complete information on constructing client
- applications. Here is a summary of the steps involved.
-
- 1. Include the PQP API header.
-
- #include "PQP.h"
-
- 2. Create two instances of PQP_Model.
-
- PQP_Model m1, m2;
-
- 3. Specify the triangles of each PQP_Model.
-
- Note that PQP uses the PQP_REAL type for all its floating point
- values. This can be set in "PQP_Compile.h", and is "double" by
- default
-
- // begin m1
-
- m1.BeginModel();
-
- // create some triangles
-
- PQP_REAL p1[3], p2[3], p3[3];
- PQP_REAL q1[3], q2[3], q3[3];
- PQP_REAL r1[3], r2[3], r3[3];
-
- // initialize the points
- .
- .
- .
-
- // add triangles that will belong to m1
-
- m1.AddTri(p1, p2, p3, 0);
- m1.AddTri(q1, q2, q3, 1);
- m1.AddTri(r1, r2, r3, 2);
-
- // end m1, which builds the model
-
- m1.EndModel();
-
- 4. Specify the orientation and position of each model.
-
- The position of a model is specified as a 3 vector giving the
- position of its frame in the world, stored in a PQP_REAL [3].
-
- The rotation for a model is specified as a 3x3 matrix, whose columns
- are the model frame's basis vectors, stored in row major order in
- a PQP_REAL [3][3];
-
- Note that an OpenGL 4x4 matrix has column major storage.
-
- 5. Perform any of the three proximity queries.
-
- // collision
-
- PQP_CollideResult cres;
- PQP_Collide(&cres,R1,T1,&m1,R2,T2,&m2);
-
- // distance
-
- PQP_DistanceResult dres;
- double rel_err = 0.0, abs_err = 0.0;
- PQP_Distance(&dres,R1,T1,&m1,R2,T2,&m2,rel_err,abs_err);
-
- // tolerance
-
- PQP_ToleranceResult tres;
- double tolerance = 1.0;
- PQP_Tolerance(&tres,R1,T1,&m1,R2,T2,&m2,tolerance);
-
- See "PQP.h" for complete information.
-
- 6. Access the result structure passed in the query call.
-
- int colliding = cres.Colliding();
- double distance = dres.Distance();
- int closer = tres.CloserThanTolerance();
-
- See "PQP.h" for the complete interface to each result structure.
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/Makefile b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/Makefile
deleted file mode 100644
index 15bdeebbc92b1e3973e4fdf946d059d195befdaa..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/Makefile
+++ /dev/null
@@ -1,16 +0,0 @@
-all:
- cd sample; \
- make
- cd spinning; \
- make
- cd falling; \
- make
-
-clean:
- cd sample; \
- make clean
- cd spinning; \
- make clean
- cd falling; \
- make clean
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.dsp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.dsp
deleted file mode 100644
index 34512688a1b527719347b5cdff9ec0fd540f3891..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.dsp
+++ /dev/null
@@ -1,83 +0,0 @@
-# Microsoft Developer Studio Project File - Name="demos" - Package Owner=<4>
-# Microsoft Developer Studio Generated Build File, Format Version 5.00
-# ** DO NOT EDIT **
-
-# TARGTYPE "Win32 (x86) Console Application" 0x0103
-
-CFG=demos - Win32 Debug
-!MESSAGE This is not a valid makefile. To build this project using NMAKE,
-!MESSAGE use the Export Makefile command and run
-!MESSAGE
-!MESSAGE NMAKE /f "demos.mak".
-!MESSAGE
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-!MESSAGE by defining the macro CFG on the command line. For example:
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-!MESSAGE
-!MESSAGE Possible choices for configuration are:
-!MESSAGE
-!MESSAGE "demos - Win32 Release" (based on "Win32 (x86) Console Application")
-!MESSAGE "demos - Win32 Debug" (based on "Win32 (x86) Console Application")
-!MESSAGE
-
-# Begin Project
-# PROP Scc_ProjName ""
-# PROP Scc_LocalPath ""
-CPP=cl.exe
-RSC=rc.exe
-
-!IF "$(CFG)" == "demos - Win32 Release"
-
-# PROP BASE Use_MFC 0
-# PROP BASE Use_Debug_Libraries 0
-# PROP BASE Output_Dir "Release"
-# PROP BASE Intermediate_Dir "Release"
-# PROP BASE Target_Dir ""
-# PROP Use_MFC 0
-# PROP Use_Debug_Libraries 0
-# PROP Output_Dir "Release"
-# PROP Intermediate_Dir "Release"
-# PROP Target_Dir ""
-# ADD BASE CPP /nologo /W3 /GX /O2 /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD CPP /nologo /W3 /GX /O2 /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD BASE RSC /l 0x409 /d "NDEBUG"
-# ADD RSC /l 0x409 /d "NDEBUG"
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LINK32=link.exe
-# ADD BASE LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /machine:I386
-# ADD LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /machine:I386
-
-!ELSEIF "$(CFG)" == "demos - Win32 Debug"
-
-# PROP BASE Use_MFC 0
-# PROP BASE Use_Debug_Libraries 1
-# PROP BASE Output_Dir "Debug"
-# PROP BASE Intermediate_Dir "Debug"
-# PROP BASE Target_Dir ""
-# PROP Use_MFC 0
-# PROP Use_Debug_Libraries 1
-# PROP Output_Dir "Debug"
-# PROP Intermediate_Dir "Debug"
-# PROP Target_Dir ""
-# ADD BASE CPP /nologo /W3 /Gm /GX /Zi /Od /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD CPP /nologo /W3 /Gm /GX /Zi /Od /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD BASE RSC /l 0x409 /d "_DEBUG"
-# ADD RSC /l 0x409 /d "_DEBUG"
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LINK32=link.exe
-# ADD BASE LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /debug /machine:I386 /pdbtype:sept
-# ADD LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /debug /machine:I386 /pdbtype:sept
-
-!ENDIF
-
-# Begin Target
-
-# Name "demos - Win32 Release"
-# Name "demos - Win32 Debug"
-# End Target
-# End Project
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.dsw b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.dsw
deleted file mode 100644
index 9aad33f880f074a0ba63898ca098fd71c2bf2918..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.dsw
+++ /dev/null
@@ -1,53 +0,0 @@
-Microsoft Developer Studio Workspace File, Format Version 5.00
-# WARNING: DO NOT EDIT OR DELETE THIS WORKSPACE FILE!
-
-###############################################################################
-
-Project: "falling"=.\falling\falling.dsp - Package Owner=<4>
-
-Package=<5>
-{{{
-}}}
-
-Package=<4>
-{{{
-}}}
-
-###############################################################################
-
-Project: "sample"=.\sample\sample.dsp - Package Owner=<4>
-
-Package=<5>
-{{{
-}}}
-
-Package=<4>
-{{{
-}}}
-
-###############################################################################
-
-Project: "spinning"=.\spinning\spinning.dsp - Package Owner=<4>
-
-Package=<5>
-{{{
-}}}
-
-Package=<4>
-{{{
-}}}
-
-###############################################################################
-
-Global:
-
-Package=<5>
-{{{
-}}}
-
-Package=<3>
-{{{
-}}}
-
-###############################################################################
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.ncb b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.ncb
deleted file mode 100644
index 40973a4585e91f24a69abe9182fb8287ab8bc0e0..0000000000000000000000000000000000000000
Binary files a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.ncb and /dev/null differ
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.opt b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.opt
deleted file mode 100644
index 237033d492a5833edc67a26a5e6693866af97436..0000000000000000000000000000000000000000
Binary files a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/demos.opt and /dev/null differ
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/Makefile b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/Makefile
deleted file mode 100644
index 89b45db8bb5354413b38ac4e5572525223e67533..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/Makefile
+++ /dev/null
@@ -1,33 +0,0 @@
-# Must set these gl and glut locations to build 'falling'
-
-GL_INCPATH = -I/usr/local/include/
-GL_LIBPATH = -L/usr/local/lib/ -L/usr/X11R6/lib/
-GL_LIBS = -lglut -lGLU -lGL -lXext -lXmu -lXi -lX11
-
-.SUFFIXES: .cpp
-
-CC = g++
-CFLAGS = -O2 -I. -I../../include $(GL_INCPATH)
-LDFLAGS = -L. -L../../lib $(GL_LIBPATH)
-LDLIBS = -lPQP -lm $(GL_LIBS)
-
-SRCS = main.cpp model.cpp
-
-OBJECTS = main.o model.o
-
-TARGET = falling
-
-CLEAN = $(OBJECTS) $(TARGET)
-
-.cpp.o:
- $(CC) ${CFLAGS} -c $<
-
-$(TARGET): $(OBJECTS)
- $(CC) $(CFLAGS) -o $(TARGET) $(OBJECTS) -L. $(LDFLAGS) $(LDLIBS)
-
-run: $(TARGET)
- $(TARGET)
-
-clean:
- /bin/rm -f $(CLEAN)
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/MatVec.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/MatVec.h
deleted file mode 100644
index 3d90522f84c19bd070f0d18fb1aff5078bdaa753..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/MatVec.h
+++ /dev/null
@@ -1,881 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_MATVEC_H
-#define PQP_MATVEC_H
-
-#include <math.h>
-#include <stdio.h>
-#include "PQP_Compile.h"
-
-#ifndef M_PI
-const double M_PI = 3.14159265359;
-#endif
-
-#ifdef gnu
-#include "zzzz.h"
-
-#ifdef hppa
-#define myfabs(x) \
- ({double __value, __arg = (x); \
- asm("fabs,dbl %1, %0": "=f" (__value): "f" (__arg)); \
- __value; \
-});
-#endif
-
-#ifdef mips
-#define myfabs(x) \
- ({double __value, __arg = (x); \
- asm("abs.d %0, %1": "=f" (__value): "f" (__arg)); \
- __value; \
-});
-#endif
-
-#else
-
-#define myfabs(x) ((x < 0) ? -x : x)
-
-#endif
-
-
-inline
-void
-Mprintg(const PQP_REAL M[3][3])
-{
- printf("%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-
-inline
-void
-Mfprint(FILE *f, const PQP_REAL M[3][3])
-{
- fprintf(f, "%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-inline
-void
-Mprint(const PQP_REAL M[3][3])
-{
- printf("%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-inline
-void
-Vprintg(const PQP_REAL V[3])
-{
- printf("%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Vfprint(FILE *f, const PQP_REAL V[3])
-{
- fprintf(f, "%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Vprint(const PQP_REAL V[3])
-{
- printf("%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Midentity(PQP_REAL M[3][3])
-{
- M[0][0] = M[1][1] = M[2][2] = 1.0;
- M[0][1] = M[1][2] = M[2][0] = 0.0;
- M[0][2] = M[1][0] = M[2][1] = 0.0;
-}
-
-inline
-void
-Videntity(PQP_REAL T[3])
-{
- T[0] = T[1] = T[2] = 0.0;
-}
-
-inline
-void
-McM(PQP_REAL Mr[3][3], const PQP_REAL M[3][3])
-{
- Mr[0][0] = M[0][0]; Mr[0][1] = M[0][1]; Mr[0][2] = M[0][2];
- Mr[1][0] = M[1][0]; Mr[1][1] = M[1][1]; Mr[1][2] = M[1][2];
- Mr[2][0] = M[2][0]; Mr[2][1] = M[2][1]; Mr[2][2] = M[2][2];
-}
-
-inline
-void
-MTcM(PQP_REAL Mr[3][3], const PQP_REAL M[3][3])
-{
- Mr[0][0] = M[0][0]; Mr[1][0] = M[0][1]; Mr[2][0] = M[0][2];
- Mr[0][1] = M[1][0]; Mr[1][1] = M[1][1]; Mr[2][1] = M[1][2];
- Mr[0][2] = M[2][0]; Mr[1][2] = M[2][1]; Mr[2][2] = M[2][2];
-}
-
-inline
-void
-VcV(PQP_REAL Vr[3], const PQP_REAL V[3])
-{
- Vr[0] = V[0]; Vr[1] = V[1]; Vr[2] = V[2];
-}
-
-inline
-void
-McolcV(PQP_REAL Vr[3], const PQP_REAL M[3][3], int c)
-{
- Vr[0] = M[0][c];
- Vr[1] = M[1][c];
- Vr[2] = M[2][c];
-}
-
-inline
-void
-McolcMcol(PQP_REAL Mr[3][3], int cr, const PQP_REAL M[3][3], int c)
-{
- Mr[0][cr] = M[0][c];
- Mr[1][cr] = M[1][c];
- Mr[2][cr] = M[2][c];
-}
-
-inline
-void
-MxMpV(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3], const PQP_REAL T[3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[1][0] +
- M1[0][2] * M2[2][0] +
- T[0]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[1][2] * M2[2][0] +
- T[1]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[1][0] +
- M1[2][2] * M2[2][0] +
- T[2]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[2][1] +
- T[0]);
- Mr[1][1] = (M1[1][0] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[2][1] +
- T[1]);
- Mr[2][1] = (M1[2][0] * M2[0][1] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[2][1] +
- T[2]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[0][1] * M2[1][2] +
- M1[0][2] * M2[2][2] +
- T[0]);
- Mr[1][2] = (M1[1][0] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[1][2] * M2[2][2] +
- T[1]);
- Mr[2][2] = (M1[2][0] * M2[0][2] +
- M1[2][1] * M2[1][2] +
- M1[2][2] * M2[2][2] +
- T[2]);
-}
-
-inline
-void
-MxM(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[1][0] +
- M1[0][2] * M2[2][0]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[1][2] * M2[2][0]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[1][0] +
- M1[2][2] * M2[2][0]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[2][1]);
- Mr[1][1] = (M1[1][0] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[2][1]);
- Mr[2][1] = (M1[2][0] * M2[0][1] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[2][1]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[0][1] * M2[1][2] +
- M1[0][2] * M2[2][2]);
- Mr[1][2] = (M1[1][0] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[1][2] * M2[2][2]);
- Mr[2][2] = (M1[2][0] * M2[0][2] +
- M1[2][1] * M2[1][2] +
- M1[2][2] * M2[2][2]);
-}
-
-
-inline
-void
-MxMT(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[0][1] +
- M1[0][2] * M2[0][2]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[0][1] +
- M1[1][2] * M2[0][2]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[0][1] +
- M1[2][2] * M2[0][2]);
- Mr[0][1] = (M1[0][0] * M2[1][0] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[1][2]);
- Mr[1][1] = (M1[1][0] * M2[1][0] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[1][2]);
- Mr[2][1] = (M1[2][0] * M2[1][0] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[1][2]);
- Mr[0][2] = (M1[0][0] * M2[2][0] +
- M1[0][1] * M2[2][1] +
- M1[0][2] * M2[2][2]);
- Mr[1][2] = (M1[1][0] * M2[2][0] +
- M1[1][1] * M2[2][1] +
- M1[1][2] * M2[2][2]);
- Mr[2][2] = (M1[2][0] * M2[2][0] +
- M1[2][1] * M2[2][1] +
- M1[2][2] * M2[2][2]);
-}
-
-inline
-void
-MTxM(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[1][0] * M2[1][0] +
- M1[2][0] * M2[2][0]);
- Mr[1][0] = (M1[0][1] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[2][1] * M2[2][0]);
- Mr[2][0] = (M1[0][2] * M2[0][0] +
- M1[1][2] * M2[1][0] +
- M1[2][2] * M2[2][0]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[1][0] * M2[1][1] +
- M1[2][0] * M2[2][1]);
- Mr[1][1] = (M1[0][1] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[2][1] * M2[2][1]);
- Mr[2][1] = (M1[0][2] * M2[0][1] +
- M1[1][2] * M2[1][1] +
- M1[2][2] * M2[2][1]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[1][0] * M2[1][2] +
- M1[2][0] * M2[2][2]);
- Mr[1][2] = (M1[0][1] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[2][1] * M2[2][2]);
- Mr[2][2] = (M1[0][2] * M2[0][2] +
- M1[1][2] * M2[1][2] +
- M1[2][2] * M2[2][2]);
-}
-
-inline
-void
-MxV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]);
- Vr[1] = (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]);
- Vr[2] = (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-
-inline
-void
-MxVpV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2] +
- V2[0]);
- Vr[1] = (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2] +
- V2[1]);
- Vr[2] = (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2] +
- V2[2]);
-}
-
-
-inline
-void
-sMxVpV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = s1 * (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]) +
- V2[0];
- Vr[1] = s1 * (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]) +
- V2[1];
- Vr[2] = s1 * (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]) +
- V2[2];
-}
-
-inline
-void
-MTxV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[1][0] * V1[1] +
- M1[2][0] * V1[2]);
- Vr[1] = (M1[0][1] * V1[0] +
- M1[1][1] * V1[1] +
- M1[2][1] * V1[2]);
- Vr[2] = (M1[0][2] * V1[0] +
- M1[1][2] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-inline
-void
-sMTxV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = s1*(M1[0][0] * V1[0] +
- M1[1][0] * V1[1] +
- M1[2][0] * V1[2]);
- Vr[1] = s1*(M1[0][1] * V1[0] +
- M1[1][1] * V1[1] +
- M1[2][1] * V1[2]);
- Vr[2] = s1*(M1[0][2] * V1[0] +
- M1[1][2] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-inline
-void
-sMxV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = s1*(M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]);
- Vr[1] = s1*(M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]);
- Vr[2] = s1*(M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-
-inline
-void
-VmV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[0] - V2[0];
- Vr[1] = V1[1] - V2[1];
- Vr[2] = V1[2] - V2[2];
-}
-
-inline
-void
-VpV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[0] + V2[0];
- Vr[1] = V1[1] + V2[1];
- Vr[2] = V1[2] + V2[2];
-}
-
-inline
-void
-VpVxS(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3], PQP_REAL s)
-{
- Vr[0] = V1[0] + V2[0] * s;
- Vr[1] = V1[1] + V2[1] * s;
- Vr[2] = V1[2] + V2[2] * s;
-}
-
-inline
-void
-MskewV(PQP_REAL M[3][3], const PQP_REAL v[3])
-{
- M[0][0] = M[1][1] = M[2][2] = 0.0;
- M[1][0] = v[2];
- M[0][1] = -v[2];
- M[0][2] = v[1];
- M[2][0] = -v[1];
- M[1][2] = -v[0];
- M[2][1] = v[0];
-}
-
-
-inline
-void
-VcrossV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[1]*V2[2] - V1[2]*V2[1];
- Vr[1] = V1[2]*V2[0] - V1[0]*V2[2];
- Vr[2] = V1[0]*V2[1] - V1[1]*V2[0];
-}
-
-
-inline
-PQP_REAL
-Vlength(PQP_REAL V[3])
-{
- return sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
-}
-
-inline
-void
-Vnormalize(PQP_REAL V[3])
-{
- PQP_REAL d = (PQP_REAL)1.0 / sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
- V[0] *= d;
- V[1] *= d;
- V[2] *= d;
-}
-
-
-inline
-PQP_REAL
-VdotV(const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- return (V1[0]*V2[0] + V1[1]*V2[1] + V1[2]*V2[2]);
-}
-
-
-inline
-PQP_REAL
-VdistV2(const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- return ( (V1[0]-V2[0]) * (V1[0]-V2[0]) +
- (V1[1]-V2[1]) * (V1[1]-V2[1]) +
- (V1[2]-V2[2]) * (V1[2]-V2[2]));
-}
-
-inline
-void
-VxS(PQP_REAL Vr[3], const PQP_REAL V[3], PQP_REAL s)
-{
- Vr[0] = V[0] * s;
- Vr[1] = V[1] * s;
- Vr[2] = V[2] * s;
-}
-
-inline
-void
-MRotZ(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[0][0] = cos(t);
- Mr[1][0] = sin(t);
- Mr[0][1] = -Mr[1][0];
- Mr[1][1] = Mr[0][0];
- Mr[2][0] = Mr[2][1] = 0.0;
- Mr[0][2] = Mr[1][2] = 0.0;
- Mr[2][2] = 1.0;
-}
-
-
-inline
-void
-MRotX(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[1][1] = cos(t);
- Mr[2][1] = sin(t);
- Mr[1][2] = -Mr[2][1];
- Mr[2][2] = Mr[1][1];
- Mr[0][1] = Mr[0][2] = 0.0;
- Mr[1][0] = Mr[2][0] = 0.0;
- Mr[0][0] = 1.0;
-}
-
-inline
-void
-MRotY(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[2][2] = cos(t);
- Mr[0][2] = sin(t);
- Mr[2][0] = -Mr[0][2];
- Mr[0][0] = Mr[2][2];
- Mr[1][2] = Mr[1][0] = 0.0;
- Mr[2][1] = Mr[0][1] = 0.0;
- Mr[1][1] = 1.0;
-}
-
-inline
-void
-MVtoOGL(double oglm[16], const PQP_REAL R[3][3], const PQP_REAL T[3])
-{
- oglm[0] = (double)R[0][0];
- oglm[1] = (double)R[1][0];
- oglm[2] = (double)R[2][0];
- oglm[3] = 0.0;
- oglm[4] = (double)R[0][1];
- oglm[5] = (double)R[1][1];
- oglm[6] = (double)R[2][1];
- oglm[7] = 0.0;
- oglm[8] = (double)R[0][2];
- oglm[9] = (double)R[1][2];
- oglm[10] = (double)R[2][2];
- oglm[11] = 0.0;
- oglm[12] = (double)T[0];
- oglm[13] = (double)T[1];
- oglm[14] = (double)T[2];
- oglm[15] = 1.0;
-}
-
-inline
-void
-OGLtoMV(PQP_REAL R[3][3], PQP_REAL T[3], const double oglm[16])
-{
- R[0][0] = (PQP_REAL)oglm[0];
- R[1][0] = (PQP_REAL)oglm[1];
- R[2][0] = (PQP_REAL)oglm[2];
-
- R[0][1] = (PQP_REAL)oglm[4];
- R[1][1] = (PQP_REAL)oglm[5];
- R[2][1] = (PQP_REAL)oglm[6];
-
- R[0][2] = (PQP_REAL)oglm[8];
- R[1][2] = (PQP_REAL)oglm[9];
- R[2][2] = (PQP_REAL)oglm[10];
-
- T[0] = (PQP_REAL)oglm[12];
- T[1] = (PQP_REAL)oglm[13];
- T[2] = (PQP_REAL)oglm[14];
-}
-
-// taken from quatlib, written by Richard Holloway
-const int QX = 0;
-const int QY = 1;
-const int QZ = 2;
-const int QW = 3;
-
-inline
-void
-MRotQ(PQP_REAL destMatrix[3][3], PQP_REAL srcQuat[4])
-{
- PQP_REAL s;
- PQP_REAL xs, ys, zs,
- wx, wy, wz,
- xx, xy, xz,
- yy, yz, zz;
-
- /*
- * For unit srcQuat, just set s = 2.0; or set xs = srcQuat[QX] +
- * srcQuat[QX], etc.
- */
-
- s = (PQP_REAL)2.0 / (srcQuat[QX]*srcQuat[QX] + srcQuat[QY]*srcQuat[QY] +
- srcQuat[QZ]*srcQuat[QZ] + srcQuat[QW]*srcQuat[QW]);
-
- xs = srcQuat[QX] * s; ys = srcQuat[QY] * s; zs = srcQuat[QZ] * s;
- wx = srcQuat[QW] * xs; wy = srcQuat[QW] * ys; wz = srcQuat[QW] * zs;
- xx = srcQuat[QX] * xs; xy = srcQuat[QX] * ys; xz = srcQuat[QX] * zs;
- yy = srcQuat[QY] * ys; yz = srcQuat[QY] * zs; zz = srcQuat[QZ] * zs;
-
- destMatrix[QX][QX] = (PQP_REAL)1.0 - (yy + zz);
- destMatrix[QX][QY] = xy + wz;
- destMatrix[QX][QZ] = xz - wy;
-
- destMatrix[QY][QX] = xy - wz;
- destMatrix[QY][QY] = (PQP_REAL)1.0 - (xx + zz);
- destMatrix[QY][QZ] = yz + wx;
-
- destMatrix[QZ][QX] = xz + wy;
- destMatrix[QZ][QY] = yz - wx;
- destMatrix[QZ][QZ] = (PQP_REAL)1.0 - (xx + yy);
-}
-
-inline
-void
-Mqinverse(PQP_REAL Mr[3][3], PQP_REAL m[3][3])
-{
- int i,j;
-
- for(i=0; i<3; i++)
- for(j=0; j<3; j++)
- {
- int i1 = (i+1)%3;
- int i2 = (i+2)%3;
- int j1 = (j+1)%3;
- int j2 = (j+2)%3;
- Mr[i][j] = (m[j1][i1]*m[j2][i2] - m[j1][i2]*m[j2][i1]);
- }
-}
-
-// Meigen from Numerical Recipes in C
-
-#if 0
-
-#define rfabs(x) ((x < 0) ? -x : x)
-
-#define ROT(a,i,j,k,l) g=a[i][j]; h=a[k][l]; a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
-
-int
-inline
-Meigen(PQP_REAL vout[3][3], PQP_REAL dout[3], PQP_REAL a[3][3])
-{
- int i;
- PQP_REAL tresh,theta,tau,t,sm,s,h,g,c;
- int nrot;
- PQP_REAL b[3];
- PQP_REAL z[3];
- PQP_REAL v[3][3];
- PQP_REAL d[3];
-
- v[0][0] = v[1][1] = v[2][2] = 1.0;
- v[0][1] = v[1][2] = v[2][0] = 0.0;
- v[0][2] = v[1][0] = v[2][1] = 0.0;
-
- b[0] = a[0][0]; d[0] = a[0][0]; z[0] = 0.0;
- b[1] = a[1][1]; d[1] = a[1][1]; z[1] = 0.0;
- b[2] = a[2][2]; d[2] = a[2][2]; z[2] = 0.0;
-
- nrot = 0;
-
-
- for(i=0; i<50; i++)
- {
-
- printf("2\n");
-
- sm=0.0; sm+=fabs(a[0][1]); sm+=fabs(a[0][2]); sm+=fabs(a[1][2]);
- if (sm == 0.0) { McM(vout,v); VcV(dout,d); return i; }
-
- if (i < 3) tresh=0.2*sm/(3*3); else tresh=0.0;
-
- {
- g = 100.0*rfabs(a[0][1]);
- if (i>3 && rfabs(d[0])+g==rfabs(d[0]) && rfabs(d[1])+g==rfabs(d[1]))
- a[0][1]=0.0;
- else if (rfabs(a[0][1])>tresh)
- {
- h = d[1]-d[0];
- if (rfabs(h)+g == rfabs(h)) t=(a[0][1])/h;
- else
- {
- theta=0.5*h/(a[0][1]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[0][1];
- z[0] -= h; z[1] += h; d[0] -= h; d[1] += h;
- a[0][1]=0.0;
- ROT(a,0,2,1,2); ROT(v,0,0,0,1); ROT(v,1,0,1,1); ROT(v,2,0,2,1);
- nrot++;
- }
- }
-
- {
- g = 100.0*rfabs(a[0][2]);
- if (i>3 && rfabs(d[0])+g==rfabs(d[0]) && rfabs(d[2])+g==rfabs(d[2]))
- a[0][2]=0.0;
- else if (rfabs(a[0][2])>tresh)
- {
- h = d[2]-d[0];
- if (rfabs(h)+g == rfabs(h)) t=(a[0][2])/h;
- else
- {
- theta=0.5*h/(a[0][2]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[0][2];
- z[0] -= h; z[2] += h; d[0] -= h; d[2] += h;
- a[0][2]=0.0;
- ROT(a,0,1,1,2); ROT(v,0,0,0,2); ROT(v,1,0,1,2); ROT(v,2,0,2,2);
- nrot++;
- }
- }
-
-
- {
- g = 100.0*rfabs(a[1][2]);
- if (i>3 && rfabs(d[1])+g==rfabs(d[1]) && rfabs(d[2])+g==rfabs(d[2]))
- a[1][2]=0.0;
- else if (rfabs(a[1][2])>tresh)
- {
- h = d[2]-d[1];
- if (rfabs(h)+g == rfabs(h)) t=(a[1][2])/h;
- else
- {
- theta=0.5*h/(a[1][2]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[1][2];
- z[1] -= h; z[2] += h; d[1] -= h; d[2] += h;
- a[1][2]=0.0;
- ROT(a,0,1,0,2); ROT(v,0,1,0,2); ROT(v,1,1,1,2); ROT(v,2,1,2,2);
- nrot++;
- }
- }
-
- b[0] += z[0]; d[0] = b[0]; z[0] = 0.0;
- b[1] += z[1]; d[1] = b[1]; z[1] = 0.0;
- b[2] += z[2]; d[2] = b[2]; z[2] = 0.0;
-
- }
-
- fprintf(stderr, "eigen: too many iterations in Jacobi transform (%d).\n", i);
-
- return i;
-}
-
-#else
-
-
-
-#define ROTATE(a,i,j,k,l) g=a[i][j]; h=a[k][l]; a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
-
-void
-inline
-Meigen(PQP_REAL vout[3][3], PQP_REAL dout[3], PQP_REAL a[3][3])
-{
- int n = 3;
- int j,iq,ip,i;
- PQP_REAL tresh,theta,tau,t,sm,s,h,g,c;
- int nrot;
- PQP_REAL b[3];
- PQP_REAL z[3];
- PQP_REAL v[3][3];
- PQP_REAL d[3];
-
- Midentity(v);
- for(ip=0; ip<n; ip++)
- {
- b[ip] = a[ip][ip];
- d[ip] = a[ip][ip];
- z[ip] = 0.0;
- }
-
- nrot = 0;
-
- for(i=0; i<50; i++)
- {
-
- sm=0.0;
- for(ip=0;ip<n;ip++) for(iq=ip+1;iq<n;iq++) sm+=fabs(a[ip][iq]);
- if (sm == 0.0)
- {
- McM(vout, v);
- VcV(dout, d);
- return;
- }
-
-
- if (i < 3) tresh=(PQP_REAL)0.2*sm/(n*n);
- else tresh=0.0;
-
- for(ip=0; ip<n; ip++) for(iq=ip+1; iq<n; iq++)
- {
- g = (PQP_REAL)100.0*fabs(a[ip][iq]);
- if (i>3 &&
- fabs(d[ip])+g==fabs(d[ip]) &&
- fabs(d[iq])+g==fabs(d[iq]))
- a[ip][iq]=0.0;
- else if (fabs(a[ip][iq])>tresh)
- {
- h = d[iq]-d[ip];
- if (fabs(h)+g == fabs(h)) t=(a[ip][iq])/h;
- else
- {
- theta=(PQP_REAL)0.5*h/(a[ip][iq]);
- t=(PQP_REAL)(1.0/(fabs(theta)+sqrt(1.0+theta*theta)));
- if (theta < 0.0) t = -t;
- }
- c=(PQP_REAL)1.0/sqrt(1+t*t);
- s=t*c;
- tau=s/((PQP_REAL)1.0+c);
- h=t*a[ip][iq];
- z[ip] -= h;
- z[iq] += h;
- d[ip] -= h;
- d[iq] += h;
- a[ip][iq]=0.0;
- for(j=0;j<ip;j++) { ROTATE(a,j,ip,j,iq); }
- for(j=ip+1;j<iq;j++) { ROTATE(a,ip,j,j,iq); }
- for(j=iq+1;j<n;j++) { ROTATE(a,ip,j,iq,j); }
- for(j=0;j<n;j++) { ROTATE(v,j,ip,j,iq); }
- nrot++;
- }
- }
- for(ip=0;ip<n;ip++)
- {
- b[ip] += z[ip];
- d[ip] = b[ip];
- z[ip] = 0.0;
- }
- }
-
- fprintf(stderr, "eigen: too many iterations in Jacobi transform.\n");
-
- return;
-}
-
-
-#endif
-
-#endif
-/* MATVEC_H */
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/falling.dsp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/falling.dsp
deleted file mode 100644
index f7108cdd472bd3eb69b2e77497978756cf28b4b1..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/falling.dsp
+++ /dev/null
@@ -1,95 +0,0 @@
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-# PROP Use_MFC 0
-# PROP Use_Debug_Libraries 1
-# PROP Output_Dir "./"
-# PROP Intermediate_Dir "Debug"
-# PROP Target_Dir ""
-# ADD BASE CPP /nologo /W3 /Gm /GX /Zi /Od /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD CPP /nologo /W3 /Gm /GX /Zi /Od /I "..\..\include" /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD BASE RSC /l 0x409 /d "_DEBUG"
-# ADD RSC /l 0x409 /d "_DEBUG"
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LINK32=xilink5.exe
-# ADD BASE LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /debug /machine:I386 /pdbtype:sept
-# ADD LINK32 glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /debug /machine:I386 /pdbtype:sept /libpath:"..\..\lib"
-
-!ENDIF
-
-# Begin Target
-
-# Name "falling - Win32 Release"
-# Name "falling - Win32 Debug"
-# Begin Source File
-
-SOURCE=.\main.cpp
-# End Source File
-# Begin Source File
-
-SOURCE=.\model.cpp
-# End Source File
-# Begin Source File
-
-SOURCE=.\model.h
-# End Source File
-# End Target
-# End Project
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/falling.plg b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/falling.plg
deleted file mode 100644
index b133bcb0471035b149a109f3cdd2904a302b85a1..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/falling.plg
+++ /dev/null
@@ -1,21 +0,0 @@
---------------------Configuration: falling - Win32 Release--------------------
-Begining build with project "C:\WIN95\DESKTOP\PQP_v1.2.1\demos\falling\falling.dsp", at root.
-Active configuration is Win32 (x86) Console Application (based on Win32 (x86) Console Application)
-
-Project's tools are:
- "32-bit C/C++ Compiler for 80x86" with flags "/nologo /ML /W3 /GX /O2 /I "..\..\include" /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /Fp"Release/falling.pch" /YX /Fo"Release/" /Fd"Release/" /FD /c "
- "Win32 Resource Compiler" with flags "/l 0x409 /d "NDEBUG" "
- "Browser Database Maker" with flags "/nologo /o"./falling.bsc" "
- "COFF Linker for 80x86" with flags "glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /incremental:no /pdb:"./falling.pdb" /machine:I386 /out:"./falling.exe" /libpath:"..\..\lib" "
- "Custom Build" with flags ""
- "<Component 0xa>" with flags ""
-
-Creating temp file "C:\WIN95\TEMP\RSP4360.TMP" with contents <glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /incremental:no /pdb:"./falling.pdb" /machine:I386 /out:"./falling.exe" /libpath:"..\..\lib"
-.\Release\main.obj
-.\Release\model.obj>
-Creating command line "link.exe @C:\WIN95\TEMP\RSP4360.TMP"
-Linking...
-
-
-
-falling.exe - 0 error(s), 0 warning(s)
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/main.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/main.cpp
deleted file mode 100644
index ee0ea92e25150f7d6d3523a8f5c6ab71934f599e..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/main.cpp
+++ /dev/null
@@ -1,537 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <math.h>
-#include <GL/glut.h>
-#include "model.h"
-#include "PQP.h"
-#include "MatVec.h"
-
-PQP_Model *torus1_tested,*torus2_tested;
-Model *torus1_drawn, *torus2_drawn;
-
-int mode;
-double beginx, beginy;
-double dis = 10.0, azim = 0.0, elev = 0.0;
-double ddis = 0.0, dazim = 0.0, delev = 0.0;
-
-int animating = 1;
-int step = 0;
-int number_of_steps;
-int query_type = 0;
-double tolerance = .05;
-
-PQP_REAL (*R1)[3][3];
-PQP_REAL (*T1)[3];
-PQP_REAL (*R2)[3][3];
-PQP_REAL (*T2)[3];
-
-void
-init_viewer_window()
-{
- GLfloat Ambient[] = { 0.2f, 0.2f, 0.2f, 1.0f };
- GLfloat Diffuse[] = { 0.8f, 0.8f, 0.8f, 1.0f };
- GLfloat Specular[] = { 0.1f, 0.1f, 0.1f, 1.0f };
- GLfloat SpecularExp[] = { 50 };
- GLfloat Emission[] = { 0.1f, 0.1f, 0.1f, 1.0f };
-
- glMaterialfv(GL_FRONT, GL_AMBIENT, Ambient);
- glMaterialfv(GL_FRONT, GL_DIFFUSE, Diffuse);
- glMaterialfv(GL_FRONT, GL_SPECULAR, Specular);
- glMaterialfv(GL_FRONT, GL_SHININESS, SpecularExp);
- glMaterialfv(GL_FRONT, GL_EMISSION, Emission);
-
- glMaterialfv(GL_BACK, GL_AMBIENT, Ambient);
- glMaterialfv(GL_BACK, GL_DIFFUSE, Diffuse);
- glMaterialfv(GL_BACK, GL_SPECULAR, Specular);
- glMaterialfv(GL_BACK, GL_SHININESS, SpecularExp);
- glMaterialfv(GL_BACK, GL_EMISSION, Emission);
-
- glColorMaterial(GL_FRONT_AND_BACK, GL_DIFFUSE);
-
- glEnable(GL_COLOR_MATERIAL);
-
- GLfloat light_position[] = { 1.0, 1.0, 1.0, 0.0 };
- glLightfv(GL_LIGHT0, GL_POSITION, light_position);
- glEnable(GL_LIGHT0);
- glEnable(GL_LIGHTING);
- glLightModeli(GL_LIGHT_MODEL_TWO_SIDE, GL_TRUE);
-
- glDepthFunc(GL_LEQUAL);
- glEnable(GL_DEPTH_TEST);
- glEnable(GL_CULL_FACE);
- glCullFace(GL_BACK);
-
- glShadeModel(GL_FLAT);
- glClearColor(0.0, 0.0, 0.0, 0.0);
-
- glMatrixMode(GL_PROJECTION);
- glLoadIdentity();
- glFrustum(-0.004,0.004,-0.004,0.004,.01,100.0);
-
- glMatrixMode(GL_MODELVIEW);
-}
-
-void
-cb_mouse(int _b, int _s, int _x, int _y)
-{
- if (_s == GLUT_UP)
- {
- dis += ddis;
- if (dis < .1) dis = .1;
- azim += dazim;
- elev += delev;
- ddis = 0.0;
- dazim = 0.0;
- delev = 0.0;
- return;
- }
-
- if (_b == GLUT_RIGHT_BUTTON)
- {
- mode = 0;
- beginy = _y;
- return;
- }
- else
- {
- mode = 1;
- beginx = _x;
- beginy = _y;
- }
-}
-
-void
-cb_motion(int _x, int _y)
-{
- if (mode == 0)
- {
- ddis = dis * (double)(_y - beginy)/200.0;
- }
- else
- {
- dazim = (_x - beginx)/5;
- delev = (_y - beginy)/5;
- }
-
- glutPostRedisplay();
-}
-
-void cb_keyboard(unsigned char key, int x, int y)
-{
- switch(key)
- {
- case 'q':
- delete torus1_drawn;
- delete torus2_drawn;
- delete torus1_tested;
- delete torus2_tested;
- delete [] R1;
- delete [] T1;
- delete [] R2;
- delete [] T2;
- exit(0);
- case '0': query_type = 0; break;
- case '1': query_type = 1; break;
- case '2': query_type = 2; break;
- case '3': query_type = 3; break;
- case '-':
- tolerance -= .01;
- if (tolerance < 0.0) tolerance = 0.0;
- break;
- case '=':
- tolerance += .01;
- break;
- default: animating = 1 - animating;
- }
-
- glutPostRedisplay();
-}
-
-void cb_idle()
-{
- if (animating)
- {
- step = (step + 1) % number_of_steps;
- glutPostRedisplay();
- }
-}
-
-void
-BeginDraw()
-{
- glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
-
- glLoadIdentity();
- glTranslatef(0.0, 0.0, -(dis+ddis));
- glRotated(elev+delev, 1.0, 0.0, 0.0);
- glRotated(azim+dazim, 0.0, 1.0, 0.0);
- glRotated(90.0,-1.0,0.0,0.0);
-}
-
-void
-EndDraw()
-{
- glFlush();
- glutSwapBuffers();
-}
-
-inline void glVertex3v(float V[3]) { glVertex3fv(V); }
-inline void glVertex3v(double V[3]) { glVertex3dv(V); }
-
-void
-cb_display()
-{
- BeginDraw();
-
- int i;
- PQP_CollideResult cres;
- PQP_DistanceResult dres;
- PQP_ToleranceResult tres;
- double oglm[16];
-
- switch(query_type)
- {
- case 0:
-
- // draw model 1
-
- glColor3f(1,1,1); // setup color and transform
- MVtoOGL(oglm,R1[step],T1[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus1_drawn->Draw(); // do gl rendering
- glPopMatrix(); // restore transform
-
- // draw model 2
-
- MVtoOGL(oglm,R2[step],T2[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus2_drawn->Draw();
- glPopMatrix();
-
- break;
-
- case 1:
-
- // perform collision query
-
- PQP_Collide(&cres,R1[step],T1[step],torus1_tested,
- R2[step],T2[step],torus2_tested,
- PQP_ALL_CONTACTS);
-
- // draw model 1 and its overlapping tris
-
- MVtoOGL(oglm,R1[step],T1[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- glColor3f(1,1,1);
- torus1_drawn->Draw();
- glColor3f(1,0,0);
- for(i = 0; i < cres.NumPairs(); i++)
- {
- torus1_drawn->DrawTri(cres.Id1(i));
- }
- glPopMatrix();
-
- // draw model 2 and its overlapping tris
-
- MVtoOGL(oglm,R2[step],T2[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- glColor3f(1,1,1);
- torus2_drawn->Draw();
- glColor3f(1,0,0);
- for(i = 0; i < cres.NumPairs(); i++)
- {
- torus2_drawn->DrawTri(cres.Id2(i));
- }
- glPopMatrix();
-
- break;
-
- case 2:
-
- // perform distance query
-
- PQP_Distance(&dres,R1[step],T1[step],torus1_tested,
- R2[step],T2[step],torus2_tested,
- 0.0,0.0);
-
- // draw models
-
- glColor3f(1,1,1);
-
- MVtoOGL(oglm,R1[step],T1[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus1_drawn->Draw();
- glPopMatrix();
-
- MVtoOGL(oglm,R2[step],T2[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus2_drawn->Draw();
- glPopMatrix();
-
- // draw the closest points as small spheres
-
- glColor3f(0,1,0);
-
- PQP_REAL P1[3],P2[3],V1[3],V2[3];
- VcV(P1,dres.P1());
- VcV(P2,dres.P2());
-
- // each point is in the space of its model;
- // transform to world space
-
- MxVpV(V1,R1[step],P1,T1[step]);
-
- glPushMatrix();
- glTranslated(V1[0],V1[1],V1[2]);
- glutSolidSphere(.01,15,15);
- glPopMatrix();
-
- MxVpV(V2,R2[step],P2,T2[step]);
-
- glPushMatrix();
- glTranslated(V2[0],V2[1],V2[2]);
- glutSolidSphere(.01,15,15);
- glPopMatrix();
-
- // draw the line between the closest points
-
- glDisable(GL_LIGHTING);
- glBegin(GL_LINES);
- glVertex3v(V1);
- glVertex3v(V2);
- glEnd();
- glEnable(GL_LIGHTING);
- break;
-
- case 3:
-
- // perform tolerance query
-
- PQP_Tolerance(&tres,R1[step],T1[step],torus1_tested,
- R2[step],T2[step],torus2_tested,
- tolerance);
-
- if (tres.CloserThanTolerance())
- glColor3f(0,0,1);
- else
- glColor3f(1,1,1);
-
- // draw models
-
- MVtoOGL(oglm,R1[step],T1[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus1_drawn->Draw();
- glPopMatrix();
-
- MVtoOGL(oglm,R2[step],T2[step]);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus2_drawn->Draw();
- glPopMatrix();
-
- break;
-
- }
-
- EndDraw();
-}
-
-void LoadPath(PQP_REAL (* &R)[3][3], PQP_REAL (* &T)[3], char *filename)
-{
- FILE *fp;
- if ( (fp = fopen(filename, "r")) == NULL )
- {
- fprintf(stderr, "Error opening file %s\n", filename);
- exit(1);
- }
- fscanf(fp, "%d", &number_of_steps);
-
- R = new PQP_REAL[number_of_steps][3][3];
- T = new PQP_REAL[number_of_steps][3];
-
- for (int i = 0; i < number_of_steps; i++)
- {
- double a, b, c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- R[i][0][0] = (PQP_REAL)a;
- R[i][0][1] = (PQP_REAL)b;
- R[i][0][2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- R[i][1][0] = (PQP_REAL)a;
- R[i][1][1] = (PQP_REAL)b;
- R[i][1][2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- R[i][2][0] = (PQP_REAL)a;
- R[i][2][1] = (PQP_REAL)b;
- R[i][2][2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- T[i][0] = (PQP_REAL)a;
- T[i][1] = (PQP_REAL)b;
- T[i][2] = (PQP_REAL)c;
- }
-
- fclose(fp);
-}
-
-void main(int argc, char **argv)
-{
- // init glut
-
- glutInit(&argc, argv);
- glutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH | GLUT_MULTISAMPLE);
-
- // create the window
-
- glutCreateWindow("PQP Demo - Falling");
-
- // set OpenGL graphics state -- material props, perspective, etc.
-
- init_viewer_window();
-
- // set the callbacks
-
- glutDisplayFunc(cb_display);
- glutMouseFunc(cb_mouse);
- glutMotionFunc(cb_motion);
- glutKeyboardFunc(cb_keyboard);
- glutIdleFunc(cb_idle);
-
- // create models
-
- FILE *fp;
- int ntris, i;
- double a,b,c;
- PQP_REAL p1[3],p2[3],p3[3];
-
- // model 1
-
- torus1_drawn = new Model("torus1.tris");
-
- torus1_tested = new PQP_Model();
-
- fp = fopen("torus1.tris","r");
- fscanf(fp,"%d",&ntris);
-
- torus1_tested->BeginModel();
- for (i = 0; i < ntris; i++)
- {
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- p1[0] = (PQP_REAL)a;
- p1[1] = (PQP_REAL)b;
- p1[2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- p2[0] = (PQP_REAL)a;
- p2[1] = (PQP_REAL)b;
- p2[2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- p3[0] = (PQP_REAL)a;
- p3[1] = (PQP_REAL)b;
- p3[2] = (PQP_REAL)c;
- torus1_tested->AddTri(p1,p2,p3,i);
- }
- torus1_tested->EndModel();
-
- fclose(fp);
-
- // model 2
-
- torus2_drawn = new Model("torus2.tris");
-
- torus2_tested = new PQP_Model();
-
- fp = fopen("torus2.tris","r");
- fscanf(fp,"%d",&ntris);
-
- torus2_tested->BeginModel();
- for (i = 0; i < ntris; i++)
- {
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- p1[0] = (PQP_REAL)a;
- p1[1] = (PQP_REAL)b;
- p1[2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- p2[0] = (PQP_REAL)a;
- p2[1] = (PQP_REAL)b;
- p2[2] = (PQP_REAL)c;
- fscanf(fp,"%lf %lf %lf",&a,&b,&c);
- p3[0] = (PQP_REAL)a;
- p3[1] = (PQP_REAL)b;
- p3[2] = (PQP_REAL)c;
- torus2_tested->AddTri(p1,p2,p3,i);
- }
- torus2_tested->EndModel();
-
- fclose(fp);
-
- // load paths
-
- LoadPath(R1,T1,"torus1.path");
- LoadPath(R2,T2,"torus2.path");
-
- // print instructions
-
- printf("PQP Demo - Falling:\n"
- "Press:\n"
- "0 - no proximity query, just animation\n"
- "1 - collision query\n"
- " overlapping triangles shown in red.\n"
- "2 - distance query\n"
- " closest points connected by a line.\n"
- "3 - tolerance query\n"
- " reduce/increase tolerance with -/= keys.\n"
- " models turn blue when closer than the tolerance.\n"
- "any other key to toggle animation on/off\n");
-
- // Enter the main loop.
-
- glutMainLoop();
-}
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/model.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/model.cpp
deleted file mode 100644
index e145b31baebd18fa43a6ca0a86314a3b230c1fb8..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/model.cpp
+++ /dev/null
@@ -1,144 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <math.h>
-#include "GL/glut.h"
-#include "model.h"
-
-inline
-void
-VmV(double Vr[3], const double V1[3], const double V2[3])
-{
- Vr[0] = V1[0] - V2[0];
- Vr[1] = V1[1] - V2[1];
- Vr[2] = V1[2] - V2[2];
-}
-
-inline
-void
-VcrossV(double Vr[3], const double V1[3], const double V2[3])
-{
- Vr[0] = V1[1]*V2[2] - V1[2]*V2[1];
- Vr[1] = V1[2]*V2[0] - V1[0]*V2[2];
- Vr[2] = V1[0]*V2[1] - V1[1]*V2[0];
-}
-
-inline
-void
-Vnormalize(double V[3])
-{
- double d = 1.0 / sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
- V[0] *= d;
- V[1] *= d;
- V[2] *= d;
-}
-
-Model::Model(char *tris_file)
-{
- FILE *fp = fopen(tris_file,"r");
- if (fp == NULL)
- {
- fprintf(stderr,"Model Constructor: Couldn't open %s\n",tris_file);
- exit(-1);
- }
-
- fscanf(fp,"%d",&ntris);
- tri = new ModelTri[ntris];
-
- int i;
-
- for (i = 0; i < ntris; i++)
- {
- // read the tri verts
-
- fscanf(fp,"%lf %lf %lf %lf %lf %lf %lf %lf %lf",
- &tri[i].p0[0], &tri[i].p0[1], &tri[i].p0[2],
- &tri[i].p1[0], &tri[i].p1[1], &tri[i].p1[2],
- &tri[i].p2[0], &tri[i].p2[1], &tri[i].p2[2]);
-
- // set the normal
-
- double a[3],b[3];
- VmV(a,tri[i].p1,tri[i].p0);
- VmV(b,tri[i].p2,tri[i].p0);
- VcrossV(tri[i].n,a,b);
- Vnormalize(tri[i].n);
- }
-
- fclose(fp);
-
- // generate display list
-
- display_list = glGenLists(1);
- glNewList(display_list,GL_COMPILE);
- glBegin(GL_TRIANGLES);
- for (i = 0; i < ntris; i++)
- {
- glNormal3dv(tri[i].n);
- glVertex3dv(tri[i].p0);
- glVertex3dv(tri[i].p1);
- glVertex3dv(tri[i].p2);
- }
- glEnd();
- glEndList();
-}
-
-Model::~Model()
-{
- delete [] tri;
-}
-
-void
-Model::Draw()
-{
- glCallList(display_list);
-}
-
-void
-Model::DrawTri(int index)
-{
- glBegin(GL_TRIANGLES);
- glVertex3dv(tri[index].p0);
- glVertex3dv(tri[index].p1);
- glVertex3dv(tri[index].p2);
- glEnd();
-}
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/model.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/model.h
deleted file mode 100644
index df352e4e8959b4902e5cab308060d13bac9c3c86..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/model.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef MODEL_H
-#define MODEL_H
-
-struct ModelTri
-{
- double p0[3], p1[3], p2[3];
- double n[3];
-};
-
-class Model
-{
- int ntris;
- ModelTri *tri;
- int display_list;
-
-public:
- Model(char *tris_file);
- ~Model();
- void Draw();
- void DrawTri(int index);
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus1.path b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus1.path
deleted file mode 100644
index 3cc39f84bbb1106f2e57985b60de7a0827cc315e..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus1.path
+++ /dev/null
@@ -1,11991 +0,0 @@
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus1.tris b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus1.tris
deleted file mode 100644
index cfb261e314c2ecc53768821833210de385db29d7..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus1.tris
+++ /dev/null
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus2.path b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus2.path
deleted file mode 100644
index 54752acccd99324d9b6123e3fde05e3bde238f2f..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus2.path
+++ /dev/null
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus2.tris b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus2.tris
deleted file mode 100644
index 115e4e2d5daf266621ecc3baf65258c16bf9f5b6..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/falling/torus2.tris
+++ /dev/null
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/Makefile b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/Makefile
deleted file mode 100644
index fff3b2a8dc75e179090064722331c0b3c302137c..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/Makefile
+++ /dev/null
@@ -1,28 +0,0 @@
-CC = g++
-
-CFLAGS = -O2 -I. -I../../include
-LDFLAGS = -L. -L../../lib
-LDLIBS = -lPQP -lm
-
-.SUFFIXES: .cpp
-
-SRCS = main.cpp
-
-OBJECTS = main.o
-
-TARGET = sample
-
-CLEAN = $(OBJECTS) $(TARGET)
-
-.cpp.o:
- $(CC) ${CFLAGS} -c $<
-
-$(TARGET): $(OBJECTS)
- $(CC) $(CFLAGS) -o $(TARGET) $(OBJECTS) -L. $(LDFLAGS) $(LDLIBS)
-
-run: $(TARGET)
- $(TARGET)
-
-clean:
- /bin/rm -f $(CLEAN)
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/main.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/main.cpp
deleted file mode 100644
index f81dfdba98e0257182a4aaac9c049f3776a32bd3..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/main.cpp
+++ /dev/null
@@ -1,301 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <math.h>
-#include "PQP.h"
-
-#define PI 3.14159265359
-#define LISTS 0
-
-main()
-{
- // initialize PQP model pointers
-
- PQP_Model *b1 = new PQP_Model;
- PQP_Model *b2 = new PQP_Model;
-
- // Add trianges to form tori
-
- fprintf(stderr, "loading tris into PQP_Model objects..."); fflush(stderr);
-
- PQP_REAL a = (PQP_REAL)1.0; // major radius of the tori
- PQP_REAL b = (PQP_REAL)0.2; // minor radius of the tori
-
- int n1 = 50; // tori will have n1*n2*2 triangles each
- int n2 = 50;
-
- int uc, vc;
- int count = 0;
-
- b1->BeginModel();
- b2->BeginModel();
- for(uc=0; uc<n1; uc++)
- {
- for(vc=0; vc<n2; vc++)
- {
- PQP_REAL u1 = (PQP_REAL)(2.0*PI*uc) / n1;
- PQP_REAL u2 = (PQP_REAL)(2.0*PI*(uc+1)) / n1;
- PQP_REAL v1 = (PQP_REAL)(2.0*PI*vc) / n2;
- PQP_REAL v2 = (PQP_REAL)(2.0*PI*(vc+1)) / n2;
-
- PQP_REAL p1[3], p2[3], p3[3], p4[3];
-
- p1[0] = (a - b * cos(v1)) * cos(u1);
- p2[0] = (a - b * cos(v1)) * cos(u2);
- p3[0] = (a - b * cos(v2)) * cos(u1);
- p4[0] = (a - b * cos(v2)) * cos(u2);
- p1[1] = (a - b * cos(v1)) * sin(u1);
- p2[1] = (a - b * cos(v1)) * sin(u2);
- p3[1] = (a - b * cos(v2)) * sin(u1);
- p4[1] = (a - b * cos(v2)) * sin(u2);
- p1[2] = b * sin(v1);
- p2[2] = b * sin(v1);
- p3[2] = b * sin(v2);
- p4[2] = b * sin(v2);
-
- b1->AddTri(p1, p2, p3, count);
- b1->AddTri(p4, p2, p3, count+1);
- b2->AddTri(p1, p2, p3, count);
- b2->AddTri(p4, p2, p3, count+1);
-
- count += 2;
- }
- }
-
- fprintf(stderr, "done\n"); fflush(stderr);
- fprintf(stderr, "Tori have %d triangles each.\n", count);
- fprintf(stderr, "building hierarchies..."); fflush(stderr);
- b1->EndModel();
- b2->EndModel();
- fprintf(stderr, "done.\n");
- b1->MemUsage(1);
- b2->MemUsage(1);
- fflush(stderr);
-
- // now we are free to call the proximity routines.
- // but first, construct the transformations that define the placement
- // of our two hierarchies in world space:
-
- // this placement causes them to overlap a large amount.
-
- PQP_REAL R1[3][3], R2[3][3], T1[3], T2[3];
-
- R1[0][0] = R1[1][1] = R1[2][2] = 1.0;
- R1[0][1] = R1[1][0] = R1[2][0] = 0.0;
- R1[0][2] = R1[1][2] = R1[2][1] = 0.0;
-
- R2[0][0] = R2[1][1] = R2[2][2] = 1.0;
- R2[0][1] = R2[1][0] = R2[2][0] = 0.0;
- R2[0][2] = R2[1][2] = R2[2][1] = 0.0;
-
- T1[0] = 1.0; T1[1] = 0.0; T1[2] = 0.0;
- T2[0] = 0.0; T2[1] = 0.0; T2[2] = 0.0;
-
- // perform a collision query
-
- PQP_CollideResult cres;
- PQP_Collide(&cres, R1, T1, b1, R2, T2, b2, PQP_ALL_CONTACTS);
-
- // looking at the report, we can see where all the contacts were, and
- // also how many tests were necessary:
-
- printf("\nAll contact collision query between overlapping tori:\n");
- printf("Num BV tests: %d\n", cres.NumBVTests());
- printf("Num Tri tests: %d\n", cres.NumTriTests());
- printf("Num contact pairs: %d\n", cres.NumPairs());
-#if LISTS
- int i;
- for(i=0; i<cres.NumPairs(); i++)
- {
- printf("\t contact %4d: tri %4d and tri %4d\n",
- i,
- cres.Id1(i),
- cres.Id2(i));
- }
-#endif
-
- // Notice the PQP_ALL_CONTACTS flag we used in the call to PQP_Collide.
- // The alternative is to use the PQP_FIRST_CONTACT flag, instead.
- // The result is that the collide routine searches for any contact,
- // but not all of them. It can take many many fewer tests to locate a single
- // contact.
-
- PQP_Collide(&cres, R1, T1, b1, R2, T2, b2, PQP_FIRST_CONTACT);
-
- printf("\nFirst contact collision query between overlapping tori:\n");
- printf("Num BV tests: %d\n", cres.NumBVTests());
- printf("Num Tri tests: %d\n", cres.NumTriTests());
- printf("Num contact pairs: %d\n", cres.NumPairs());
-#if LISTS
- for(i=0; i<cres.NumPairs(); i++)
- {
- printf("\t contact %4d: tri %4d and tri %4d\n",
- i,
- cres.Id1(i),
- cres.Id2(i));
- }
-#endif
-
- // Perform a distance query, which should return a distance of 0.0
-
- PQP_DistanceResult dres;
- PQP_Distance(&dres, R1, T1, b1, R2, T2, b2, 0.0, 0.0);
-
- printf("\nDistance query between overlapping tori\n");
- printf("Num BV tests: %d\n", dres.NumBVTests());
- printf("Num Tri tests: %d\n", dres.NumTriTests());
- printf("Distance: %lf\n", dres.Distance());
-
- // by rotating one of them around the x-axis 90 degrees, they
- // are now interlocked, but not quite touching.
-
- R1[0][0] = 1.0; R1[0][1] = 0.0; R1[0][2] = 0.0;
- R1[1][0] = 0.0; R1[1][1] = 0.0; R1[1][2] =-1.0;
- R1[2][0] = 0.0; R1[2][1] = 1.0; R1[2][2] = 0.0;
-
- PQP_Collide(&cres, R1, T1, b1, R2, T2, b2, PQP_FIRST_CONTACT);
-
- printf("\nCollision query between interlocked but nontouching tori:\n");
- printf("Num BV tests: %d\n", cres.NumBVTests());
- printf("Num Tri tests: %d\n", cres.NumTriTests());
- printf("Num contact pairs: %d\n", cres.NumPairs());
-#if LISTS
- for(i=0; i<cres.NumPairs(); i++)
- {
- printf("\t contact %4d: tri %4d and tri %4d\n",
- i,
- cres.Id1(i),
- cres.Id2(i));
- }
-#endif
-
- // Perform a distance query - the distance found should be greater than zero
-
- PQP_Distance(&dres, R1, T1, b1, R2, T2, b2, 0.0, 0.0);
-
- printf("\nDistance query between interlocked but nontouching tori\n");
- printf("Num BV tests: %d\n", dres.NumBVTests());
- printf("Num Tri tests: %d\n", dres.NumTriTests());
- printf("Distance: %lf\n", dres.Distance());
-
- // Perform two tolerance queries. One tolerance setting is greater than the
- // distance between the models, and one tolerance is less than the distance.
-
- PQP_ToleranceResult tres;
- PQP_REAL tolerance = (PQP_REAL).60;
- PQP_Tolerance(&tres, R1, T1, b1, R2, T2, b2, tolerance);
-
- printf("\nTolerance query between interlocked but nontouching tori\n"
- "with tolerance %lf\n", tolerance);
- printf("Num BV tests: %d\n", tres.NumBVTests());
- printf("Num Tri tests: %d\n", tres.NumTriTests());
- printf("Closer than tolerance? ",tolerance);
- if (tres.CloserThanTolerance()) printf("yes.\n"); else printf("no.\n");
-
- tolerance = (PQP_REAL).40;
- PQP_Tolerance(&tres, R1, T1, b1, R2, T2, b2, tolerance);
-
- printf("\nTolerance query between interlocked but nontouching tori\n"
- "with tolerance %lf\n", tolerance);
- printf("Num BV tests: %d\n", tres.NumBVTests());
- printf("Num Tri tests: %d\n", tres.NumTriTests());
- printf("Closer than tolerance? ",tolerance);
- if (tres.CloserThanTolerance()) printf("yes.\n"); else printf("no.\n");
-
- // by moving one of the tori closer to the other, they
- // almost touch. This is the case that requires a lot
- // of work wiht methods which use bounding boxes of limited
- // aspect ratio. Oriented bounding boxes are more efficient
- // at determining noncontact than spheres, octree, or axis-aligned
- // bounding boxes for scenarios like this. In this case, the interlocked
- // tori are separated by 0.0001 at their closest point.
-
-
- T1[0] = (PQP_REAL)1.5999;
-
- PQP_Collide(&cres, R1, T1, b1, R2, T2, b2, PQP_FIRST_CONTACT);
-
- printf("\nCollision query on interlocked and almost touching tori:\n");
- printf("Num BV tests: %d\n", cres.NumBVTests());
- printf("Num Tri tests: %d\n", cres.NumTriTests());
- printf("Num contact pairs: %d\n", cres.NumPairs());
-#if LISTS
- for(i=0; i<cres.NumPairs(); i++)
- {
- printf("\t contact %4d: tri %4d and tri %4d\n",
- i,
- cres.Id1(i),
- cres.Id2(i));
- }
-#endif
-
- PQP_Distance(&dres, R1, T1, b1, R2, T2, b2, 0.0, 0.0);
-
- printf("\nDistance query between interlocked and almost touching tori\n");
- printf("Num BV tests: %d\n", dres.NumBVTests());
- printf("Num Tri tests: %d\n", dres.NumTriTests());
- printf("Distance: %lf\n", dres.Distance());
-
- tolerance = (PQP_REAL)0.00015;
- PQP_Tolerance(&tres, R1, T1, b1, R2, T2, b2, tolerance);
-
- printf("\nTolerance query between interlocked and almost touching tori\n"
- "with tolerance %lf\n", tolerance);
- printf("Num BV tests: %d\n", tres.NumBVTests());
- printf("Num Tri tests: %d\n", tres.NumTriTests());
- printf("Closer than tolerance? ",tolerance);
- if (tres.CloserThanTolerance()) printf("yes.\n"); else printf("no.\n");
-
- tolerance = (PQP_REAL)0.00005;
- PQP_Tolerance(&tres, R1, T1, b1, R2, T2, b2, tolerance);
-
- printf("\nTolerance query between interlocked and almost touching tori\n"
- "with tolerance %lf\n", tolerance);
- printf("Num BV tests: %d\n", tres.NumBVTests());
- printf("Num Tri tests: %d\n", tres.NumTriTests());
- printf("Closer than tolerance? ",tolerance);
- if (tres.CloserThanTolerance()) printf("yes.\n"); else printf("no.\n");
-
- delete b1;
- delete b2;
-
- return 0;
-}
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/sample.dsp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/sample.dsp
deleted file mode 100644
index aec7603d4701b8ac86e3cd3443cf937e17cee9ec..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/sample.dsp
+++ /dev/null
@@ -1,91 +0,0 @@
-# Microsoft Developer Studio Project File - Name="sample" - Package Owner=<4>
-# Microsoft Developer Studio Generated Build File, Format Version 5.00
-# ** DO NOT EDIT **
-
-# TARGTYPE "Win32 (x86) Console Application" 0x0103
-
-CFG=sample - Win32 Debug
-!MESSAGE This is not a valid makefile. To build this project using NMAKE,
-!MESSAGE use the Export Makefile command and run
-!MESSAGE
-!MESSAGE NMAKE /f "sample.mak".
-!MESSAGE
-!MESSAGE You can specify a configuration when running NMAKE
-!MESSAGE by defining the macro CFG on the command line. For example:
-!MESSAGE
-!MESSAGE NMAKE /f "sample.mak" CFG="sample - Win32 Debug"
-!MESSAGE
-!MESSAGE Possible choices for configuration are:
-!MESSAGE
-!MESSAGE "sample - Win32 Release" (based on "Win32 (x86) Console Application")
-!MESSAGE "sample - Win32 Debug" (based on "Win32 (x86) Console Application")
-!MESSAGE
-
-# Begin Project
-# PROP Scc_ProjName ""
-# PROP Scc_LocalPath ""
-CPP=cl.exe
-RSC=rc.exe
-
-!IF "$(CFG)" == "sample - Win32 Release"
-
-# PROP BASE Use_MFC 0
-# PROP BASE Use_Debug_Libraries 0
-# PROP BASE Output_Dir "Release"
-# PROP BASE Intermediate_Dir "Release"
-# PROP BASE Target_Dir ""
-# PROP Use_MFC 0
-# PROP Use_Debug_Libraries 0
-# PROP Output_Dir "./"
-# PROP Intermediate_Dir "Release"
-# PROP Ignore_Export_Lib 0
-# PROP Target_Dir ""
-# ADD BASE CPP /nologo /W3 /GX /O2 /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD CPP /nologo /W3 /GX /O2 /I "..\..\include" /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD BASE RSC /l 0x409 /d "NDEBUG"
-# ADD RSC /l 0x409 /d "NDEBUG"
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LINK32=link.exe
-# ADD BASE LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /machine:I386
-# ADD LINK32 pqp.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib PQP.lib /nologo /subsystem:console /machine:I386 /libpath:"..\..\lib"
-# SUBTRACT LINK32 /nodefaultlib
-
-!ELSEIF "$(CFG)" == "sample - Win32 Debug"
-
-# PROP BASE Use_MFC 0
-# PROP BASE Use_Debug_Libraries 1
-# PROP BASE Output_Dir "Debug"
-# PROP BASE Intermediate_Dir "Debug"
-# PROP BASE Target_Dir ""
-# PROP Use_MFC 0
-# PROP Use_Debug_Libraries 1
-# PROP Output_Dir "./"
-# PROP Intermediate_Dir "Debug"
-# PROP Ignore_Export_Lib 0
-# PROP Target_Dir ""
-# ADD BASE CPP /nologo /W3 /Gm /GX /Zi /Od /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD CPP /nologo /W3 /GX /Od /I "..\..\include" /D "WIN32" /D "_DEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD BASE RSC /l 0x409 /d "_DEBUG"
-# ADD RSC /l 0x409 /d "_DEBUG"
-BSC32=bscmake.exe
-# ADD BASE BSC32 /nologo
-# ADD BSC32 /nologo
-LINK32=link.exe
-# ADD BASE LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /debug /machine:I386 /pdbtype:sept
-# ADD LINK32 PQP.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /debug /machine:I386 /pdbtype:sept /libpath:"..\..\lib"
-# SUBTRACT LINK32 /nodefaultlib
-
-!ENDIF
-
-# Begin Target
-
-# Name "sample - Win32 Release"
-# Name "sample - Win32 Debug"
-# Begin Source File
-
-SOURCE=.\main.cpp
-# End Source File
-# End Target
-# End Project
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/sample.plg b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/sample.plg
deleted file mode 100644
index 958f67aee4cec28709a6d7d2c0982aaf2456ec4e..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/sample/sample.plg
+++ /dev/null
@@ -1,20 +0,0 @@
---------------------Configuration: sample - Win32 Release--------------------
-Begining build with project "C:\Win95\Desktop\PQP_v1.2.1\demos\sample\sample.dsp", at root.
-Active configuration is Win32 (x86) Console Application (based on Win32 (x86) Console Application)
-
-Project's tools are:
- "32-bit C/C++ Compiler for 80x86" with flags "/nologo /ML /W3 /GX /O2 /I "..\..\include" /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /Fp"Release/sample.pch" /YX /Fo"Release/" /Fd"Release/" /FD /c "
- "Win32 Resource Compiler" with flags "/l 0x409 /d "NDEBUG" "
- "Browser Database Maker" with flags "/nologo /o"./sample.bsc" "
- "COFF Linker for 80x86" with flags "pqp.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib PQP.lib /nologo /subsystem:console /incremental:no /pdb:"./sample.pdb" /machine:I386 /out:"./sample.exe" /libpath:"..\..\lib" "
- "Custom Build" with flags ""
- "<Component 0xa>" with flags ""
-
-Creating temp file "C:\WIN95\TEMP\RSP6314.TMP" with contents <pqp.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib PQP.lib /nologo /subsystem:console /incremental:no /pdb:"./sample.pdb" /machine:I386 /out:"./sample.exe" /libpath:"..\..\lib"
-.\Release\main.obj>
-Creating command line "link.exe @C:\WIN95\TEMP\RSP6314.TMP"
-Linking...
-
-
-
-sample.exe - 0 error(s), 0 warning(s)
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/Makefile b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/Makefile
deleted file mode 100644
index 9289a9b19b4222c6d3eafc0ce607a1718a5cafef..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/Makefile
+++ /dev/null
@@ -1,36 +0,0 @@
-# Must set these gl and glut locations to build 'spinning'
-
-CC = g++
-
-GL_INCPATH = -I/usr/include/
-GL_LIBPATH = -L/usr/lib/ -L/usr/X11R6/lib/
-GL_LIBS = -lGLU -lGL -lXext -lXmu -lXi -lX11 -lglut
-
-.SUFFIXES: .cpp
-
-CC = g++
-CFLAGS = -g -O2 -I. -I../../include $(GL_INCPATH)
-LDFLAGS = -L. -L../../lib -L/usr/lib/ -L/usr/X11R6/lib/
-LDLIBS = -lPQP -lm $(GL_LIBS)
-
-OBJS = main.o model.o
-TARGET = spinning
-
-.cpp.o:
- $(CC) ${CFLAGS} -c $<
-
-$(TARGET): $(OBJS)
- $(CC) $(CFLAGS) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LDLIBS)
-
-run: $(TARGET)
- $(TARGET)
-
-clean:
- rm -f *~ $(OBJS) $(TARGET)
-
-
-
-
-
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/MatVec.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/MatVec.h
deleted file mode 100644
index 3d90522f84c19bd070f0d18fb1aff5078bdaa753..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/MatVec.h
+++ /dev/null
@@ -1,881 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_MATVEC_H
-#define PQP_MATVEC_H
-
-#include <math.h>
-#include <stdio.h>
-#include "PQP_Compile.h"
-
-#ifndef M_PI
-const double M_PI = 3.14159265359;
-#endif
-
-#ifdef gnu
-#include "zzzz.h"
-
-#ifdef hppa
-#define myfabs(x) \
- ({double __value, __arg = (x); \
- asm("fabs,dbl %1, %0": "=f" (__value): "f" (__arg)); \
- __value; \
-});
-#endif
-
-#ifdef mips
-#define myfabs(x) \
- ({double __value, __arg = (x); \
- asm("abs.d %0, %1": "=f" (__value): "f" (__arg)); \
- __value; \
-});
-#endif
-
-#else
-
-#define myfabs(x) ((x < 0) ? -x : x)
-
-#endif
-
-
-inline
-void
-Mprintg(const PQP_REAL M[3][3])
-{
- printf("%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-
-inline
-void
-Mfprint(FILE *f, const PQP_REAL M[3][3])
-{
- fprintf(f, "%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-inline
-void
-Mprint(const PQP_REAL M[3][3])
-{
- printf("%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-inline
-void
-Vprintg(const PQP_REAL V[3])
-{
- printf("%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Vfprint(FILE *f, const PQP_REAL V[3])
-{
- fprintf(f, "%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Vprint(const PQP_REAL V[3])
-{
- printf("%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Midentity(PQP_REAL M[3][3])
-{
- M[0][0] = M[1][1] = M[2][2] = 1.0;
- M[0][1] = M[1][2] = M[2][0] = 0.0;
- M[0][2] = M[1][0] = M[2][1] = 0.0;
-}
-
-inline
-void
-Videntity(PQP_REAL T[3])
-{
- T[0] = T[1] = T[2] = 0.0;
-}
-
-inline
-void
-McM(PQP_REAL Mr[3][3], const PQP_REAL M[3][3])
-{
- Mr[0][0] = M[0][0]; Mr[0][1] = M[0][1]; Mr[0][2] = M[0][2];
- Mr[1][0] = M[1][0]; Mr[1][1] = M[1][1]; Mr[1][2] = M[1][2];
- Mr[2][0] = M[2][0]; Mr[2][1] = M[2][1]; Mr[2][2] = M[2][2];
-}
-
-inline
-void
-MTcM(PQP_REAL Mr[3][3], const PQP_REAL M[3][3])
-{
- Mr[0][0] = M[0][0]; Mr[1][0] = M[0][1]; Mr[2][0] = M[0][2];
- Mr[0][1] = M[1][0]; Mr[1][1] = M[1][1]; Mr[2][1] = M[1][2];
- Mr[0][2] = M[2][0]; Mr[1][2] = M[2][1]; Mr[2][2] = M[2][2];
-}
-
-inline
-void
-VcV(PQP_REAL Vr[3], const PQP_REAL V[3])
-{
- Vr[0] = V[0]; Vr[1] = V[1]; Vr[2] = V[2];
-}
-
-inline
-void
-McolcV(PQP_REAL Vr[3], const PQP_REAL M[3][3], int c)
-{
- Vr[0] = M[0][c];
- Vr[1] = M[1][c];
- Vr[2] = M[2][c];
-}
-
-inline
-void
-McolcMcol(PQP_REAL Mr[3][3], int cr, const PQP_REAL M[3][3], int c)
-{
- Mr[0][cr] = M[0][c];
- Mr[1][cr] = M[1][c];
- Mr[2][cr] = M[2][c];
-}
-
-inline
-void
-MxMpV(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3], const PQP_REAL T[3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[1][0] +
- M1[0][2] * M2[2][0] +
- T[0]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[1][2] * M2[2][0] +
- T[1]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[1][0] +
- M1[2][2] * M2[2][0] +
- T[2]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[2][1] +
- T[0]);
- Mr[1][1] = (M1[1][0] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[2][1] +
- T[1]);
- Mr[2][1] = (M1[2][0] * M2[0][1] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[2][1] +
- T[2]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[0][1] * M2[1][2] +
- M1[0][2] * M2[2][2] +
- T[0]);
- Mr[1][2] = (M1[1][0] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[1][2] * M2[2][2] +
- T[1]);
- Mr[2][2] = (M1[2][0] * M2[0][2] +
- M1[2][1] * M2[1][2] +
- M1[2][2] * M2[2][2] +
- T[2]);
-}
-
-inline
-void
-MxM(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[1][0] +
- M1[0][2] * M2[2][0]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[1][2] * M2[2][0]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[1][0] +
- M1[2][2] * M2[2][0]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[2][1]);
- Mr[1][1] = (M1[1][0] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[2][1]);
- Mr[2][1] = (M1[2][0] * M2[0][1] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[2][1]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[0][1] * M2[1][2] +
- M1[0][2] * M2[2][2]);
- Mr[1][2] = (M1[1][0] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[1][2] * M2[2][2]);
- Mr[2][2] = (M1[2][0] * M2[0][2] +
- M1[2][1] * M2[1][2] +
- M1[2][2] * M2[2][2]);
-}
-
-
-inline
-void
-MxMT(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[0][1] +
- M1[0][2] * M2[0][2]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[0][1] +
- M1[1][2] * M2[0][2]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[0][1] +
- M1[2][2] * M2[0][2]);
- Mr[0][1] = (M1[0][0] * M2[1][0] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[1][2]);
- Mr[1][1] = (M1[1][0] * M2[1][0] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[1][2]);
- Mr[2][1] = (M1[2][0] * M2[1][0] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[1][2]);
- Mr[0][2] = (M1[0][0] * M2[2][0] +
- M1[0][1] * M2[2][1] +
- M1[0][2] * M2[2][2]);
- Mr[1][2] = (M1[1][0] * M2[2][0] +
- M1[1][1] * M2[2][1] +
- M1[1][2] * M2[2][2]);
- Mr[2][2] = (M1[2][0] * M2[2][0] +
- M1[2][1] * M2[2][1] +
- M1[2][2] * M2[2][2]);
-}
-
-inline
-void
-MTxM(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[1][0] * M2[1][0] +
- M1[2][0] * M2[2][0]);
- Mr[1][0] = (M1[0][1] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[2][1] * M2[2][0]);
- Mr[2][0] = (M1[0][2] * M2[0][0] +
- M1[1][2] * M2[1][0] +
- M1[2][2] * M2[2][0]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[1][0] * M2[1][1] +
- M1[2][0] * M2[2][1]);
- Mr[1][1] = (M1[0][1] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[2][1] * M2[2][1]);
- Mr[2][1] = (M1[0][2] * M2[0][1] +
- M1[1][2] * M2[1][1] +
- M1[2][2] * M2[2][1]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[1][0] * M2[1][2] +
- M1[2][0] * M2[2][2]);
- Mr[1][2] = (M1[0][1] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[2][1] * M2[2][2]);
- Mr[2][2] = (M1[0][2] * M2[0][2] +
- M1[1][2] * M2[1][2] +
- M1[2][2] * M2[2][2]);
-}
-
-inline
-void
-MxV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]);
- Vr[1] = (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]);
- Vr[2] = (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-
-inline
-void
-MxVpV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2] +
- V2[0]);
- Vr[1] = (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2] +
- V2[1]);
- Vr[2] = (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2] +
- V2[2]);
-}
-
-
-inline
-void
-sMxVpV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = s1 * (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]) +
- V2[0];
- Vr[1] = s1 * (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]) +
- V2[1];
- Vr[2] = s1 * (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]) +
- V2[2];
-}
-
-inline
-void
-MTxV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[1][0] * V1[1] +
- M1[2][0] * V1[2]);
- Vr[1] = (M1[0][1] * V1[0] +
- M1[1][1] * V1[1] +
- M1[2][1] * V1[2]);
- Vr[2] = (M1[0][2] * V1[0] +
- M1[1][2] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-inline
-void
-sMTxV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = s1*(M1[0][0] * V1[0] +
- M1[1][0] * V1[1] +
- M1[2][0] * V1[2]);
- Vr[1] = s1*(M1[0][1] * V1[0] +
- M1[1][1] * V1[1] +
- M1[2][1] * V1[2]);
- Vr[2] = s1*(M1[0][2] * V1[0] +
- M1[1][2] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-inline
-void
-sMxV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = s1*(M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]);
- Vr[1] = s1*(M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]);
- Vr[2] = s1*(M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-
-inline
-void
-VmV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[0] - V2[0];
- Vr[1] = V1[1] - V2[1];
- Vr[2] = V1[2] - V2[2];
-}
-
-inline
-void
-VpV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[0] + V2[0];
- Vr[1] = V1[1] + V2[1];
- Vr[2] = V1[2] + V2[2];
-}
-
-inline
-void
-VpVxS(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3], PQP_REAL s)
-{
- Vr[0] = V1[0] + V2[0] * s;
- Vr[1] = V1[1] + V2[1] * s;
- Vr[2] = V1[2] + V2[2] * s;
-}
-
-inline
-void
-MskewV(PQP_REAL M[3][3], const PQP_REAL v[3])
-{
- M[0][0] = M[1][1] = M[2][2] = 0.0;
- M[1][0] = v[2];
- M[0][1] = -v[2];
- M[0][2] = v[1];
- M[2][0] = -v[1];
- M[1][2] = -v[0];
- M[2][1] = v[0];
-}
-
-
-inline
-void
-VcrossV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[1]*V2[2] - V1[2]*V2[1];
- Vr[1] = V1[2]*V2[0] - V1[0]*V2[2];
- Vr[2] = V1[0]*V2[1] - V1[1]*V2[0];
-}
-
-
-inline
-PQP_REAL
-Vlength(PQP_REAL V[3])
-{
- return sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
-}
-
-inline
-void
-Vnormalize(PQP_REAL V[3])
-{
- PQP_REAL d = (PQP_REAL)1.0 / sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
- V[0] *= d;
- V[1] *= d;
- V[2] *= d;
-}
-
-
-inline
-PQP_REAL
-VdotV(const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- return (V1[0]*V2[0] + V1[1]*V2[1] + V1[2]*V2[2]);
-}
-
-
-inline
-PQP_REAL
-VdistV2(const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- return ( (V1[0]-V2[0]) * (V1[0]-V2[0]) +
- (V1[1]-V2[1]) * (V1[1]-V2[1]) +
- (V1[2]-V2[2]) * (V1[2]-V2[2]));
-}
-
-inline
-void
-VxS(PQP_REAL Vr[3], const PQP_REAL V[3], PQP_REAL s)
-{
- Vr[0] = V[0] * s;
- Vr[1] = V[1] * s;
- Vr[2] = V[2] * s;
-}
-
-inline
-void
-MRotZ(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[0][0] = cos(t);
- Mr[1][0] = sin(t);
- Mr[0][1] = -Mr[1][0];
- Mr[1][1] = Mr[0][0];
- Mr[2][0] = Mr[2][1] = 0.0;
- Mr[0][2] = Mr[1][2] = 0.0;
- Mr[2][2] = 1.0;
-}
-
-
-inline
-void
-MRotX(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[1][1] = cos(t);
- Mr[2][1] = sin(t);
- Mr[1][2] = -Mr[2][1];
- Mr[2][2] = Mr[1][1];
- Mr[0][1] = Mr[0][2] = 0.0;
- Mr[1][0] = Mr[2][0] = 0.0;
- Mr[0][0] = 1.0;
-}
-
-inline
-void
-MRotY(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[2][2] = cos(t);
- Mr[0][2] = sin(t);
- Mr[2][0] = -Mr[0][2];
- Mr[0][0] = Mr[2][2];
- Mr[1][2] = Mr[1][0] = 0.0;
- Mr[2][1] = Mr[0][1] = 0.0;
- Mr[1][1] = 1.0;
-}
-
-inline
-void
-MVtoOGL(double oglm[16], const PQP_REAL R[3][3], const PQP_REAL T[3])
-{
- oglm[0] = (double)R[0][0];
- oglm[1] = (double)R[1][0];
- oglm[2] = (double)R[2][0];
- oglm[3] = 0.0;
- oglm[4] = (double)R[0][1];
- oglm[5] = (double)R[1][1];
- oglm[6] = (double)R[2][1];
- oglm[7] = 0.0;
- oglm[8] = (double)R[0][2];
- oglm[9] = (double)R[1][2];
- oglm[10] = (double)R[2][2];
- oglm[11] = 0.0;
- oglm[12] = (double)T[0];
- oglm[13] = (double)T[1];
- oglm[14] = (double)T[2];
- oglm[15] = 1.0;
-}
-
-inline
-void
-OGLtoMV(PQP_REAL R[3][3], PQP_REAL T[3], const double oglm[16])
-{
- R[0][0] = (PQP_REAL)oglm[0];
- R[1][0] = (PQP_REAL)oglm[1];
- R[2][0] = (PQP_REAL)oglm[2];
-
- R[0][1] = (PQP_REAL)oglm[4];
- R[1][1] = (PQP_REAL)oglm[5];
- R[2][1] = (PQP_REAL)oglm[6];
-
- R[0][2] = (PQP_REAL)oglm[8];
- R[1][2] = (PQP_REAL)oglm[9];
- R[2][2] = (PQP_REAL)oglm[10];
-
- T[0] = (PQP_REAL)oglm[12];
- T[1] = (PQP_REAL)oglm[13];
- T[2] = (PQP_REAL)oglm[14];
-}
-
-// taken from quatlib, written by Richard Holloway
-const int QX = 0;
-const int QY = 1;
-const int QZ = 2;
-const int QW = 3;
-
-inline
-void
-MRotQ(PQP_REAL destMatrix[3][3], PQP_REAL srcQuat[4])
-{
- PQP_REAL s;
- PQP_REAL xs, ys, zs,
- wx, wy, wz,
- xx, xy, xz,
- yy, yz, zz;
-
- /*
- * For unit srcQuat, just set s = 2.0; or set xs = srcQuat[QX] +
- * srcQuat[QX], etc.
- */
-
- s = (PQP_REAL)2.0 / (srcQuat[QX]*srcQuat[QX] + srcQuat[QY]*srcQuat[QY] +
- srcQuat[QZ]*srcQuat[QZ] + srcQuat[QW]*srcQuat[QW]);
-
- xs = srcQuat[QX] * s; ys = srcQuat[QY] * s; zs = srcQuat[QZ] * s;
- wx = srcQuat[QW] * xs; wy = srcQuat[QW] * ys; wz = srcQuat[QW] * zs;
- xx = srcQuat[QX] * xs; xy = srcQuat[QX] * ys; xz = srcQuat[QX] * zs;
- yy = srcQuat[QY] * ys; yz = srcQuat[QY] * zs; zz = srcQuat[QZ] * zs;
-
- destMatrix[QX][QX] = (PQP_REAL)1.0 - (yy + zz);
- destMatrix[QX][QY] = xy + wz;
- destMatrix[QX][QZ] = xz - wy;
-
- destMatrix[QY][QX] = xy - wz;
- destMatrix[QY][QY] = (PQP_REAL)1.0 - (xx + zz);
- destMatrix[QY][QZ] = yz + wx;
-
- destMatrix[QZ][QX] = xz + wy;
- destMatrix[QZ][QY] = yz - wx;
- destMatrix[QZ][QZ] = (PQP_REAL)1.0 - (xx + yy);
-}
-
-inline
-void
-Mqinverse(PQP_REAL Mr[3][3], PQP_REAL m[3][3])
-{
- int i,j;
-
- for(i=0; i<3; i++)
- for(j=0; j<3; j++)
- {
- int i1 = (i+1)%3;
- int i2 = (i+2)%3;
- int j1 = (j+1)%3;
- int j2 = (j+2)%3;
- Mr[i][j] = (m[j1][i1]*m[j2][i2] - m[j1][i2]*m[j2][i1]);
- }
-}
-
-// Meigen from Numerical Recipes in C
-
-#if 0
-
-#define rfabs(x) ((x < 0) ? -x : x)
-
-#define ROT(a,i,j,k,l) g=a[i][j]; h=a[k][l]; a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
-
-int
-inline
-Meigen(PQP_REAL vout[3][3], PQP_REAL dout[3], PQP_REAL a[3][3])
-{
- int i;
- PQP_REAL tresh,theta,tau,t,sm,s,h,g,c;
- int nrot;
- PQP_REAL b[3];
- PQP_REAL z[3];
- PQP_REAL v[3][3];
- PQP_REAL d[3];
-
- v[0][0] = v[1][1] = v[2][2] = 1.0;
- v[0][1] = v[1][2] = v[2][0] = 0.0;
- v[0][2] = v[1][0] = v[2][1] = 0.0;
-
- b[0] = a[0][0]; d[0] = a[0][0]; z[0] = 0.0;
- b[1] = a[1][1]; d[1] = a[1][1]; z[1] = 0.0;
- b[2] = a[2][2]; d[2] = a[2][2]; z[2] = 0.0;
-
- nrot = 0;
-
-
- for(i=0; i<50; i++)
- {
-
- printf("2\n");
-
- sm=0.0; sm+=fabs(a[0][1]); sm+=fabs(a[0][2]); sm+=fabs(a[1][2]);
- if (sm == 0.0) { McM(vout,v); VcV(dout,d); return i; }
-
- if (i < 3) tresh=0.2*sm/(3*3); else tresh=0.0;
-
- {
- g = 100.0*rfabs(a[0][1]);
- if (i>3 && rfabs(d[0])+g==rfabs(d[0]) && rfabs(d[1])+g==rfabs(d[1]))
- a[0][1]=0.0;
- else if (rfabs(a[0][1])>tresh)
- {
- h = d[1]-d[0];
- if (rfabs(h)+g == rfabs(h)) t=(a[0][1])/h;
- else
- {
- theta=0.5*h/(a[0][1]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[0][1];
- z[0] -= h; z[1] += h; d[0] -= h; d[1] += h;
- a[0][1]=0.0;
- ROT(a,0,2,1,2); ROT(v,0,0,0,1); ROT(v,1,0,1,1); ROT(v,2,0,2,1);
- nrot++;
- }
- }
-
- {
- g = 100.0*rfabs(a[0][2]);
- if (i>3 && rfabs(d[0])+g==rfabs(d[0]) && rfabs(d[2])+g==rfabs(d[2]))
- a[0][2]=0.0;
- else if (rfabs(a[0][2])>tresh)
- {
- h = d[2]-d[0];
- if (rfabs(h)+g == rfabs(h)) t=(a[0][2])/h;
- else
- {
- theta=0.5*h/(a[0][2]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[0][2];
- z[0] -= h; z[2] += h; d[0] -= h; d[2] += h;
- a[0][2]=0.0;
- ROT(a,0,1,1,2); ROT(v,0,0,0,2); ROT(v,1,0,1,2); ROT(v,2,0,2,2);
- nrot++;
- }
- }
-
-
- {
- g = 100.0*rfabs(a[1][2]);
- if (i>3 && rfabs(d[1])+g==rfabs(d[1]) && rfabs(d[2])+g==rfabs(d[2]))
- a[1][2]=0.0;
- else if (rfabs(a[1][2])>tresh)
- {
- h = d[2]-d[1];
- if (rfabs(h)+g == rfabs(h)) t=(a[1][2])/h;
- else
- {
- theta=0.5*h/(a[1][2]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[1][2];
- z[1] -= h; z[2] += h; d[1] -= h; d[2] += h;
- a[1][2]=0.0;
- ROT(a,0,1,0,2); ROT(v,0,1,0,2); ROT(v,1,1,1,2); ROT(v,2,1,2,2);
- nrot++;
- }
- }
-
- b[0] += z[0]; d[0] = b[0]; z[0] = 0.0;
- b[1] += z[1]; d[1] = b[1]; z[1] = 0.0;
- b[2] += z[2]; d[2] = b[2]; z[2] = 0.0;
-
- }
-
- fprintf(stderr, "eigen: too many iterations in Jacobi transform (%d).\n", i);
-
- return i;
-}
-
-#else
-
-
-
-#define ROTATE(a,i,j,k,l) g=a[i][j]; h=a[k][l]; a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
-
-void
-inline
-Meigen(PQP_REAL vout[3][3], PQP_REAL dout[3], PQP_REAL a[3][3])
-{
- int n = 3;
- int j,iq,ip,i;
- PQP_REAL tresh,theta,tau,t,sm,s,h,g,c;
- int nrot;
- PQP_REAL b[3];
- PQP_REAL z[3];
- PQP_REAL v[3][3];
- PQP_REAL d[3];
-
- Midentity(v);
- for(ip=0; ip<n; ip++)
- {
- b[ip] = a[ip][ip];
- d[ip] = a[ip][ip];
- z[ip] = 0.0;
- }
-
- nrot = 0;
-
- for(i=0; i<50; i++)
- {
-
- sm=0.0;
- for(ip=0;ip<n;ip++) for(iq=ip+1;iq<n;iq++) sm+=fabs(a[ip][iq]);
- if (sm == 0.0)
- {
- McM(vout, v);
- VcV(dout, d);
- return;
- }
-
-
- if (i < 3) tresh=(PQP_REAL)0.2*sm/(n*n);
- else tresh=0.0;
-
- for(ip=0; ip<n; ip++) for(iq=ip+1; iq<n; iq++)
- {
- g = (PQP_REAL)100.0*fabs(a[ip][iq]);
- if (i>3 &&
- fabs(d[ip])+g==fabs(d[ip]) &&
- fabs(d[iq])+g==fabs(d[iq]))
- a[ip][iq]=0.0;
- else if (fabs(a[ip][iq])>tresh)
- {
- h = d[iq]-d[ip];
- if (fabs(h)+g == fabs(h)) t=(a[ip][iq])/h;
- else
- {
- theta=(PQP_REAL)0.5*h/(a[ip][iq]);
- t=(PQP_REAL)(1.0/(fabs(theta)+sqrt(1.0+theta*theta)));
- if (theta < 0.0) t = -t;
- }
- c=(PQP_REAL)1.0/sqrt(1+t*t);
- s=t*c;
- tau=s/((PQP_REAL)1.0+c);
- h=t*a[ip][iq];
- z[ip] -= h;
- z[iq] += h;
- d[ip] -= h;
- d[iq] += h;
- a[ip][iq]=0.0;
- for(j=0;j<ip;j++) { ROTATE(a,j,ip,j,iq); }
- for(j=ip+1;j<iq;j++) { ROTATE(a,ip,j,j,iq); }
- for(j=iq+1;j<n;j++) { ROTATE(a,ip,j,iq,j); }
- for(j=0;j<n;j++) { ROTATE(v,j,ip,j,iq); }
- nrot++;
- }
- }
- for(ip=0;ip<n;ip++)
- {
- b[ip] += z[ip];
- d[ip] = b[ip];
- z[ip] = 0.0;
- }
- }
-
- fprintf(stderr, "eigen: too many iterations in Jacobi transform.\n");
-
- return;
-}
-
-
-#endif
-
-#endif
-/* MATVEC_H */
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/bunny.tris b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/bunny.tris
deleted file mode 100644
index 5df21722968d7d884cd123b87847e45b8bddaaf2..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/bunny.tris
+++ /dev/null
@@ -1,8817 +0,0 @@
-2204
--0.61915501 0.44778999 0.11374
--0.65483498 0.44209999 0.23309999
--0.54955502 0.46242001 0.21086
-
--0.67527496 -0.04277 0.26872
--0.69119499 -0.03813 0.21893999
--0.70878502 -0.09154 0.23915001
-
--0.369095 -0.54116001 0.13157
--0.38878502 -0.53153 0.12594
--0.41275501 -0.52455002 0.07158
-
--0.75503502 0.05844 0.12856
--0.73905502 0.04444 0.0987
--0.75351501 0.04522 0.14867
-
--0.66683502 0.43922001 0.22899
--0.65483498 0.44209999 0.23309999
--0.61915501 0.44778999 0.11374
-
-0.100405 0.1927 0.26025
-0.21620501 0.20631001 0.22993
-0.147305 0.21093 0.16179001
-
--0.49363499 0.44341 -0.49014
--0.47512501 0.32431999 -0.07113
--0.51723499 0.35522999 -0.21364
-
--0.33272499 -0.76374001 -0.11234
--0.31717501 -0.73977997 -0.10929
--0.31505501 -0.76257004 -0.12831
-
-0.013465 -0.32881001 0.57973
--0.006605 -0.27245001 0.59221001
--0.042595 -0.29021999 0.57817001
-
--0.478615 0.61969002 -0.45830002
--0.55654499 0.64793999 -0.39986
--0.541525 0.60362999 -0.27988001
-
--0.69841499 -0.28159 0.17025999
--0.67350502 -0.31801001 0.21348
--0.70689499 -0.24051001 0.17004999
-
--0.53543499 0.41438 -0.43537998
--0.52343498 0.42028999 -0.45174999
--0.52669498 0.41201 -0.43175999
-
--0.73406502 0.22117001 0.0777
--0.69706497 0.27559999 0.04741
--0.67165497 0.20761999 -0.01075
-
-0.032965 0.15014 0.30976
-0.119035 0.13868 0.35504002
-0.100405 0.1927 0.26025
-
--0.48884499 0.70297997 -0.56327
--0.543125 0.70637001 -0.51022999
--0.478615 0.61969002 -0.45830002
-
--0.56956501 0.37901001 0.42835999
--0.62949501 0.42409 0.33152
--0.62565498 0.37287998 0.41800999
-
--0.69841499 -0.28159 0.17025999
--0.67290497 -0.32478001 0.06036
--0.67019501 -0.33046001 0.17235001
-
-0.26532499 0.11682 0.36832001
-0.279865 0.18177999 0.28193001
-0.185485 0.15646 0.33446999
-
-0.100405 0.1927 0.26025
-0.19949499 0.18938999 0.2817
-0.21620501 0.20631001 0.22993
-
--0.051545 0.75455002 -0.12812
--0.075495 0.73363998 -0.09516
--0.036455 0.74396004 -0.12342
-
--0.085275 0.1548 0.17931
--0.087935 0.12239 0.26698
-0.014575 0.18521999 0.18579
-
-0.100405 0.1927 0.26025
-0.147305 0.21093 0.16179001
-0.014575 0.18521999 0.18579
-
--0.440755 0.59640999 -0.59333
--0.43966499 0.60813999 -0.59201
--0.44141499 0.60801998 -0.58164001
-
-0.068505 0.67084 -0.27976999
-0.073585 0.64028999 -0.27017
-0.046245 0.64051003 -0.26128
-
--0.722985 0.29028 0.13737
--0.65177498 0.38554001 0.03709
--0.685625 0.31711 0.06703
-
--0.53012501 0.29573 0.48049
--0.59655499 0.21528 0.54181
--0.52195499 0.23915001 0.49179001
-
--0.49343498 0.53766998 -0.56959999
--0.46016499 0.52960999 -0.57521999
--0.49435501 0.49361 -0.54973999
-
-0.62454498 -0.39271999 0.04435
-0.59800499 -0.28384001 -0.03253
-0.616395 -0.19808001 0.01714
-
--0.40461498 0.42754002 0.32105999
--0.45004501 0.41863998 0.36409
--0.43137501 0.38099998 0.37743
-
--0.62663502 0.17285 0.54678001
--0.59655499 0.21528 0.54181
--0.65234497 0.19790001 0.53490002
-
--0.59655499 0.21528 0.54181
--0.60633499 0.29861 0.49606998
--0.65054497 0.23882 0.51790001
-
-0.63150501 -0.32306999 0.09721
-0.62454498 -0.39271999 0.04435
-0.616395 -0.19808001 0.01714
-
--0.087935 0.12239 0.26698
-0.100405 0.1927 0.26025
-0.014575 0.18521999 0.18579
-
--0.40461498 0.42754002 0.32105999
--0.38591499 0.35167 0.31952
--0.36774502 0.40701 0.25933001
-
--0.62663502 0.17285 0.54678001
--0.59962502 0.11646 0.54145
--0.59655499 0.21528 0.54181
-
--0.48044498 -0.07672 -0.15554
--0.40962502 -0.24707001 -0.19660999
--0.39932499 -0.27525 -0.19733
-
--0.67873497 0.42423 0.10789
--0.61915501 0.44778999 0.11374
--0.65177498 0.41154999 0.04812
-
--0.71519501 -0.22723 0.07012
--0.68001503 -0.24351 -0.00998
--0.63365501 -0.34187 -0.01984
-
--0.112925 0.44946999 -0.01904
--0.29133499 0.43453999 0.10392
--0.28335501 0.37535999 0.07935
-
-0.20192499 0.21209999 0.08168
-0.267075 0.20905001 0.1627
-0.30644501 0.19247999 0.06287
-
--0.31738501 0.43448002 0.11911
--0.049325 0.62575001 -0.12564
--0.112215 0.62556 -0.07594
-
--0.50442501 0.51681 -0.12221
--0.497005 0.44953999 -0.01774
--0.462285 0.43570999 -0.09058
-
-0.147305 0.21093 0.16179001
-0.21620501 0.20631001 0.22993
-0.267075 0.20905001 0.1627
-
--0.38455502 -0.62816002 0.13858
--0.35919498 -0.65380997 0.20221001
--0.334715 -0.61401001 0.18212
-
--0.17445499 -0.76107002 0.26183001
--0.189415 -0.75043999 0.19530001
--0.18321501 -0.75733002 0.1487
-
--0.43966499 0.60813999 -0.59201
--0.440755 0.59640999 -0.59333
--0.43943501 0.61122002 -0.59380001
-
--0.68663498 0.30483 0.43856998
--0.66231499 0.29212999 0.47123001
--0.62565498 0.37287998 0.41800999
-
--0.58244499 0.64759003 -0.39550999
--0.541525 0.60362999 -0.27988001
--0.55654499 0.64793999 -0.39986
-
-0.61706501 -0.15582 0.06717
-0.63150501 -0.32306999 0.09721
-0.616395 -0.19808001 0.01714
-
-0.119035 0.13868 0.35504002
-0.185485 0.15646 0.33446999
-0.19949499 0.18938999 0.2817
-
--0.439925 0.61355999 -0.59553001
--0.44219501 0.62398998 -0.59071999
--0.43966499 0.60813999 -0.59201
-
--0.297925 -0.53887001 -0.09417
--0.26883499 -0.37657001 -0.16448
--0.238365 -0.47976002 -0.11273
-
--0.67350502 -0.31801001 0.21348
--0.65677498 -0.28486 0.31427999
--0.71858498 -0.18570999 0.23955999
-
--0.445825 0.62064999 -0.60304001
--0.44350498 0.63459 -0.59969002
--0.439925 0.61355999 -0.59553001
-
-0.36023499 -0.12728 -0.21188999
-0.38315498 -0.105 -0.19958
-0.44883499 -0.17316 -0.18752001
-
-0.004935 0.49839001 -0.08707
--0.112925 0.44946999 -0.01904
--0.34072498 0.34067001 0.0194
-
--0.52690498 0.36866001 0.4325
--0.56956501 0.37901001 0.42835999
--0.60633499 0.29861 0.49606998
-
--0.722985 0.29028 0.13737
--0.69845497 0.34506001 0.09724
--0.65177498 0.38554001 0.03709
-
-0.21620501 0.20631001 0.22993
-0.31116501 0.18573 0.23601
-0.267075 0.20905001 0.1627
-
--0.53012501 0.29573 0.48049
--0.52690498 0.36866001 0.4325
--0.60633499 0.29861 0.49606998
-
-0.66172501 -0.52612 -0.03485
-0.66172501 -0.51227001 -0.03209
-0.701595 -0.51362999 -0.01377
-
--0.27186501 0.17989 0.1998
--0.28657499 0.21525999 0.16419001
--0.318915 0.22754999 0.2701
-
--0.096095 0.71514 -0.06997
--0.236595 0.58076 0.04404
--0.166485 0.62580002 -0.00026
-
-0.38503502 0.15941 0.19188999
-0.41226501 0.13985 0.08791
-0.30644501 0.19247999 0.06287
-
-0.046245 0.64051003 -0.26128
--0.027095 0.51598 -0.13203
--0.048255 0.52195999 -0.13641
-
-0.059645 0.68377998 -0.28246
-0.068505 0.67084 -0.27976999
-0.046245 0.64051003 -0.26128
-
--0.44495499 0.61160999 -0.60196999
--0.45517502 0.62153999 -0.60337002
--0.445825 0.62064999 -0.60304001
-
--0.040395 -0.02881 -0.20667999
-0.027045 -0.06751 -0.21985001
-0.074325 -0.09863 -0.22704
-
-0.100405 0.1927 0.26025
-0.119035 0.13868 0.35504002
-0.19949499 0.18938999 0.2817
-
-0.17338499 -0.16284 0.56652
-0.223505 -0.23148001 0.58157001
-0.25977501 -0.2282 0.57007
-
--0.52043499 0.07819 -0.06974
--0.43113499 0.13668 -0.06854
--0.43526501 0.09176 -0.08455
-
--0.551175 -0.15953 0.43046001
--0.50651501 -0.10104 0.42856998
--0.57303501 -0.09277 0.39316002
-
-0.63273499 -0.64609001 0.00875
-0.63202499 -0.59748001 -0.03384
-0.68647499 -0.58339001 -0.0017
-
--0.50564499 -0.26843 0.44533001
--0.44656502 -0.19707001 0.46938
--0.51212502 -0.2273 0.45049999
-
-0.143465 0.09524 0.39535999
-0.119035 0.13868 0.35504002
-0.013225 0.0811 0.38118
-
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--0.43411499 -0.75475998 0.44608002
--0.307395 -0.74459 0.46473999
-
-0.247805 -0.73291 -0.06195
-0.231395 -0.71984001 -0.09604
-0.28172501 -0.75823997 -0.05062
-
-0.182815 -0.75714996 0.16868999
-0.227675 -0.76184998 0.16388
-0.23355499 -0.75731003 0.17916
-
--0.096785 -0.71277 0.39598999
--0.100605 -0.71330002 0.37792
--0.126875 -0.71792 0.35866001
-
--0.212265 -0.71635002 -0.05771
--0.157735 -0.71690002 -0.13885
--0.098815 -0.71488998 -0.14756
-
--0.139575 -0.71986 0.03333
--0.149585 -0.71949997 0.03095
--0.152335 -0.71831001 0.01116
-
--0.18321501 -0.75733002 0.1487
--0.20473499 -0.76521004 0.06109
--0.166565 -0.76378998 0.10376
-
--0.252265 -0.76543999 0.06978
--0.18321501 -0.75733002 0.1487
--0.23188499 -0.74691002 0.11138
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/main.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/main.cpp
deleted file mode 100644
index 816f1881a65db74f6191bbd4f62f376a316d0b1e..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/main.cpp
+++ /dev/null
@@ -1,372 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <math.h>
-#include <GL/glut.h>
-#include "PQP.h"
-#include "model.h"
-#include "MatVec.h"
-
-PQP_Model bunny, torus;
-Model *bunny_to_draw, *torus_to_draw;
-
-int mode;
-double beginx, beginy;
-double dis = 10.0, azim = 0.0, elev = 0.0;
-double ddis = 0.0, dazim = 0.0, delev = 0.0;
-double rot1 = 0.0, rot2 = 0.0, rot3 = 0.0;
-int animate = 0;
-
-void
-InitViewerWindow()
-{
- GLfloat Ambient[] = { 0.2f, 0.2f, 0.2f, 1.0f };
- GLfloat Diffuse[] = { 0.8f, 0.8f, 0.8f, 1.0f };
- GLfloat Specular[] = { 0.2f, 0.2f, 0.2f, 1.0f };
- GLfloat SpecularExp[] = { 50 };
- GLfloat Emission[] = { 0.1f, 0.1f, 0.1f, 1.0f };
-
- glMaterialfv(GL_FRONT, GL_AMBIENT, Ambient);
- glMaterialfv(GL_FRONT, GL_DIFFUSE, Diffuse);
- glMaterialfv(GL_FRONT, GL_SPECULAR, Specular);
- glMaterialfv(GL_FRONT, GL_SHININESS, SpecularExp);
- glMaterialfv(GL_FRONT, GL_EMISSION, Emission);
-
- glMaterialfv(GL_BACK, GL_AMBIENT, Ambient);
- glMaterialfv(GL_BACK, GL_DIFFUSE, Diffuse);
- glMaterialfv(GL_BACK, GL_SPECULAR, Specular);
- glMaterialfv(GL_BACK, GL_SHININESS, SpecularExp);
- glMaterialfv(GL_BACK, GL_EMISSION, Emission);
-
- glColorMaterial(GL_FRONT_AND_BACK, GL_DIFFUSE);
-
- glEnable(GL_COLOR_MATERIAL);
-
- GLfloat light_position[] = { 1.0, 1.0, 1.0, 0.0 };
- glLightfv(GL_LIGHT0, GL_POSITION, light_position);
- glEnable(GL_LIGHT0);
- glEnable(GL_LIGHTING);
- glLightModeli(GL_LIGHT_MODEL_TWO_SIDE, GL_TRUE);
-
- glDepthFunc(GL_LEQUAL);
- glEnable(GL_DEPTH_TEST);
-
- glShadeModel(GL_FLAT);
- glClearColor(0.0, 0.0, 0.0, 0.0);
-
- glEnable(GL_CULL_FACE);
- glCullFace(GL_BACK);
- glEnable(GL_NORMALIZE);
-
- glMatrixMode(GL_PROJECTION);
- glLoadIdentity();
- glFrustum(-0.004,0.004,-0.004,0.004,.01,100.0);
-
- glMatrixMode(GL_MODELVIEW);
-}
-
-void
-KeyboardCB(unsigned char key, int x, int y)
-{
- switch(key)
- {
- case 'q': delete bunny_to_draw; delete torus_to_draw; exit(0);
- default: animate = 1 - animate;
- }
-
- glutPostRedisplay();
-}
-
-void
-MouseCB(int _b, int _s, int _x, int _y)
-{
- if (_s == GLUT_UP)
- {
- dis += ddis;
- azim += dazim;
- elev += delev;
- ddis = 0.0;
- dazim = 0.0;
- delev = 0.0;
- return;
- }
-
- if (_b == GLUT_RIGHT_BUTTON)
- {
- mode = 0;
- beginy = _y;
- return;
- }
- else
- {
- mode = 1;
- beginx = _x;
- beginy = _y;
- }
-}
-
-void
-MotionCB(int _x, int _y)
-{
- if (mode == 0)
- {
- ddis = dis * (_y - beginy)/200.0;
- }
- else
- {
- dazim = (_x - beginx)/5.0;
- delev = (_y - beginy)/5.0;
- }
-
- glutPostRedisplay();
-}
-
-inline void glVertex3v(float V[3]) { glVertex3fv(V); }
-inline void glVertex3v(double V[3]) { glVertex3dv(V); }
-
-void
-BeginDraw()
-{
- glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
-
- glLoadIdentity();
- glTranslatef(0.0, 0.0, -(dis+ddis));
- glRotated(elev+delev, 1.0, 0.0, 0.0);
- glRotated(azim+dazim, 0.0, 1.0, 0.0);
-}
-
-void
-EndDraw()
-{
- glFlush();
- glutSwapBuffers();
-}
-
-void
-IdleCB()
-{
- glutPostRedisplay();
-}
-
-void
-DisplayCB()
-{
- BeginDraw();
-
- // set up model transformations
-
- if (animate)
- {
- rot1 += .1;
- rot2 += .2;
- rot3 += .3;
- }
-
- PQP_REAL R1[3][3],R2[3][3],T1[3],T2[3];
- PQP_REAL M1[3][3],M2[3][3],M3[3][3];
-
- T1[0] = -1;
- T1[1] = 0.0;
- T1[2] = 0.0;
-
- T2[0] = 1;
- T2[1] = 0.0;
- T2[2] = 0.0;
-
- MRotX(M1,rot1);
- MRotY(M2,rot2);
- MxM(M3,M1,M2);
- MRotZ(M1,rot3);
- MxM(R1,M3,M1);
-
- MRotX(M1,rot3);
- MRotY(M2,rot1);
- MxM(M3,M1,M2);
- MRotZ(M1,rot2);
- MxM(R2,M3,M1);
-
- // perform distance query
-
- PQP_REAL rel_err = 0.0;
- PQP_REAL abs_err = 0.0;
- PQP_DistanceResult res;
- PQP_Distance(&res,R1,T1,&bunny,R2,T2,&torus,rel_err,abs_err);
-
- // draw the models
-
- glColor3d(0.0,0.0,1.0);
- double oglm[16];
- MVtoOGL(oglm,R1,T1);
- glPushMatrix();
- glMultMatrixd(oglm);
- bunny_to_draw->Draw();
- glPopMatrix();
-
- glColor3d(0.0,1.0,0.0);
- MVtoOGL(oglm,R2,T2);
- glPushMatrix();
- glMultMatrixd(oglm);
- torus_to_draw->Draw();
- glPopMatrix();
-
- // draw the closest points as small spheres
-
- glColor3d(1.0,0.0,0.0);
-
- PQP_REAL P1[3],P2[3],V1[3],V2[3];
- VcV(P1,res.P1());
- VcV(P2,res.P2());
-
- // each point is in the space of its model;
- // transform to world space
-
- MxVpV(V1,R1,P1,T1);
-
- glPushMatrix();
- glTranslated(V1[0],V1[1],V1[2]);
- glutSolidSphere(.05,15,15);
- glPopMatrix();
-
- MxVpV(V2,R2,P2,T2);
-
- glPushMatrix();
- glTranslated(V2[0],V2[1],V2[2]);
- glutSolidSphere(.05,15,15);
- glPopMatrix();
-
- // draw the line between the closest points
-
- glDisable(GL_LIGHTING);
- glBegin(GL_LINES);
- glVertex3v(V1);
- glVertex3v(V2);
- glEnd();
- glEnable(GL_LIGHTING);
-
- EndDraw();
-}
-
-void main(int argc, char **argv)
-{
- glutInit(&argc, argv);
- glutInitDisplayMode (GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH | GLUT_MULTISAMPLE);
-
- // create the window
-
- glutCreateWindow("PQP Demo - Spinning");
-
- // set OpenGL graphics state -- material props, perspective, etc.
-
- InitViewerWindow();
-
- // set the callbacks
-
- glutDisplayFunc(DisplayCB);
- glutIdleFunc(IdleCB);
- glutMouseFunc(MouseCB);
- glutMotionFunc(MotionCB);
- glutKeyboardFunc(KeyboardCB);
-
- // initialize the bunny
-
- FILE *fp;
- int i, ntris;
-
- bunny_to_draw = new Model("bunny.tris");
-
- fp = fopen("bunny.tris","r");
- if (fp == NULL) { fprintf(stderr,"Couldn't open bunny.tris\n"); exit(-1); }
- fscanf(fp,"%d",&ntris);
-
- bunny.BeginModel();
- for (i = 0; i < ntris; i++)
- {
- double p1x,p1y,p1z,p2x,p2y,p2z,p3x,p3y,p3z;
- fscanf(fp,"%lf %lf %lf %lf %lf %lf %lf %lf %lf",
- &p1x,&p1y,&p1z,&p2x,&p2y,&p2z,&p3x,&p3y,&p3z);
- PQP_REAL p1[3],p2[3],p3[3];
- p1[0] = (PQP_REAL)p1x; p1[1] = (PQP_REAL)p1y; p1[2] = (PQP_REAL)p1z;
- p2[0] = (PQP_REAL)p2x; p2[1] = (PQP_REAL)p2y; p2[2] = (PQP_REAL)p2z;
- p3[0] = (PQP_REAL)p3x; p3[1] = (PQP_REAL)p3y; p3[2] = (PQP_REAL)p3z;
- bunny.AddTri(p1,p2,p3,i);
- }
- bunny.EndModel();
- fclose(fp);
-
- // initialize the torus
-
- torus_to_draw = new Model("torus.tris");
-
- fp = fopen("torus.tris","r");
- if (fp == NULL) { fprintf(stderr,"Couldn't open torus.tris\n"); exit(-1); }
- fscanf(fp,"%d",&ntris);
-
- torus.BeginModel();
- for (i = 0; i < ntris; i++)
- {
- double p1x,p1y,p1z,p2x,p2y,p2z,p3x,p3y,p3z;
- fscanf(fp,"%lf %lf %lf %lf %lf %lf %lf %lf %lf",
- &p1x,&p1y,&p1z,&p2x,&p2y,&p2z,&p3x,&p3y,&p3z);
- PQP_REAL p1[3],p2[3],p3[3];
- p1[0] = (PQP_REAL)p1x; p1[1] = (PQP_REAL)p1y; p1[2] = (PQP_REAL)p1z;
- p2[0] = (PQP_REAL)p2x; p2[1] = (PQP_REAL)p2y; p2[2] = (PQP_REAL)p2z;
- p3[0] = (PQP_REAL)p3x; p3[1] = (PQP_REAL)p3y; p3[2] = (PQP_REAL)p3z;
- torus.AddTri(p1,p2,p3,i);
- }
- torus.EndModel();
- fclose(fp);
-
- // print instructions
-
- printf("PQP Demo - Spinning:\n"
- "Press 'q' to quit.\n"
- "Press any other key to toggle animation.\n"
- "Left-drag left & right to change angle of view.\n"
- "Left-drag up & down to change elevation of view.\n"
- "Right-drag up & down to change distance of view.\n");
-
- // Enter the main loop.
-
- glutMainLoop();
-}
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/model.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/model.cpp
deleted file mode 100644
index e145b31baebd18fa43a6ca0a86314a3b230c1fb8..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/model.cpp
+++ /dev/null
@@ -1,144 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <math.h>
-#include "GL/glut.h"
-#include "model.h"
-
-inline
-void
-VmV(double Vr[3], const double V1[3], const double V2[3])
-{
- Vr[0] = V1[0] - V2[0];
- Vr[1] = V1[1] - V2[1];
- Vr[2] = V1[2] - V2[2];
-}
-
-inline
-void
-VcrossV(double Vr[3], const double V1[3], const double V2[3])
-{
- Vr[0] = V1[1]*V2[2] - V1[2]*V2[1];
- Vr[1] = V1[2]*V2[0] - V1[0]*V2[2];
- Vr[2] = V1[0]*V2[1] - V1[1]*V2[0];
-}
-
-inline
-void
-Vnormalize(double V[3])
-{
- double d = 1.0 / sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
- V[0] *= d;
- V[1] *= d;
- V[2] *= d;
-}
-
-Model::Model(char *tris_file)
-{
- FILE *fp = fopen(tris_file,"r");
- if (fp == NULL)
- {
- fprintf(stderr,"Model Constructor: Couldn't open %s\n",tris_file);
- exit(-1);
- }
-
- fscanf(fp,"%d",&ntris);
- tri = new ModelTri[ntris];
-
- int i;
-
- for (i = 0; i < ntris; i++)
- {
- // read the tri verts
-
- fscanf(fp,"%lf %lf %lf %lf %lf %lf %lf %lf %lf",
- &tri[i].p0[0], &tri[i].p0[1], &tri[i].p0[2],
- &tri[i].p1[0], &tri[i].p1[1], &tri[i].p1[2],
- &tri[i].p2[0], &tri[i].p2[1], &tri[i].p2[2]);
-
- // set the normal
-
- double a[3],b[3];
- VmV(a,tri[i].p1,tri[i].p0);
- VmV(b,tri[i].p2,tri[i].p0);
- VcrossV(tri[i].n,a,b);
- Vnormalize(tri[i].n);
- }
-
- fclose(fp);
-
- // generate display list
-
- display_list = glGenLists(1);
- glNewList(display_list,GL_COMPILE);
- glBegin(GL_TRIANGLES);
- for (i = 0; i < ntris; i++)
- {
- glNormal3dv(tri[i].n);
- glVertex3dv(tri[i].p0);
- glVertex3dv(tri[i].p1);
- glVertex3dv(tri[i].p2);
- }
- glEnd();
- glEndList();
-}
-
-Model::~Model()
-{
- delete [] tri;
-}
-
-void
-Model::Draw()
-{
- glCallList(display_list);
-}
-
-void
-Model::DrawTri(int index)
-{
- glBegin(GL_TRIANGLES);
- glVertex3dv(tri[index].p0);
- glVertex3dv(tri[index].p1);
- glVertex3dv(tri[index].p2);
- glEnd();
-}
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/model.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/model.h
deleted file mode 100644
index df352e4e8959b4902e5cab308060d13bac9c3c86..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/model.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef MODEL_H
-#define MODEL_H
-
-struct ModelTri
-{
- double p0[3], p1[3], p2[3];
- double n[3];
-};
-
-class Model
-{
- int ntris;
- ModelTri *tri;
- int display_list;
-
-public:
- Model(char *tris_file);
- ~Model();
- void Draw();
- void DrawTri(int index);
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/spinning.dsp b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/spinning.dsp
deleted file mode 100644
index b31912aa0c3a8fcda214a653ab3cb0f48eb383d5..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/spinning.dsp
+++ /dev/null
@@ -1,98 +0,0 @@
-# Microsoft Developer Studio Project File - Name="spinning" - Package Owner=<4>
-# Microsoft Developer Studio Generated Build File, Format Version 5.00
-# ** DO NOT EDIT **
-
-# TARGTYPE "Win32 (x86) Console Application" 0x0103
-
-CFG=spinning - Win32 Debug
-!MESSAGE This is not a valid makefile. To build this project using NMAKE,
-!MESSAGE use the Export Makefile command and run
-!MESSAGE
-!MESSAGE NMAKE /f "spinning.mak".
-!MESSAGE
-!MESSAGE You can specify a configuration when running NMAKE
-!MESSAGE by defining the macro CFG on the command line. For example:
-!MESSAGE
-!MESSAGE NMAKE /f "spinning.mak" CFG="spinning - Win32 Debug"
-!MESSAGE
-!MESSAGE Possible choices for configuration are:
-!MESSAGE
-!MESSAGE "spinning - Win32 Release" (based on\
- "Win32 (x86) Console Application")
-!MESSAGE "spinning - Win32 Debug" (based on "Win32 (x86) Console Application")
-!MESSAGE
-
-# Begin Project
-# PROP Scc_ProjName ""
-# PROP Scc_LocalPath ""
-CPP=xicl5.exe
-RSC=rc.exe
-
-!IF "$(CFG)" == "spinning - Win32 Release"
-
-# PROP BASE Use_MFC 0
-# PROP BASE Use_Debug_Libraries 0
-# PROP BASE Output_Dir "Release"
-# PROP BASE Intermediate_Dir "Release"
-# PROP BASE Target_Dir ""
-# PROP Use_MFC 0
-# PROP Use_Debug_Libraries 0
-# PROP Output_Dir "./"
-# PROP Intermediate_Dir "Release"
-# PROP Ignore_Export_Lib 0
-# PROP Target_Dir ""
-# ADD BASE CPP /nologo /W3 /GX /O2 /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD CPP /nologo /W3 /GX /O2 /I "..\..\include" /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /YX /FD /c
-# ADD BASE RSC /l 0x409 /d "NDEBUG"
-# ADD RSC /l 0x409 /d "NDEBUG"
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-# ADD BASE LINK32 kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /nologo /subsystem:console /machine:I386
-# ADD LINK32 glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /pdb:none /machine:I386 /libpath:"..\..\lib"
-
-!ELSEIF "$(CFG)" == "spinning - Win32 Debug"
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-# ADD LINK32 glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /pdb:none /debug /machine:I386 /libpath:"..\..\lib"
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-# Name "spinning - Win32 Debug"
-# Begin Source File
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-SOURCE=.\main.cpp
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/spinning.plg b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/spinning.plg
deleted file mode 100644
index d8ee3728795b3cc580b04f82e75480862e4be371..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/spinning.plg
+++ /dev/null
@@ -1,27 +0,0 @@
---------------------Configuration: spinning - Win32 Release--------------------
-Begining build with project "C:\WIN95\DESKTOP\PQP_v1.2.1\demos\spinning\spinning.dsp", at root.
-Active configuration is Win32 (x86) Console Application (based on Win32 (x86) Console Application)
-
-Project's tools are:
- "32-bit C/C++ Compiler for 80x86" with flags "/nologo /ML /W3 /GX /O2 /I "..\..\include" /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /Fp"Release/spinning.pch" /YX /Fo"Release/" /Fd"Release/" /FD /c "
- "Win32 Resource Compiler" with flags "/l 0x409 /d "NDEBUG" "
- "Browser Database Maker" with flags "/nologo /o"./spinning.bsc" "
- "COFF Linker for 80x86" with flags "glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /pdb:none /machine:I386 /out:"./spinning.exe" /libpath:"..\..\lib" "
- "Custom Build" with flags ""
- "<Component 0xa>" with flags ""
-
-Creating temp file "C:\WIN95\TEMP\RSP9380.TMP" with contents </nologo /ML /W3 /GX /O2 /I "..\..\include" /D "WIN32" /D "NDEBUG" /D "_CONSOLE" /D "_MBCS" /Fp"Release/spinning.pch" /YX /Fo"Release/" /Fd"Release/" /FD /c
-"C:\WIN95\DESKTOP\PQP_v1.2.1\demos\spinning\main.cpp"
->
-Creating command line "cl.exe @C:\WIN95\TEMP\RSP9380.TMP"
-Creating temp file "C:\WIN95\TEMP\RSP9381.TMP" with contents <glut32.lib opengl32.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib pqp.lib /nologo /subsystem:console /pdb:none /machine:I386 /out:"./spinning.exe" /libpath:"..\..\lib"
-.\Release\main.obj
-.\Release\model.obj>
-Creating command line "link.exe @C:\WIN95\TEMP\RSP9381.TMP"
-Compiling...
-main.cpp
-Linking...
-
-
-
-spinning.exe - 0 error(s), 0 warning(s)
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/torus.tris b/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/torus.tris
deleted file mode 100644
index a0bc4507a8b37d2868603a6974c4c49ff64037f9..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/demos/spinning/torus.tris
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diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/BV.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/include/BV.h
deleted file mode 100644
index cfe42c73785d1ebad055dfca953870eeb32d5a19..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/BV.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_BV_H
-#define PQP_BV_H
-
-#include <math.h>
-#include "Tri.h"
-#include "PQP_Compile.h"
-
-struct BV
-{
- PQP_REAL R[3][3]; // orientation of RSS & OBB
-
-#if PQP_BV_TYPE & RSS_TYPE
- PQP_REAL Tr[3]; // position of rectangle
- PQP_REAL l[2]; // side lengths of rectangle
- PQP_REAL r; // radius of sphere summed with rectangle to form RSS
-#endif
-
-#if PQP_BV_TYPE & OBB_TYPE
- PQP_REAL To[3]; // position of obb
- PQP_REAL d[3]; // (half) dimensions of obb
-#endif
-
- int first_child; // positive value is index of first_child bv
- // negative value is -(index + 1) of triangle
-
- BV();
- ~BV();
- int Leaf() { return first_child < 0; }
- PQP_REAL GetSize();
- void FitToTris(PQP_REAL O[3][3], Tri *tris, int num_tris);
-};
-
-inline
-PQP_REAL
-BV::GetSize()
-{
-#if PQP_BV_TYPE & RSS_TYPE
- return (sqrt(l[0]*l[0] + l[1]*l[1]) + 2*r);
-#else
- return (d[0]*d[0] + d[1]*d[1] + d[2]*d[2]);
-#endif
-}
-
-int
-BV_Overlap(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2);
-
-#if PQP_BV_TYPE & RSS_TYPE
-PQP_REAL
-BV_Distance(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2);
-#endif
-
-#endif
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP.h
deleted file mode 100644
index f6f3e539581c4e118d2afecace8e8a35d10c3db2..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP.h
+++ /dev/null
@@ -1,338 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_H
-#define PQP_H
-
-#include "PQP_Compile.h"
-#include "PQP_Internal.h"
-
-//----------------------------------------------------------------------------
-//
-// PQP API Return Values
-//
-//----------------------------------------------------------------------------
-
-const int PQP_OK = 0;
- // Used by all API routines upon successful completion except
- // constructors and destructors
-
-const int PQP_ERR_MODEL_OUT_OF_MEMORY = -1;
- // Returned when an API function cannot obtain enough memory to
- // store or process a PQP_Model object.
-
-const int PQP_ERR_OUT_OF_MEMORY = -2;
- // Returned when a PQP query cannot allocate enough storage to
- // compute or hold query information. In this case, the returned
- // data should not be trusted.
-
-const int PQP_ERR_UNPROCESSED_MODEL = -3;
- // Returned when an unprocessed model is passed to a function which
- // expects only processed models, such as PQP_Collide() or
- // PQP_Distance().
-
-const int PQP_ERR_BUILD_OUT_OF_SEQUENCE = -4;
- // Returned when:
- // 1. AddTri() is called before BeginModel().
- // 2. BeginModel() is called immediately after AddTri().
- // This error code is something like a warning: the invoked
- // operation takes place anyway, and PQP does what makes "most
- // sense", but the returned error code may tip off the client that
- // something out of the ordinary is happenning.
-
-const int PQP_ERR_BUILD_EMPTY_MODEL = -5;
- // Returned when EndModel() is called on a model to which no
- // triangles have been added. This is similar in spirit to the
- // OUT_OF_SEQUENCE return code, except that the requested operation
- // has FAILED -- the model remains "unprocessed", and the client may
- // NOT use it in queries.
-
-//----------------------------------------------------------------------------
-//
-// PQP_REAL
-//
-// The floating point type used throughout the package. The type is defined
-// in PQP_Compile.h, and by default is "double"
-//
-//----------------------------------------------------------------------------
-
-//----------------------------------------------------------------------------
-//
-// PQP_Model
-//
-// A PQP_Model stores geometry to be used in a proximity query.
-// The geometry is loaded with a call to BeginModel(), at least one call to
-// AddTri(), and then a call to EndModel().
-//
-// // create a two triangle model, m
-//
-// PQP_Model m;
-//
-// PQP_REAL p1[3],p2[3],p3[3]; // 3 points will make triangle p
-// PQP_REAL q1[3],q2[3],q3[3]; // another 3 points for triangle q
-//
-// // some initialization of these vertices not shown
-//
-// m.BeginModel(); // begin the model
-// m.AddTri(p1,p2,p3,0); // add triangle p
-// m.AddTri(q1,q2,q3,1); // add triangle q
-// m.EndModel(); // end (build) the model
-//
-// The last parameter of AddTri() is the number to be associated with the
-// triangle. These numbers are used to identify the triangles that overlap.
-//
-// AddTri() copies into the PQP_Model the data pointed to by the three vertex
-// pointers, so that it is safe to delete vertex data after you have
-// passed it to AddTri().
-//
-//----------------------------------------------------------------------------
-//
-// class PQP_Model - declaration contained in PQP_Internal.h
-// {
-//
-// public:
-// PQP_Model();
-// ~PQP_Model();
-//
-// int BeginModel(int num_tris = 8); // preallocate for num_tris triangles;
-// // the parameter is optional, since
-// // arrays are reallocated as needed
-//
-// int AddTri(const PQP_REAL *p1, const PQP_REAL *p2, const PQP_REAL *p3,
-// int id);
-//
-// int EndModel();
-// int MemUsage(int msg); // returns model mem usage in bytes
-// // prints message to stderr if msg == TRUE
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_CollideResult
-//
-// This saves and reports results from a collision query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_CollideResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // free the list of contact pairs; ordinarily this list is reused
-// // for each query, and only deleted in the destructor.
-//
-// void FreePairsList();
-//
-// // query results
-//
-// int Colliding();
-// int NumPairs();
-// int Id1(int k);
-// int Id2(int k);
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Collide() - detects collision between two PQP_Models
-//
-//
-// Declare a PQP_CollideResult struct and pass its pointer to collect
-// collision data.
-//
-// [R1, T1] is the placement of model 1 in the world &
-// [R2, T2] is the placement of model 2 in the world.
-// The columns of each 3x3 matrix are the basis vectors for the model
-// in world coordinates, and the matrices are in row-major order:
-// R(row r, col c) = R[r][c].
-//
-// If PQP_ALL_CONTACTS is the flag value, after calling PQP_Collide(),
-// the PQP_CollideResult object will contain an array with all
-// colliding triangle pairs. Suppose CR is a pointer to the
-// PQP_CollideResult object. The number of pairs is gotten from
-// CR->NumPairs(), and the ids of the 15'th pair of colliding
-// triangles is gotten from CR->Id1(14) and CR->Id2(14).
-//
-// If PQP_FIRST_CONTACT is the flag value, the PQP_CollideResult array
-// will only get the first colliding triangle pair found. Thus
-// CR->NumPairs() will be at most 1, and if 1, CR->Id1(0) and
-// CR->Id2(0) give the ids of the colliding triangle pair.
-//
-//----------------------------------------------------------------------------
-
-const int PQP_ALL_CONTACTS = 1; // find all pairwise intersecting triangles
-const int PQP_FIRST_CONTACT = 2; // report first intersecting tri pair found
-
-int
-PQP_Collide(PQP_CollideResult *result,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- int flag = PQP_ALL_CONTACTS);
-
-
-#if PQP_BV_TYPE & RSS_TYPE // this is true by default,
- // and explained in PQP_Compile.h
-
-//----------------------------------------------------------------------------
-//
-// PQP_DistanceResult
-//
-// This saves and reports results from a distance query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_DistanceResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // The following distance and points established the minimum distance
-// // for the models, within the relative and absolute error bounds
-// // specified.
-//
-// PQP_REAL Distance();
-// const PQP_REAL *P1(); // pointers to three PQP_REALs
-// const PQP_REAL *P2();
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Distance() - computes the distance between two PQP_Models
-//
-//
-// Declare a PQP_DistanceResult struct and pass its pointer to collect
-// distance information.
-//
-// "rel_err" is the relative error margin from actual distance.
-// "abs_err" is the absolute error margin from actual distance. The
-// smaller of the two will be satisfied, so set one large to nullify
-// its effect.
-//
-// "qsize" is an optional parameter controlling the size of a priority
-// queue used to direct the search for closest points. A larger queue
-// can help the algorithm discover the minimum with fewer steps, but
-// will increase the cost of each step. It is not beneficial to increase
-// qsize if the application has frame-to-frame coherence, i.e., the
-// pair of models take small steps between each call, since another
-// speedup trick already accelerates this situation with no overhead.
-//
-// However, a queue size of 100 to 200 has been seen to save time in a
-// planning application with "non-coherent" placements of models.
-//
-//----------------------------------------------------------------------------
-
-int
-PQP_Distance(PQP_DistanceResult *result,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL rel_err, PQP_REAL abs_err,
- int qsize = 2);
-
-//----------------------------------------------------------------------------
-//
-// PQP_ToleranceResult
-//
-// This saves and reports results from a tolerance query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_ToleranceResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // If the models are closer than ( <= ) tolerance, these points
-// // and distance were what established this. Otherwise,
-// // distance and point values are not meaningful.
-//
-// PQP_REAL Distance();
-// const PQP_REAL *P1();
-// const PQP_REAL *P2();
-//
-// // boolean says whether models are closer than tolerance distance
-//
-// int CloserThanTolerance();
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Tolerance() - checks if distance between PQP_Models is <= tolerance
-//
-//
-// Declare a PQP_ToleranceResult and pass its pointer to collect
-// tolerance information.
-//
-// The algorithm returns whether the true distance is <= or >
-// "tolerance". This routine does not simply compute true distance
-// and compare to the tolerance - models can often be shown closer or
-// farther than the tolerance more trivially. In most cases this
-// query should run faster than a distance query would on the same
-// models and configurations.
-//
-// "qsize" again controls the size of a priority queue used for
-// searching. Not setting qsize is the current recommendation, since
-// increasing it has only slowed down our applications.
-//
-//----------------------------------------------------------------------------
-
-int
-PQP_Tolerance(PQP_ToleranceResult *res,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL tolerance,
- int qsize = 2);
-
-#endif
-#endif
-
-
-
-
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP_Compile.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP_Compile.h
deleted file mode 100644
index f76c981370ecd5833f257fa7e14564d5704ce2e6..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP_Compile.h
+++ /dev/null
@@ -1,101 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_COMPILE_H
-#define PQP_COMPILE_H
-
-// prevents compiler warnings when PQP_REAL is float
-
-#include <math.h>
-inline float sqrt(float x) { return (float)sqrt((double)x); }
-inline float cos(float x) { return (float)cos((double)x); }
-inline float sin(float x) { return (float)sin((double)x); }
-inline float fabs(float x) { return (float)fabs((double)x); }
-
-//-------------------------------------------------------------------------
-//
-// PQP_REAL
-//
-// This is the floating point type used throughout PQP. doubles are
-// recommended, both for their precision and because the software has
-// mainly been tested using them. However, floats appear to be faster
-// (by 60% on some machines).
-//
-//-------------------------------------------------------------------------
-
-typedef double PQP_REAL;
-
-//-------------------------------------------------------------------------
-//
-// PQP_BV_TYPE
-//
-// PQP introduces a bounding volume (BV) type known as the "rectangle
-// swept sphere" (RSS) - the volume created by sweeping a sphere so
-// that its center visits every point on a rectangle; it looks
-// something like a rounded box.
-//
-// In our experiments, the RSS type is comparable to the oriented
-// bounding box (OBB) in terms of the number of BV-pair and triangle-pair
-// tests incurred. However, with our present implementations, overlap
-// tests are cheaper for OBBs, while distance tests are cheaper for the
-// RSS type (we used a public gjk implementation for the OBB distance test).
-//
-// Consequently, PQP is configured to use the RSS type in distance and
-// tolerance queries (which use BV distance tests) and to use OBBs for
-// collision queries (which use BV overlap tests). Using both requires six
-// more PQP_REALs per BV node than using just one type.
-//
-// To save space, you can configure PQP to use only one type, however,
-// with RSS alone, collision queries will typically be slower. With OBB's
-// alone, distance and tolerance queries are currently not supported, since
-// we have not developed our own OBB distance test. The three options are:
-//
-// #define PQP_BV_TYPE RSS_TYPE
-// #define PQP_BV_TYPE OBB_TYPE
-// #define PQP_BV_TYPE RSS_TYPE | OBB_TYPE
-//
-//-------------------------------------------------------------------------
-
-#define RSS_TYPE 1
-#define OBB_TYPE 2
-
-#define PQP_BV_TYPE RSS_TYPE | OBB_TYPE
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP_Internal.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP_Internal.h
deleted file mode 100644
index 90cedcfa2d7f3ec96c3d378774c48f9e9a5a6d29..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/PQP_Internal.h
+++ /dev/null
@@ -1,203 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include "Tri.h"
-#include "BV.h"
-
-class PQP_Model
-{
-
-public:
-
- int build_state;
-
- Tri *tris;
- int num_tris;
- int num_tris_alloced;
-
- BV *b;
- int num_bvs;
- int num_bvs_alloced;
-
- Tri *last_tri; // closest tri on this model in last distance test
-
- BV *child(int n) { return &b[n]; }
-
- PQP_Model();
- ~PQP_Model();
-
- int BeginModel(int num_tris = 8); // preallocate for num_tris triangles;
- // the parameter is optional, since
- // arrays are reallocated as needed
- int AddTri(const PQP_REAL *p1, const PQP_REAL *p2, const PQP_REAL *p3,
- int id);
- int EndModel();
- int MemUsage(int msg); // returns model mem usage.
- // prints message to stderr if msg == TRUE
-};
-
-struct CollisionPair
-{
- int id1;
- int id2;
-};
-
-struct PQP_CollideResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- int num_pairs_alloced;
- int num_pairs;
- CollisionPair *pairs;
-
- void SizeTo(int n);
- void Add(int i1, int i2);
-
- PQP_CollideResult();
- ~PQP_CollideResult();
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // free the list of contact pairs; ordinarily this list is reused
- // for each query, and only deleted in the destructor.
-
- void FreePairsList();
-
- // query results
-
- int Colliding() { return (num_pairs > 0); }
- int NumPairs() { return num_pairs; }
- int Id1(int k) { return pairs[k].id1; }
- int Id2(int k) { return pairs[k].id2; }
-};
-
-#if PQP_BV_TYPE & RSS_TYPE // distance/tolerance are only available with RSS
-
-struct PQP_DistanceResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- PQP_REAL rel_err;
- PQP_REAL abs_err;
-
- PQP_REAL distance;
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- int qsize;
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // The following distance and points established the minimum distance
- // for the models, within the relative and absolute error bounds
- // specified.
- // Points are defined: PQP_REAL p1[3], p2[3];
-
- PQP_REAL Distance() { return distance; }
- const PQP_REAL *P1() { return p1; }
- const PQP_REAL *P2() { return p2; }
-};
-
-struct PQP_ToleranceResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- int closer_than_tolerance;
- PQP_REAL tolerance;
-
- PQP_REAL distance;
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- int qsize;
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // If the models are closer than ( <= ) tolerance, these points
- // and distance were what established this. Otherwise,
- // distance and point values are not meaningful.
-
- PQP_REAL Distance() { return distance; }
- const PQP_REAL *P1() { return p1; }
- const PQP_REAL *P2() { return p2; }
-
- // boolean says whether models are closer than tolerance distance
-
- int CloserThanTolerance() { return closer_than_tolerance; }
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/Tri.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/include/Tri.h
deleted file mode 100644
index 496cddd928e5ab4fe8c5b1046f55f2a6079f4c71..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/include/Tri.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_TRI_H
-#define PQP_TRI_H
-
-#include "PQP_Compile.h"
-
-struct Tri
-{
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- PQP_REAL p3[3];
- int id;
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BV.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BV.cpp
deleted file mode 100644
index adbe2fc139c29710d0567017f29b8ec8aba59f2b..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BV.cpp
+++ /dev/null
@@ -1,323 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdlib.h>
-#include <math.h>
-#include "BV.h"
-#include "MatVec.h"
-#include "RectDist.h"
-#include "OBB_Disjoint.h"
-
-BV::BV()
-{
- first_child = 0;
-}
-
-BV::~BV()
-{
-}
-
-static
-inline
-PQP_REAL
-MaxOfTwo(PQP_REAL a, PQP_REAL b)
-{
- if (a > b) return a;
- return b;
-}
-
-void
-BV::FitToTris(PQP_REAL O[3][3], Tri *tris, int num_tris)
-{
- // store orientation
-
- McM(R,O);
-
- // project points of tris to R coordinates
-
- int num_points = 3*num_tris;
- PQP_REAL (*P)[3] = new PQP_REAL[num_points][3];
- int point = 0;
- int i;
- for (i = 0; i < num_tris; i++)
- {
- MTxV(P[point],R,tris[i].p1);
- point++;
-
- MTxV(P[point],R,tris[i].p2);
- point++;
-
- MTxV(P[point],R,tris[i].p3);
- point++;
- }
-
- PQP_REAL minx, maxx, miny, maxy, minz, maxz, c[3];
-
-#if PQP_BV_TYPE & OBB_TYPE
- minx = maxx = P[0][0];
- miny = maxy = P[0][1];
- minz = maxz = P[0][2];
- for (i = 1; i < num_points; i++)
- {
- if (P[i][0] < minx) minx = P[i][0];
- else if (P[i][0] > maxx) maxx = P[i][0];
- if (P[i][1] < miny) miny = P[i][1];
- else if (P[i][1] > maxy) maxy = P[i][1];
- if (P[i][2] < minz) minz = P[i][2];
- else if (P[i][2] > maxz) maxz = P[i][2];
- }
- c[0] = (PQP_REAL)0.5*(maxx + minx);
- c[1] = (PQP_REAL)0.5*(maxy + miny);
- c[2] = (PQP_REAL)0.5*(maxz + minz);
- MxV(To,R,c);
-
- d[0] = (PQP_REAL)0.5*(maxx - minx);
- d[1] = (PQP_REAL)0.5*(maxy - miny);
- d[2] = (PQP_REAL)0.5*(maxz - minz);
-#endif
-
-#if PQP_BV_TYPE & RSS_TYPE
-
- // compute thickness, which determines radius, and z of rectangle corner
-
- PQP_REAL cz,radsqr;
- minz = maxz = P[0][2];
- for (i = 1; i < num_points; i++)
- {
- if (P[i][2] < minz) minz = P[i][2];
- else if (P[i][2] > maxz) maxz = P[i][2];
- }
- r = (PQP_REAL)0.5*(maxz - minz);
- radsqr = r*r;
- cz = (PQP_REAL)0.5*(maxz + minz);
-
- // compute an initial length of rectangle along x direction
-
- // find minx and maxx as starting points
-
- int minindex, maxindex;
- minindex = maxindex = 0;
- for (i = 1; i < num_points; i++)
- {
- if (P[i][0] < P[minindex][0]) minindex = i;
- else if (P[i][0] > P[maxindex][0]) maxindex = i;
- }
- PQP_REAL x, dz;
- dz = P[minindex][2] - cz;
- minx = P[minindex][0] + sqrt(MaxOfTwo(radsqr - dz*dz,0));
- dz = P[maxindex][2] - cz;
- maxx = P[maxindex][0] - sqrt(MaxOfTwo(radsqr - dz*dz,0));
-
- // grow minx
-
- for (i = 0; i < num_points; i++)
- {
- if (P[i][0] < minx)
- {
- dz = P[i][2] - cz;
- x = P[i][0] + sqrt(MaxOfTwo(radsqr - dz*dz,0));
- if (x < minx) minx = x;
- }
- }
-
- // grow maxx
-
- for (i = 0; i < num_points; i++)
- {
- if (P[i][0] > maxx)
- {
- dz = P[i][2] - cz;
- x = P[i][0] - sqrt(MaxOfTwo(radsqr - dz*dz,0));
- if (x > maxx) maxx = x;
- }
- }
-
- // compute an initial length of rectangle along y direction
-
- // find miny and maxy as starting points
-
- minindex = maxindex = 0;
- for (i = 1; i < num_points; i++)
- {
- if (P[i][1] < P[minindex][1]) minindex = i;
- else if (P[i][1] > P[maxindex][1]) maxindex = i;
- }
- PQP_REAL y;
- dz = P[minindex][2] - cz;
- miny = P[minindex][1] + sqrt(MaxOfTwo(radsqr - dz*dz,0));
- dz = P[maxindex][2] - cz;
- maxy = P[maxindex][1] - sqrt(MaxOfTwo(radsqr - dz*dz,0));
-
- // grow miny
-
- for (i = 0; i < num_points; i++)
- {
- if (P[i][1] < miny)
- {
- dz = P[i][2] - cz;
- y = P[i][1] + sqrt(MaxOfTwo(radsqr - dz*dz,0));
- if (y < miny) miny = y;
- }
- }
-
- // grow maxy
-
- for (i = 0; i < num_points; i++)
- {
- if (P[i][1] > maxy)
- {
- dz = P[i][2] - cz;
- y = P[i][1] - sqrt(MaxOfTwo(radsqr - dz*dz,0));
- if (y > maxy) maxy = y;
- }
- }
-
- // corners may have some points which are not covered - grow lengths if
- // necessary
-
- PQP_REAL dx, dy, u, t;
- PQP_REAL a = sqrt((PQP_REAL)0.5);
- for (i = 0; i < num_points; i++)
- {
- if (P[i][0] > maxx)
- {
- if (P[i][1] > maxy)
- {
- dx = P[i][0] - maxx;
- dy = P[i][1] - maxy;
- u = dx*a + dy*a;
- t = (a*u - dx)*(a*u - dx) +
- (a*u - dy)*(a*u - dy) +
- (cz - P[i][2])*(cz - P[i][2]);
- u = u - sqrt(MaxOfTwo(radsqr - t,0));
- if (u > 0)
- {
- maxx += u*a;
- maxy += u*a;
- }
- }
- else if (P[i][1] < miny)
- {
- dx = P[i][0] - maxx;
- dy = P[i][1] - miny;
- u = dx*a - dy*a;
- t = (a*u - dx)*(a*u - dx) +
- (-a*u - dy)*(-a*u - dy) +
- (cz - P[i][2])*(cz - P[i][2]);
- u = u - sqrt(MaxOfTwo(radsqr - t,0));
- if (u > 0)
- {
- maxx += u*a;
- miny -= u*a;
- }
- }
- }
- else if (P[i][0] < minx)
- {
- if (P[i][1] > maxy)
- {
- dx = P[i][0] - minx;
- dy = P[i][1] - maxy;
- u = dy*a - dx*a;
- t = (-a*u - dx)*(-a*u - dx) +
- (a*u - dy)*(a*u - dy) +
- (cz - P[i][2])*(cz - P[i][2]);
- u = u - sqrt(MaxOfTwo(radsqr - t,0));
- if (u > 0)
- {
- minx -= u*a;
- maxy += u*a;
- }
- }
- else if (P[i][1] < miny)
- {
- dx = P[i][0] - minx;
- dy = P[i][1] - miny;
- u = -dx*a - dy*a;
- t = (-a*u - dx)*(-a*u - dx) +
- (-a*u - dy)*(-a*u - dy) +
- (cz - P[i][2])*(cz - P[i][2]);
- u = u - sqrt(MaxOfTwo(radsqr - t,0));
- if (u > 0)
- {
- minx -= u*a;
- miny -= u*a;
- }
- }
- }
- }
-
- c[0] = minx;
- c[1] = miny;
- c[2] = cz;
- MxV(Tr,R,c);
-
- l[0] = maxx - minx;
- if (l[0] < 0) l[0] = 0;
- l[1] = maxy - miny;
- if (l[1] < 0) l[1] = 0;
-#endif
-
- delete [] P;
-}
-
-int
-BV_Overlap(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2)
-{
-#if PQP_BV_TYPE & OBB_TYPE
- return (obb_disjoint(R,T,b1->d,b2->d) == 0);
-#else
- PQP_REAL dist = RectDist(R,T,b1->l,b2->l);
- if (dist <= (b1->r + b2->r)) return 1;
- return 0;
-#endif
-}
-
-#if PQP_BV_TYPE & RSS_TYPE
-PQP_REAL
-BV_Distance(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2)
-{
- PQP_REAL dist = RectDist(R,T,b1->l,b2->l);
- dist -= (b1->r + b2->r);
- return (dist < (PQP_REAL)0.0)? (PQP_REAL)0.0 : dist;
-}
-#endif
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BV.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BV.h
deleted file mode 100644
index cfe42c73785d1ebad055dfca953870eeb32d5a19..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BV.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_BV_H
-#define PQP_BV_H
-
-#include <math.h>
-#include "Tri.h"
-#include "PQP_Compile.h"
-
-struct BV
-{
- PQP_REAL R[3][3]; // orientation of RSS & OBB
-
-#if PQP_BV_TYPE & RSS_TYPE
- PQP_REAL Tr[3]; // position of rectangle
- PQP_REAL l[2]; // side lengths of rectangle
- PQP_REAL r; // radius of sphere summed with rectangle to form RSS
-#endif
-
-#if PQP_BV_TYPE & OBB_TYPE
- PQP_REAL To[3]; // position of obb
- PQP_REAL d[3]; // (half) dimensions of obb
-#endif
-
- int first_child; // positive value is index of first_child bv
- // negative value is -(index + 1) of triangle
-
- BV();
- ~BV();
- int Leaf() { return first_child < 0; }
- PQP_REAL GetSize();
- void FitToTris(PQP_REAL O[3][3], Tri *tris, int num_tris);
-};
-
-inline
-PQP_REAL
-BV::GetSize()
-{
-#if PQP_BV_TYPE & RSS_TYPE
- return (sqrt(l[0]*l[0] + l[1]*l[1]) + 2*r);
-#else
- return (d[0]*d[0] + d[1]*d[1] + d[2]*d[2]);
-#endif
-}
-
-int
-BV_Overlap(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2);
-
-#if PQP_BV_TYPE & RSS_TYPE
-PQP_REAL
-BV_Distance(PQP_REAL R[3][3], PQP_REAL T[3], BV *b1, BV *b2);
-#endif
-
-#endif
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BVTQ.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BVTQ.h
deleted file mode 100644
index 94a6fc788fea5556f633b384e392f8e00cc2717c..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/BVTQ.h
+++ /dev/null
@@ -1,214 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_BVTQ_H
-#define PQP_BVTQ_H
-
-#include <stdio.h>
-#include <stdlib.h>
-#include "PQP_Compile.h"
-
-inline
-int
-LChild(int p)
-{
- return (2*p + 1);
-}
-
-inline
-int
-Parent(int c)
-{
- return ((c - 1)/2);
-}
-
-struct BVT
-{
- PQP_REAL d; // distance between the bvs
- int b1, b2; // bv numbers - b1 is from model 1, b2 from model 2
- PQP_REAL R[3][3]; // the relative rotation from b1 to b2
- PQP_REAL T[3]; // the relative translation from b1 to b2
- int pindex; // the index of the pointer that points to this -
- // needed when filling the hole left by an ExtractMin
-};
-
-class BVTQ
-{
- int size; // max number of bv tests
- int numtests; // number of bv tests in queue
- BVT *bvt; // an array of bv tests - seems faster than 'new' for each
- BVT **bvtp; // the queue: an array of pointers to elts of bvt
-
-public:
- BVTQ(int sz)
- {
- size = sz;
- bvt = new BVT[size];
- bvtp = new BVT*[size];
- numtests = 0;
- }
- ~BVTQ() { delete [] bvt; delete [] bvtp; }
- int Empty() { return (numtests == 0); }
- int GetNumTests() { return numtests; }
- int GetSize() { return size; }
- PQP_REAL MinTest() { return bvtp[0]->d; }
- BVT ExtractMinTest();
- void AddTest(BVT &);
-};
-
-inline
-void
-BVTQ::AddTest(BVT &t)
-{
- bvtp[numtests] = &bvt[numtests];
-
- *bvtp[numtests] = t;
- bvtp[numtests]->pindex = numtests;
-
- BVT *temp;
- int c = numtests;
- int p;
-
- while ((c != 0) && (bvtp[(p = Parent(c))]->d >= bvtp[c]->d))
- {
- // swap p and c pointers
-
- temp = bvtp[p];
- bvtp[p] = bvtp[c];
- bvtp[c] = temp;
-
- // the bv tests pointed to by p and c need new indices
-
- bvtp[p]->pindex = p;
- bvtp[c]->pindex = c;
-
- c = p;
- }
- numtests++;
-}
-
-inline
-BVT
-BVTQ::ExtractMinTest()
-{
- // store min test to be extracted
-
- BVT min_test = *bvtp[0];
-
- // copy last bvt to the empty space;
- // reset the pointer to this moved bvt
-
- *bvtp[0] = bvt[numtests-1];
- bvtp[bvt[numtests-1].pindex] = bvtp[0];
-
- // copy the last pointer to the first
-
- bvtp[0] = bvtp[numtests-1];
-
- numtests--;
-
- BVT *temp;
- int p = 0;
- int c1,c2,c;
-
- while(1)
- {
- c1 = LChild(p);
- c2 = c1+1;
-
- if (c1 < numtests)
- {
- if (c2 < numtests)
- {
- // p has both children, promote the minimum
-
- if (bvtp[c1]->d < bvtp[c2]->d) c = c1; else c = c2;
-
- if (bvtp[c]->d < bvtp[p]->d)
- {
- temp = bvtp[p];
- bvtp[p] = bvtp[c];
- bvtp[c] = temp;
-
- bvtp[p]->pindex = p;
- bvtp[c]->pindex = c;
-
- p = c;
- }
- else
- {
- break;
- }
- }
- else
- {
- // p has only left child
-
- if (bvtp[c1]->d < bvtp[p]->d)
- {
- temp = bvtp[p];
- bvtp[p] = bvtp[c1];
- bvtp[c1] = temp;
-
- bvtp[p]->pindex = p;
- bvtp[c1]->pindex = c1;
-
- p = c1;
- }
- else
- {
- break;
- }
- }
- }
- else
- {
- // p has no children
-
- break;
- }
- }
-
- return min_test;
-}
-
-#endif
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Build.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Build.cpp
deleted file mode 100644
index 4e37b16cc30a71276ed87c8dd8a0ab42733b4f68..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Build.cpp
+++ /dev/null
@@ -1,551 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include "PQP.h"
-#include "MatVec.h"
-
-// If this is set, build routines will use covariance matrix
-// and mean finding code from RAPID 2.
-
-#define RAPID2_FIT 0
-
-#if RAPID2_FIT
-
-struct moment
-{
- PQP_REAL A;
- PQP_REAL m[3];
- PQP_REAL s[3][3];
-};
-
-struct accum
-{
- PQP_REAL A;
- PQP_REAL m[3];
- PQP_REAL s[3][3];
-};
-
-inline
-void
-clear_accum(accum &a)
-{
- a.m[0] = a.m[1] = a.m[2] = 0.0;
- a.s[0][0] = a.s[0][1] = a.s[0][2] = 0.0;
- a.s[1][0] = a.s[1][1] = a.s[1][2] = 0.0;
- a.s[2][0] = a.s[2][1] = a.s[2][2] = 0.0;
- a.A = 0.0;
-}
-
-inline
-void
-accum_moment(accum &a, moment &b)
-{
- a.m[0] += b.m[0] * b.A;
- a.m[1] += b.m[1] * b.A;
- a.m[2] += b.m[2] * b.A;
-
- a.s[0][0] += b.s[0][0];
- a.s[0][1] += b.s[0][1];
- a.s[0][2] += b.s[0][2];
- a.s[1][0] += b.s[1][0];
- a.s[1][1] += b.s[1][1];
- a.s[1][2] += b.s[1][2];
- a.s[2][0] += b.s[2][0];
- a.s[2][1] += b.s[2][1];
- a.s[2][2] += b.s[2][2];
-
- a.A += b.A;
-}
-
-inline
-void
-mean_from_moment(PQP_REAL M[3], moment &m)
-{
- M[0] = m.m[0];
- M[1] = m.m[1];
- M[2] = m.m[2];
-}
-
-inline
-void
-mean_from_accum(PQP_REAL M[3], accum &a)
-{
- M[0] = a.m[0] / a.A;
- M[1] = a.m[1] / a.A;
- M[2] = a.m[2] / a.A;
-}
-
-inline
-void
-covariance_from_accum(PQP_REAL C[3][3], accum &a)
-{
- int i,j;
- for(i=0; i<3; i++)
- for(j=0; j<3; j++)
- C[i][j] = a.s[i][j] - a.m[i]*a.m[j]/a.A;
-}
-
-inline
-void
-compute_moment(moment &M, PQP_REAL p[3], PQP_REAL q[3], PQP_REAL r[3])
-{
- PQP_REAL u[3], v[3], w[3];
-
- // compute the area of the triangle
- VmV(u, q, p);
- VmV(v, r, p);
- VcrossV(w, u, v);
- M.A = 0.5 * Vlength(w);
-
- if (M.A == 0.0)
- {
- // This triangle has zero area. The second order components
- // would be eliminated with the usual formula, so, for the
- // sake of robustness we use an alternative form. These are the
- // centroid and second-order components of the triangle's vertices.
-
- // centroid
- M.m[0] = (p[0] + q[0] + r[0]) /3;
- M.m[1] = (p[1] + q[1] + r[1]) /3;
- M.m[2] = (p[2] + q[2] + r[2]) /3;
-
- // second-order components
- M.s[0][0] = (p[0]*p[0] + q[0]*q[0] + r[0]*r[0]);
- M.s[0][1] = (p[0]*p[1] + q[0]*q[1] + r[0]*r[1]);
- M.s[0][2] = (p[0]*p[2] + q[0]*q[2] + r[0]*r[2]);
- M.s[1][1] = (p[1]*p[1] + q[1]*q[1] + r[1]*r[1]);
- M.s[1][2] = (p[1]*p[2] + q[1]*q[2] + r[1]*r[2]);
- M.s[2][2] = (p[2]*p[2] + q[2]*q[2] + r[2]*r[2]);
- M.s[2][1] = M.s[1][2];
- M.s[1][0] = M.s[0][1];
- M.s[2][0] = M.s[0][2];
-
- return;
- }
-
- // get the centroid
- M.m[0] = (p[0] + q[0] + r[0])/3;
- M.m[1] = (p[1] + q[1] + r[1])/3;
- M.m[2] = (p[2] + q[2] + r[2])/3;
-
- // get the second order components -- note the weighting by the area
- M.s[0][0] = M.A*(9*M.m[0]*M.m[0]+p[0]*p[0]+q[0]*q[0]+r[0]*r[0])/12;
- M.s[0][1] = M.A*(9*M.m[0]*M.m[1]+p[0]*p[1]+q[0]*q[1]+r[0]*r[1])/12;
- M.s[1][1] = M.A*(9*M.m[1]*M.m[1]+p[1]*p[1]+q[1]*q[1]+r[1]*r[1])/12;
- M.s[0][2] = M.A*(9*M.m[0]*M.m[2]+p[0]*p[2]+q[0]*q[2]+r[0]*r[2])/12;
- M.s[1][2] = M.A*(9*M.m[1]*M.m[2]+p[1]*p[2]+q[1]*q[2]+r[1]*r[2])/12;
- M.s[2][2] = M.A*(9*M.m[2]*M.m[2]+p[2]*p[2]+q[2]*q[2]+r[2]*r[2])/12;
- M.s[2][1] = M.s[1][2];
- M.s[1][0] = M.s[0][1];
- M.s[2][0] = M.s[0][2];
-}
-
-inline
-void
-compute_moments(moment *M, Tri *tris, int num_tris)
-{
- int i;
-
- // first collect all the moments, and obtain the area of the
- // smallest nonzero area triangle.
-
- PQP_REAL Amin = 0.0;
- int zero = 0;
- int nonzero = 0;
- for(i=0; i<num_tris; i++)
- {
- compute_moment(M[i],
- tris[i].p1,
- tris[i].p2,
- tris[i].p3);
- if (M[i].A == 0.0)
- {
- zero = 1;
- }
- else
- {
- nonzero = 1;
- if (Amin == 0.0) Amin = M[i].A;
- else if (M[i].A < Amin) Amin = M[i].A;
- }
- }
-
- if (zero)
- {
- fprintf(stderr, "----\n");
- fprintf(stderr, "Warning! Some triangles have zero area!\n");
- fprintf(stderr, "----\n");
-
- // if there are any zero area triangles, go back and set their area
-
- // if ALL the triangles have zero area, then set the area thingy
- // to some arbitrary value.
- if (Amin == 0.0) Amin = 1.0;
-
- for(i=0; i<num_tris; i++)
- {
- if (M[i].A == 0.0) M[i].A = Amin;
- }
- }
-}
-
-#else
-
-PQP_REAL max(PQP_REAL a, PQP_REAL b, PQP_REAL c, PQP_REAL d)
-{
- PQP_REAL t = a;
- if (b > t) t = b;
- if (c > t) t = c;
- if (d > t) t = d;
- return t;
-}
-
-PQP_REAL min(PQP_REAL a, PQP_REAL b, PQP_REAL c, PQP_REAL d)
-{
- PQP_REAL t = a;
- if (b < t) t = b;
- if (c < t) t = c;
- if (d < t) t = d;
- return t;
-}
-
-void
-get_centroid_triverts(PQP_REAL c[3], Tri *tris, int num_tris)
-{
- int i;
-
- c[0] = c[1] = c[2] = 0.0;
-
- // get center of mass
- for(i=0; i<num_tris; i++)
- {
- PQP_REAL *p1 = tris[i].p1;
- PQP_REAL *p2 = tris[i].p2;
- PQP_REAL *p3 = tris[i].p3;
-
- c[0] += p1[0] + p2[0] + p3[0];
- c[1] += p1[1] + p2[1] + p3[1];
- c[2] += p1[2] + p2[2] + p3[2];
- }
-
- PQP_REAL n = (PQP_REAL)(3 * num_tris);
-
- c[0] /= n;
- c[1] /= n;
- c[2] /= n;
-}
-
-void
-get_covariance_triverts(PQP_REAL M[3][3], Tri *tris, int num_tris)
-{
- int i;
- PQP_REAL S1[3];
- PQP_REAL S2[3][3];
-
- S1[0] = S1[1] = S1[2] = 0.0;
- S2[0][0] = S2[1][0] = S2[2][0] = 0.0;
- S2[0][1] = S2[1][1] = S2[2][1] = 0.0;
- S2[0][2] = S2[1][2] = S2[2][2] = 0.0;
-
- // get center of mass
- for(i=0; i<num_tris; i++)
- {
- PQP_REAL *p1 = tris[i].p1;
- PQP_REAL *p2 = tris[i].p2;
- PQP_REAL *p3 = tris[i].p3;
-
- S1[0] += p1[0] + p2[0] + p3[0];
- S1[1] += p1[1] + p2[1] + p3[1];
- S1[2] += p1[2] + p2[2] + p3[2];
-
- S2[0][0] += (p1[0] * p1[0] +
- p2[0] * p2[0] +
- p3[0] * p3[0]);
- S2[1][1] += (p1[1] * p1[1] +
- p2[1] * p2[1] +
- p3[1] * p3[1]);
- S2[2][2] += (p1[2] * p1[2] +
- p2[2] * p2[2] +
- p3[2] * p3[2]);
- S2[0][1] += (p1[0] * p1[1] +
- p2[0] * p2[1] +
- p3[0] * p3[1]);
- S2[0][2] += (p1[0] * p1[2] +
- p2[0] * p2[2] +
- p3[0] * p3[2]);
- S2[1][2] += (p1[1] * p1[2] +
- p2[1] * p2[2] +
- p3[1] * p3[2]);
- }
-
- PQP_REAL n = (PQP_REAL)(3 * num_tris);
-
- // now get covariances
-
- M[0][0] = S2[0][0] - S1[0]*S1[0] / n;
- M[1][1] = S2[1][1] - S1[1]*S1[1] / n;
- M[2][2] = S2[2][2] - S1[2]*S1[2] / n;
- M[0][1] = S2[0][1] - S1[0]*S1[1] / n;
- M[1][2] = S2[1][2] - S1[1]*S1[2] / n;
- M[0][2] = S2[0][2] - S1[0]*S1[2] / n;
- M[1][0] = M[0][1];
- M[2][0] = M[0][2];
- M[2][1] = M[1][2];
-}
-
-#endif
-
-// given a list of triangles, a splitting axis, and a coordinate on
-// that axis, partition the triangles into two groups according to
-// where their centroids fall on the axis (under axial projection).
-// Returns the number of tris in the first half
-
-int
-split_tris(Tri *tris, int num_tris, PQP_REAL a[3], PQP_REAL c)
-{
- int i;
- int c1 = 0;
- PQP_REAL p[3];
- PQP_REAL x;
- Tri temp;
-
- for(i = 0; i < num_tris; i++)
- {
- // loop invariant: up to (but not including) index c1 in group 1,
- // then up to (but not including) index i in group 2
- //
- // [1] [1] [1] [1] [2] [2] [2] [x] [x] ... [x]
- // c1 i
- //
- VcV(p, tris[i].p1);
- VpV(p, p, tris[i].p2);
- VpV(p, p, tris[i].p3);
- x = VdotV(p, a);
- x /= 3.0;
- if (x <= c)
- {
- // group 1
- temp = tris[i];
- tris[i] = tris[c1];
- tris[c1] = temp;
- c1++;
- }
- else
- {
- // group 2 -- do nothing
- }
- }
-
- // split arbitrarily if one group empty
-
- if ((c1 == 0) || (c1 == num_tris)) c1 = num_tris/2;
-
- return c1;
-}
-
-// Fits m->child(bn) to the num_tris triangles starting at first_tri
-// Then, if num_tris is greater than one, partitions the tris into two
-// sets, and recursively builds two children of m->child(bn)
-
-int
-build_recurse(PQP_Model *m, int bn, int first_tri, int num_tris)
-{
- BV *b = m->child(bn);
-
- // compute a rotation matrix
-
- PQP_REAL C[3][3], E[3][3], R[3][3], s[3], axis[3], mean[3], coord;
-
-#if RAPID2_FIT
- moment *tri_moment = new moment[num_tris];
- compute_moments(tri_moment, &(m->tris[first_tri]), num_tris);
- accum acc;
- clear_accum(acc);
- for(int i = 0; i < num_tris; i++) accum_moment(acc, tri_moment[i]);
- delete [] tri_moment;
- covariance_from_accum(C,acc);
-#else
- get_covariance_triverts(C,&m->tris[first_tri],num_tris);
-#endif
-
- Meigen(E, s, C);
-
- // place axes of E in order of increasing s
-
- int min, mid, max;
- if (s[0] > s[1]) { max = 0; min = 1; }
- else { min = 0; max = 1; }
- if (s[2] < s[min]) { mid = min; min = 2; }
- else if (s[2] > s[max]) { mid = max; max = 2; }
- else { mid = 2; }
- McolcMcol(R,0,E,max);
- McolcMcol(R,1,E,mid);
- R[0][2] = E[1][max]*E[2][mid] - E[1][mid]*E[2][max];
- R[1][2] = E[0][mid]*E[2][max] - E[0][max]*E[2][mid];
- R[2][2] = E[0][max]*E[1][mid] - E[0][mid]*E[1][max];
-
- // fit the BV
-
- b->FitToTris(R, &m->tris[first_tri], num_tris);
-
- if (num_tris == 1)
- {
- // BV is a leaf BV - first_child will index a triangle
-
- b->first_child = -(first_tri + 1);
- }
- else if (num_tris > 1)
- {
- // BV not a leaf - first_child will index a BV
-
- b->first_child = m->num_bvs;
- m->num_bvs+=2;
-
- // choose splitting axis and splitting coord
-
- McolcV(axis,R,0);
-
-#if RAPID2_FIT
- mean_from_accum(mean,acc);
-#else
- get_centroid_triverts(mean,&m->tris[first_tri],num_tris);
-#endif
- coord = VdotV(axis, mean);
-
- // now split
-
- int num_first_half = split_tris(&m->tris[first_tri], num_tris,
- axis, coord);
-
- // recursively build the children
-
- build_recurse(m, m->child(bn)->first_child, first_tri, num_first_half);
- build_recurse(m, m->child(bn)->first_child + 1,
- first_tri + num_first_half, num_tris - num_first_half);
- }
- return PQP_OK;
-}
-
-// this descends the hierarchy, converting world-relative
-// transforms to parent-relative transforms
-
-void
-make_parent_relative(PQP_Model *m, int bn,
- const PQP_REAL parentR[3][3]
-#if PQP_BV_TYPE & RSS_TYPE
- ,const PQP_REAL parentTr[3]
-#endif
-#if PQP_BV_TYPE & OBB_TYPE
- ,const PQP_REAL parentTo[3]
-#endif
- )
-{
- PQP_REAL Rpc[3][3], Tpc[3];
-
- if (!m->child(bn)->Leaf())
- {
- // make children parent-relative
-
- make_parent_relative(m,m->child(bn)->first_child,
- m->child(bn)->R
-#if PQP_BV_TYPE & RSS_TYPE
- ,m->child(bn)->Tr
-#endif
-#if PQP_BV_TYPE & OBB_TYPE
- ,m->child(bn)->To
-#endif
- );
- make_parent_relative(m,m->child(bn)->first_child+1,
- m->child(bn)->R
-#if PQP_BV_TYPE & RSS_TYPE
- ,m->child(bn)->Tr
-#endif
-#if PQP_BV_TYPE & OBB_TYPE
- ,m->child(bn)->To
-#endif
- );
- }
-
- // make self parent relative
-
- MTxM(Rpc,parentR,m->child(bn)->R);
- McM(m->child(bn)->R,Rpc);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Tpc,m->child(bn)->Tr,parentTr);
- MTxV(m->child(bn)->Tr,parentR,Tpc);
-#endif
-#if PQP_BV_TYPE & OBB_TYPE
- VmV(Tpc,m->child(bn)->To,parentTo);
- MTxV(m->child(bn)->To,parentR,Tpc);
-#endif
-
-}
-
-int
-build_model(PQP_Model *m)
-{
- // set num_bvs to 1, the first index for a child bv
-
- m->num_bvs = 1;
-
- // build recursively
-
- build_recurse(m, 0, 0, m->num_tris);
-
- // change BV orientations from world-relative to parent-relative
-
- PQP_REAL R[3][3],T[3];
- Midentity(R);
- Videntity(T);
-
- make_parent_relative(m,0,R
-#if PQP_BV_TYPE & RSS_TYPE
- ,T
-#endif
-#if PQP_BV_TYPE & OBB_TYPE
- ,T
-#endif
- );
-
- return PQP_OK;
-}
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Build.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Build.h
deleted file mode 100644
index bab05dd2e07e39f16bdbc50f788566699ebb8e9e..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Build.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_BUILD_H
-#define PQP_BUILD_H
-
-#include "PQP.h"
-
-int
-build_model(PQP_Model *m);
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/GetTime.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/GetTime.h
deleted file mode 100644
index 5529a08fb98d31ccdc04c584c58f3f8cc4e42447..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/GetTime.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_GETTIME_H
-#define PQP_GETTIME_H
-
-#ifdef WIN32
-
- #include <time.h>
- #include <sys/timeb.h>
- inline
- double
- GetTime()
- {
- struct _timeb thistime;
- _ftime(&thistime);
- return (thistime.time + thistime.millitm * 1e-3);
- }
-
-#else
-
- #include <sys/time.h>
- inline
- double
- GetTime()
- {
- struct timeval thistime;
- gettimeofday(&thistime, 0);
- return (thistime.tv_sec + thistime.tv_usec * 1e-6);
- }
-
-#endif
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/MatVec.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/MatVec.h
deleted file mode 100644
index c0198ad77aa2b60297f4a35cd5550c5d1bf21d75..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/MatVec.h
+++ /dev/null
@@ -1,877 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_MATVEC_H
-#define PQP_MATVEC_H
-
-#include <math.h>
-#include <stdio.h>
-#include "PQP_Compile.h"
-
-#ifndef M_PI
-const PQP_REAL M_PI = (PQP_REAL)3.14159265359;
-#endif
-
-#ifdef gnu
-#include "zzzz.h"
-
-#ifdef hppa
-#define myfabs(x) \
- ({double __value, __arg = (x); \
- asm("fabs,dbl %1, %0": "=f" (__value): "f" (__arg)); \
- __value; \
-});
-#endif
-
-#ifdef mips
-#define myfabs(x) \
- ({double __value, __arg = (x); \
- asm("abs.d %0, %1": "=f" (__value): "f" (__arg)); \
- __value; \
-});
-#endif
-
-#else
-
-#define myfabs(x) ((x < 0) ? -x : x)
-
-#endif
-
-
-inline
-void
-Mprintg(const PQP_REAL M[3][3])
-{
- printf("%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-
-inline
-void
-Mfprint(FILE *f, const PQP_REAL M[3][3])
-{
- fprintf(f, "%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-inline
-void
-Mprint(const PQP_REAL M[3][3])
-{
- printf("%g %g %g\n%g %g %g\n%g %g %g\n",
- M[0][0], M[0][1], M[0][2],
- M[1][0], M[1][1], M[1][2],
- M[2][0], M[2][1], M[2][2]);
-}
-
-inline
-void
-Vprintg(const PQP_REAL V[3])
-{
- printf("%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Vfprint(FILE *f, const PQP_REAL V[3])
-{
- fprintf(f, "%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Vprint(const PQP_REAL V[3])
-{
- printf("%g %g %g\n", V[0], V[1], V[2]);
-}
-
-inline
-void
-Midentity(PQP_REAL M[3][3])
-{
- M[0][0] = M[1][1] = M[2][2] = 1.0;
- M[0][1] = M[1][2] = M[2][0] = 0.0;
- M[0][2] = M[1][0] = M[2][1] = 0.0;
-}
-
-inline
-void
-Videntity(PQP_REAL T[3])
-{
- T[0] = T[1] = T[2] = 0.0;
-}
-
-inline
-void
-McM(PQP_REAL Mr[3][3], const PQP_REAL M[3][3])
-{
- Mr[0][0] = M[0][0]; Mr[0][1] = M[0][1]; Mr[0][2] = M[0][2];
- Mr[1][0] = M[1][0]; Mr[1][1] = M[1][1]; Mr[1][2] = M[1][2];
- Mr[2][0] = M[2][0]; Mr[2][1] = M[2][1]; Mr[2][2] = M[2][2];
-}
-
-inline
-void
-MTcM(PQP_REAL Mr[3][3], const PQP_REAL M[3][3])
-{
- Mr[0][0] = M[0][0]; Mr[1][0] = M[0][1]; Mr[2][0] = M[0][2];
- Mr[0][1] = M[1][0]; Mr[1][1] = M[1][1]; Mr[2][1] = M[1][2];
- Mr[0][2] = M[2][0]; Mr[1][2] = M[2][1]; Mr[2][2] = M[2][2];
-}
-
-inline
-void
-VcV(PQP_REAL Vr[3], const PQP_REAL V[3])
-{
- Vr[0] = V[0]; Vr[1] = V[1]; Vr[2] = V[2];
-}
-
-inline
-void
-McolcV(PQP_REAL Vr[3], const PQP_REAL M[3][3], int c)
-{
- Vr[0] = M[0][c];
- Vr[1] = M[1][c];
- Vr[2] = M[2][c];
-}
-
-inline
-void
-McolcMcol(PQP_REAL Mr[3][3], int cr, const PQP_REAL M[3][3], int c)
-{
- Mr[0][cr] = M[0][c];
- Mr[1][cr] = M[1][c];
- Mr[2][cr] = M[2][c];
-}
-
-inline
-void
-MxMpV(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3], const PQP_REAL T[3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[1][0] +
- M1[0][2] * M2[2][0] +
- T[0]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[1][2] * M2[2][0] +
- T[1]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[1][0] +
- M1[2][2] * M2[2][0] +
- T[2]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[2][1] +
- T[0]);
- Mr[1][1] = (M1[1][0] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[2][1] +
- T[1]);
- Mr[2][1] = (M1[2][0] * M2[0][1] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[2][1] +
- T[2]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[0][1] * M2[1][2] +
- M1[0][2] * M2[2][2] +
- T[0]);
- Mr[1][2] = (M1[1][0] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[1][2] * M2[2][2] +
- T[1]);
- Mr[2][2] = (M1[2][0] * M2[0][2] +
- M1[2][1] * M2[1][2] +
- M1[2][2] * M2[2][2] +
- T[2]);
-}
-
-inline
-void
-MxM(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[1][0] +
- M1[0][2] * M2[2][0]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[1][2] * M2[2][0]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[1][0] +
- M1[2][2] * M2[2][0]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[2][1]);
- Mr[1][1] = (M1[1][0] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[2][1]);
- Mr[2][1] = (M1[2][0] * M2[0][1] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[2][1]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[0][1] * M2[1][2] +
- M1[0][2] * M2[2][2]);
- Mr[1][2] = (M1[1][0] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[1][2] * M2[2][2]);
- Mr[2][2] = (M1[2][0] * M2[0][2] +
- M1[2][1] * M2[1][2] +
- M1[2][2] * M2[2][2]);
-}
-
-
-inline
-void
-MxMT(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[0][1] * M2[0][1] +
- M1[0][2] * M2[0][2]);
- Mr[1][0] = (M1[1][0] * M2[0][0] +
- M1[1][1] * M2[0][1] +
- M1[1][2] * M2[0][2]);
- Mr[2][0] = (M1[2][0] * M2[0][0] +
- M1[2][1] * M2[0][1] +
- M1[2][2] * M2[0][2]);
- Mr[0][1] = (M1[0][0] * M2[1][0] +
- M1[0][1] * M2[1][1] +
- M1[0][2] * M2[1][2]);
- Mr[1][1] = (M1[1][0] * M2[1][0] +
- M1[1][1] * M2[1][1] +
- M1[1][2] * M2[1][2]);
- Mr[2][1] = (M1[2][0] * M2[1][0] +
- M1[2][1] * M2[1][1] +
- M1[2][2] * M2[1][2]);
- Mr[0][2] = (M1[0][0] * M2[2][0] +
- M1[0][1] * M2[2][1] +
- M1[0][2] * M2[2][2]);
- Mr[1][2] = (M1[1][0] * M2[2][0] +
- M1[1][1] * M2[2][1] +
- M1[1][2] * M2[2][2]);
- Mr[2][2] = (M1[2][0] * M2[2][0] +
- M1[2][1] * M2[2][1] +
- M1[2][2] * M2[2][2]);
-}
-
-inline
-void
-MTxM(PQP_REAL Mr[3][3], const PQP_REAL M1[3][3], const PQP_REAL M2[3][3])
-{
- Mr[0][0] = (M1[0][0] * M2[0][0] +
- M1[1][0] * M2[1][0] +
- M1[2][0] * M2[2][0]);
- Mr[1][0] = (M1[0][1] * M2[0][0] +
- M1[1][1] * M2[1][0] +
- M1[2][1] * M2[2][0]);
- Mr[2][0] = (M1[0][2] * M2[0][0] +
- M1[1][2] * M2[1][0] +
- M1[2][2] * M2[2][0]);
- Mr[0][1] = (M1[0][0] * M2[0][1] +
- M1[1][0] * M2[1][1] +
- M1[2][0] * M2[2][1]);
- Mr[1][1] = (M1[0][1] * M2[0][1] +
- M1[1][1] * M2[1][1] +
- M1[2][1] * M2[2][1]);
- Mr[2][1] = (M1[0][2] * M2[0][1] +
- M1[1][2] * M2[1][1] +
- M1[2][2] * M2[2][1]);
- Mr[0][2] = (M1[0][0] * M2[0][2] +
- M1[1][0] * M2[1][2] +
- M1[2][0] * M2[2][2]);
- Mr[1][2] = (M1[0][1] * M2[0][2] +
- M1[1][1] * M2[1][2] +
- M1[2][1] * M2[2][2]);
- Mr[2][2] = (M1[0][2] * M2[0][2] +
- M1[1][2] * M2[1][2] +
- M1[2][2] * M2[2][2]);
-}
-
-inline
-void
-MxV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]);
- Vr[1] = (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]);
- Vr[2] = (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-
-inline
-void
-MxVpV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2] +
- V2[0]);
- Vr[1] = (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2] +
- V2[1]);
- Vr[2] = (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2] +
- V2[2]);
-}
-
-
-inline
-void
-sMxVpV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = s1 * (M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]) +
- V2[0];
- Vr[1] = s1 * (M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]) +
- V2[1];
- Vr[2] = s1 * (M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]) +
- V2[2];
-}
-
-inline
-void
-MTxV(PQP_REAL Vr[3], const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = (M1[0][0] * V1[0] +
- M1[1][0] * V1[1] +
- M1[2][0] * V1[2]);
- Vr[1] = (M1[0][1] * V1[0] +
- M1[1][1] * V1[1] +
- M1[2][1] * V1[2]);
- Vr[2] = (M1[0][2] * V1[0] +
- M1[1][2] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-inline
-void
-sMTxV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = s1*(M1[0][0] * V1[0] +
- M1[1][0] * V1[1] +
- M1[2][0] * V1[2]);
- Vr[1] = s1*(M1[0][1] * V1[0] +
- M1[1][1] * V1[1] +
- M1[2][1] * V1[2]);
- Vr[2] = s1*(M1[0][2] * V1[0] +
- M1[1][2] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-inline
-void
-sMxV(PQP_REAL Vr[3], PQP_REAL s1, const PQP_REAL M1[3][3], const PQP_REAL V1[3])
-{
- Vr[0] = s1*(M1[0][0] * V1[0] +
- M1[0][1] * V1[1] +
- M1[0][2] * V1[2]);
- Vr[1] = s1*(M1[1][0] * V1[0] +
- M1[1][1] * V1[1] +
- M1[1][2] * V1[2]);
- Vr[2] = s1*(M1[2][0] * V1[0] +
- M1[2][1] * V1[1] +
- M1[2][2] * V1[2]);
-}
-
-
-inline
-void
-VmV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[0] - V2[0];
- Vr[1] = V1[1] - V2[1];
- Vr[2] = V1[2] - V2[2];
-}
-
-inline
-void
-VpV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[0] + V2[0];
- Vr[1] = V1[1] + V2[1];
- Vr[2] = V1[2] + V2[2];
-}
-
-inline
-void
-VpVxS(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3], PQP_REAL s)
-{
- Vr[0] = V1[0] + V2[0] * s;
- Vr[1] = V1[1] + V2[1] * s;
- Vr[2] = V1[2] + V2[2] * s;
-}
-
-inline
-void
-MskewV(PQP_REAL M[3][3], const PQP_REAL v[3])
-{
- M[0][0] = M[1][1] = M[2][2] = 0.0;
- M[1][0] = v[2];
- M[0][1] = -v[2];
- M[0][2] = v[1];
- M[2][0] = -v[1];
- M[1][2] = -v[0];
- M[2][1] = v[0];
-}
-
-
-inline
-void
-VcrossV(PQP_REAL Vr[3], const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- Vr[0] = V1[1]*V2[2] - V1[2]*V2[1];
- Vr[1] = V1[2]*V2[0] - V1[0]*V2[2];
- Vr[2] = V1[0]*V2[1] - V1[1]*V2[0];
-}
-
-inline
-PQP_REAL
-Vlength(PQP_REAL V[3])
-{
- return sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
-}
-
-inline
-void
-Vnormalize(PQP_REAL V[3])
-{
- PQP_REAL d = (PQP_REAL)1.0 / sqrt(V[0]*V[0] + V[1]*V[1] + V[2]*V[2]);
- V[0] *= d;
- V[1] *= d;
- V[2] *= d;
-}
-
-inline
-PQP_REAL
-VdotV(const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- return (V1[0]*V2[0] + V1[1]*V2[1] + V1[2]*V2[2]);
-}
-
-inline
-PQP_REAL
-VdistV2(const PQP_REAL V1[3], const PQP_REAL V2[3])
-{
- return ( (V1[0]-V2[0]) * (V1[0]-V2[0]) +
- (V1[1]-V2[1]) * (V1[1]-V2[1]) +
- (V1[2]-V2[2]) * (V1[2]-V2[2]));
-}
-
-inline
-void
-VxS(PQP_REAL Vr[3], const PQP_REAL V[3], PQP_REAL s)
-{
- Vr[0] = V[0] * s;
- Vr[1] = V[1] * s;
- Vr[2] = V[2] * s;
-}
-
-inline
-void
-MRotZ(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[0][0] = cos(t);
- Mr[1][0] = sin(t);
- Mr[0][1] = -Mr[1][0];
- Mr[1][1] = Mr[0][0];
- Mr[2][0] = Mr[2][1] = 0.0;
- Mr[0][2] = Mr[1][2] = 0.0;
- Mr[2][2] = 1.0;
-}
-
-inline
-void
-MRotX(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[1][1] = cos(t);
- Mr[2][1] = sin(t);
- Mr[1][2] = -Mr[2][1];
- Mr[2][2] = Mr[1][1];
- Mr[0][1] = Mr[0][2] = 0.0;
- Mr[1][0] = Mr[2][0] = 0.0;
- Mr[0][0] = 1.0;
-}
-
-inline
-void
-MRotY(PQP_REAL Mr[3][3], PQP_REAL t)
-{
- Mr[2][2] = cos(t);
- Mr[0][2] = sin(t);
- Mr[2][0] = -Mr[0][2];
- Mr[0][0] = Mr[2][2];
- Mr[1][2] = Mr[1][0] = 0.0;
- Mr[2][1] = Mr[0][1] = 0.0;
- Mr[1][1] = 1.0;
-}
-
-inline
-void
-MVtoOGL(double oglm[16], const PQP_REAL R[3][3], const PQP_REAL T[3])
-{
- oglm[0] = (double)R[0][0];
- oglm[1] = (double)R[1][0];
- oglm[2] = (double)R[2][0];
- oglm[3] = 0.0;
- oglm[4] = (double)R[0][1];
- oglm[5] = (double)R[1][1];
- oglm[6] = (double)R[2][1];
- oglm[7] = 0.0;
- oglm[8] = (double)R[0][2];
- oglm[9] = (double)R[1][2];
- oglm[10] = (double)R[2][2];
- oglm[11] = 0.0;
- oglm[12] = (double)T[0];
- oglm[13] = (double)T[1];
- oglm[14] = (double)T[2];
- oglm[15] = 1.0;
-}
-
-inline
-void
-OGLtoMV(PQP_REAL R[3][3], PQP_REAL T[3], const double oglm[16])
-{
- R[0][0] = (PQP_REAL)oglm[0];
- R[1][0] = (PQP_REAL)oglm[1];
- R[2][0] = (PQP_REAL)oglm[2];
-
- R[0][1] = (PQP_REAL)oglm[4];
- R[1][1] = (PQP_REAL)oglm[5];
- R[2][1] = (PQP_REAL)oglm[6];
-
- R[0][2] = (PQP_REAL)oglm[8];
- R[1][2] = (PQP_REAL)oglm[9];
- R[2][2] = (PQP_REAL)oglm[10];
-
- T[0] = (PQP_REAL)oglm[12];
- T[1] = (PQP_REAL)oglm[13];
- T[2] = (PQP_REAL)oglm[14];
-}
-
-// taken from quatlib, written by Richard Holloway
-const int QX = 0;
-const int QY = 1;
-const int QZ = 2;
-const int QW = 3;
-
-inline
-void
-MRotQ(PQP_REAL destMatrix[3][3], PQP_REAL srcQuat[4])
-{
- PQP_REAL s;
- PQP_REAL xs, ys, zs,
- wx, wy, wz,
- xx, xy, xz,
- yy, yz, zz;
-
- /*
- * For unit srcQuat, just set s = 2.0; or set xs = srcQuat[QX] +
- * srcQuat[QX], etc.
- */
-
- s = (PQP_REAL)2.0 / (srcQuat[QX]*srcQuat[QX] + srcQuat[QY]*srcQuat[QY] +
- srcQuat[QZ]*srcQuat[QZ] + srcQuat[QW]*srcQuat[QW]);
-
- xs = srcQuat[QX] * s; ys = srcQuat[QY] * s; zs = srcQuat[QZ] * s;
- wx = srcQuat[QW] * xs; wy = srcQuat[QW] * ys; wz = srcQuat[QW] * zs;
- xx = srcQuat[QX] * xs; xy = srcQuat[QX] * ys; xz = srcQuat[QX] * zs;
- yy = srcQuat[QY] * ys; yz = srcQuat[QY] * zs; zz = srcQuat[QZ] * zs;
-
- destMatrix[QX][QX] = (PQP_REAL)1.0 - (yy + zz);
- destMatrix[QX][QY] = xy + wz;
- destMatrix[QX][QZ] = xz - wy;
-
- destMatrix[QY][QX] = xy - wz;
- destMatrix[QY][QY] = (PQP_REAL)1.0 - (xx + zz);
- destMatrix[QY][QZ] = yz + wx;
-
- destMatrix[QZ][QX] = xz + wy;
- destMatrix[QZ][QY] = yz - wx;
- destMatrix[QZ][QZ] = (PQP_REAL)1.0 - (xx + yy);
-}
-
-inline
-void
-Mqinverse(PQP_REAL Mr[3][3], PQP_REAL m[3][3])
-{
- int i,j;
-
- for(i=0; i<3; i++)
- for(j=0; j<3; j++)
- {
- int i1 = (i+1)%3;
- int i2 = (i+2)%3;
- int j1 = (j+1)%3;
- int j2 = (j+2)%3;
- Mr[i][j] = (m[j1][i1]*m[j2][i2] - m[j1][i2]*m[j2][i1]);
- }
-}
-
-// Meigen from Numerical Recipes in C
-
-#if 0
-
-#define rfabs(x) ((x < 0) ? -x : x)
-
-#define ROT(a,i,j,k,l) g=a[i][j]; h=a[k][l]; a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
-
-int
-inline
-Meigen(PQP_REAL vout[3][3], PQP_REAL dout[3], PQP_REAL a[3][3])
-{
- int i;
- PQP_REAL tresh,theta,tau,t,sm,s,h,g,c;
- int nrot;
- PQP_REAL b[3];
- PQP_REAL z[3];
- PQP_REAL v[3][3];
- PQP_REAL d[3];
-
- v[0][0] = v[1][1] = v[2][2] = 1.0;
- v[0][1] = v[1][2] = v[2][0] = 0.0;
- v[0][2] = v[1][0] = v[2][1] = 0.0;
-
- b[0] = a[0][0]; d[0] = a[0][0]; z[0] = 0.0;
- b[1] = a[1][1]; d[1] = a[1][1]; z[1] = 0.0;
- b[2] = a[2][2]; d[2] = a[2][2]; z[2] = 0.0;
-
- nrot = 0;
-
-
- for(i=0; i<50; i++)
- {
-
- printf("2\n");
-
- sm=0.0; sm+=fabs(a[0][1]); sm+=fabs(a[0][2]); sm+=fabs(a[1][2]);
- if (sm == 0.0) { McM(vout,v); VcV(dout,d); return i; }
-
- if (i < 3) tresh=0.2*sm/(3*3); else tresh=0.0;
-
- {
- g = 100.0*rfabs(a[0][1]);
- if (i>3 && rfabs(d[0])+g==rfabs(d[0]) && rfabs(d[1])+g==rfabs(d[1]))
- a[0][1]=0.0;
- else if (rfabs(a[0][1])>tresh)
- {
- h = d[1]-d[0];
- if (rfabs(h)+g == rfabs(h)) t=(a[0][1])/h;
- else
- {
- theta=0.5*h/(a[0][1]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[0][1];
- z[0] -= h; z[1] += h; d[0] -= h; d[1] += h;
- a[0][1]=0.0;
- ROT(a,0,2,1,2); ROT(v,0,0,0,1); ROT(v,1,0,1,1); ROT(v,2,0,2,1);
- nrot++;
- }
- }
-
- {
- g = 100.0*rfabs(a[0][2]);
- if (i>3 && rfabs(d[0])+g==rfabs(d[0]) && rfabs(d[2])+g==rfabs(d[2]))
- a[0][2]=0.0;
- else if (rfabs(a[0][2])>tresh)
- {
- h = d[2]-d[0];
- if (rfabs(h)+g == rfabs(h)) t=(a[0][2])/h;
- else
- {
- theta=0.5*h/(a[0][2]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[0][2];
- z[0] -= h; z[2] += h; d[0] -= h; d[2] += h;
- a[0][2]=0.0;
- ROT(a,0,1,1,2); ROT(v,0,0,0,2); ROT(v,1,0,1,2); ROT(v,2,0,2,2);
- nrot++;
- }
- }
-
-
- {
- g = 100.0*rfabs(a[1][2]);
- if (i>3 && rfabs(d[1])+g==rfabs(d[1]) && rfabs(d[2])+g==rfabs(d[2]))
- a[1][2]=0.0;
- else if (rfabs(a[1][2])>tresh)
- {
- h = d[2]-d[1];
- if (rfabs(h)+g == rfabs(h)) t=(a[1][2])/h;
- else
- {
- theta=0.5*h/(a[1][2]);
- t=1.0/(rfabs(theta)+sqrt(1.0+theta*theta));
- if (theta < 0.0) t = -t;
- }
- c=1.0/sqrt(1+t*t); s=t*c; tau=s/(1.0+c); h=t*a[1][2];
- z[1] -= h; z[2] += h; d[1] -= h; d[2] += h;
- a[1][2]=0.0;
- ROT(a,0,1,0,2); ROT(v,0,1,0,2); ROT(v,1,1,1,2); ROT(v,2,1,2,2);
- nrot++;
- }
- }
-
- b[0] += z[0]; d[0] = b[0]; z[0] = 0.0;
- b[1] += z[1]; d[1] = b[1]; z[1] = 0.0;
- b[2] += z[2]; d[2] = b[2]; z[2] = 0.0;
-
- }
-
- fprintf(stderr, "eigen: too many iterations in Jacobi transform (%d).\n", i);
-
- return i;
-}
-
-#else
-
-
-
-#define ROTATE(a,i,j,k,l) g=a[i][j]; h=a[k][l]; a[i][j]=g-s*(h+g*tau); a[k][l]=h+s*(g-h*tau);
-
-void
-inline
-Meigen(PQP_REAL vout[3][3], PQP_REAL dout[3], PQP_REAL a[3][3])
-{
- int n = 3;
- int j,iq,ip,i;
- PQP_REAL tresh,theta,tau,t,sm,s,h,g,c;
- int nrot;
- PQP_REAL b[3];
- PQP_REAL z[3];
- PQP_REAL v[3][3];
- PQP_REAL d[3];
-
- Midentity(v);
- for(ip=0; ip<n; ip++)
- {
- b[ip] = a[ip][ip];
- d[ip] = a[ip][ip];
- z[ip] = 0.0;
- }
-
- nrot = 0;
-
- for(i=0; i<50; i++)
- {
-
- sm=0.0;
- for(ip=0;ip<n;ip++) for(iq=ip+1;iq<n;iq++) sm+=fabs(a[ip][iq]);
- if (sm == 0.0)
- {
- McM(vout, v);
- VcV(dout, d);
- return;
- }
-
-
- if (i < 3) tresh=(PQP_REAL)0.2*sm/(n*n);
- else tresh=0.0;
-
- for(ip=0; ip<n; ip++) for(iq=ip+1; iq<n; iq++)
- {
- g = (PQP_REAL)100.0*fabs(a[ip][iq]);
- if (i>3 &&
- fabs(d[ip])+g==fabs(d[ip]) &&
- fabs(d[iq])+g==fabs(d[iq]))
- a[ip][iq]=0.0;
- else if (fabs(a[ip][iq])>tresh)
- {
- h = d[iq]-d[ip];
- if (fabs(h)+g == fabs(h)) t=(a[ip][iq])/h;
- else
- {
- theta=(PQP_REAL)0.5*h/(a[ip][iq]);
- t=(PQP_REAL)(1.0/(fabs(theta)+sqrt(1.0+theta*theta)));
- if (theta < 0.0) t = -t;
- }
- c=(PQP_REAL)1.0/sqrt(1+t*t);
- s=t*c;
- tau=s/((PQP_REAL)1.0+c);
- h=t*a[ip][iq];
- z[ip] -= h;
- z[iq] += h;
- d[ip] -= h;
- d[iq] += h;
- a[ip][iq]=0.0;
- for(j=0;j<ip;j++) { ROTATE(a,j,ip,j,iq); }
- for(j=ip+1;j<iq;j++) { ROTATE(a,ip,j,j,iq); }
- for(j=iq+1;j<n;j++) { ROTATE(a,ip,j,iq,j); }
- for(j=0;j<n;j++) { ROTATE(v,j,ip,j,iq); }
- nrot++;
- }
- }
- for(ip=0;ip<n;ip++)
- {
- b[ip] += z[ip];
- d[ip] = b[ip];
- z[ip] = 0.0;
- }
- }
-
- fprintf(stderr, "eigen: too many iterations in Jacobi transform.\n");
-
- return;
-}
-
-
-#endif
-
-#endif
-// MATVEC_H
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/OBB_Disjoint.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/OBB_Disjoint.h
deleted file mode 100644
index 4a73203171ac3d21622246358fbceba12179e857..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/OBB_Disjoint.h
+++ /dev/null
@@ -1,216 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_OBB_DISJOINT
-#define PQP_OBB_DISJOINT
-
-#include "MatVec.h"
-#include "PQP_Compile.h"
-
-// int
-// obb_disjoint(PQP_REAL B[3][3], PQP_REAL T[3], PQP_REAL a[3], PQP_REAL b[3]);
-//
-// This is a test between two boxes, box A and box B. It is assumed that
-// the coordinate system is aligned and centered on box A. The 3x3
-// matrix B specifies box B's orientation with respect to box A.
-// Specifically, the columns of B are the basis vectors (axis vectors) of
-// box B. The center of box B is located at the vector T. The
-// dimensions of box B are given in the array b. The orientation and
-// placement of box A, in this coordinate system, are the identity matrix
-// and zero vector, respectively, so they need not be specified. The
-// dimensions of box A are given in array a.
-
-inline
-int
-obb_disjoint(PQP_REAL B[3][3], PQP_REAL T[3], PQP_REAL a[3], PQP_REAL b[3])
-{
- register PQP_REAL t, s;
- register int r;
- PQP_REAL Bf[3][3];
- const PQP_REAL reps = (PQP_REAL)1e-6;
-
- // Bf = fabs(B)
- Bf[0][0] = myfabs(B[0][0]); Bf[0][0] += reps;
- Bf[0][1] = myfabs(B[0][1]); Bf[0][1] += reps;
- Bf[0][2] = myfabs(B[0][2]); Bf[0][2] += reps;
- Bf[1][0] = myfabs(B[1][0]); Bf[1][0] += reps;
- Bf[1][1] = myfabs(B[1][1]); Bf[1][1] += reps;
- Bf[1][2] = myfabs(B[1][2]); Bf[1][2] += reps;
- Bf[2][0] = myfabs(B[2][0]); Bf[2][0] += reps;
- Bf[2][1] = myfabs(B[2][1]); Bf[2][1] += reps;
- Bf[2][2] = myfabs(B[2][2]); Bf[2][2] += reps;
-
- // if any of these tests are one-sided, then the polyhedra are disjoint
- r = 1;
-
- // A1 x A2 = A0
- t = myfabs(T[0]);
-
- r &= (t <=
- (a[0] + b[0] * Bf[0][0] + b[1] * Bf[0][1] + b[2] * Bf[0][2]));
- if (!r) return 1;
-
- // B1 x B2 = B0
- s = T[0]*B[0][0] + T[1]*B[1][0] + T[2]*B[2][0];
- t = myfabs(s);
-
- r &= ( t <=
- (b[0] + a[0] * Bf[0][0] + a[1] * Bf[1][0] + a[2] * Bf[2][0]));
- if (!r) return 2;
-
- // A2 x A0 = A1
- t = myfabs(T[1]);
-
- r &= ( t <=
- (a[1] + b[0] * Bf[1][0] + b[1] * Bf[1][1] + b[2] * Bf[1][2]));
- if (!r) return 3;
-
- // A0 x A1 = A2
- t = myfabs(T[2]);
-
- r &= ( t <=
- (a[2] + b[0] * Bf[2][0] + b[1] * Bf[2][1] + b[2] * Bf[2][2]));
- if (!r) return 4;
-
- // B2 x B0 = B1
- s = T[0]*B[0][1] + T[1]*B[1][1] + T[2]*B[2][1];
- t = myfabs(s);
-
- r &= ( t <=
- (b[1] + a[0] * Bf[0][1] + a[1] * Bf[1][1] + a[2] * Bf[2][1]));
- if (!r) return 5;
-
- // B0 x B1 = B2
- s = T[0]*B[0][2] + T[1]*B[1][2] + T[2]*B[2][2];
- t = myfabs(s);
-
- r &= ( t <=
- (b[2] + a[0] * Bf[0][2] + a[1] * Bf[1][2] + a[2] * Bf[2][2]));
- if (!r) return 6;
-
- // A0 x B0
- s = T[2] * B[1][0] - T[1] * B[2][0];
- t = myfabs(s);
-
- r &= ( t <=
- (a[1] * Bf[2][0] + a[2] * Bf[1][0] +
- b[1] * Bf[0][2] + b[2] * Bf[0][1]));
- if (!r) return 7;
-
- // A0 x B1
- s = T[2] * B[1][1] - T[1] * B[2][1];
- t = myfabs(s);
-
- r &= ( t <=
- (a[1] * Bf[2][1] + a[2] * Bf[1][1] +
- b[0] * Bf[0][2] + b[2] * Bf[0][0]));
- if (!r) return 8;
-
- // A0 x B2
- s = T[2] * B[1][2] - T[1] * B[2][2];
- t = myfabs(s);
-
- r &= ( t <=
- (a[1] * Bf[2][2] + a[2] * Bf[1][2] +
- b[0] * Bf[0][1] + b[1] * Bf[0][0]));
- if (!r) return 9;
-
- // A1 x B0
- s = T[0] * B[2][0] - T[2] * B[0][0];
- t = myfabs(s);
-
- r &= ( t <=
- (a[0] * Bf[2][0] + a[2] * Bf[0][0] +
- b[1] * Bf[1][2] + b[2] * Bf[1][1]));
- if (!r) return 10;
-
- // A1 x B1
- s = T[0] * B[2][1] - T[2] * B[0][1];
- t = myfabs(s);
-
- r &= ( t <=
- (a[0] * Bf[2][1] + a[2] * Bf[0][1] +
- b[0] * Bf[1][2] + b[2] * Bf[1][0]));
- if (!r) return 11;
-
- // A1 x B2
- s = T[0] * B[2][2] - T[2] * B[0][2];
- t = myfabs(s);
-
- r &= (t <=
- (a[0] * Bf[2][2] + a[2] * Bf[0][2] +
- b[0] * Bf[1][1] + b[1] * Bf[1][0]));
- if (!r) return 12;
-
- // A2 x B0
- s = T[1] * B[0][0] - T[0] * B[1][0];
- t = myfabs(s);
-
- r &= (t <=
- (a[0] * Bf[1][0] + a[1] * Bf[0][0] +
- b[1] * Bf[2][2] + b[2] * Bf[2][1]));
- if (!r) return 13;
-
- // A2 x B1
- s = T[1] * B[0][1] - T[0] * B[1][1];
- t = myfabs(s);
-
- r &= ( t <=
- (a[0] * Bf[1][1] + a[1] * Bf[0][1] +
- b[0] * Bf[2][2] + b[2] * Bf[2][0]));
- if (!r) return 14;
-
- // A2 x B2
- s = T[1] * B[0][2] - T[0] * B[1][2];
- t = myfabs(s);
-
- r &= ( t <=
- (a[0] * Bf[1][2] + a[1] * Bf[0][2] +
- b[0] * Bf[2][1] + b[1] * Bf[2][0]));
- if (!r) return 15;
-
- return 0; // should equal 0
-}
-
-#endif
-
-
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP.cpp
deleted file mode 100644
index c185750345501c0866070b8bf7766ee4a2285b4b..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP.cpp
+++ /dev/null
@@ -1,1376 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include <stdio.h>
-#include <string.h>
-#include "PQP.h"
-#include "BVTQ.h"
-#include "Build.h"
-#include "MatVec.h"
-#include "GetTime.h"
-#include "TriDist.h"
-
-enum BUILD_STATE
-{
- PQP_BUILD_STATE_EMPTY, // empty state, immediately after constructor
- PQP_BUILD_STATE_BEGUN, // after BeginModel(), state for adding triangles
- PQP_BUILD_STATE_PROCESSED // after tree has been built, ready to use
-};
-
-PQP_Model::PQP_Model()
-{
- // no bounding volume tree yet
-
- b = 0;
- num_bvs_alloced = 0;
- num_bvs = 0;
-
- // no tri list yet
-
- tris = 0;
- num_tris = 0;
- num_tris_alloced = 0;
-
- last_tri = 0;
-
- build_state = PQP_BUILD_STATE_EMPTY;
-}
-
-PQP_Model::~PQP_Model()
-{
- if (b != NULL)
- delete [] b;
- if (tris != NULL)
- delete [] tris;
-}
-
-int
-PQP_Model::BeginModel(int n)
-{
- // reset to initial state if necessary
-
- if (build_state != PQP_BUILD_STATE_EMPTY)
- {
- delete [] b;
- delete [] tris;
-
- num_tris = num_bvs = num_tris_alloced = num_bvs_alloced = 0;
- }
-
- // prepare model for addition of triangles
-
- if (n <= 0) n = 8;
- num_tris_alloced = n;
- tris = new Tri[n];
- if (!tris)
- {
- fprintf(stderr, "PQP Error! Out of memory for tri array on "
- "BeginModel() call!\n");
- return PQP_ERR_MODEL_OUT_OF_MEMORY;
- }
-
- // give a warning if called out of sequence
-
- if (build_state != PQP_BUILD_STATE_EMPTY)
- {
- fprintf(stderr,
- "PQP Warning! Called BeginModel() on a PQP_Model that \n"
- "was not empty. This model was cleared and previous\n"
- "triangle additions were lost.\n");
- build_state = PQP_BUILD_STATE_BEGUN;
- return PQP_ERR_BUILD_OUT_OF_SEQUENCE;
- }
-
- build_state = PQP_BUILD_STATE_BEGUN;
- return PQP_OK;
-}
-
-int
-PQP_Model::AddTri(const PQP_REAL *p1,
- const PQP_REAL *p2,
- const PQP_REAL *p3,
- int id)
-{
- if (build_state == PQP_BUILD_STATE_EMPTY)
- {
- BeginModel();
- }
- else if (build_state == PQP_BUILD_STATE_PROCESSED)
- {
- fprintf(stderr,"PQP Warning! Called AddTri() on PQP_Model \n"
- "object that was already ended. AddTri() was\n"
- "ignored. Must do a BeginModel() to clear the\n"
- "model for addition of new triangles\n");
- return PQP_ERR_BUILD_OUT_OF_SEQUENCE;
- }
-
- // allocate for new triangles
-
- if (num_tris >= num_tris_alloced)
- {
- Tri *temp;
- temp = new Tri[num_tris_alloced*2];
- if (!temp)
- {
- fprintf(stderr, "PQP Error! Out of memory for tri array on"
- " AddTri() call!\n");
- return PQP_ERR_MODEL_OUT_OF_MEMORY;
- }
- memcpy(temp, tris, sizeof(Tri)*num_tris);
- delete [] tris;
- tris = temp;
- num_tris_alloced = num_tris_alloced*2;
- }
-
- // initialize the new triangle
-
- tris[num_tris].p1[0] = p1[0];
- tris[num_tris].p1[1] = p1[1];
- tris[num_tris].p1[2] = p1[2];
-
- tris[num_tris].p2[0] = p2[0];
- tris[num_tris].p2[1] = p2[1];
- tris[num_tris].p2[2] = p2[2];
-
- tris[num_tris].p3[0] = p3[0];
- tris[num_tris].p3[1] = p3[1];
- tris[num_tris].p3[2] = p3[2];
-
- tris[num_tris].id = id;
-
- num_tris += 1;
-
- return PQP_OK;
-}
-
-int
-PQP_Model::EndModel()
-{
- if (build_state == PQP_BUILD_STATE_PROCESSED)
- {
- fprintf(stderr,"PQP Warning! Called EndModel() on PQP_Model \n"
- "object that was already ended. EndModel() was\n"
- "ignored. Must do a BeginModel() to clear the\n"
- "model for addition of new triangles\n");
- return PQP_ERR_BUILD_OUT_OF_SEQUENCE;
- }
-
- // report error is no tris
-
- if (num_tris == 0)
- {
- fprintf(stderr,"PQP Error! EndModel() called on model with"
- " no triangles\n");
- return PQP_ERR_BUILD_EMPTY_MODEL;
- }
-
- // shrink fit tris array
-
- if (num_tris_alloced > num_tris)
- {
- Tri *new_tris = new Tri[num_tris];
- if (!new_tris)
- {
- fprintf(stderr, "PQP Error! Out of memory for tri array "
- "in EndModel() call!\n");
- return PQP_ERR_MODEL_OUT_OF_MEMORY;
- }
- memcpy(new_tris, tris, sizeof(Tri)*num_tris);
- delete [] tris;
- tris = new_tris;
- num_tris_alloced = num_tris;
- }
-
- // create an array of BVs for the model
-
- b = new BV[2*num_tris - 1];
- if (!b)
- {
- fprintf(stderr,"PQP Error! out of memory for BV array "
- "in EndModel()\n");
- return PQP_ERR_MODEL_OUT_OF_MEMORY;
- }
- num_bvs_alloced = 2*num_tris - 1;
- num_bvs = 0;
-
- // we should build the model now.
-
- build_model(this);
- build_state = PQP_BUILD_STATE_PROCESSED;
-
- last_tri = tris;
-
- return PQP_OK;
-}
-
-int
-PQP_Model::MemUsage(int msg)
-{
- int mem_bv_list = sizeof(BV)*num_bvs;
- int mem_tri_list = sizeof(Tri)*num_tris;
-
- int total_mem = mem_bv_list + mem_tri_list + sizeof(PQP_Model);
-
- if (msg)
- {
- fprintf(stderr,"Total for model %x: %d bytes\n", this, total_mem);
- fprintf(stderr,"BVs: %d alloced, take %d bytes each\n",
- num_bvs, sizeof(BV));
- fprintf(stderr,"Tris: %d alloced, take %d bytes each\n",
- num_tris, sizeof(Tri));
- }
-
- return total_mem;
-}
-
-// COLLIDE STUFF
-//
-//--------------------------------------------------------------------------
-
-PQP_CollideResult::PQP_CollideResult()
-{
- pairs = 0;
- num_pairs = num_pairs_alloced = 0;
- num_bv_tests = 0;
- num_tri_tests = 0;
-}
-
-PQP_CollideResult::~PQP_CollideResult()
-{
- delete [] pairs;
-}
-
-void
-PQP_CollideResult::FreePairsList()
-{
- num_pairs = num_pairs_alloced = 0;
- delete [] pairs;
- pairs = 0;
-}
-
-// may increase OR reduce mem usage
-void
-PQP_CollideResult::SizeTo(int n)
-{
- CollisionPair *temp;
-
- if (n < num_pairs)
- {
- fprintf(stderr, "PQP Error: Internal error in "
- "'PQP_CollideResult::SizeTo(int n)'\n");
- fprintf(stderr, " n = %d, but num_pairs = %d\n", n, num_pairs);
- return;
- }
-
- temp = new CollisionPair[n];
- memcpy(temp, pairs, num_pairs*sizeof(CollisionPair));
- delete [] pairs;
- pairs = temp;
- num_pairs_alloced = n;
- return;
-}
-
-void
-PQP_CollideResult::Add(int a, int b)
-{
- if (num_pairs >= num_pairs_alloced)
- {
- // allocate more
-
- SizeTo(num_pairs_alloced*2+8);
- }
-
- // now proceed as usual
-
- pairs[num_pairs].id1 = a;
- pairs[num_pairs].id2 = b;
- num_pairs++;
-}
-
-// TRIANGLE OVERLAP TEST
-
-inline
-PQP_REAL
-max(PQP_REAL a, PQP_REAL b, PQP_REAL c)
-{
- PQP_REAL t = a;
- if (b > t) t = b;
- if (c > t) t = c;
- return t;
-}
-
-inline
-PQP_REAL
-min(PQP_REAL a, PQP_REAL b, PQP_REAL c)
-{
- PQP_REAL t = a;
- if (b < t) t = b;
- if (c < t) t = c;
- return t;
-}
-
-int
-project6(PQP_REAL *ax,
- PQP_REAL *p1, PQP_REAL *p2, PQP_REAL *p3,
- PQP_REAL *q1, PQP_REAL *q2, PQP_REAL *q3)
-{
- PQP_REAL P1 = VdotV(ax, p1);
- PQP_REAL P2 = VdotV(ax, p2);
- PQP_REAL P3 = VdotV(ax, p3);
- PQP_REAL Q1 = VdotV(ax, q1);
- PQP_REAL Q2 = VdotV(ax, q2);
- PQP_REAL Q3 = VdotV(ax, q3);
-
- PQP_REAL mx1 = max(P1, P2, P3);
- PQP_REAL mn1 = min(P1, P2, P3);
- PQP_REAL mx2 = max(Q1, Q2, Q3);
- PQP_REAL mn2 = min(Q1, Q2, Q3);
-
- if (mn1 > mx2) return 0;
- if (mn2 > mx1) return 0;
- return 1;
-}
-
-// very robust triangle intersection test
-// uses no divisions
-// works on coplanar triangles
-int
-TriContact(PQP_REAL *P1, PQP_REAL *P2, PQP_REAL *P3,
- PQP_REAL *Q1, PQP_REAL *Q2, PQP_REAL *Q3)
-{
-
- // One triangle is (p1,p2,p3). Other is (q1,q2,q3).
- // Edges are (e1,e2,e3) and (f1,f2,f3).
- // Normals are n1 and m1
- // Outwards are (g1,g2,g3) and (h1,h2,h3).
- //
- // We assume that the triangle vertices are in the same coordinate system.
- //
- // First thing we do is establish a new c.s. so that p1 is at (0,0,0).
-
- PQP_REAL p1[3], p2[3], p3[3];
- PQP_REAL q1[3], q2[3], q3[3];
- PQP_REAL e1[3], e2[3], e3[3];
- PQP_REAL f1[3], f2[3], f3[3];
- PQP_REAL g1[3], g2[3], g3[3];
- PQP_REAL h1[3], h2[3], h3[3];
- PQP_REAL n1[3], m1[3];
-
- PQP_REAL ef11[3], ef12[3], ef13[3];
- PQP_REAL ef21[3], ef22[3], ef23[3];
- PQP_REAL ef31[3], ef32[3], ef33[3];
-
- p1[0] = P1[0] - P1[0]; p1[1] = P1[1] - P1[1]; p1[2] = P1[2] - P1[2];
- p2[0] = P2[0] - P1[0]; p2[1] = P2[1] - P1[1]; p2[2] = P2[2] - P1[2];
- p3[0] = P3[0] - P1[0]; p3[1] = P3[1] - P1[1]; p3[2] = P3[2] - P1[2];
-
- q1[0] = Q1[0] - P1[0]; q1[1] = Q1[1] - P1[1]; q1[2] = Q1[2] - P1[2];
- q2[0] = Q2[0] - P1[0]; q2[1] = Q2[1] - P1[1]; q2[2] = Q2[2] - P1[2];
- q3[0] = Q3[0] - P1[0]; q3[1] = Q3[1] - P1[1]; q3[2] = Q3[2] - P1[2];
-
- e1[0] = p2[0] - p1[0]; e1[1] = p2[1] - p1[1]; e1[2] = p2[2] - p1[2];
- e2[0] = p3[0] - p2[0]; e2[1] = p3[1] - p2[1]; e2[2] = p3[2] - p2[2];
- e3[0] = p1[0] - p3[0]; e3[1] = p1[1] - p3[1]; e3[2] = p1[2] - p3[2];
-
- f1[0] = q2[0] - q1[0]; f1[1] = q2[1] - q1[1]; f1[2] = q2[2] - q1[2];
- f2[0] = q3[0] - q2[0]; f2[1] = q3[1] - q2[1]; f2[2] = q3[2] - q2[2];
- f3[0] = q1[0] - q3[0]; f3[1] = q1[1] - q3[1]; f3[2] = q1[2] - q3[2];
-
- VcrossV(n1, e1, e2);
- VcrossV(m1, f1, f2);
-
- VcrossV(g1, e1, n1);
- VcrossV(g2, e2, n1);
- VcrossV(g3, e3, n1);
- VcrossV(h1, f1, m1);
- VcrossV(h2, f2, m1);
- VcrossV(h3, f3, m1);
-
- VcrossV(ef11, e1, f1);
- VcrossV(ef12, e1, f2);
- VcrossV(ef13, e1, f3);
- VcrossV(ef21, e2, f1);
- VcrossV(ef22, e2, f2);
- VcrossV(ef23, e2, f3);
- VcrossV(ef31, e3, f1);
- VcrossV(ef32, e3, f2);
- VcrossV(ef33, e3, f3);
-
- // now begin the series of tests
-
- if (!project6(n1, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(m1, p1, p2, p3, q1, q2, q3)) return 0;
-
- if (!project6(ef11, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef12, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef13, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef21, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef22, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef23, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef31, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef32, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(ef33, p1, p2, p3, q1, q2, q3)) return 0;
-
- if (!project6(g1, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(g2, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(g3, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(h1, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(h2, p1, p2, p3, q1, q2, q3)) return 0;
- if (!project6(h3, p1, p2, p3, q1, q2, q3)) return 0;
-
- return 1;
-}
-
-inline
-PQP_REAL
-TriDistance(PQP_REAL R[3][3], PQP_REAL T[3], Tri *t1, Tri *t2,
- PQP_REAL p[3], PQP_REAL q[3])
-{
- // transform tri 2 into same space as tri 1
-
- PQP_REAL tri1[3][3], tri2[3][3];
-
- VcV(tri1[0], t1->p1);
- VcV(tri1[1], t1->p2);
- VcV(tri1[2], t1->p3);
- MxVpV(tri2[0], R, t2->p1, T);
- MxVpV(tri2[1], R, t2->p2, T);
- MxVpV(tri2[2], R, t2->p3, T);
-
- return TriDist(p,q,tri1,tri2);
-}
-
-
-void
-CollideRecurse(PQP_CollideResult *res,
- PQP_REAL R[3][3], PQP_REAL T[3], // b2 relative to b1
- PQP_Model *o1, int b1,
- PQP_Model *o2, int b2, int flag)
-{
- // first thing, see if we're overlapping
-
- res->num_bv_tests++;
-
- if (!BV_Overlap(R, T, o1->child(b1), o2->child(b2))) return;
-
- // if we are, see if we test triangles next
-
- int l1 = o1->child(b1)->Leaf();
- int l2 = o2->child(b2)->Leaf();
-
- if (l1 && l2)
- {
- res->num_tri_tests++;
-
-#if 1
- // transform the points in b2 into space of b1, then compare
-
- Tri *t1 = &o1->tris[-o1->child(b1)->first_child - 1];
- Tri *t2 = &o2->tris[-o2->child(b2)->first_child - 1];
- PQP_REAL q1[3], q2[3], q3[3];
- PQP_REAL *p1 = t1->p1;
- PQP_REAL *p2 = t1->p2;
- PQP_REAL *p3 = t1->p3;
- MxVpV(q1, res->R, t2->p1, res->T);
- MxVpV(q2, res->R, t2->p2, res->T);
- MxVpV(q3, res->R, t2->p3, res->T);
- if (TriContact(p1, p2, p3, q1, q2, q3))
- {
- // add this to result
-
- res->Add(t1->id, t2->id);
- }
-#else
- PQP_REAL p[3], q[3];
-
- Tri *t1 = &o1->tris[-o1->child(b1)->first_child - 1];
- Tri *t2 = &o2->tris[-o2->child(b2)->first_child - 1];
-
- if (TriDistance(res->R,res->T,t1,t2,p,q) == 0.0)
- {
- // add this to result
-
- res->Add(t1->id, t2->id);
- }
-#endif
-
- return;
- }
-
- // we dont, so decide whose children to visit next
-
- PQP_REAL sz1 = o1->child(b1)->GetSize();
- PQP_REAL sz2 = o2->child(b2)->GetSize();
-
- PQP_REAL Rc[3][3],Tc[3],Ttemp[3];
-
- if (l2 || (!l1 && (sz1 > sz2)))
- {
- int c1 = o1->child(b1)->first_child;
- int c2 = c1 + 1;
-
- MTxM(Rc,o1->child(c1)->R,R);
-#if PQP_BV_TYPE & OBB_TYPE
- VmV(Ttemp,T,o1->child(c1)->To);
-#else
- VmV(Ttemp,T,o1->child(c1)->Tr);
-#endif
- MTxV(Tc,o1->child(c1)->R,Ttemp);
- CollideRecurse(res,Rc,Tc,o1,c1,o2,b2,flag);
-
- if ((flag == PQP_FIRST_CONTACT) && (res->num_pairs > 0)) return;
-
- MTxM(Rc,o1->child(c2)->R,R);
-#if PQP_BV_TYPE & OBB_TYPE
- VmV(Ttemp,T,o1->child(c2)->To);
-#else
- VmV(Ttemp,T,o1->child(c2)->Tr);
-#endif
- MTxV(Tc,o1->child(c2)->R,Ttemp);
- CollideRecurse(res,Rc,Tc,o1,c2,o2,b2,flag);
- }
- else
- {
- int c1 = o2->child(b2)->first_child;
- int c2 = c1 + 1;
-
- MxM(Rc,R,o2->child(c1)->R);
-#if PQP_BV_TYPE & OBB_TYPE
- MxVpV(Tc,R,o2->child(c1)->To,T);
-#else
- MxVpV(Tc,R,o2->child(c1)->Tr,T);
-#endif
- CollideRecurse(res,Rc,Tc,o1,b1,o2,c1,flag);
-
- if ((flag == PQP_FIRST_CONTACT) && (res->num_pairs > 0)) return;
-
- MxM(Rc,R,o2->child(c2)->R);
-#if PQP_BV_TYPE & OBB_TYPE
- MxVpV(Tc,R,o2->child(c2)->To,T);
-#else
- MxVpV(Tc,R,o2->child(c2)->Tr,T);
-#endif
- CollideRecurse(res,Rc,Tc,o1,b1,o2,c2,flag);
- }
-}
-
-int
-PQP_Collide(PQP_CollideResult *res,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- int flag)
-{
- double t1 = GetTime();
-
- // make sure that the models are built
-
- if (o1->build_state != PQP_BUILD_STATE_PROCESSED)
- return PQP_ERR_UNPROCESSED_MODEL;
- if (o2->build_state != PQP_BUILD_STATE_PROCESSED)
- return PQP_ERR_UNPROCESSED_MODEL;
-
- // clear the stats
-
- res->num_bv_tests = 0;
- res->num_tri_tests = 0;
-
- // don't release the memory, but reset the num_pairs counter
-
- res->num_pairs = 0;
-
- // Okay, compute what transform [R,T] that takes us from cs1 to cs2.
- // [R,T] = [R1,T1]'[R2,T2] = [R1',-R1'T][R2,T2] = [R1'R2, R1'(T2-T1)]
- // First compute the rotation part, then translation part
-
- MTxM(res->R,R1,R2);
- PQP_REAL Ttemp[3];
- VmV(Ttemp, T2, T1);
- MTxV(res->T, R1, Ttemp);
-
- // compute the transform from o1->child(0) to o2->child(0)
-
- PQP_REAL Rtemp[3][3], R[3][3], T[3];
-
- MxM(Rtemp,res->R,o2->child(0)->R);
- MTxM(R,o1->child(0)->R,Rtemp);
-
-#if PQP_BV_TYPE & OBB_TYPE
- MxVpV(Ttemp,res->R,o2->child(0)->To,res->T);
- VmV(Ttemp,Ttemp,o1->child(0)->To);
-#else
- MxVpV(Ttemp,res->R,o2->child(0)->Tr,res->T);
- VmV(Ttemp,Ttemp,o1->child(0)->Tr);
-#endif
-
- MTxV(T,o1->child(0)->R,Ttemp);
-
- // now start with both top level BVs
-
- CollideRecurse(res,R,T,o1,0,o2,0,flag);
-
- double t2 = GetTime();
- res->query_time_secs = t2 - t1;
-
- return PQP_OK;
-}
-
-#if PQP_BV_TYPE & RSS_TYPE // distance/tolerance only available with RSS
- // unless an OBB distance test is supplied in
- // BV.cpp
-
-// DISTANCE STUFF
-//
-//--------------------------------------------------------------------------
-
-void
-DistanceRecurse(PQP_DistanceResult *res,
- PQP_REAL R[3][3], PQP_REAL T[3], // b2 relative to b1
- PQP_Model *o1, int b1,
- PQP_Model *o2, int b2)
-{
- PQP_REAL sz1 = o1->child(b1)->GetSize();
- PQP_REAL sz2 = o2->child(b2)->GetSize();
- int l1 = o1->child(b1)->Leaf();
- int l2 = o2->child(b2)->Leaf();
-
- if (l1 && l2)
- {
- // both leaves. Test the triangles beneath them.
-
- res->num_tri_tests++;
-
- PQP_REAL p[3], q[3];
-
- Tri *t1 = &o1->tris[-o1->child(b1)->first_child - 1];
- Tri *t2 = &o2->tris[-o2->child(b2)->first_child - 1];
-
- PQP_REAL d = TriDistance(res->R,res->T,t1,t2,p,q);
-
- if (d < res->distance)
- {
- res->distance = d;
-
- VcV(res->p1, p); // p already in c.s. 1
- VcV(res->p2, q); // q must be transformed
- // into c.s. 2 later
- o1->last_tri = t1;
- o2->last_tri = t2;
- }
-
- return;
- }
-
- // First, perform distance tests on the children. Then traverse
- // them recursively, but test the closer pair first, the further
- // pair second.
-
- int a1,a2,c1,c2; // new bv tests 'a' and 'c'
- PQP_REAL R1[3][3], T1[3], R2[3][3], T2[3], Ttemp[3];
-
- if (l2 || (!l1 && (sz1 > sz2)))
- {
- // visit the children of b1
-
- a1 = o1->child(b1)->first_child;
- a2 = b2;
- c1 = o1->child(b1)->first_child+1;
- c2 = b2;
-
- MTxM(R1,o1->child(a1)->R,R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,T,o1->child(a1)->Tr);
-#else
- VmV(Ttemp,T,o1->child(a1)->To);
-#endif
- MTxV(T1,o1->child(a1)->R,Ttemp);
-
- MTxM(R2,o1->child(c1)->R,R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,T,o1->child(c1)->Tr);
-#else
- VmV(Ttemp,T,o1->child(c1)->To);
-#endif
- MTxV(T2,o1->child(c1)->R,Ttemp);
- }
- else
- {
- // visit the children of b2
-
- a1 = b1;
- a2 = o2->child(b2)->first_child;
- c1 = b1;
- c2 = o2->child(b2)->first_child+1;
-
- MxM(R1,R,o2->child(a2)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(T1,R,o2->child(a2)->Tr,T);
-#else
- MxVpV(T1,R,o2->child(a2)->To,T);
-#endif
-
- MxM(R2,R,o2->child(c2)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(T2,R,o2->child(c2)->Tr,T);
-#else
- MxVpV(T2,R,o2->child(c2)->To,T);
-#endif
- }
-
- res->num_bv_tests += 2;
-
- PQP_REAL d1 = BV_Distance(R1, T1, o1->child(a1), o2->child(a2));
- PQP_REAL d2 = BV_Distance(R2, T2, o1->child(c1), o2->child(c2));
-
- if (d2 < d1)
- {
- if ((d2 < (res->distance - res->abs_err)) ||
- (d2*(1 + res->rel_err) < res->distance))
- {
- DistanceRecurse(res, R2, T2, o1, c1, o2, c2);
- }
-
- if ((d1 < (res->distance - res->abs_err)) ||
- (d1*(1 + res->rel_err) < res->distance))
- {
- DistanceRecurse(res, R1, T1, o1, a1, o2, a2);
- }
- }
- else
- {
- if ((d1 < (res->distance - res->abs_err)) ||
- (d1*(1 + res->rel_err) < res->distance))
- {
- DistanceRecurse(res, R1, T1, o1, a1, o2, a2);
- }
-
- if ((d2 < (res->distance - res->abs_err)) ||
- (d2*(1 + res->rel_err) < res->distance))
- {
- DistanceRecurse(res, R2, T2, o1, c1, o2, c2);
- }
- }
-}
-
-void
-DistanceQueueRecurse(PQP_DistanceResult *res,
- PQP_REAL R[3][3], PQP_REAL T[3],
- PQP_Model *o1, int b1,
- PQP_Model *o2, int b2)
-{
- BVTQ bvtq(res->qsize);
-
- BVT min_test;
- min_test.b1 = b1;
- min_test.b2 = b2;
- McM(min_test.R,R);
- VcV(min_test.T,T);
-
- while(1)
- {
- int l1 = o1->child(min_test.b1)->Leaf();
- int l2 = o2->child(min_test.b2)->Leaf();
-
- if (l1 && l2)
- {
- // both leaves. Test the triangles beneath them.
-
- res->num_tri_tests++;
-
- PQP_REAL p[3], q[3];
-
- Tri *t1 = &o1->tris[-o1->child(min_test.b1)->first_child - 1];
- Tri *t2 = &o2->tris[-o2->child(min_test.b2)->first_child - 1];
-
- PQP_REAL d = TriDistance(res->R,res->T,t1,t2,p,q);
-
- if (d < res->distance)
- {
- res->distance = d;
-
- VcV(res->p1, p); // p already in c.s. 1
- VcV(res->p2, q); // q must be transformed
- // into c.s. 2 later
- o1->last_tri = t1;
- o2->last_tri = t2;
- }
- }
- else if (bvtq.GetNumTests() == bvtq.GetSize() - 1)
- {
- // queue can't get two more tests, recur
-
- DistanceQueueRecurse(res,min_test.R,min_test.T,
- o1,min_test.b1,o2,min_test.b2);
- }
- else
- {
- // decide how to descend to children
-
- PQP_REAL sz1 = o1->child(min_test.b1)->GetSize();
- PQP_REAL sz2 = o2->child(min_test.b2)->GetSize();
-
- res->num_bv_tests += 2;
-
- BVT bvt1,bvt2;
- PQP_REAL Ttemp[3];
-
- if (l2 || (!l1 && (sz1 > sz2)))
- {
- // put new tests on queue consisting of min_test.b2
- // with children of min_test.b1
-
- int c1 = o1->child(min_test.b1)->first_child;
- int c2 = c1 + 1;
-
- // init bv test 1
-
- bvt1.b1 = c1;
- bvt1.b2 = min_test.b2;
- MTxM(bvt1.R,o1->child(c1)->R,min_test.R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,min_test.T,o1->child(c1)->Tr);
-#else
- VmV(Ttemp,min_test.T,o1->child(c1)->To);
-#endif
- MTxV(bvt1.T,o1->child(c1)->R,Ttemp);
- bvt1.d = BV_Distance(bvt1.R,bvt1.T,
- o1->child(bvt1.b1),o2->child(bvt1.b2));
-
- // init bv test 2
-
- bvt2.b1 = c2;
- bvt2.b2 = min_test.b2;
- MTxM(bvt2.R,o1->child(c2)->R,min_test.R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,min_test.T,o1->child(c2)->Tr);
-#else
- VmV(Ttemp,min_test.T,o1->child(c2)->To);
-#endif
- MTxV(bvt2.T,o1->child(c2)->R,Ttemp);
- bvt2.d = BV_Distance(bvt2.R,bvt2.T,
- o1->child(bvt2.b1),o2->child(bvt2.b2));
- }
- else
- {
- // put new tests on queue consisting of min_test.b1
- // with children of min_test.b2
-
- int c1 = o2->child(min_test.b2)->first_child;
- int c2 = c1 + 1;
-
- // init bv test 1
-
- bvt1.b1 = min_test.b1;
- bvt1.b2 = c1;
- MxM(bvt1.R,min_test.R,o2->child(c1)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(bvt1.T,min_test.R,o2->child(c1)->Tr,min_test.T);
-#else
- MxVpV(bvt1.T,min_test.R,o2->child(c1)->To,min_test.T);
-#endif
- bvt1.d = BV_Distance(bvt1.R,bvt1.T,
- o1->child(bvt1.b1),o2->child(bvt1.b2));
-
- // init bv test 2
-
- bvt2.b1 = min_test.b1;
- bvt2.b2 = c2;
- MxM(bvt2.R,min_test.R,o2->child(c2)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(bvt2.T,min_test.R,o2->child(c2)->Tr,min_test.T);
-#else
- MxVpV(bvt2.T,min_test.R,o2->child(c2)->To,min_test.T);
-#endif
- bvt2.d = BV_Distance(bvt2.R,bvt2.T,
- o1->child(bvt2.b1),o2->child(bvt2.b2));
- }
-
- bvtq.AddTest(bvt1);
- bvtq.AddTest(bvt2);
- }
-
- if (bvtq.Empty())
- {
- break;
- }
- else
- {
- min_test = bvtq.ExtractMinTest();
-
- if ((min_test.d + res->abs_err >= res->distance) &&
- ((min_test.d * (1 + res->rel_err)) >= res->distance))
- {
- break;
- }
- }
- }
-}
-
-int
-PQP_Distance(PQP_DistanceResult *res,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL rel_err, PQP_REAL abs_err,
- int qsize)
-{
-
- double time1 = GetTime();
-
- // make sure that the models are built
-
- if (o1->build_state != PQP_BUILD_STATE_PROCESSED)
- return PQP_ERR_UNPROCESSED_MODEL;
- if (o2->build_state != PQP_BUILD_STATE_PROCESSED)
- return PQP_ERR_UNPROCESSED_MODEL;
-
- // Okay, compute what transform [R,T] that takes us from cs2 to cs1.
- // [R,T] = [R1,T1]'[R2,T2] = [R1',-R1'T][R2,T2] = [R1'R2, R1'(T2-T1)]
- // First compute the rotation part, then translation part
-
- MTxM(res->R,R1,R2);
- PQP_REAL Ttemp[3];
- VmV(Ttemp, T2, T1);
- MTxV(res->T, R1, Ttemp);
-
- // establish initial upper bound using last triangles which
- // provided the minimum distance
-
- PQP_REAL p[3],q[3];
- res->distance = TriDistance(res->R,res->T,o1->last_tri,o2->last_tri,p,q);
- VcV(res->p1,p);
- VcV(res->p2,q);
-
- // initialize error bounds
-
- res->abs_err = abs_err;
- res->rel_err = rel_err;
-
- // clear the stats
-
- res->num_bv_tests = 0;
- res->num_tri_tests = 0;
-
- // compute the transform from o1->child(0) to o2->child(0)
-
- PQP_REAL Rtemp[3][3], R[3][3], T[3];
-
- MxM(Rtemp,res->R,o2->child(0)->R);
- MTxM(R,o1->child(0)->R,Rtemp);
-
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(Ttemp,res->R,o2->child(0)->Tr,res->T);
- VmV(Ttemp,Ttemp,o1->child(0)->Tr);
-#else
- MxVpV(Ttemp,res->R,o2->child(0)->To,res->T);
- VmV(Ttemp,Ttemp,o1->child(0)->To);
-#endif
- MTxV(T,o1->child(0)->R,Ttemp);
-
- // choose routine according to queue size
-
- if (qsize <= 2)
- {
- DistanceRecurse(res,R,T,o1,0,o2,0);
- }
- else
- {
- res->qsize = qsize;
-
- DistanceQueueRecurse(res,R,T,o1,0,o2,0);
- }
-
- // res->p2 is in cs 1 ; transform it to cs 2
-
- PQP_REAL u[3];
- VmV(u, res->p2, res->T);
- MTxV(res->p2, res->R, u);
-
- double time2 = GetTime();
- res->query_time_secs = time2 - time1;
-
- return PQP_OK;
-}
-
-// Tolerance Stuff
-//
-//---------------------------------------------------------------------------
-void
-ToleranceRecurse(PQP_ToleranceResult *res,
- PQP_REAL R[3][3], PQP_REAL T[3],
- PQP_Model *o1, int b1, PQP_Model *o2, int b2)
-{
- PQP_REAL sz1 = o1->child(b1)->GetSize();
- PQP_REAL sz2 = o2->child(b2)->GetSize();
- int l1 = o1->child(b1)->Leaf();
- int l2 = o2->child(b2)->Leaf();
-
- if (l1 && l2)
- {
- // both leaves - find if tri pair within tolerance
-
- res->num_tri_tests++;
-
- PQP_REAL p[3], q[3];
-
- Tri *t1 = &o1->tris[-o1->child(b1)->first_child - 1];
- Tri *t2 = &o2->tris[-o2->child(b2)->first_child - 1];
-
- PQP_REAL d = TriDistance(res->R,res->T,t1,t2,p,q);
-
- if (d <= res->tolerance)
- {
- // triangle pair distance less than tolerance
-
- res->closer_than_tolerance = 1;
- res->distance = d;
- VcV(res->p1, p); // p already in c.s. 1
- VcV(res->p2, q); // q must be transformed
- // into c.s. 2 later
- }
-
- return;
- }
-
- int a1,a2,c1,c2; // new bv tests 'a' and 'c'
- PQP_REAL R1[3][3], T1[3], R2[3][3], T2[3], Ttemp[3];
-
- if (l2 || (!l1 && (sz1 > sz2)))
- {
- // visit the children of b1
-
- a1 = o1->child(b1)->first_child;
- a2 = b2;
- c1 = o1->child(b1)->first_child+1;
- c2 = b2;
-
- MTxM(R1,o1->child(a1)->R,R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,T,o1->child(a1)->Tr);
-#else
- VmV(Ttemp,T,o1->child(a1)->To);
-#endif
- MTxV(T1,o1->child(a1)->R,Ttemp);
-
- MTxM(R2,o1->child(c1)->R,R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,T,o1->child(c1)->Tr);
-#else
- VmV(Ttemp,T,o1->child(c1)->To);
-#endif
- MTxV(T2,o1->child(c1)->R,Ttemp);
- }
- else
- {
- // visit the children of b2
-
- a1 = b1;
- a2 = o2->child(b2)->first_child;
- c1 = b1;
- c2 = o2->child(b2)->first_child+1;
-
- MxM(R1,R,o2->child(a2)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(T1,R,o2->child(a2)->Tr,T);
-#else
- MxVpV(T1,R,o2->child(a2)->To,T);
-#endif
- MxM(R2,R,o2->child(c2)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(T2,R,o2->child(c2)->Tr,T);
-#else
- MxVpV(T2,R,o2->child(c2)->To,T);
-#endif
- }
-
- res->num_bv_tests += 2;
-
- PQP_REAL d1 = BV_Distance(R1, T1, o1->child(a1), o2->child(a2));
- PQP_REAL d2 = BV_Distance(R2, T2, o1->child(c1), o2->child(c2));
-
- if (d2 < d1)
- {
- if (d2 <= res->tolerance) ToleranceRecurse(res, R2, T2, o1, c1, o2, c2);
- if (res->closer_than_tolerance) return;
- if (d1 <= res->tolerance) ToleranceRecurse(res, R1, T1, o1, a1, o2, a2);
- }
- else
- {
- if (d1 <= res->tolerance) ToleranceRecurse(res, R1, T1, o1, a1, o2, a2);
- if (res->closer_than_tolerance) return;
- if (d2 <= res->tolerance) ToleranceRecurse(res, R2, T2, o1, c1, o2, c2);
- }
-}
-
-void
-ToleranceQueueRecurse(PQP_ToleranceResult *res,
- PQP_REAL R[3][3], PQP_REAL T[3],
- PQP_Model *o1, int b1,
- PQP_Model *o2, int b2)
-{
- BVTQ bvtq(res->qsize);
- BVT min_test;
- min_test.b1 = b1;
- min_test.b2 = b2;
- McM(min_test.R,R);
- VcV(min_test.T,T);
-
- while(1)
- {
- int l1 = o1->child(min_test.b1)->Leaf();
- int l2 = o2->child(min_test.b2)->Leaf();
-
- if (l1 && l2)
- {
- // both leaves - find if tri pair within tolerance
-
- res->num_tri_tests++;
-
- PQP_REAL p[3], q[3];
-
- Tri *t1 = &o1->tris[-o1->child(min_test.b1)->first_child - 1];
- Tri *t2 = &o2->tris[-o2->child(min_test.b2)->first_child - 1];
-
- PQP_REAL d = TriDistance(res->R,res->T,t1,t2,p,q);
-
- if (d <= res->tolerance)
- {
- // triangle pair distance less than tolerance
-
- res->closer_than_tolerance = 1;
- res->distance = d;
- VcV(res->p1, p); // p already in c.s. 1
- VcV(res->p2, q); // q must be transformed
- // into c.s. 2 later
- return;
- }
- }
- else if (bvtq.GetNumTests() == bvtq.GetSize() - 1)
- {
- // queue can't get two more tests, recur
-
- ToleranceQueueRecurse(res,min_test.R,min_test.T,
- o1,min_test.b1,o2,min_test.b2);
- if (res->closer_than_tolerance == 1) return;
- }
- else
- {
- // decide how to descend to children
-
- PQP_REAL sz1 = o1->child(min_test.b1)->GetSize();
- PQP_REAL sz2 = o2->child(min_test.b2)->GetSize();
-
- res->num_bv_tests += 2;
-
- BVT bvt1,bvt2;
- PQP_REAL Ttemp[3];
-
- if (l2 || (!l1 && (sz1 > sz2)))
- {
- // add two new tests to queue, consisting of min_test.b2
- // with the children of min_test.b1
-
- int c1 = o1->child(min_test.b1)->first_child;
- int c2 = c1 + 1;
-
- // init bv test 1
-
- bvt1.b1 = c1;
- bvt1.b2 = min_test.b2;
- MTxM(bvt1.R,o1->child(c1)->R,min_test.R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,min_test.T,o1->child(c1)->Tr);
-#else
- VmV(Ttemp,min_test.T,o1->child(c1)->To);
-#endif
- MTxV(bvt1.T,o1->child(c1)->R,Ttemp);
- bvt1.d = BV_Distance(bvt1.R,bvt1.T,
- o1->child(bvt1.b1),o2->child(bvt1.b2));
-
- // init bv test 2
-
- bvt2.b1 = c2;
- bvt2.b2 = min_test.b2;
- MTxM(bvt2.R,o1->child(c2)->R,min_test.R);
-#if PQP_BV_TYPE & RSS_TYPE
- VmV(Ttemp,min_test.T,o1->child(c2)->Tr);
-#else
- VmV(Ttemp,min_test.T,o1->child(c2)->To);
-#endif
- MTxV(bvt2.T,o1->child(c2)->R,Ttemp);
- bvt2.d = BV_Distance(bvt2.R,bvt2.T,
- o1->child(bvt2.b1),o2->child(bvt2.b2));
- }
- else
- {
- // add two new tests to queue, consisting of min_test.b1
- // with the children of min_test.b2
-
- int c1 = o2->child(min_test.b2)->first_child;
- int c2 = c1 + 1;
-
- // init bv test 1
-
- bvt1.b1 = min_test.b1;
- bvt1.b2 = c1;
- MxM(bvt1.R,min_test.R,o2->child(c1)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(bvt1.T,min_test.R,o2->child(c1)->Tr,min_test.T);
-#else
- MxVpV(bvt1.T,min_test.R,o2->child(c1)->To,min_test.T);
-#endif
- bvt1.d = BV_Distance(bvt1.R,bvt1.T,
- o1->child(bvt1.b1),o2->child(bvt1.b2));
-
- // init bv test 2
-
- bvt2.b1 = min_test.b1;
- bvt2.b2 = c2;
- MxM(bvt2.R,min_test.R,o2->child(c2)->R);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(bvt2.T,min_test.R,o2->child(c2)->Tr,min_test.T);
-#else
- MxVpV(bvt2.T,min_test.R,o2->child(c2)->To,min_test.T);
-#endif
- bvt2.d = BV_Distance(bvt2.R,bvt2.T,
- o1->child(bvt2.b1),o2->child(bvt2.b2));
- }
-
- // put children tests in queue
-
- if (bvt1.d <= res->tolerance) bvtq.AddTest(bvt1);
- if (bvt2.d <= res->tolerance) bvtq.AddTest(bvt2);
- }
-
- if (bvtq.Empty() || (bvtq.MinTest() > res->tolerance))
- {
- res->closer_than_tolerance = 0;
- return;
- }
- else
- {
- min_test = bvtq.ExtractMinTest();
- }
- }
-}
-
-int
-PQP_Tolerance(PQP_ToleranceResult *res,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL tolerance,
- int qsize)
-{
- double time1 = GetTime();
-
- // make sure that the models are built
-
- if (o1->build_state != PQP_BUILD_STATE_PROCESSED)
- return PQP_ERR_UNPROCESSED_MODEL;
- if (o2->build_state != PQP_BUILD_STATE_PROCESSED)
- return PQP_ERR_UNPROCESSED_MODEL;
-
- // Compute the transform [R,T] that takes us from cs2 to cs1.
- // [R,T] = [R1,T1]'[R2,T2] = [R1',-R1'T][R2,T2] = [R1'R2, R1'(T2-T1)]
-
- MTxM(res->R,R1,R2);
- PQP_REAL Ttemp[3];
- VmV(Ttemp, T2, T1);
- MTxV(res->T, R1, Ttemp);
-
- // set tolerance, used to prune the search
-
- if (tolerance < 0.0) tolerance = 0.0;
- res->tolerance = tolerance;
-
- // clear the stats
-
- res->num_bv_tests = 0;
- res->num_tri_tests = 0;
-
- // initially assume not closer than tolerance
-
- res->closer_than_tolerance = 0;
-
- // compute the transform from o1->child(0) to o2->child(0)
-
- PQP_REAL Rtemp[3][3], R[3][3], T[3];
-
- MxM(Rtemp,res->R,o2->child(0)->R);
- MTxM(R,o1->child(0)->R,Rtemp);
-#if PQP_BV_TYPE & RSS_TYPE
- MxVpV(Ttemp,res->R,o2->child(0)->Tr,res->T);
- VmV(Ttemp,Ttemp,o1->child(0)->Tr);
-#else
- MxVpV(Ttemp,res->R,o2->child(0)->To,res->T);
- VmV(Ttemp,Ttemp,o1->child(0)->To);
-#endif
- MTxV(T,o1->child(0)->R,Ttemp);
-
- // find a distance lower bound for trivial reject
-
- PQP_REAL d = BV_Distance(R, T, o1->child(0), o2->child(0));
-
- if (d <= res->tolerance)
- {
- // more work needed - choose routine according to queue size
-
- if (qsize <= 2)
- {
- ToleranceRecurse(res, R, T, o1, 0, o2, 0);
- }
- else
- {
- res->qsize = qsize;
- ToleranceQueueRecurse(res, R, T, o1, 0, o2, 0);
- }
- }
-
- // res->p2 is in cs 1 ; transform it to cs 2
-
- PQP_REAL u[3];
- VmV(u, res->p2, res->T);
- MTxV(res->p2, res->R, u);
-
- double time2 = GetTime();
- res->query_time_secs = time2 - time1;
-
- return PQP_OK;
-}
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP.h
deleted file mode 100644
index f6f3e539581c4e118d2afecace8e8a35d10c3db2..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP.h
+++ /dev/null
@@ -1,338 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_H
-#define PQP_H
-
-#include "PQP_Compile.h"
-#include "PQP_Internal.h"
-
-//----------------------------------------------------------------------------
-//
-// PQP API Return Values
-//
-//----------------------------------------------------------------------------
-
-const int PQP_OK = 0;
- // Used by all API routines upon successful completion except
- // constructors and destructors
-
-const int PQP_ERR_MODEL_OUT_OF_MEMORY = -1;
- // Returned when an API function cannot obtain enough memory to
- // store or process a PQP_Model object.
-
-const int PQP_ERR_OUT_OF_MEMORY = -2;
- // Returned when a PQP query cannot allocate enough storage to
- // compute or hold query information. In this case, the returned
- // data should not be trusted.
-
-const int PQP_ERR_UNPROCESSED_MODEL = -3;
- // Returned when an unprocessed model is passed to a function which
- // expects only processed models, such as PQP_Collide() or
- // PQP_Distance().
-
-const int PQP_ERR_BUILD_OUT_OF_SEQUENCE = -4;
- // Returned when:
- // 1. AddTri() is called before BeginModel().
- // 2. BeginModel() is called immediately after AddTri().
- // This error code is something like a warning: the invoked
- // operation takes place anyway, and PQP does what makes "most
- // sense", but the returned error code may tip off the client that
- // something out of the ordinary is happenning.
-
-const int PQP_ERR_BUILD_EMPTY_MODEL = -5;
- // Returned when EndModel() is called on a model to which no
- // triangles have been added. This is similar in spirit to the
- // OUT_OF_SEQUENCE return code, except that the requested operation
- // has FAILED -- the model remains "unprocessed", and the client may
- // NOT use it in queries.
-
-//----------------------------------------------------------------------------
-//
-// PQP_REAL
-//
-// The floating point type used throughout the package. The type is defined
-// in PQP_Compile.h, and by default is "double"
-//
-//----------------------------------------------------------------------------
-
-//----------------------------------------------------------------------------
-//
-// PQP_Model
-//
-// A PQP_Model stores geometry to be used in a proximity query.
-// The geometry is loaded with a call to BeginModel(), at least one call to
-// AddTri(), and then a call to EndModel().
-//
-// // create a two triangle model, m
-//
-// PQP_Model m;
-//
-// PQP_REAL p1[3],p2[3],p3[3]; // 3 points will make triangle p
-// PQP_REAL q1[3],q2[3],q3[3]; // another 3 points for triangle q
-//
-// // some initialization of these vertices not shown
-//
-// m.BeginModel(); // begin the model
-// m.AddTri(p1,p2,p3,0); // add triangle p
-// m.AddTri(q1,q2,q3,1); // add triangle q
-// m.EndModel(); // end (build) the model
-//
-// The last parameter of AddTri() is the number to be associated with the
-// triangle. These numbers are used to identify the triangles that overlap.
-//
-// AddTri() copies into the PQP_Model the data pointed to by the three vertex
-// pointers, so that it is safe to delete vertex data after you have
-// passed it to AddTri().
-//
-//----------------------------------------------------------------------------
-//
-// class PQP_Model - declaration contained in PQP_Internal.h
-// {
-//
-// public:
-// PQP_Model();
-// ~PQP_Model();
-//
-// int BeginModel(int num_tris = 8); // preallocate for num_tris triangles;
-// // the parameter is optional, since
-// // arrays are reallocated as needed
-//
-// int AddTri(const PQP_REAL *p1, const PQP_REAL *p2, const PQP_REAL *p3,
-// int id);
-//
-// int EndModel();
-// int MemUsage(int msg); // returns model mem usage in bytes
-// // prints message to stderr if msg == TRUE
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_CollideResult
-//
-// This saves and reports results from a collision query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_CollideResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // free the list of contact pairs; ordinarily this list is reused
-// // for each query, and only deleted in the destructor.
-//
-// void FreePairsList();
-//
-// // query results
-//
-// int Colliding();
-// int NumPairs();
-// int Id1(int k);
-// int Id2(int k);
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Collide() - detects collision between two PQP_Models
-//
-//
-// Declare a PQP_CollideResult struct and pass its pointer to collect
-// collision data.
-//
-// [R1, T1] is the placement of model 1 in the world &
-// [R2, T2] is the placement of model 2 in the world.
-// The columns of each 3x3 matrix are the basis vectors for the model
-// in world coordinates, and the matrices are in row-major order:
-// R(row r, col c) = R[r][c].
-//
-// If PQP_ALL_CONTACTS is the flag value, after calling PQP_Collide(),
-// the PQP_CollideResult object will contain an array with all
-// colliding triangle pairs. Suppose CR is a pointer to the
-// PQP_CollideResult object. The number of pairs is gotten from
-// CR->NumPairs(), and the ids of the 15'th pair of colliding
-// triangles is gotten from CR->Id1(14) and CR->Id2(14).
-//
-// If PQP_FIRST_CONTACT is the flag value, the PQP_CollideResult array
-// will only get the first colliding triangle pair found. Thus
-// CR->NumPairs() will be at most 1, and if 1, CR->Id1(0) and
-// CR->Id2(0) give the ids of the colliding triangle pair.
-//
-//----------------------------------------------------------------------------
-
-const int PQP_ALL_CONTACTS = 1; // find all pairwise intersecting triangles
-const int PQP_FIRST_CONTACT = 2; // report first intersecting tri pair found
-
-int
-PQP_Collide(PQP_CollideResult *result,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- int flag = PQP_ALL_CONTACTS);
-
-
-#if PQP_BV_TYPE & RSS_TYPE // this is true by default,
- // and explained in PQP_Compile.h
-
-//----------------------------------------------------------------------------
-//
-// PQP_DistanceResult
-//
-// This saves and reports results from a distance query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_DistanceResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // The following distance and points established the minimum distance
-// // for the models, within the relative and absolute error bounds
-// // specified.
-//
-// PQP_REAL Distance();
-// const PQP_REAL *P1(); // pointers to three PQP_REALs
-// const PQP_REAL *P2();
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Distance() - computes the distance between two PQP_Models
-//
-//
-// Declare a PQP_DistanceResult struct and pass its pointer to collect
-// distance information.
-//
-// "rel_err" is the relative error margin from actual distance.
-// "abs_err" is the absolute error margin from actual distance. The
-// smaller of the two will be satisfied, so set one large to nullify
-// its effect.
-//
-// "qsize" is an optional parameter controlling the size of a priority
-// queue used to direct the search for closest points. A larger queue
-// can help the algorithm discover the minimum with fewer steps, but
-// will increase the cost of each step. It is not beneficial to increase
-// qsize if the application has frame-to-frame coherence, i.e., the
-// pair of models take small steps between each call, since another
-// speedup trick already accelerates this situation with no overhead.
-//
-// However, a queue size of 100 to 200 has been seen to save time in a
-// planning application with "non-coherent" placements of models.
-//
-//----------------------------------------------------------------------------
-
-int
-PQP_Distance(PQP_DistanceResult *result,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL rel_err, PQP_REAL abs_err,
- int qsize = 2);
-
-//----------------------------------------------------------------------------
-//
-// PQP_ToleranceResult
-//
-// This saves and reports results from a tolerance query.
-//
-//----------------------------------------------------------------------------
-//
-// struct PQP_ToleranceResult - declaration contained in PQP_Internal.h
-// {
-// // statistics
-//
-// int NumBVTests();
-// int NumTriTests();
-// PQP_REAL QueryTimeSecs();
-//
-// // If the models are closer than ( <= ) tolerance, these points
-// // and distance were what established this. Otherwise,
-// // distance and point values are not meaningful.
-//
-// PQP_REAL Distance();
-// const PQP_REAL *P1();
-// const PQP_REAL *P2();
-//
-// // boolean says whether models are closer than tolerance distance
-//
-// int CloserThanTolerance();
-// };
-
-//----------------------------------------------------------------------------
-//
-// PQP_Tolerance() - checks if distance between PQP_Models is <= tolerance
-//
-//
-// Declare a PQP_ToleranceResult and pass its pointer to collect
-// tolerance information.
-//
-// The algorithm returns whether the true distance is <= or >
-// "tolerance". This routine does not simply compute true distance
-// and compare to the tolerance - models can often be shown closer or
-// farther than the tolerance more trivially. In most cases this
-// query should run faster than a distance query would on the same
-// models and configurations.
-//
-// "qsize" again controls the size of a priority queue used for
-// searching. Not setting qsize is the current recommendation, since
-// increasing it has only slowed down our applications.
-//
-//----------------------------------------------------------------------------
-
-int
-PQP_Tolerance(PQP_ToleranceResult *res,
- PQP_REAL R1[3][3], PQP_REAL T1[3], PQP_Model *o1,
- PQP_REAL R2[3][3], PQP_REAL T2[3], PQP_Model *o2,
- PQP_REAL tolerance,
- int qsize = 2);
-
-#endif
-#endif
-
-
-
-
-
-
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP_Compile.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP_Compile.h
deleted file mode 100644
index f76c981370ecd5833f257fa7e14564d5704ce2e6..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP_Compile.h
+++ /dev/null
@@ -1,101 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_COMPILE_H
-#define PQP_COMPILE_H
-
-// prevents compiler warnings when PQP_REAL is float
-
-#include <math.h>
-inline float sqrt(float x) { return (float)sqrt((double)x); }
-inline float cos(float x) { return (float)cos((double)x); }
-inline float sin(float x) { return (float)sin((double)x); }
-inline float fabs(float x) { return (float)fabs((double)x); }
-
-//-------------------------------------------------------------------------
-//
-// PQP_REAL
-//
-// This is the floating point type used throughout PQP. doubles are
-// recommended, both for their precision and because the software has
-// mainly been tested using them. However, floats appear to be faster
-// (by 60% on some machines).
-//
-//-------------------------------------------------------------------------
-
-typedef double PQP_REAL;
-
-//-------------------------------------------------------------------------
-//
-// PQP_BV_TYPE
-//
-// PQP introduces a bounding volume (BV) type known as the "rectangle
-// swept sphere" (RSS) - the volume created by sweeping a sphere so
-// that its center visits every point on a rectangle; it looks
-// something like a rounded box.
-//
-// In our experiments, the RSS type is comparable to the oriented
-// bounding box (OBB) in terms of the number of BV-pair and triangle-pair
-// tests incurred. However, with our present implementations, overlap
-// tests are cheaper for OBBs, while distance tests are cheaper for the
-// RSS type (we used a public gjk implementation for the OBB distance test).
-//
-// Consequently, PQP is configured to use the RSS type in distance and
-// tolerance queries (which use BV distance tests) and to use OBBs for
-// collision queries (which use BV overlap tests). Using both requires six
-// more PQP_REALs per BV node than using just one type.
-//
-// To save space, you can configure PQP to use only one type, however,
-// with RSS alone, collision queries will typically be slower. With OBB's
-// alone, distance and tolerance queries are currently not supported, since
-// we have not developed our own OBB distance test. The three options are:
-//
-// #define PQP_BV_TYPE RSS_TYPE
-// #define PQP_BV_TYPE OBB_TYPE
-// #define PQP_BV_TYPE RSS_TYPE | OBB_TYPE
-//
-//-------------------------------------------------------------------------
-
-#define RSS_TYPE 1
-#define OBB_TYPE 2
-
-#define PQP_BV_TYPE RSS_TYPE | OBB_TYPE
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP_Internal.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP_Internal.h
deleted file mode 100644
index 90cedcfa2d7f3ec96c3d378774c48f9e9a5a6d29..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/PQP_Internal.h
+++ /dev/null
@@ -1,203 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk, E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#include "Tri.h"
-#include "BV.h"
-
-class PQP_Model
-{
-
-public:
-
- int build_state;
-
- Tri *tris;
- int num_tris;
- int num_tris_alloced;
-
- BV *b;
- int num_bvs;
- int num_bvs_alloced;
-
- Tri *last_tri; // closest tri on this model in last distance test
-
- BV *child(int n) { return &b[n]; }
-
- PQP_Model();
- ~PQP_Model();
-
- int BeginModel(int num_tris = 8); // preallocate for num_tris triangles;
- // the parameter is optional, since
- // arrays are reallocated as needed
- int AddTri(const PQP_REAL *p1, const PQP_REAL *p2, const PQP_REAL *p3,
- int id);
- int EndModel();
- int MemUsage(int msg); // returns model mem usage.
- // prints message to stderr if msg == TRUE
-};
-
-struct CollisionPair
-{
- int id1;
- int id2;
-};
-
-struct PQP_CollideResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- int num_pairs_alloced;
- int num_pairs;
- CollisionPair *pairs;
-
- void SizeTo(int n);
- void Add(int i1, int i2);
-
- PQP_CollideResult();
- ~PQP_CollideResult();
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // free the list of contact pairs; ordinarily this list is reused
- // for each query, and only deleted in the destructor.
-
- void FreePairsList();
-
- // query results
-
- int Colliding() { return (num_pairs > 0); }
- int NumPairs() { return num_pairs; }
- int Id1(int k) { return pairs[k].id1; }
- int Id2(int k) { return pairs[k].id2; }
-};
-
-#if PQP_BV_TYPE & RSS_TYPE // distance/tolerance are only available with RSS
-
-struct PQP_DistanceResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- PQP_REAL rel_err;
- PQP_REAL abs_err;
-
- PQP_REAL distance;
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- int qsize;
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // The following distance and points established the minimum distance
- // for the models, within the relative and absolute error bounds
- // specified.
- // Points are defined: PQP_REAL p1[3], p2[3];
-
- PQP_REAL Distance() { return distance; }
- const PQP_REAL *P1() { return p1; }
- const PQP_REAL *P2() { return p2; }
-};
-
-struct PQP_ToleranceResult
-{
- // stats
-
- int num_bv_tests;
- int num_tri_tests;
- double query_time_secs;
-
- // xform from model 1 to model 2
-
- PQP_REAL R[3][3];
- PQP_REAL T[3];
-
- int closer_than_tolerance;
- PQP_REAL tolerance;
-
- PQP_REAL distance;
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- int qsize;
-
- // statistics
-
- int NumBVTests() { return num_bv_tests; }
- int NumTriTests() { return num_tri_tests; }
- double QueryTimeSecs() { return query_time_secs; }
-
- // If the models are closer than ( <= ) tolerance, these points
- // and distance were what established this. Otherwise,
- // distance and point values are not meaningful.
-
- PQP_REAL Distance() { return distance; }
- const PQP_REAL *P1() { return p1; }
- const PQP_REAL *P2() { return p2; }
-
- // boolean says whether models are closer than tolerance distance
-
- int CloserThanTolerance() { return closer_than_tolerance; }
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/RectDist.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/RectDist.h
deleted file mode 100644
index 429d2c71633771ff5b2db7ca3cffe71bfaf7b93e..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/RectDist.h
+++ /dev/null
@@ -1,753 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_RECTDIST_H
-#define PQP_RECTDIST_H
-
-#include <math.h>
-#include "MatVec.h"
-#include "PQP_Compile.h"
-
-// ClipToRange
-//
-// clips val between a and b
-
-inline
-void
-ClipToRange(PQP_REAL &val, const PQP_REAL &a, const PQP_REAL &b)
-{
- if (val < a) val = a;
- else if (val > b) val = b;
-}
-
-// SegCoords
-//
-// finds the parameters t & u corresponding to the two closest points
-// on a pair of line segments
-//
-// The first segment is defined as
-//
-// Pa + A*t, 0 <= t <= a,
-//
-// where "Pa" is one endpoint of the segment, "A" is a unit vector
-// pointing to the other endpoint, and t is a scalar that produces
-// all the points between the two endpoints. Since "A" is a unit
-// vector, "a" is the segment's length.
-//
-// The second segment is
-//
-// Pb + B*u, 0 <= u <= b
-//
-// In my application, many of the terms needed by the algorithm
-// are already computed for other purposes, so I pass these terms to
-// the function instead of complete specifications of each segment.
-// "T" in the dot products is the vector between Pa and Pb.
-//
-// The algorithm is from
-//
-// Vladimir J. Lumelsky,
-// On fast computation of distance between line segments.
-// In Information Processing Letters, no. 21, pages 55-61, 1985.
-
-inline
-void
-SegCoords(PQP_REAL& t, PQP_REAL& u,
- const PQP_REAL& a, const PQP_REAL& b,
- const PQP_REAL& A_dot_B,
- const PQP_REAL& A_dot_T,
- const PQP_REAL& B_dot_T)
-{
- PQP_REAL denom = 1 - (A_dot_B)*(A_dot_B);
-
- if (denom == 0) t = 0;
- else
- {
- t = (A_dot_T - B_dot_T*A_dot_B)/denom;
- ClipToRange(t,0,a);
- }
-
- u = t*A_dot_B - B_dot_T;
- if (u < 0)
- {
- u = 0;
- t = A_dot_T;
- ClipToRange(t,0,a);
- }
- else if (u > b)
- {
- u = b;
- t = u*A_dot_B + A_dot_T;
- ClipToRange(t,0,a);
- }
-}
-
-// InVoronoi
-//
-// returns whether the nearest point on rectangle edge
-// Pb + B*u, 0 <= u <= b, to the rectangle edge,
-// Pa + A*t, 0 <= t <= a, is within the half space
-// determined by the point Pa and the direction Anorm.
-//
-// A,B, and Anorm are unit vectors.
-// T is the vector between Pa and Pb.
-
-inline
-int
-InVoronoi(const PQP_REAL &a,
- const PQP_REAL &b,
- const PQP_REAL &Anorm_dot_B,
- const PQP_REAL &Anorm_dot_T,
- const PQP_REAL &A_dot_B,
- const PQP_REAL &A_dot_T,
- const PQP_REAL &B_dot_T)
-{
- if (myfabs(Anorm_dot_B) < 1e-7) return 0;
-
- PQP_REAL t, u, v;
-
- u = -Anorm_dot_T / Anorm_dot_B;
- ClipToRange(u,0,b);
-
- t = u*A_dot_B + A_dot_T;
- ClipToRange(t,0,a);
-
- v = t*A_dot_B - B_dot_T;
-
- if (Anorm_dot_B > 0)
- {
- if (v > (u + 1e-7)) return 1;
- }
- else
- {
- if (v < (u - 1e-7)) return 1;
- }
- return 0;
-}
-
-
-// RectDist
-//
-// Finds the distance between two rectangles A and B. A is assumed
-// to have its corner on the origin, one side aligned with
-// x, the other side aligned with y, and its normal aligned with z.
-//
-// [Rab,Tab] gives the orientation and corner position of rectangle B
-//
-// a[2] are the side lengths of A, b[2] are the side lengths of B
-
-inline
-PQP_REAL
-RectDist(PQP_REAL Rab[3][3], PQP_REAL Tab[3],
- PQP_REAL a[2], PQP_REAL b[2])
-{
- PQP_REAL A0_dot_B0, A0_dot_B1, A1_dot_B0, A1_dot_B1;
-
- A0_dot_B0 = Rab[0][0];
- A0_dot_B1 = Rab[0][1];
- A1_dot_B0 = Rab[1][0];
- A1_dot_B1 = Rab[1][1];
-
- PQP_REAL aA0_dot_B0, aA0_dot_B1, aA1_dot_B0, aA1_dot_B1;
- PQP_REAL bA0_dot_B0, bA0_dot_B1, bA1_dot_B0, bA1_dot_B1;
-
- aA0_dot_B0 = a[0]*A0_dot_B0;
- aA0_dot_B1 = a[0]*A0_dot_B1;
- aA1_dot_B0 = a[1]*A1_dot_B0;
- aA1_dot_B1 = a[1]*A1_dot_B1;
- bA0_dot_B0 = b[0]*A0_dot_B0;
- bA1_dot_B0 = b[0]*A1_dot_B0;
- bA0_dot_B1 = b[1]*A0_dot_B1;
- bA1_dot_B1 = b[1]*A1_dot_B1;
-
- PQP_REAL Tba[3];
- MTxV(Tba,Rab,Tab);
-
- PQP_REAL S[3], t, u;
-
- // determine if any edge pair contains the closest points
-
- PQP_REAL ALL_x, ALU_x, AUL_x, AUU_x;
- PQP_REAL BLL_x, BLU_x, BUL_x, BUU_x;
- PQP_REAL LA1_lx, LA1_ux, UA1_lx, UA1_ux, LB1_lx, LB1_ux, UB1_lx, UB1_ux;
-
- ALL_x = -Tba[0];
- ALU_x = ALL_x + aA1_dot_B0;
- AUL_x = ALL_x + aA0_dot_B0;
- AUU_x = ALU_x + aA0_dot_B0;
-
- if (ALL_x < ALU_x)
- {
- LA1_lx = ALL_x;
- LA1_ux = ALU_x;
- UA1_lx = AUL_x;
- UA1_ux = AUU_x;
- }
- else
- {
- LA1_lx = ALU_x;
- LA1_ux = ALL_x;
- UA1_lx = AUU_x;
- UA1_ux = AUL_x;
- }
-
- BLL_x = Tab[0];
- BLU_x = BLL_x + bA0_dot_B1;
- BUL_x = BLL_x + bA0_dot_B0;
- BUU_x = BLU_x + bA0_dot_B0;
-
- if (BLL_x < BLU_x)
- {
- LB1_lx = BLL_x;
- LB1_ux = BLU_x;
- UB1_lx = BUL_x;
- UB1_ux = BUU_x;
- }
- else
- {
- LB1_lx = BLU_x;
- LB1_ux = BLL_x;
- UB1_lx = BUU_x;
- UB1_ux = BUL_x;
- }
-
- // UA1, UB1
-
- if ((UA1_ux > b[0]) && (UB1_ux > a[0]))
- {
- if (((UA1_lx > b[0]) ||
- InVoronoi(b[1],a[1],A1_dot_B0,aA0_dot_B0 - b[0] - Tba[0],
- A1_dot_B1, aA0_dot_B1 - Tba[1],
- -Tab[1] - bA1_dot_B0))
- &&
-
- ((UB1_lx > a[0]) ||
- InVoronoi(a[1],b[1],A0_dot_B1,Tab[0] + bA0_dot_B0 - a[0],
- A1_dot_B1,Tab[1] + bA1_dot_B0,Tba[1] - aA0_dot_B1)))
- {
- SegCoords(t,u,a[1],b[1],A1_dot_B1,Tab[1] + bA1_dot_B0,
- Tba[1] - aA0_dot_B1);
-
- S[0] = Tab[0] + Rab[0][0]*b[0] + Rab[0][1]*u - a[0] ;
- S[1] = Tab[1] + Rab[1][0]*b[0] + Rab[1][1]*u - t;
- S[2] = Tab[2] + Rab[2][0]*b[0] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
-
- // UA1, LB1
-
- if ((UA1_lx < 0) && (LB1_ux > a[0]))
- {
- if (((UA1_ux < 0) ||
- InVoronoi(b[1],a[1],-A1_dot_B0,Tba[0] - aA0_dot_B0,
- A1_dot_B1, aA0_dot_B1 - Tba[1], -Tab[1]))
- &&
-
- ((LB1_lx > a[0]) ||
- InVoronoi(a[1],b[1],A0_dot_B1,Tab[0] - a[0],
- A1_dot_B1,Tab[1],Tba[1] - aA0_dot_B1)))
- {
- SegCoords(t,u,a[1],b[1],A1_dot_B1,Tab[1],Tba[1] - aA0_dot_B1);
-
- S[0] = Tab[0] + Rab[0][1]*u - a[0];
- S[1] = Tab[1] + Rab[1][1]*u - t;
- S[2] = Tab[2] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA1, UB1
-
- if ((LA1_ux > b[0]) && (UB1_lx < 0))
- {
- if (((LA1_lx > b[0]) ||
- InVoronoi(b[1],a[1],A1_dot_B0,-Tba[0] - b[0],
- A1_dot_B1,-Tba[1], -Tab[1] - bA1_dot_B0))
- &&
-
- ((UB1_ux < 0) ||
- InVoronoi(a[1],b[1],-A0_dot_B1, -Tab[0] - bA0_dot_B0,
- A1_dot_B1, Tab[1] + bA1_dot_B0,Tba[1])))
- {
-
- SegCoords(t,u,a[1],b[1],A1_dot_B1,Tab[1] + bA1_dot_B0,Tba[1]);
-
- S[0] = Tab[0] + Rab[0][0]*b[0] + Rab[0][1]*u;
- S[1] = Tab[1] + Rab[1][0]*b[0] + Rab[1][1]*u - t;
- S[2] = Tab[2] + Rab[2][0]*b[0] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA1, LB1
-
- if ((LA1_lx < 0) && (LB1_lx < 0))
- {
- if (((LA1_ux < 0) ||
- InVoronoi(b[1],a[1],-A1_dot_B0,Tba[0],A1_dot_B1,
- -Tba[1],-Tab[1]))
- &&
-
- ((LB1_ux < 0) ||
- InVoronoi(a[1],b[1],-A0_dot_B1,-Tab[0],A1_dot_B1,
- Tab[1], Tba[1])))
- {
- SegCoords(t,u,a[1],b[1],A1_dot_B1,Tab[1],Tba[1]);
-
- S[0] = Tab[0] + Rab[0][1]*u;
- S[1] = Tab[1] + Rab[1][1]*u - t;
- S[2] = Tab[2] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- PQP_REAL ALL_y, ALU_y, AUL_y, AUU_y;
-
- ALL_y = -Tba[1];
- ALU_y = ALL_y + aA1_dot_B1;
- AUL_y = ALL_y + aA0_dot_B1;
- AUU_y = ALU_y + aA0_dot_B1;
-
- PQP_REAL LA1_ly, LA1_uy, UA1_ly, UA1_uy, LB0_lx, LB0_ux, UB0_lx, UB0_ux;
-
- if (ALL_y < ALU_y)
- {
- LA1_ly = ALL_y;
- LA1_uy = ALU_y;
- UA1_ly = AUL_y;
- UA1_uy = AUU_y;
- }
- else
- {
- LA1_ly = ALU_y;
- LA1_uy = ALL_y;
- UA1_ly = AUU_y;
- UA1_uy = AUL_y;
- }
-
- if (BLL_x < BUL_x)
- {
- LB0_lx = BLL_x;
- LB0_ux = BUL_x;
- UB0_lx = BLU_x;
- UB0_ux = BUU_x;
- }
- else
- {
- LB0_lx = BUL_x;
- LB0_ux = BLL_x;
- UB0_lx = BUU_x;
- UB0_ux = BLU_x;
- }
-
- // UA1, UB0
-
- if ((UA1_uy > b[1]) && (UB0_ux > a[0]))
- {
- if (((UA1_ly > b[1]) ||
- InVoronoi(b[0],a[1],A1_dot_B1, aA0_dot_B1 - Tba[1] - b[1],
- A1_dot_B0, aA0_dot_B0 - Tba[0], -Tab[1] - bA1_dot_B1))
- &&
-
- ((UB0_lx > a[0]) ||
- InVoronoi(a[1],b[0],A0_dot_B0, Tab[0] - a[0] + bA0_dot_B1,
- A1_dot_B0, Tab[1] + bA1_dot_B1, Tba[0] - aA0_dot_B0)))
- {
- SegCoords(t,u,a[1],b[0],A1_dot_B0,Tab[1] + bA1_dot_B1,
- Tba[0] - aA0_dot_B0);
-
- S[0] = Tab[0] + Rab[0][1]*b[1] + Rab[0][0]*u - a[0] ;
- S[1] = Tab[1] + Rab[1][1]*b[1] + Rab[1][0]*u - t;
- S[2] = Tab[2] + Rab[2][1]*b[1] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // UA1, LB0
-
- if ((UA1_ly < 0) && (LB0_ux > a[0]))
- {
- if (((UA1_uy < 0) ||
- InVoronoi(b[0],a[1],-A1_dot_B1, Tba[1] - aA0_dot_B1,A1_dot_B0,
- aA0_dot_B0 - Tba[0], -Tab[1]))
- &&
-
- ((LB0_lx > a[0]) ||
- InVoronoi(a[1],b[0],A0_dot_B0,Tab[0] - a[0],
- A1_dot_B0,Tab[1],Tba[0] - aA0_dot_B0)))
- {
- SegCoords(t,u,a[1],b[0],A1_dot_B0,Tab[1],Tba[0] - aA0_dot_B0);
-
- S[0] = Tab[0] + Rab[0][0]*u - a[0];
- S[1] = Tab[1] + Rab[1][0]*u - t;
- S[2] = Tab[2] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA1, UB0
-
- if ((LA1_uy > b[1]) && (UB0_lx < 0))
- {
- if (((LA1_ly > b[1]) ||
- InVoronoi(b[0],a[1],A1_dot_B1,-Tba[1] - b[1],
- A1_dot_B0, -Tba[0], -Tab[1] - bA1_dot_B1))
- &&
-
- ((UB0_ux < 0) ||
- InVoronoi(a[1],b[0],-A0_dot_B0, -Tab[0] - bA0_dot_B1,A1_dot_B0,
- Tab[1] + bA1_dot_B1,Tba[0])))
- {
- SegCoords(t,u,a[1],b[0],A1_dot_B0,Tab[1] + bA1_dot_B1,Tba[0]);
-
- S[0] = Tab[0] + Rab[0][1]*b[1] + Rab[0][0]*u;
- S[1] = Tab[1] + Rab[1][1]*b[1] + Rab[1][0]*u - t;
- S[2] = Tab[2] + Rab[2][1]*b[1] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA1, LB0
-
- if ((LA1_ly < 0) && (LB0_lx < 0))
- {
- if (((LA1_uy < 0) ||
- InVoronoi(b[0],a[1],-A1_dot_B1,Tba[1],A1_dot_B0,
- -Tba[0],-Tab[1]))
- &&
-
- ((LB0_ux < 0) ||
- InVoronoi(a[1],b[0],-A0_dot_B0,-Tab[0],A1_dot_B0,
- Tab[1],Tba[0])))
- {
- SegCoords(t,u,a[1],b[0],A1_dot_B0,Tab[1],Tba[0]);
-
- S[0] = Tab[0] + Rab[0][0]*u;
- S[1] = Tab[1] + Rab[1][0]*u - t;
- S[2] = Tab[2] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- PQP_REAL BLL_y, BLU_y, BUL_y, BUU_y;
-
- BLL_y = Tab[1];
- BLU_y = BLL_y + bA1_dot_B1;
- BUL_y = BLL_y + bA1_dot_B0;
- BUU_y = BLU_y + bA1_dot_B0;
-
- PQP_REAL LA0_lx, LA0_ux, UA0_lx, UA0_ux, LB1_ly, LB1_uy, UB1_ly, UB1_uy;
-
- if (ALL_x < AUL_x)
- {
- LA0_lx = ALL_x;
- LA0_ux = AUL_x;
- UA0_lx = ALU_x;
- UA0_ux = AUU_x;
- }
- else
- {
- LA0_lx = AUL_x;
- LA0_ux = ALL_x;
- UA0_lx = AUU_x;
- UA0_ux = ALU_x;
- }
-
- if (BLL_y < BLU_y)
- {
- LB1_ly = BLL_y;
- LB1_uy = BLU_y;
- UB1_ly = BUL_y;
- UB1_uy = BUU_y;
- }
- else
- {
- LB1_ly = BLU_y;
- LB1_uy = BLL_y;
- UB1_ly = BUU_y;
- UB1_uy = BUL_y;
- }
-
- // UA0, UB1
-
- if ((UA0_ux > b[0]) && (UB1_uy > a[1]))
- {
- if (((UA0_lx > b[0]) ||
- InVoronoi(b[1],a[0],A0_dot_B0, aA1_dot_B0 - Tba[0] - b[0],
- A0_dot_B1,aA1_dot_B1 - Tba[1], -Tab[0] - bA0_dot_B0))
- &&
-
- ((UB1_ly > a[1]) ||
- InVoronoi(a[0],b[1],A1_dot_B1, Tab[1] - a[1] + bA1_dot_B0,
- A0_dot_B1,Tab[0] + bA0_dot_B0, Tba[1] - aA1_dot_B1)))
- {
- SegCoords(t,u,a[0],b[1],A0_dot_B1,Tab[0] + bA0_dot_B0,
- Tba[1] - aA1_dot_B1);
-
- S[0] = Tab[0] + Rab[0][0]*b[0] + Rab[0][1]*u - t;
- S[1] = Tab[1] + Rab[1][0]*b[0] + Rab[1][1]*u - a[1];
- S[2] = Tab[2] + Rab[2][0]*b[0] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // UA0, LB1
-
- if ((UA0_lx < 0) && (LB1_uy > a[1]))
- {
- if (((UA0_ux < 0) ||
- InVoronoi(b[1],a[0],-A0_dot_B0, Tba[0] - aA1_dot_B0,A0_dot_B1,
- aA1_dot_B1 - Tba[1],-Tab[0]))
- &&
-
- ((LB1_ly > a[1]) ||
- InVoronoi(a[0],b[1],A1_dot_B1,Tab[1] - a[1],A0_dot_B1,Tab[0],
- Tba[1] - aA1_dot_B1)))
- {
- SegCoords(t,u,a[0],b[1],A0_dot_B1,Tab[0],Tba[1] - aA1_dot_B1);
-
- S[0] = Tab[0] + Rab[0][1]*u - t;
- S[1] = Tab[1] + Rab[1][1]*u - a[1];
- S[2] = Tab[2] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA0, UB1
-
- if ((LA0_ux > b[0]) && (UB1_ly < 0))
- {
- if (((LA0_lx > b[0]) ||
- InVoronoi(b[1],a[0],A0_dot_B0,-b[0] - Tba[0],A0_dot_B1,-Tba[1],
- -bA0_dot_B0 - Tab[0]))
- &&
-
- ((UB1_uy < 0) ||
- InVoronoi(a[0],b[1],-A1_dot_B1, -Tab[1] - bA1_dot_B0,A0_dot_B1,
- Tab[0] + bA0_dot_B0,Tba[1])))
- {
- SegCoords(t,u,a[0],b[1],A0_dot_B1,Tab[0] + bA0_dot_B0,Tba[1]);
-
- S[0] = Tab[0] + Rab[0][0]*b[0] + Rab[0][1]*u - t;
- S[1] = Tab[1] + Rab[1][0]*b[0] + Rab[1][1]*u;
- S[2] = Tab[2] + Rab[2][0]*b[0] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA0, LB1
-
- if ((LA0_lx < 0) && (LB1_ly < 0))
- {
- if (((LA0_ux < 0) ||
- InVoronoi(b[1],a[0],-A0_dot_B0,Tba[0],A0_dot_B1,-Tba[1],
- -Tab[0]))
- &&
-
- ((LB1_uy < 0) ||
- InVoronoi(a[0],b[1],-A1_dot_B1,-Tab[1],A0_dot_B1,
- Tab[0],Tba[1])))
- {
- SegCoords(t,u,a[0],b[1],A0_dot_B1,Tab[0],Tba[1]);
-
- S[0] = Tab[0] + Rab[0][1]*u - t;
- S[1] = Tab[1] + Rab[1][1]*u;
- S[2] = Tab[2] + Rab[2][1]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- PQP_REAL LA0_ly, LA0_uy, UA0_ly, UA0_uy, LB0_ly, LB0_uy, UB0_ly, UB0_uy;
-
- if (ALL_y < AUL_y)
- {
- LA0_ly = ALL_y;
- LA0_uy = AUL_y;
- UA0_ly = ALU_y;
- UA0_uy = AUU_y;
- }
- else
- {
- LA0_ly = AUL_y;
- LA0_uy = ALL_y;
- UA0_ly = AUU_y;
- UA0_uy = ALU_y;
- }
-
- if (BLL_y < BUL_y)
- {
- LB0_ly = BLL_y;
- LB0_uy = BUL_y;
- UB0_ly = BLU_y;
- UB0_uy = BUU_y;
- }
- else
- {
- LB0_ly = BUL_y;
- LB0_uy = BLL_y;
- UB0_ly = BUU_y;
- UB0_uy = BLU_y;
- }
-
- // UA0, UB0
-
- if ((UA0_uy > b[1]) && (UB0_uy > a[1]))
- {
- if (((UA0_ly > b[1]) ||
- InVoronoi(b[0],a[0],A0_dot_B1, aA1_dot_B1 - Tba[1] - b[1],
- A0_dot_B0, aA1_dot_B0 - Tba[0], -Tab[0] - bA0_dot_B1))
- &&
-
- ((UB0_ly > a[1]) ||
- InVoronoi(a[0],b[0],A1_dot_B0,Tab[1] - a[1] + bA1_dot_B1,A0_dot_B0,
- Tab[0] + bA0_dot_B1, Tba[0] - aA1_dot_B0)))
- {
- SegCoords(t,u,a[0],b[0],A0_dot_B0,Tab[0] + bA0_dot_B1,
- Tba[0] - aA1_dot_B0);
-
- S[0] = Tab[0] + Rab[0][1]*b[1] + Rab[0][0]*u - t;
- S[1] = Tab[1] + Rab[1][1]*b[1] + Rab[1][0]*u - a[1];
- S[2] = Tab[2] + Rab[2][1]*b[1] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // UA0, LB0
-
- if ((UA0_ly < 0) && (LB0_uy > a[1]))
- {
- if (((UA0_uy < 0) ||
- InVoronoi(b[0],a[0],-A0_dot_B1,Tba[1] - aA1_dot_B1,A0_dot_B0,
- aA1_dot_B0 - Tba[0],-Tab[0]))
- &&
-
- ((LB0_ly > a[1]) ||
- InVoronoi(a[0],b[0],A1_dot_B0,Tab[1] - a[1],
- A0_dot_B0,Tab[0],Tba[0] - aA1_dot_B0)))
- {
- SegCoords(t,u,a[0],b[0],A0_dot_B0,Tab[0],Tba[0] - aA1_dot_B0);
-
- S[0] = Tab[0] + Rab[0][0]*u - t;
- S[1] = Tab[1] + Rab[1][0]*u - a[1];
- S[2] = Tab[2] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA0, UB0
-
- if ((LA0_uy > b[1]) && (UB0_ly < 0))
- {
- if (((LA0_ly > b[1]) ||
- InVoronoi(b[0],a[0],A0_dot_B1,-Tba[1] - b[1], A0_dot_B0,-Tba[0],
- -Tab[0] - bA0_dot_B1))
- &&
-
- ((UB0_uy < 0) ||
- InVoronoi(a[0],b[0],-A1_dot_B0, -Tab[1] - bA1_dot_B1, A0_dot_B0,
- Tab[0] + bA0_dot_B1,Tba[0])))
- {
- SegCoords(t,u,a[0],b[0],A0_dot_B0,Tab[0] + bA0_dot_B1,Tba[0]);
-
- S[0] = Tab[0] + Rab[0][1]*b[1] + Rab[0][0]*u - t;
- S[1] = Tab[1] + Rab[1][1]*b[1] + Rab[1][0]*u;
- S[2] = Tab[2] + Rab[2][1]*b[1] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // LA0, LB0
-
- if ((LA0_ly < 0) && (LB0_ly < 0))
- {
- if (((LA0_uy < 0) ||
- InVoronoi(b[0],a[0],-A0_dot_B1,Tba[1],A0_dot_B0,
- -Tba[0],-Tab[0]))
- &&
-
- ((LB0_uy < 0) ||
- InVoronoi(a[0],b[0],-A1_dot_B0,-Tab[1],A0_dot_B0,
- Tab[0],Tba[0])))
- {
- SegCoords(t,u,a[0],b[0],A0_dot_B0,Tab[0],Tba[0]);
-
- S[0] = Tab[0] + Rab[0][0]*u - t;
- S[1] = Tab[1] + Rab[1][0]*u;
- S[2] = Tab[2] + Rab[2][0]*u;
- return sqrt(VdotV(S,S));
- }
- }
-
- // no edges passed, take max separation along face normals
-
- PQP_REAL sep1, sep2;
-
- if (Tab[2] > 0.0)
- {
- sep1 = Tab[2];
- if (Rab[2][0] < 0.0) sep1 += b[0]*Rab[2][0];
- if (Rab[2][1] < 0.0) sep1 += b[1]*Rab[2][1];
- }
- else
- {
- sep1 = -Tab[2];
- if (Rab[2][0] > 0.0) sep1 -= b[0]*Rab[2][0];
- if (Rab[2][1] > 0.0) sep1 -= b[1]*Rab[2][1];
- }
-
- if (Tba[2] < 0)
- {
- sep2 = -Tba[2];
- if (Rab[0][2] < 0.0) sep2 += a[0]*Rab[0][2];
- if (Rab[1][2] < 0.0) sep2 += a[1]*Rab[1][2];
- }
- else
- {
- sep2 = Tba[2];
- if (Rab[0][2] > 0.0) sep2 -= a[0]*Rab[0][2];
- if (Rab[1][2] > 0.0) sep2 -= a[1]*Rab[1][2];
- }
-
- PQP_REAL sep = (sep1 > sep2? sep1 : sep2);
- return (sep > 0? sep : 0);
-}
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Tri.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Tri.h
deleted file mode 100644
index 496cddd928e5ab4fe8c5b1046f55f2a6079f4c71..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/Tri.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: S. Gottschalk
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_TRI_H
-#define PQP_TRI_H
-
-#include "PQP_Compile.h"
-
-struct Tri
-{
- PQP_REAL p1[3];
- PQP_REAL p2[3];
- PQP_REAL p3[3];
- int id;
-};
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/TriDist.cpp b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/TriDist.cpp
deleted file mode 100644
index 3cbd438b7a7ec1aaa2c9eaa9181d9e03d4ec4b17..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/TriDist.cpp
+++ /dev/null
@@ -1,407 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-//--------------------------------------------------------------------------
-// File: TriDist.cpp
-// Author: Eric Larsen
-// Description:
-// contains SegPoints() for finding closest points on a pair of line
-// segments and TriDist() for finding closest points on a pair of triangles
-//--------------------------------------------------------------------------
-
-#include "MatVec.h"
-#ifdef _WIN32
-#include <float.h>
-#define isnan _isnan
-#endif
-
-//--------------------------------------------------------------------------
-// SegPoints()
-//
-// Returns closest points between an segment pair.
-// Implemented from an algorithm described in
-//
-// Vladimir J. Lumelsky,
-// On fast computation of distance between line segments.
-// In Information Processing Letters, no. 21, pages 55-61, 1985.
-//--------------------------------------------------------------------------
-
-void
-SegPoints(PQP_REAL VEC[3],
- PQP_REAL X[3], PQP_REAL Y[3], // closest points
- const PQP_REAL P[3], const PQP_REAL A[3], // seg 1 origin, vector
- const PQP_REAL Q[3], const PQP_REAL B[3]) // seg 2 origin, vector
-{
- PQP_REAL T[3], A_dot_A, B_dot_B, A_dot_B, A_dot_T, B_dot_T;
- PQP_REAL TMP[3];
-
- VmV(T,Q,P);
- A_dot_A = VdotV(A,A);
- B_dot_B = VdotV(B,B);
- A_dot_B = VdotV(A,B);
- A_dot_T = VdotV(A,T);
- B_dot_T = VdotV(B,T);
-
- // t parameterizes ray P,A
- // u parameterizes ray Q,B
-
- PQP_REAL t,u;
-
- // compute t for the closest point on ray P,A to
- // ray Q,B
-
- PQP_REAL denom = A_dot_A*B_dot_B - A_dot_B*A_dot_B;
-
- t = (A_dot_T*B_dot_B - B_dot_T*A_dot_B) / denom;
-
- // clamp result so t is on the segment P,A
-
- if ((t < 0) || isnan(t)) t = 0; else if (t > 1) t = 1;
-
- // find u for point on ray Q,B closest to point at t
-
- u = (t*A_dot_B - B_dot_T) / B_dot_B;
-
- // if u is on segment Q,B, t and u correspond to
- // closest points, otherwise, clamp u, recompute and
- // clamp t
-
- if ((u <= 0) || isnan(u)) {
-
- VcV(Y, Q);
-
- t = A_dot_T / A_dot_A;
-
- if ((t <= 0) || isnan(t)) {
- VcV(X, P);
- VmV(VEC, Q, P);
- }
- else if (t >= 1) {
- VpV(X, P, A);
- VmV(VEC, Q, X);
- }
- else {
- VpVxS(X, P, A, t);
- VcrossV(TMP, T, A);
- VcrossV(VEC, A, TMP);
- }
- }
- else if (u >= 1) {
-
- VpV(Y, Q, B);
-
- t = (A_dot_B + A_dot_T) / A_dot_A;
-
- if ((t <= 0) || isnan(t)) {
- VcV(X, P);
- VmV(VEC, Y, P);
- }
- else if (t >= 1) {
- VpV(X, P, A);
- VmV(VEC, Y, X);
- }
- else {
- VpVxS(X, P, A, t);
- VmV(T, Y, P);
- VcrossV(TMP, T, A);
- VcrossV(VEC, A, TMP);
- }
- }
- else {
-
- VpVxS(Y, Q, B, u);
-
- if ((t <= 0) || isnan(t)) {
- VcV(X, P);
- VcrossV(TMP, T, B);
- VcrossV(VEC, B, TMP);
- }
- else if (t >= 1) {
- VpV(X, P, A);
- VmV(T, Q, X);
- VcrossV(TMP, T, B);
- VcrossV(VEC, B, TMP);
- }
- else {
- VpVxS(X, P, A, t);
- VcrossV(VEC, A, B);
- if (VdotV(VEC, T) < 0) {
- VxS(VEC, VEC, -1);
- }
- }
- }
-}
-
-//--------------------------------------------------------------------------
-// TriDist()
-//
-// Computes the closest points on two triangles, and returns the
-// distance between them.
-//
-// S and T are the triangles, stored tri[point][dimension].
-//
-// If the triangles are disjoint, P and Q give the closest points of
-// S and T respectively. However, if the triangles overlap, P and Q
-// are basically a random pair of points from the triangles, not
-// coincident points on the intersection of the triangles, as might
-// be expected.
-//--------------------------------------------------------------------------
-
-PQP_REAL
-TriDist(PQP_REAL P[3], PQP_REAL Q[3],
- const PQP_REAL S[3][3], const PQP_REAL T[3][3])
-{
- // Compute vectors along the 6 sides
-
- PQP_REAL Sv[3][3], Tv[3][3];
- PQP_REAL VEC[3];
-
- VmV(Sv[0],S[1],S[0]);
- VmV(Sv[1],S[2],S[1]);
- VmV(Sv[2],S[0],S[2]);
-
- VmV(Tv[0],T[1],T[0]);
- VmV(Tv[1],T[2],T[1]);
- VmV(Tv[2],T[0],T[2]);
-
- // For each edge pair, the vector connecting the closest points
- // of the edges defines a slab (parallel planes at head and tail
- // enclose the slab). If we can show that the off-edge vertex of
- // each triangle is outside of the slab, then the closest points
- // of the edges are the closest points for the triangles.
- // Even if these tests fail, it may be helpful to know the closest
- // points found, and whether the triangles were shown disjoint
-
- PQP_REAL V[3];
- PQP_REAL Z[3];
- PQP_REAL minP[3], minQ[3], mindd;
- int shown_disjoint = 0;
-
- mindd = VdistV2(S[0],T[0]) + 1; // Set first minimum safely high
-
- for (int i = 0; i < 3; i++)
- {
- for (int j = 0; j < 3; j++)
- {
- // Find closest points on edges i & j, plus the
- // vector (and distance squared) between these points
-
- SegPoints(VEC,P,Q,S[i],Sv[i],T[j],Tv[j]);
-
- VmV(V,Q,P);
- PQP_REAL dd = VdotV(V,V);
-
- // Verify this closest point pair only if the distance
- // squared is less than the minimum found thus far.
-
- if (dd <= mindd)
- {
- VcV(minP,P);
- VcV(minQ,Q);
- mindd = dd;
-
- VmV(Z,S[(i+2)%3],P);
- PQP_REAL a = VdotV(Z,VEC);
- VmV(Z,T[(j+2)%3],Q);
- PQP_REAL b = VdotV(Z,VEC);
-
- if ((a <= 0) && (b >= 0)) return sqrt(dd);
-
- PQP_REAL p = VdotV(V, VEC);
-
- if (a < 0) a = 0;
- if (b > 0) b = 0;
- if ((p - a + b) > 0) shown_disjoint = 1;
- }
- }
- }
-
- // No edge pairs contained the closest points.
- // either:
- // 1. one of the closest points is a vertex, and the
- // other point is interior to a face.
- // 2. the triangles are overlapping.
- // 3. an edge of one triangle is parallel to the other's face. If
- // cases 1 and 2 are not true, then the closest points from the 9
- // edge pairs checks above can be taken as closest points for the
- // triangles.
- // 4. possibly, the triangles were degenerate. When the
- // triangle points are nearly colinear or coincident, one
- // of above tests might fail even though the edges tested
- // contain the closest points.
-
- // First check for case 1
-
- PQP_REAL Sn[3], Snl;
- VcrossV(Sn,Sv[0],Sv[1]); // Compute normal to S triangle
- Snl = VdotV(Sn,Sn); // Compute square of length of normal
-
- // If cross product is long enough,
-
- if (Snl > 1e-15)
- {
- // Get projection lengths of T points
-
- PQP_REAL Tp[3];
-
- VmV(V,S[0],T[0]);
- Tp[0] = VdotV(V,Sn);
-
- VmV(V,S[0],T[1]);
- Tp[1] = VdotV(V,Sn);
-
- VmV(V,S[0],T[2]);
- Tp[2] = VdotV(V,Sn);
-
- // If Sn is a separating direction,
- // find point with smallest projection
-
- int point = -1;
- if ((Tp[0] > 0) && (Tp[1] > 0) && (Tp[2] > 0))
- {
- if (Tp[0] < Tp[1]) point = 0; else point = 1;
- if (Tp[2] < Tp[point]) point = 2;
- }
- else if ((Tp[0] < 0) && (Tp[1] < 0) && (Tp[2] < 0))
- {
- if (Tp[0] > Tp[1]) point = 0; else point = 1;
- if (Tp[2] > Tp[point]) point = 2;
- }
-
- // If Sn is a separating direction,
-
- if (point >= 0)
- {
- shown_disjoint = 1;
-
- // Test whether the point found, when projected onto the
- // other triangle, lies within the face.
-
- VmV(V,T[point],S[0]);
- VcrossV(Z,Sn,Sv[0]);
- if (VdotV(V,Z) > 0)
- {
- VmV(V,T[point],S[1]);
- VcrossV(Z,Sn,Sv[1]);
- if (VdotV(V,Z) > 0)
- {
- VmV(V,T[point],S[2]);
- VcrossV(Z,Sn,Sv[2]);
- if (VdotV(V,Z) > 0)
- {
- // T[point] passed the test - it's a closest point for
- // the T triangle; the other point is on the face of S
-
- VpVxS(P,T[point],Sn,Tp[point]/Snl);
- VcV(Q,T[point]);
- return sqrt(VdistV2(P,Q));
- }
- }
- }
- }
- }
-
- PQP_REAL Tn[3], Tnl;
- VcrossV(Tn,Tv[0],Tv[1]);
- Tnl = VdotV(Tn,Tn);
-
- if (Tnl > 1e-15)
- {
- PQP_REAL Sp[3];
-
- VmV(V,T[0],S[0]);
- Sp[0] = VdotV(V,Tn);
-
- VmV(V,T[0],S[1]);
- Sp[1] = VdotV(V,Tn);
-
- VmV(V,T[0],S[2]);
- Sp[2] = VdotV(V,Tn);
-
- int point = -1;
- if ((Sp[0] > 0) && (Sp[1] > 0) && (Sp[2] > 0))
- {
- if (Sp[0] < Sp[1]) point = 0; else point = 1;
- if (Sp[2] < Sp[point]) point = 2;
- }
- else if ((Sp[0] < 0) && (Sp[1] < 0) && (Sp[2] < 0))
- {
- if (Sp[0] > Sp[1]) point = 0; else point = 1;
- if (Sp[2] > Sp[point]) point = 2;
- }
-
- if (point >= 0)
- {
- shown_disjoint = 1;
-
- VmV(V,S[point],T[0]);
- VcrossV(Z,Tn,Tv[0]);
- if (VdotV(V,Z) > 0)
- {
- VmV(V,S[point],T[1]);
- VcrossV(Z,Tn,Tv[1]);
- if (VdotV(V,Z) > 0)
- {
- VmV(V,S[point],T[2]);
- VcrossV(Z,Tn,Tv[2]);
- if (VdotV(V,Z) > 0)
- {
- VcV(P,S[point]);
- VpVxS(Q,S[point],Tn,Sp[point]/Tnl);
- return sqrt(VdistV2(P,Q));
- }
- }
- }
- }
- }
-
- // Case 1 can't be shown.
- // If one of these tests showed the triangles disjoint,
- // we assume case 3 or 4, otherwise we conclude case 2,
- // that the triangles overlap.
-
- if (shown_disjoint)
- {
- VcV(P,minP);
- VcV(Q,minQ);
- return sqrt(mindd);
- }
- else return 0;
-}
diff --git a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/TriDist.h b/trunk/PQP/build/pqp-tar/PQP_v1.3/src/TriDist.h
deleted file mode 100644
index dd20a8c3eac4fa47d53cfb012d2fa9a961542de0..0000000000000000000000000000000000000000
--- a/trunk/PQP/build/pqp-tar/PQP_v1.3/src/TriDist.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*************************************************************************\
-
- Copyright 1999 The University of North Carolina at Chapel Hill.
- All Rights Reserved.
-
- Permission to use, copy, modify and distribute this software and its
- documentation for educational, research and non-profit purposes, without
- fee, and without a written agreement is hereby granted, provided that the
- above copyright notice and the following three paragraphs appear in all
- copies.
-
- IN NO EVENT SHALL THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL BE
- LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR
- CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE
- USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY
- OF NORTH CAROLINA HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH
- DAMAGES.
-
- THE UNIVERSITY OF NORTH CAROLINA SPECIFICALLY DISCLAIM ANY
- WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE
- PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF
- NORTH CAROLINA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT,
- UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
-
- The authors may be contacted via:
-
- US Mail: E. Larsen
- Department of Computer Science
- Sitterson Hall, CB #3175
- University of N. Carolina
- Chapel Hill, NC 27599-3175
-
- Phone: (919)962-1749
-
- EMail: geom@cs.unc.edu
-
-
-\**************************************************************************/
-
-#ifndef PQP_TRIDIST_H
-#define PQP_TRIDIST_H
-
-#include "PQP_Compile.h"
-
-// TriDist()
-//
-// computes the closest points on two triangles, and returns the
-// distance between them.
-//
-// s and t are the triangles, stored tri[point][dimension].
-//
-// If the triangles are disjoint, p and q give the closest points of
-// s and t respectively. However, if the triangles overlap, p and q
-// are basically a random pair of points from the triangles, not
-// coincident points on the intersection of the triangles, as might
-// be expected.
-
-PQP_REAL
-TriDist(PQP_REAL p[3], PQP_REAL q[3],
- const PQP_REAL s[3][3], const PQP_REAL t[3][3]);
-
-#endif
diff --git a/trunk/PQP/build/pqp-tar/unpacked b/trunk/PQP/build/pqp-tar/unpacked
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/trunk/PQP/installed b/trunk/PQP/installed
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/trunk/PQP/mainpage.dox b/trunk/PQP/mainpage.dox
deleted file mode 100644
index c315b415aebe971a6949979c8bd8f869c5180c15..0000000000000000000000000000000000000000
--- a/trunk/PQP/mainpage.dox
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
-\mainpage
-\htmlinclude manifest.html
-
-\b PQP is ...
-
-<!--
-Provide an overview of your package.
--->
-
-
-\section codeapi Code API
-
-<!--
-Provide links to specific auto-generated API documentation within your
-package that is of particular interest to a reader. Doxygen will
-document pretty much every part of your code, so do your best here to
-point the reader to the actual API.
-
-If your codebase is fairly large or has different sets of APIs, you
-should use the doxygen 'group' tag to keep these APIs together. For
-example, the roscpp documentation has 'libros' group.
--->
-
-
-*/
diff --git a/trunk/PQP/manifest.xml b/trunk/PQP/manifest.xml
deleted file mode 100644
index 6e8014660196b5f473892e28ba4b28475d48b66d..0000000000000000000000000000000000000000
--- a/trunk/PQP/manifest.xml
+++ /dev/null
@@ -1,15 +0,0 @@
-<package>
- <description brief="PQP">
- This package is a wrapper on the PQP library available from <a href="http://gamma.cs.unc.edu/software/downloads/SSV">here</a>. This package does not modify the contents of the original library in any manner and only wraps it for easy distribution with the ROS packaging system. PQP is not under BSD license and is optional for FCL. Users can choose to use PQP by setting flag USE_PQP=1 in FCL.
- </description>
- <author>Maintained by Jia Pan and Sachin Chitta</author>
- <license>BSD</license>
- <review status="unreviewed" notes=""/>
- <url>http://ros.org/wiki/PQP</url>
- <export>
- <cpp cflags="-I${prefix}/PQP/include" lflags="-L${prefix}/PQP/lib -Wl,-rpath,${prefix}/PQP/lib -lPQP"/>
- </export>
-
-</package>
-
-
diff --git a/trunk/PQP/pqp.diff b/trunk/PQP/pqp.diff
deleted file mode 100644
index 9a972d9954478e40046e40d538fccfec65ac0995..0000000000000000000000000000000000000000
--- a/trunk/PQP/pqp.diff
+++ /dev/null
@@ -1,10 +0,0 @@
---- PQP_v1.3/Makefile 2002-04-21 12:55:38.000000000 -0400
-+++ PQP_v1.3/Makefile 2011-09-06 23:17:24.535133167 -0400
-@@ -1,6 +1,6 @@
- CC = g++
-
--CFLAGS = -O2 -I.
-+CFLAGS = -O2 -fPIC -I.
-
- .SUFFIXES: .C .cpp
-
diff --git a/trunk/PQP/wiped b/trunk/PQP/wiped
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/trunk/svm_light/Makefile b/trunk/svm_light/Makefile
deleted file mode 100644
index 3a68c58d65ed3b288c6112b76a2b9c4602bdf793..0000000000000000000000000000000000000000
--- a/trunk/svm_light/Makefile
+++ /dev/null
@@ -1,37 +0,0 @@
-all: installed
-
-#
-# Download, extract and compile from a released tarball:
-#
-TARBALL = build/svm_light.tar.gz
-TARBALL_URL = http://download.joachims.org/svm_light/current/svm_light.tar.gz
-TARBALL_PATCH = svm_light.diff
-UNPACK_CMD = mkdir svm_light; tar -C svm_light -xzf
-INITIAL_DIR = build/svm_light
-SOURCE_DIR = build/svm_light-tar
-include $(shell rospack find mk)/download_unpack_build.mk
-
-INSTALL_DIR = svm_light
-CMAKE = cmake
-CMAKE_ARGS = -D CMAKE_BUILD_TYPE="Release" -D CMAKE_INSTALL_PREFIX=`rospack find svm_light`/$(INSTALL_DIR)
-MAKE = make
-
-installed: wiped $(SOURCE_DIR)/unpacked
- cd $(SOURCE_DIR) && make libsvmlight_hideo $(ROS_PARALLEL_JOBS)
- mkdir -p $(INSTALL_DIR)/lib
- mkdir -p $(INSTALL_DIR)/include
- mkdir -p $(INSTALL_DIR)/include/svm_light
- cp -r $(SOURCE_DIR)/*.h $(INSTALL_DIR)/include/svm_light
- cp -r $(SOURCE_DIR)/*.so $(INSTALL_DIR)/lib
- touch installed
-
-clean:
- rm -rf build
- rm -rf $(INSTALL_DIR) installed
-
-wiped: Makefile
- make wipe
- touch wiped
-
-wipe: clean
- rm -rf build patched
diff --git a/trunk/svm_light/build/svm_light-tar/LICENSE.txt b/trunk/svm_light/build/svm_light-tar/LICENSE.txt
deleted file mode 100755
index 28d6db09f54f5efad2fd03db0d3a36be32844e9b..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/LICENSE.txt
+++ /dev/null
@@ -1,59 +0,0 @@
-SVM-Light
----------
-
-Available at http://svmlight.joachims.org/
-
-Author: Thorsten Joachims
- thorsten@joachims.org
-
- Cornell University
- Department of Computer Science
- 4153 Upson Hall
- Ithaca, NY 14853
- USA
-
-LICENSING TERMS
-
-This program is granted free of charge for research and education
-purposes. However you must obtain a license from the author to use it
-for commercial purposes.
-
-Scientific results produced using the software provided shall
-acknowledge the use of SVM-Light. Please cite as
-
- T. Joachims, Making large-Scale SVM Learning
- Practical. Advances in Kernel Methods - Support Vector
- Learning, B. Schölkopf and C. Burges and A. Smola (ed.),
- MIT-Press, 1999.
- http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.pdf
-
-Moreover shall the author of SVM-Light be informed about the
-publication.
-
-The software must not be modified and distributed without prior
-permission of the author.
-
-By using SVM-Light you agree to the licensing terms.
-
-
-NO WARRANTY
-
-BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT
-WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER
-PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND,
-EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE
-PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME
-THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
-REDISTRIBUTE THE PROGRAM, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO
-LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
-YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY
-OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED
-OF THE POSSIBILITY OF SUCH DAMAGES.
diff --git a/trunk/svm_light/build/svm_light-tar/Makefile b/trunk/svm_light/build/svm_light-tar/Makefile
deleted file mode 100755
index 7bb164821c86ef9bdfd1f2ea69f8a3ed1690e34e..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/Makefile
+++ /dev/null
@@ -1,105 +0,0 @@
-#
-# makefile for svm_light
-#
-# Thorsten Joachims, 2002
-#
-
-#Use the following to compile under unix or cygwin
-CC = gcc
-LD = gcc
-
-#Uncomment the following line to make CYGWIN produce stand-alone Windows executables
-#SFLAGS= -mno-cygwin
-
-CFLAGS= $(SFLAGS) -fPIC -O3 # release C-Compiler flags
-LFLAGS= $(SFLAGS) -O3 # release linker flags
-#CFLAGS= $(SFLAGS) -pg -Wall -pedantic # debugging C-Compiler flags
-#LFLAGS= $(SFLAGS) -pg # debugging linker flags
-LIBS=-L. -lm # used libraries
-
-all: svm_learn_hideo svm_classify
-
-tidy:
- rm -f *.o
- rm -f pr_loqo/*.o
-
-clean: tidy
- rm -f svm_learn
- rm -f svm_classify
- rm -f libsvmlight.so
-
-help: info
-
-info:
- @echo
- @echo "make for SVM-light Thorsten Joachims, 1998"
- @echo
- @echo "Thanks to Ralf Herbrich for the initial version."
- @echo
- @echo "USAGE: make [svm_learn | svm_learn_loqo | svm_learn_hideo | "
- @echo " libsvmlight_hideo | libsvmlight_loqo | "
- @echo " svm_classify | all | clean | tidy]"
- @echo
- @echo " svm_learn builds the learning module (prefers HIDEO)"
- @echo " svm_learn_hideo builds the learning module using HIDEO optimizer"
- @echo " svm_learn_loqo builds the learning module using PR_LOQO optimizer"
- @echo " svm_classify builds the classfication module"
- @echo " libsvmlight_hideo builds shared object library that can be linked into"
- @echo " other code using HIDEO"
- @echo " libsvmlight_loqo builds shared object library that can be linked into"
- @echo " other code using PR_LOQO"
- @echo " all (default) builds svm_learn + svm_classify"
- @echo " clean removes .o and target files"
- @echo " tidy removes .o files"
- @echo
-
-# Create executables svm_learn and svm_classify
-
-svm_learn_hideo: svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o
- $(LD) $(LFLAGS) svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o -o svm_learn $(LIBS)
-
-#svm_learn_loqo: svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o loqo
-# $(LD) $(LFLAGS) svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o pr_loqo/pr_loqo.o -o svm_learn $(LIBS)
-
-svm_classify: svm_classify.o svm_common.o
- $(LD) $(LFLAGS) svm_classify.o svm_common.o -o svm_classify $(LIBS)
-
-
-# Create library libsvmlight.so, so that external code can get access to the
-# learning and classification functions of svm-light by linking this library.
-
-svm_learn_hideo_noexe: svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o
-
-libsvmlight_hideo: svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o
- $(LD) -shared svm_learn.o svm_common.o svm_hideo.o -o libsvmlight.so
-
-#svm_learn_loqo_noexe: svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o loqo
-
-#libsvmlight_loqo: svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o
-# $(LD) -shared svm_learn.o svm_common.o svm_loqo.o pr_loqo/pr_loqo.o -o libsvmlight.so
-
-# Compile components
-
-svm_hideo.o: svm_hideo.c
- $(CC) -c $(CFLAGS) svm_hideo.c -o svm_hideo.o
-
-#svm_loqo.o: svm_loqo.c
-# $(CC) -c $(CFLAGS) svm_loqo.c -o svm_loqo.o
-
-svm_common.o: svm_common.c svm_common.h kernel.h
- $(CC) -c $(CFLAGS) svm_common.c -o svm_common.o
-
-svm_learn.o: svm_learn.c svm_common.h
- $(CC) -c $(CFLAGS) svm_learn.c -o svm_learn.o
-
-svm_learn_main.o: svm_learn_main.c svm_learn.h svm_common.h
- $(CC) -c $(CFLAGS) svm_learn_main.c -o svm_learn_main.o
-
-svm_classify.o: svm_classify.c svm_common.h kernel.h
- $(CC) -c $(CFLAGS) svm_classify.c -o svm_classify.o
-
-#loqo: pr_loqo/pr_loqo.o
-
-#pr_loqo/pr_loqo.o: pr_loqo/pr_loqo.c
-# $(CC) -c $(CFLAGS) pr_loqo/pr_loqo.c -o pr_loqo/pr_loqo.o
-
diff --git a/trunk/svm_light/build/svm_light-tar/kernel.h b/trunk/svm_light/build/svm_light-tar/kernel.h
deleted file mode 100755
index 0133b00661fe990c832eabe2f110166c991b814e..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/kernel.h
+++ /dev/null
@@ -1,40 +0,0 @@
-/************************************************************************/
-/* */
-/* kernel.h */
-/* */
-/* User defined kernel function. Feel free to plug in your own. */
-/* */
-/* Copyright: Thorsten Joachims */
-/* Date: 16.12.97 */
-/* */
-/************************************************************************/
-
-/* KERNEL_PARM is defined in svm_common.h The field 'custom' is reserved for */
-/* parameters of the user defined kernel. You can also access and use */
-/* the parameters of the other kernels. Just replace the line
- return((double)(1.0));
- with your own kernel. */
-
- /* Example: The following computes the polynomial kernel. sprod_ss
- computes the inner product between two sparse vectors.
-
- return((CFLOAT)pow(kernel_parm->coef_lin*sprod_ss(a->words,b->words)
- +kernel_parm->coef_const,(double)kernel_parm->poly_degree));
- */
-
-/* If you are implementing a kernel that is not based on a
- feature/value representation, you might want to make use of the
- field "userdefined" in SVECTOR. By default, this field will contain
- whatever string you put behind a # sign in the example file. So, if
- a line in your training file looks like
-
- -1 1:3 5:6 #abcdefg
-
- then the SVECTOR field "words" will contain the vector 1:3 5:6, and
- "userdefined" will contain the string "abcdefg". */
-
-double custom_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b)
- /* plug in you favorite kernel */
-{
- return((double)(1.0));
-}
diff --git a/trunk/svm_light/build/svm_light-tar/svm_classify.c b/trunk/svm_light/build/svm_light-tar/svm_classify.c
deleted file mode 100755
index 0b0333b05b6df2aa0ede76ee76e4b323c1e67c6d..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_classify.c
+++ /dev/null
@@ -1,197 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_classify.c */
-/* */
-/* Classification module of Support Vector Machine. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/************************************************************************/
-
-# include "svm_common.h"
-
-char docfile[200];
-char modelfile[200];
-char predictionsfile[200];
-
-void read_input_parameters(int, char **, char *, char *, char *, long *,
- long *);
-void print_help(void);
-
-
-int main (int argc, char* argv[])
-{
- DOC *doc; /* test example */
- WORD *words;
- long max_docs,max_words_doc,lld;
- long totdoc=0,queryid,slackid;
- long correct=0,incorrect=0,no_accuracy=0;
- long res_a=0,res_b=0,res_c=0,res_d=0,wnum,pred_format;
- long j;
- double t1,runtime=0;
- double dist,doc_label,costfactor;
- char *line,*comment;
- FILE *predfl,*docfl;
- MODEL *model;
-
- read_input_parameters(argc,argv,docfile,modelfile,predictionsfile,
- &verbosity,&pred_format);
-
- nol_ll(docfile,&max_docs,&max_words_doc,&lld); /* scan size of input file */
- max_words_doc+=2;
- lld+=2;
-
- line = (char *)my_malloc(sizeof(char)*lld);
- words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10));
-
- model=read_model(modelfile);
-
- if(model->kernel_parm.kernel_type == 0) { /* linear kernel */
- /* compute weight vector */
- add_weight_vector_to_linear_model(model);
- }
-
- if(verbosity>=2) {
- printf("Classifying test examples.."); fflush(stdout);
- }
-
- if ((docfl = fopen (docfile, "r")) == NULL)
- { perror (docfile); exit (1); }
- if ((predfl = fopen (predictionsfile, "w")) == NULL)
- { perror (predictionsfile); exit (1); }
-
- while((!feof(docfl)) && fgets(line,(int)lld,docfl)) {
- if(line[0] == '#') continue; /* line contains comments */
- parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,&wnum,
- max_words_doc,&comment);
- totdoc++;
- if(model->kernel_parm.kernel_type == 0) { /* linear kernel */
- for(j=0;(words[j]).wnum != 0;j++) { /* Check if feature numbers */
- if((words[j]).wnum>model->totwords) /* are not larger than in */
- (words[j]).wnum=0; /* model. Remove feature if */
- } /* necessary. */
- doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0));
- t1=get_runtime();
- dist=classify_example_linear(model,doc);
- runtime+=(get_runtime()-t1);
- free_example(doc,1);
- }
- else { /* non-linear kernel */
- doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0));
- t1=get_runtime();
- dist=classify_example(model,doc);
- runtime+=(get_runtime()-t1);
- free_example(doc,1);
- }
- if(dist>0) {
- if(pred_format==0) { /* old weired output format */
- fprintf(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist);
- }
- if(doc_label>0) correct++; else incorrect++;
- if(doc_label>0) res_a++; else res_b++;
- }
- else {
- if(pred_format==0) { /* old weired output format */
- fprintf(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist);
- }
- if(doc_label<0) correct++; else incorrect++;
- if(doc_label>0) res_c++; else res_d++;
- }
- if(pred_format==1) { /* output the value of decision function */
- fprintf(predfl,"%.8g\n",dist);
- }
- if((int)(0.01+(doc_label*doc_label)) != 1)
- { no_accuracy=1; } /* test data is not binary labeled */
- if(verbosity>=2) {
- if(totdoc % 100 == 0) {
- printf("%ld..",totdoc); fflush(stdout);
- }
- }
- }
- fclose(predfl);
- fclose(docfl);
- free(line);
- free(words);
- free_model(model,1);
-
- if(verbosity>=2) {
- printf("done\n");
-
-/* Note by Gary Boone Date: 29 April 2000 */
-/* o Timing is inaccurate. The timer has 0.01 second resolution. */
-/* Because classification of a single vector takes less than */
-/* 0.01 secs, the timer was underflowing. */
- printf("Runtime (without IO) in cpu-seconds: %.2f\n",
- (float)(runtime/100.0));
-
- }
- if((!no_accuracy) && (verbosity>=1)) {
- printf("Accuracy on test set: %.2f%% (%ld correct, %ld incorrect, %ld total)\n",(float)(correct)*100.0/totdoc,correct,incorrect,totdoc);
- printf("Precision/recall on test set: %.2f%%/%.2f%%\n",(float)(res_a)*100.0/(res_a+res_b),(float)(res_a)*100.0/(res_a+res_c));
- }
-
- return(0);
-}
-
-void read_input_parameters(int argc, char **argv, char *docfile,
- char *modelfile, char *predictionsfile,
- long int *verbosity, long int *pred_format)
-{
- long i;
-
- /* set default */
- strcpy (modelfile, "svm_model");
- strcpy (predictionsfile, "svm_predictions");
- (*verbosity)=2;
- (*pred_format)=1;
-
- for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
- switch ((argv[i])[1])
- {
- case 'h': print_help(); exit(0);
- case 'v': i++; (*verbosity)=atol(argv[i]); break;
- case 'f': i++; (*pred_format)=atol(argv[i]); break;
- default: printf("\nUnrecognized option %s!\n\n",argv[i]);
- print_help();
- exit(0);
- }
- }
- if((i+1)>=argc) {
- printf("\nNot enough input parameters!\n\n");
- print_help();
- exit(0);
- }
- strcpy (docfile, argv[i]);
- strcpy (modelfile, argv[i+1]);
- if((i+2)<argc) {
- strcpy (predictionsfile, argv[i+2]);
- }
- if(((*pred_format) != 0) && ((*pred_format) != 1)) {
- printf("\nOutput format can only take the values 0 or 1!\n\n");
- print_help();
- exit(0);
- }
-}
-
-void print_help(void)
-{
- printf("\nSVM-light %s: Support Vector Machine, classification module %s\n",VERSION,VERSION_DATE);
- copyright_notice();
- printf(" usage: svm_classify [options] example_file model_file output_file\n\n");
- printf("options: -h -> this help\n");
- printf(" -v [0..3] -> verbosity level (default 2)\n");
- printf(" -f [0,1] -> 0: old output format of V1.0\n");
- printf(" -> 1: output the value of decision function (default)\n\n");
-}
-
-
-
-
diff --git a/trunk/svm_light/build/svm_light-tar/svm_common.c b/trunk/svm_light/build/svm_light-tar/svm_common.c
deleted file mode 100755
index 61e72800e3aef9af54111b35cfe432ff275b8205..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_common.c
+++ /dev/null
@@ -1,985 +0,0 @@
-/************************************************************************/
-/* */
-/* svm_common.c */
-/* */
-/* Definitions and functions used in both svm_learn and svm_classify. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.04 */
-/* */
-/* Copyright (c) 2004 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/************************************************************************/
-
-# include "ctype.h"
-# include "svm_common.h"
-# include "kernel.h" /* this contains a user supplied kernel */
-
-long verbosity; /* verbosity level (0-4) */
-long kernel_cache_statistic;
-
-double classify_example(MODEL *model, DOC *ex)
- /* classifies one example */
-{
- register long i;
- register double dist;
-
- if((model->kernel_parm.kernel_type == LINEAR) && (model->lin_weights))
- return(classify_example_linear(model,ex));
-
- dist=0;
- for(i=1;i<model->sv_num;i++) {
- dist+=kernel(&model->kernel_parm,model->supvec[i],ex)*model->alpha[i];
- }
- return(dist-model->b);
-}
-
-double classify_example_linear(MODEL *model, DOC *ex)
- /* classifies example for linear kernel */
-
- /* important: the model must have the linear weight vector computed */
- /* use: add_weight_vector_to_linear_model(&model); */
-
-
- /* important: the feature numbers in the example to classify must */
- /* not be larger than the weight vector! */
-{
- double sum=0;
- SVECTOR *f;
-
- for(f=ex->fvec;f;f=f->next)
- sum+=f->factor*sprod_ns(model->lin_weights,f);
- return(sum-model->b);
-}
-
-
-double kernel(KERNEL_PARM *kernel_parm, DOC *a, DOC *b)
- /* calculate the kernel function */
-{
- double sum=0;
- SVECTOR *fa,*fb;
-
- /* in case the constraints are sums of feature vector as represented
- as a list of SVECTOR's with their coefficient factor in the sum,
- take the kernel between all pairs */
- for(fa=a->fvec;fa;fa=fa->next) {
- for(fb=b->fvec;fb;fb=fb->next) {
- if(fa->kernel_id == fb->kernel_id)
- sum+=fa->factor*fb->factor*single_kernel(kernel_parm,fa,fb);
- }
- }
- return(sum);
-}
-
-double single_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b)
- /* calculate the kernel function between two vectors */
-{
- kernel_cache_statistic++;
- switch(kernel_parm->kernel_type) {
- case 0: /* linear */
- return(sprod_ss(a,b));
- case 1: /* polynomial */
- return(pow(kernel_parm->coef_lin*sprod_ss(a,b)+kernel_parm->coef_const,(double)kernel_parm->poly_degree));
- case 2: /* radial basis function */
- return(exp(-kernel_parm->rbf_gamma*(a->twonorm_sq-2*sprod_ss(a,b)+b->twonorm_sq)));
- case 3: /* sigmoid neural net */
- return(tanh(kernel_parm->coef_lin*sprod_ss(a,b)+kernel_parm->coef_const));
- case 4: /* custom-kernel supplied in file kernel.h*/
- return(custom_kernel(kernel_parm,a,b));
- default: printf("Error: Unknown kernel function\n"); exit(1);
- }
-}
-
-
-SVECTOR *create_svector(WORD *words,char *userdefined,double factor)
-{
- SVECTOR *vec;
- long fnum,i;
-
- fnum=0;
- while(words[fnum].wnum) {
- fnum++;
- }
- fnum++;
- vec = (SVECTOR *)my_malloc(sizeof(SVECTOR));
- vec->words = (WORD *)my_malloc(sizeof(WORD)*(fnum));
- for(i=0;i<fnum;i++) {
- vec->words[i]=words[i];
- }
- vec->twonorm_sq=sprod_ss(vec,vec);
-
- fnum=0;
- while(userdefined[fnum]) {
- fnum++;
- }
- fnum++;
- vec->userdefined = (char *)my_malloc(sizeof(char)*(fnum));
- for(i=0;i<fnum;i++) {
- vec->userdefined[i]=userdefined[i];
- }
- vec->kernel_id=0;
- vec->next=NULL;
- vec->factor=factor;
- return(vec);
-}
-
-SVECTOR *copy_svector(SVECTOR *vec)
-{
- SVECTOR *newvec=NULL;
- if(vec) {
- newvec=create_svector(vec->words,vec->userdefined,vec->factor);
- newvec->next=copy_svector(vec->next);
- }
- return(newvec);
-}
-
-void free_svector(SVECTOR *vec)
-{
- if(vec) {
- free(vec->words);
- if(vec->userdefined)
- free(vec->userdefined);
- free_svector(vec->next);
- free(vec);
- }
-}
-
-double sprod_ss(SVECTOR *a, SVECTOR *b)
- /* compute the inner product of two sparse vectors */
-{
- register double sum=0;
- register WORD *ai,*bj;
- ai=a->words;
- bj=b->words;
- while (ai->wnum && bj->wnum) {
- if(ai->wnum > bj->wnum) {
- bj++;
- }
- else if (ai->wnum < bj->wnum) {
- ai++;
- }
- else {
- sum+=(ai->weight) * (bj->weight);
- ai++;
- bj++;
- }
- }
- return((double)sum);
-}
-
-SVECTOR* sub_ss(SVECTOR *a, SVECTOR *b)
- /* compute the difference a-b of two sparse vectors */
- /* Note: SVECTOR lists are not followed, but only the first
- SVECTOR is used */
-{
- SVECTOR *vec;
- register WORD *sum,*sumi;
- register WORD *ai,*bj;
- long veclength;
-
- ai=a->words;
- bj=b->words;
- veclength=0;
- while (ai->wnum && bj->wnum) {
- if(ai->wnum > bj->wnum) {
- veclength++;
- bj++;
- }
- else if (ai->wnum < bj->wnum) {
- veclength++;
- ai++;
- }
- else {
- veclength++;
- ai++;
- bj++;
- }
- }
- while (bj->wnum) {
- veclength++;
- bj++;
- }
- while (ai->wnum) {
- veclength++;
- ai++;
- }
- veclength++;
-
- sum=(WORD *)my_malloc(sizeof(WORD)*veclength);
- sumi=sum;
- ai=a->words;
- bj=b->words;
- while (ai->wnum && bj->wnum) {
- if(ai->wnum > bj->wnum) {
- (*sumi)=(*bj);
- sumi->weight*=(-1);
- sumi++;
- bj++;
- }
- else if (ai->wnum < bj->wnum) {
- (*sumi)=(*ai);
- sumi++;
- ai++;
- }
- else {
- (*sumi)=(*ai);
- sumi->weight-=bj->weight;
- if(sumi->weight != 0)
- sumi++;
- ai++;
- bj++;
- }
- }
- while (bj->wnum) {
- (*sumi)=(*bj);
- sumi->weight*=(-1);
- sumi++;
- bj++;
- }
- while (ai->wnum) {
- (*sumi)=(*ai);
- sumi++;
- ai++;
- }
- sumi->wnum=0;
-
- vec=create_svector(sum,"",1.0);
- free(sum);
-
- return(vec);
-}
-
-SVECTOR* add_ss(SVECTOR *a, SVECTOR *b)
- /* compute the sum a+b of two sparse vectors */
- /* Note: SVECTOR lists are not followed, but only the first
- SVECTOR is used */
-{
- SVECTOR *vec;
- register WORD *sum,*sumi;
- register WORD *ai,*bj;
- long veclength;
-
- ai=a->words;
- bj=b->words;
- veclength=0;
- while (ai->wnum && bj->wnum) {
- if(ai->wnum > bj->wnum) {
- veclength++;
- bj++;
- }
- else if (ai->wnum < bj->wnum) {
- veclength++;
- ai++;
- }
- else {
- veclength++;
- ai++;
- bj++;
- }
- }
- while (bj->wnum) {
- veclength++;
- bj++;
- }
- while (ai->wnum) {
- veclength++;
- ai++;
- }
- veclength++;
-
- /*** is veclength=lengSequence(a)+lengthSequence(b)? ***/
-
- sum=(WORD *)my_malloc(sizeof(WORD)*veclength);
- sumi=sum;
- ai=a->words;
- bj=b->words;
- while (ai->wnum && bj->wnum) {
- if(ai->wnum > bj->wnum) {
- (*sumi)=(*bj);
- sumi++;
- bj++;
- }
- else if (ai->wnum < bj->wnum) {
- (*sumi)=(*ai);
- sumi++;
- ai++;
- }
- else {
- (*sumi)=(*ai);
- sumi->weight+=bj->weight;
- if(sumi->weight != 0)
- sumi++;
- ai++;
- bj++;
- }
- }
- while (bj->wnum) {
- (*sumi)=(*bj);
- sumi++;
- bj++;
- }
- while (ai->wnum) {
- (*sumi)=(*ai);
- sumi++;
- ai++;
- }
- sumi->wnum=0;
-
- vec=create_svector(sum,"",1.0);
- free(sum);
-
- return(vec);
-}
-
-SVECTOR* add_list_ss(SVECTOR *a)
- /* computes the linear combination of the SVECTOR list weighted
- by the factor of each SVECTOR */
-{
- SVECTOR *scaled,*oldsum,*sum,*f;
- WORD empty[2];
-
- if(a){
- sum=smult_s(a,a->factor);
- for(f=a->next;f;f=f->next) {
- scaled=smult_s(f,f->factor);
- oldsum=sum;
- sum=add_ss(sum,scaled);
- free_svector(oldsum);
- free_svector(scaled);
- }
- sum->factor=1.0;
- }
- else {
- empty[0].wnum=0;
- sum=create_svector(empty,"",1.0);
- }
- return(sum);
-}
-
-void append_svector_list(SVECTOR *a, SVECTOR *b)
- /* appends SVECTOR b to the end of SVECTOR a. */
-{
- SVECTOR *f;
-
- for(f=a;f->next;f=f->next); /* find end of first vector list */
- f->next=b; /* append the two vector lists */
-}
-
-SVECTOR* smult_s(SVECTOR *a, double factor)
- /* scale sparse vector a by factor */
-{
- SVECTOR *vec;
- register WORD *sum,*sumi;
- register WORD *ai;
- long veclength;
-
- ai=a->words;
- veclength=0;
- while (ai->wnum) {
- veclength++;
- ai++;
- }
- veclength++;
-
- sum=(WORD *)my_malloc(sizeof(WORD)*veclength);
- sumi=sum;
- ai=a->words;
- while (ai->wnum) {
- (*sumi)=(*ai);
- sumi->weight*=factor;
- if(sumi->weight != 0)
- sumi++;
- ai++;
- }
- sumi->wnum=0;
-
- vec=create_svector(sum,a->userdefined,a->factor);
- free(sum);
-
- return(vec);
-}
-
-int featvec_eq(SVECTOR *a, SVECTOR *b)
- /* tests two sparse vectors for equality */
-{
- register WORD *ai,*bj;
- ai=a->words;
- bj=b->words;
- while (ai->wnum && bj->wnum) {
- if(ai->wnum > bj->wnum) {
- if((bj->weight) != 0)
- return(0);
- bj++;
- }
- else if (ai->wnum < bj->wnum) {
- if((ai->weight) != 0)
- return(0);
- ai++;
- }
- else {
- if((ai->weight) != (bj->weight))
- return(0);
- ai++;
- bj++;
- }
- }
- return(1);
-}
-
-double model_length_s(MODEL *model, KERNEL_PARM *kernel_parm)
- /* compute length of weight vector */
-{
- register long i,j;
- register double sum=0,alphai;
- register DOC *supveci;
-
- for(i=1;i<model->sv_num;i++) {
- alphai=model->alpha[i];
- supveci=model->supvec[i];
- for(j=1;j<model->sv_num;j++) {
- sum+=alphai*model->alpha[j]
- *kernel(kernel_parm,supveci,model->supvec[j]);
- }
- }
- return(sqrt(sum));
-}
-
-void clear_vector_n(double *vec, long int n)
-{
- register long i;
- for(i=0;i<=n;i++) vec[i]=0;
-}
-
-void add_vector_ns(double *vec_n, SVECTOR *vec_s, double faktor)
-{
- register WORD *ai;
- ai=vec_s->words;
- while (ai->wnum) {
- vec_n[ai->wnum]+=(faktor*ai->weight);
- ai++;
- }
-}
-
-double sprod_ns(double *vec_n, SVECTOR *vec_s)
-{
- register double sum=0;
- register WORD *ai;
- ai=vec_s->words;
- while (ai->wnum) {
- sum+=(vec_n[ai->wnum]*ai->weight);
- ai++;
- }
- return(sum);
-}
-
-void add_weight_vector_to_linear_model(MODEL *model)
- /* compute weight vector in linear case and add to model */
-{
- long i;
- SVECTOR *f;
-
- model->lin_weights=(double *)my_malloc(sizeof(double)*(model->totwords+1));
- clear_vector_n(model->lin_weights,model->totwords);
- for(i=1;i<model->sv_num;i++) {
- for(f=(model->supvec[i])->fvec;f;f=f->next)
- add_vector_ns(model->lin_weights,f,f->factor*model->alpha[i]);
- }
-}
-
-
-DOC *create_example(long docnum, long queryid, long slackid,
- double costfactor, SVECTOR *fvec)
-{
- DOC *example;
- example = (DOC *)my_malloc(sizeof(DOC));
- example->docnum=docnum;
- example->queryid=queryid;
- example->slackid=slackid;
- example->costfactor=costfactor;
- example->fvec=fvec;
- return(example);
-}
-
-void free_example(DOC *example, long deep)
-{
- if(example) {
- if(deep) {
- if(example->fvec)
- free_svector(example->fvec);
- }
- free(example);
- }
-}
-
-void write_model(char *modelfile, MODEL *model)
-{
- FILE *modelfl;
- long j,i,sv_num;
- SVECTOR *v;
-
- if(verbosity>=1) {
- printf("Writing model file..."); fflush(stdout);
- }
- if ((modelfl = fopen (modelfile, "w")) == NULL)
- { perror (modelfile); exit (1); }
- fprintf(modelfl,"SVM-light Version %s\n",VERSION);
- fprintf(modelfl,"%ld # kernel type\n",
- model->kernel_parm.kernel_type);
- fprintf(modelfl,"%ld # kernel parameter -d \n",
- model->kernel_parm.poly_degree);
- fprintf(modelfl,"%.8g # kernel parameter -g \n",
- model->kernel_parm.rbf_gamma);
- fprintf(modelfl,"%.8g # kernel parameter -s \n",
- model->kernel_parm.coef_lin);
- fprintf(modelfl,"%.8g # kernel parameter -r \n",
- model->kernel_parm.coef_const);
- fprintf(modelfl,"%s# kernel parameter -u \n",model->kernel_parm.custom);
- fprintf(modelfl,"%ld # highest feature index \n",model->totwords);
- fprintf(modelfl,"%ld # number of training documents \n",model->totdoc);
-
- sv_num=1;
- for(i=1;i<model->sv_num;i++) {
- for(v=model->supvec[i]->fvec;v;v=v->next)
- sv_num++;
- }
- fprintf(modelfl,"%ld # number of support vectors plus 1 \n",sv_num);
- fprintf(modelfl,"%.8g # threshold b, each following line is a SV (starting with alpha*y)\n",model->b);
-
- for(i=1;i<model->sv_num;i++) {
- for(v=model->supvec[i]->fvec;v;v=v->next) {
- fprintf(modelfl,"%.32g ",model->alpha[i]*v->factor);
- for (j=0; (v->words[j]).wnum; j++) {
- fprintf(modelfl,"%ld:%.8g ",
- (long)(v->words[j]).wnum,
- (double)(v->words[j]).weight);
- }
- fprintf(modelfl,"#%s\n",v->userdefined);
- /* NOTE: this could be made more efficient by summing the
- alpha's of identical vectors before writing them to the
- file. */
- }
- }
- fclose(modelfl);
- if(verbosity>=1) {
- printf("done\n");
- }
-}
-
-
-MODEL *read_model(char *modelfile)
-{
- FILE *modelfl;
- long i,queryid,slackid;
- double costfactor;
- long max_sv,max_words,ll,wpos;
- char *line,*comment;
- WORD *words;
- char version_buffer[100];
- MODEL *model;
-
- if(verbosity>=1) {
- printf("Reading model..."); fflush(stdout);
- }
-
- nol_ll(modelfile,&max_sv,&max_words,&ll); /* scan size of model file */
- max_words+=2;
- ll+=2;
-
- words = (WORD *)my_malloc(sizeof(WORD)*(max_words+10));
- line = (char *)my_malloc(sizeof(char)*ll);
- model = (MODEL *)my_malloc(sizeof(MODEL));
-
- if ((modelfl = fopen (modelfile, "r")) == NULL)
- { perror (modelfile); exit (1); }
-
- fscanf(modelfl,"SVM-light Version %s\n",version_buffer);
- if(strcmp(version_buffer,VERSION)) {
- perror ("Version of model-file does not match version of svm_classify!");
- exit (1);
- }
- fscanf(modelfl,"%ld%*[^\n]\n", &model->kernel_parm.kernel_type);
- fscanf(modelfl,"%ld%*[^\n]\n", &model->kernel_parm.poly_degree);
- fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.rbf_gamma);
- fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.coef_lin);
- fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.coef_const);
- fscanf(modelfl,"%[^#]%*[^\n]\n", model->kernel_parm.custom);
-
- fscanf(modelfl,"%ld%*[^\n]\n", &model->totwords);
- fscanf(modelfl,"%ld%*[^\n]\n", &model->totdoc);
- fscanf(modelfl,"%ld%*[^\n]\n", &model->sv_num);
- fscanf(modelfl,"%lf%*[^\n]\n", &model->b);
-
- model->supvec = (DOC **)my_malloc(sizeof(DOC *)*model->sv_num);
- model->alpha = (double *)my_malloc(sizeof(double)*model->sv_num);
- model->index=NULL;
- model->lin_weights=NULL;
-
- for(i=1;i<model->sv_num;i++) {
- fgets(line,(int)ll,modelfl);
- if(!parse_document(line,words,&(model->alpha[i]),&queryid,&slackid,
- &costfactor,&wpos,max_words,&comment)) {
- printf("\nParsing error while reading model file in SV %ld!\n%s",
- i,line);
- exit(1);
- }
- model->supvec[i] = create_example(-1,
- 0,0,
- 0.0,
- create_svector(words,comment,1.0));
- }
- fclose(modelfl);
- free(line);
- free(words);
- if(verbosity>=1) {
- fprintf(stdout, "OK. (%d support vectors read)\n",(int)(model->sv_num-1));
- }
- return(model);
-}
-
-MODEL *copy_model(MODEL *model)
-{
- MODEL *newmodel;
- long i;
-
- newmodel=(MODEL *)my_malloc(sizeof(MODEL));
- (*newmodel)=(*model);
- newmodel->supvec = (DOC **)my_malloc(sizeof(DOC *)*model->sv_num);
- newmodel->alpha = (double *)my_malloc(sizeof(double)*model->sv_num);
- newmodel->index = NULL; /* index is not copied */
- newmodel->supvec[0] = NULL;
- newmodel->alpha[0] = 0;
- for(i=1;i<model->sv_num;i++) {
- newmodel->alpha[i]=model->alpha[i];
- newmodel->supvec[i]=create_example(model->supvec[i]->docnum,
- model->supvec[i]->queryid,0,
- model->supvec[i]->costfactor,
- copy_svector(model->supvec[i]->fvec));
- }
- if(model->lin_weights) {
- newmodel->lin_weights = (double *)my_malloc(sizeof(double)*(model->totwords+1));
- for(i=0;i<model->totwords+1;i++)
- newmodel->lin_weights[i]=model->lin_weights[i];
- }
- return(newmodel);
-}
-
-void free_model(MODEL *model, int deep)
-{
- long i;
-
- if(model->supvec) {
- if(deep) {
- for(i=1;i<model->sv_num;i++) {
- free_example(model->supvec[i],1);
- }
- }
- free(model->supvec);
- }
- if(model->alpha) free(model->alpha);
- if(model->index) free(model->index);
- if(model->lin_weights) free(model->lin_weights);
- free(model);
-}
-
-
-void read_documents(char *docfile, DOC ***docs, double **label,
- long int *totwords, long int *totdoc)
-{
- char *line,*comment;
- WORD *words;
- long dnum=0,wpos,dpos=0,dneg=0,dunlab=0,queryid,slackid,max_docs;
- long max_words_doc, ll;
- double doc_label,costfactor;
- FILE *docfl;
-
- if(verbosity>=1) {
- printf("Scanning examples..."); fflush(stdout);
- }
- nol_ll(docfile,&max_docs,&max_words_doc,&ll); /* scan size of input file */
- max_words_doc+=2;
- ll+=2;
- max_docs+=2;
- if(verbosity>=1) {
- printf("done\n"); fflush(stdout);
- }
-
- (*docs) = (DOC **)my_malloc(sizeof(DOC *)*max_docs); /* feature vectors */
- (*label) = (double *)my_malloc(sizeof(double)*max_docs); /* target values */
- line = (char *)my_malloc(sizeof(char)*ll);
-
- if ((docfl = fopen (docfile, "r")) == NULL)
- { perror (docfile); exit (1); }
-
- words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10));
- if(verbosity>=1) {
- printf("Reading examples into memory..."); fflush(stdout);
- }
- dnum=0;
- (*totwords)=0;
- while((!feof(docfl)) && fgets(line,(int)ll,docfl)) {
- if(line[0] == '#') continue; /* line contains comments */
- if(!parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,
- &wpos,max_words_doc,&comment)) {
- printf("\nParsing error in line %ld!\n%s",dnum,line);
- exit(1);
- }
- (*label)[dnum]=doc_label;
- /* printf("docnum=%ld: Class=%f ",dnum,doc_label); */
- if(doc_label > 0) dpos++;
- if (doc_label < 0) dneg++;
- if (doc_label == 0) dunlab++;
- if((wpos>1) && ((words[wpos-2]).wnum>(*totwords)))
- (*totwords)=(words[wpos-2]).wnum;
- if((*totwords) > MAXFEATNUM) {
- printf("\nMaximum feature number exceeds limit defined in MAXFEATNUM!\n");
- printf("LINE: %s\n",line);
- exit(1);
- }
- (*docs)[dnum] = create_example(dnum,queryid,slackid,costfactor,
- create_svector(words,comment,1.0));
- /* printf("\nNorm=%f\n",((*docs)[dnum]->fvec)->twonorm_sq); */
- dnum++;
- if(verbosity>=1) {
- if((dnum % 100) == 0) {
- printf("%ld..",dnum); fflush(stdout);
- }
- }
- }
-
- fclose(docfl);
- free(line);
- free(words);
- if(verbosity>=1) {
- fprintf(stdout, "OK. (%ld examples read)\n", dnum);
- }
- (*totdoc)=dnum;
-}
-
-int parse_document(char *line, WORD *words, double *label,
- long *queryid, long *slackid, double *costfactor,
- long int *numwords, long int max_words_doc,
- char **comment)
-{
- register long wpos,pos;
- long wnum;
- double weight;
- int numread;
- char featurepair[1000],junk[1000];
-
- (*queryid)=0;
- (*slackid)=0;
- (*costfactor)=1;
-
- pos=0;
- (*comment)=NULL;
- while(line[pos] ) { /* cut off comments */
- if((line[pos] == '#') && (!(*comment))) {
- line[pos]=0;
- (*comment)=&(line[pos+1]);
- }
- if(line[pos] == '\n') { /* strip the CR */
- line[pos]=0;
- }
- pos++;
- }
- if(!(*comment)) (*comment)=&(line[pos]);
- /* printf("Comment: '%s'\n",(*comment)); */
-
- wpos=0;
- /* check, that line starts with target value or zero, but not with
- feature pair */
- if(sscanf(line,"%s",featurepair) == EOF) return(0);
- pos=0;
- while((featurepair[pos] != ':') && featurepair[pos]) pos++;
- if(featurepair[pos] == ':') {
- perror ("Line must start with label or 0!!!\n");
- printf("LINE: %s\n",line);
- exit (1);
- }
- /* read the target value */
- if(sscanf(line,"%lf",label) == EOF) return(0);
- pos=0;
- while(space_or_null((int)line[pos])) pos++;
- while((!space_or_null((int)line[pos])) && line[pos]) pos++;
- while(((numread=sscanf(line+pos,"%s",featurepair)) != EOF) &&
- (numread > 0) &&
- (wpos<max_words_doc)) {
- /* printf("%s\n",featurepair); */
- while(space_or_null((int)line[pos])) pos++;
- while((!space_or_null((int)line[pos])) && line[pos]) pos++;
- if(sscanf(featurepair,"qid:%ld%s",&wnum,junk)==1) {
- /* it is the query id */
- (*queryid)=(long)wnum;
- }
- else if(sscanf(featurepair,"sid:%ld%s",&wnum,junk)==1) {
- /* it is the slack id */
- if(wnum > 0)
- (*slackid)=(long)wnum;
- else {
- perror ("Slack-id must be greater or equal to 1!!!\n");
- printf("LINE: %s\n",line);
- exit (1);
- }
- }
- else if(sscanf(featurepair,"cost:%lf%s",&weight,junk)==1) {
- /* it is the example-dependent cost factor */
- (*costfactor)=(double)weight;
- }
- else if(sscanf(featurepair,"%ld:%lf%s",&wnum,&weight,junk)==2) {
- /* it is a regular feature */
- if(wnum<=0) {
- perror ("Feature numbers must be larger or equal to 1!!!\n");
- printf("LINE: %s\n",line);
- exit (1);
- }
- if((wpos>0) && ((words[wpos-1]).wnum >= wnum)) {
- perror ("Features must be in increasing order!!!\n");
- printf("LINE: %s\n",line);
- exit (1);
- }
- (words[wpos]).wnum=wnum;
- (words[wpos]).weight=(FVAL)weight;
- wpos++;
- }
- else {
- perror ("Cannot parse feature/value pair!!!\n");
- printf("'%s' in LINE: %s\n",featurepair,line);
- exit (1);
- }
- }
- (words[wpos]).wnum=0;
- (*numwords)=wpos+1;
- return(1);
-}
-
-double *read_alphas(char *alphafile,long totdoc)
- /* reads the alpha vector from a file as written by the
- write_alphas function */
-{
- FILE *fl;
- double *alpha;
- long dnum;
-
- if ((fl = fopen (alphafile, "r")) == NULL)
- { perror (alphafile); exit (1); }
-
- alpha = (double *)my_malloc(sizeof(double)*totdoc);
- if(verbosity>=1) {
- printf("Reading alphas..."); fflush(stdout);
- }
- dnum=0;
- while((!feof(fl)) && fscanf(fl,"%lf\n",&alpha[dnum]) && (dnum<totdoc)) {
- dnum++;
- }
- if(dnum != totdoc)
- { perror ("\nNot enough values in alpha file!"); exit (1); }
- fclose(fl);
-
- if(verbosity>=1) {
- printf("done\n"); fflush(stdout);
- }
-
- return(alpha);
-}
-
-void nol_ll(char *file, long int *nol, long int *wol, long int *ll)
- /* Grep through file and count number of lines, maximum number of
- spaces per line, and longest line. */
-{
- FILE *fl;
- int ic;
- char c;
- long current_length,current_wol;
-
- if ((fl = fopen (file, "r")) == NULL)
- { perror (file); exit (1); }
- current_length=0;
- current_wol=0;
- (*ll)=0;
- (*nol)=1;
- (*wol)=0;
- while((ic=getc(fl)) != EOF) {
- c=(char)ic;
- current_length++;
- if(space_or_null((int)c)) {
- current_wol++;
- }
- if(c == '\n') {
- (*nol)++;
- if(current_length>(*ll)) {
- (*ll)=current_length;
- }
- if(current_wol>(*wol)) {
- (*wol)=current_wol;
- }
- current_length=0;
- current_wol=0;
- }
- }
- fclose(fl);
-}
-
-long minl(long int a, long int b)
-{
- if(a<b)
- return(a);
- else
- return(b);
-}
-
-long maxl(long int a, long int b)
-{
- if(a>b)
- return(a);
- else
- return(b);
-}
-
-long get_runtime(void)
-{
- clock_t start;
- start = clock();
- return((long)((double)start*100.0/(double)CLOCKS_PER_SEC));
-}
-
-
-# ifdef _MSC_VER
-
-int isnan(double a)
-{
- return(_isnan(a));
-}
-
-# endif
-
-int space_or_null(int c) {
- if (c==0)
- return 1;
- return isspace((unsigned char)c);
-}
-
-void *my_malloc(size_t size)
-{
- void *ptr;
- if(size<=0) size=1; /* for AIX compatibility */
- ptr=(void *)malloc(size);
- if(!ptr) {
- perror ("Out of memory!\n");
- exit (1);
- }
- return(ptr);
-}
-
-void copyright_notice(void)
-{
- printf("\nCopyright: Thorsten Joachims, thorsten@joachims.org\n\n");
- printf("This software is available for non-commercial use only. It must not\n");
- printf("be modified and distributed without prior permission of the author.\n");
- printf("The author is not responsible for implications from the use of this\n");
- printf("software.\n\n");
-}
diff --git a/trunk/svm_light/build/svm_light-tar/svm_common.h b/trunk/svm_light/build/svm_light-tar/svm_common.h
deleted file mode 100755
index 6487fa1d1017d9fcb3b32d7e66cc514b4af9bddf..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_common.h
+++ /dev/null
@@ -1,301 +0,0 @@
-/************************************************************************/
-/* */
-/* svm_common.h */
-/* */
-/* Definitions and functions used in both svm_learn and svm_classify. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/************************************************************************/
-
-#ifndef SVM_COMMON
-#define SVM_COMMON
-
-# include <stdio.h>
-# include <ctype.h>
-# include <math.h>
-# include <string.h>
-# include <stdlib.h>
-# include <time.h>
-# include <float.h>
-
-# define VERSION "V6.02"
-# define VERSION_DATE "14.08.08"
-
-# define CFLOAT float /* the type of float to use for caching */
- /* kernel evaluations. Using float saves */
- /* us some memory, but you can use double, too */
-# define FNUM long /* the type used for storing feature ids */
-# define FVAL float /* the type used for storing feature values */
-# define MAXFEATNUM 99999999 /* maximum feature number (must be in
- valid range of FNUM type and long int!) */
-
-# define LINEAR 0 /* linear kernel type */
-# define POLY 1 /* polynoial kernel type */
-# define RBF 2 /* rbf kernel type */
-# define SIGMOID 3 /* sigmoid kernel type */
-
-# define CLASSIFICATION 1 /* train classification model */
-# define REGRESSION 2 /* train regression model */
-# define RANKING 3 /* train ranking model */
-# define OPTIMIZATION 4 /* train on general set of constraints */
-
-# define MAXSHRINK 50000 /* maximum number of shrinking rounds */
-
-typedef struct word {
- FNUM wnum; /* word number */
- FVAL weight; /* word weight */
-} WORD;
-
-typedef struct svector {
- WORD *words; /* The features/values in the vector by
- increasing feature-number. Feature
- numbers that are skipped are
- interpreted as having value zero. */
- double twonorm_sq; /* The squared euclidian length of the
- vector. Used to speed up the RBF kernel. */
- char *userdefined; /* You can put additional information
- here. This can be useful, if you are
- implementing your own kernel that
- does not work with feature/values
- representations (for example a
- string kernel). By default,
- svm-light will put here the string
- after the # sign from each line of
- the input file. */
- long kernel_id; /* Feature vectors with different
- kernel_id's are orthogonal (ie. the
- feature number do not match). This
- is used for computing component
- kernels for linear constraints which
- are a sum of several different
- weight vectors. (currently not
- implemented). */
- struct svector *next; /* Let's you set up a list of SVECTOR's
- for linear constraints which are a
- sum of multiple feature
- vectors. List is terminated by
- NULL. */
- double factor; /* Factor by which this feature vector
- is multiplied in the sum. */
-} SVECTOR;
-
-typedef struct doc {
- long docnum; /* Document ID. This has to be the position of
- the document in the training set array. */
- long queryid; /* for learning rankings, constraints are
- generated for documents with the same
- queryID. */
- double costfactor; /* Scales the cost of misclassifying this
- document by this factor. The effect of this
- value is, that the upper bound on the alpha
- for this example is scaled by this factor.
- The factors are set by the feature
- 'cost:<val>' in the training data. */
- long slackid; /* Index of the slack variable
- corresponding to this
- constraint. All constraints with the
- same slackid share the same slack
- variable. This can only be used for
- svm_learn_optimization. */
- SVECTOR *fvec; /* Feature vector of the example. The
- feature vector can actually be a
- list of feature vectors. For
- example, the list will have two
- elements, if this DOC is a
- preference constraint. The one
- vector that is supposed to be ranked
- higher, will have a factor of +1,
- the lower ranked one should have a
- factor of -1. */
-} DOC;
-
-typedef struct learn_parm {
- long type; /* selects between regression and
- classification */
- double svm_c; /* upper bound C on alphas */
- double eps; /* regression epsilon (eps=1.0 for
- classification */
- double svm_costratio; /* factor to multiply C for positive examples */
- double transduction_posratio;/* fraction of unlabeled examples to be */
- /* classified as positives */
- long biased_hyperplane; /* if nonzero, use hyperplane w*x+b=0
- otherwise w*x=0 */
- long sharedslack; /* if nonzero, it will use the shared
- slack variable mode in
- svm_learn_optimization. It requires
- that the slackid is set for every
- training example */
- long svm_maxqpsize; /* size q of working set */
- long svm_newvarsinqp; /* new variables to enter the working set
- in each iteration */
- long kernel_cache_size; /* size of kernel cache in megabytes */
- double epsilon_crit; /* tolerable error for distances used
- in stopping criterion */
- double epsilon_shrink; /* how much a multiplier should be above
- zero for shrinking */
- long svm_iter_to_shrink; /* iterations h after which an example can
- be removed by shrinking */
- long maxiter; /* number of iterations after which the
- optimizer terminates, if there was
- no progress in maxdiff */
- long remove_inconsistent; /* exclude examples with alpha at C and
- retrain */
- long skip_final_opt_check; /* do not check KT-Conditions at the end of
- optimization for examples removed by
- shrinking. WARNING: This might lead to
- sub-optimal solutions! */
- long compute_loo; /* if nonzero, computes leave-one-out
- estimates */
- double rho; /* parameter in xi/alpha-estimates and for
- pruning leave-one-out range [1..2] */
- long xa_depth; /* parameter in xi/alpha-estimates upper
- bounding the number of SV the current
- alpha_t is distributed over */
- char predfile[200]; /* file for predicitions on unlabeled examples
- in transduction */
- char alphafile[200]; /* file to store optimal alphas in. use
- empty string if alphas should not be
- output */
-
- /* you probably do not want to touch the following */
- double epsilon_const; /* tolerable error on eq-constraint */
- double epsilon_a; /* tolerable error on alphas at bounds */
- double opt_precision; /* precision of solver, set to e.g. 1e-21
- if you get convergence problems */
-
- /* the following are only for internal use */
- long svm_c_steps; /* do so many steps for finding optimal C */
- double svm_c_factor; /* increase C by this factor every step */
- double svm_costratio_unlab;
- double svm_unlabbound;
- double *svm_cost; /* individual upper bounds for each var */
- long totwords; /* number of features */
-} LEARN_PARM;
-
-typedef struct kernel_parm {
- long kernel_type; /* 0=linear, 1=poly, 2=rbf, 3=sigmoid, 4=custom */
- long poly_degree;
- double rbf_gamma;
- double coef_lin;
- double coef_const;
- char custom[50]; /* for user supplied kernel */
-} KERNEL_PARM;
-
-typedef struct model {
- long sv_num;
- long at_upper_bound;
- double b;
- DOC **supvec;
- double *alpha;
- long *index; /* index from docnum to position in model */
- long totwords; /* number of features */
- long totdoc; /* number of training documents */
- KERNEL_PARM kernel_parm; /* kernel */
-
- /* the following values are not written to file */
- double loo_error,loo_recall,loo_precision; /* leave-one-out estimates */
- double xa_error,xa_recall,xa_precision; /* xi/alpha estimates */
- double *lin_weights; /* weights for linear case using
- folding */
- double maxdiff; /* precision, up to which this
- model is accurate */
-} MODEL;
-
-typedef struct quadratic_program {
- long opt_n; /* number of variables */
- long opt_m; /* number of linear equality constraints */
- double *opt_ce,*opt_ce0; /* linear equality constraints */
- double *opt_g; /* hessian of objective */
- double *opt_g0; /* linear part of objective */
- double *opt_xinit; /* initial value for variables */
- double *opt_low,*opt_up; /* box constraints */
-} QP;
-
-typedef struct kernel_cache {
- long *index; /* cache some kernel evalutations */
- CFLOAT *buffer; /* to improve speed */
- long *invindex;
- long *active2totdoc;
- long *totdoc2active;
- long *lru;
- long *occu;
- long elems;
- long max_elems;
- long time;
- long activenum;
- long buffsize;
-} KERNEL_CACHE;
-
-
-typedef struct timing_profile {
- long time_kernel;
- long time_opti;
- long time_shrink;
- long time_update;
- long time_model;
- long time_check;
- long time_select;
-} TIMING;
-
-typedef struct shrink_state {
- long *active;
- long *inactive_since;
- long deactnum;
- double **a_history; /* for shrinking with non-linear kernel */
- long maxhistory;
- double *last_a; /* for shrinking with linear kernel */
- double *last_lin; /* for shrinking with linear kernel */
-} SHRINK_STATE;
-
-double classify_example(MODEL *, DOC *);
-double classify_example_linear(MODEL *, DOC *);
-double kernel(KERNEL_PARM *, DOC *, DOC *);
-double single_kernel(KERNEL_PARM *, SVECTOR *, SVECTOR *);
-double custom_kernel(KERNEL_PARM *, SVECTOR *, SVECTOR *);
-SVECTOR *create_svector(WORD *, char *, double);
-SVECTOR *copy_svector(SVECTOR *);
-void free_svector(SVECTOR *);
-double sprod_ss(SVECTOR *, SVECTOR *);
-SVECTOR* sub_ss(SVECTOR *, SVECTOR *);
-SVECTOR* add_ss(SVECTOR *, SVECTOR *);
-SVECTOR* add_list_ss(SVECTOR *);
-void append_svector_list(SVECTOR *a, SVECTOR *b);
-SVECTOR* smult_s(SVECTOR *, double);
-int featvec_eq(SVECTOR *, SVECTOR *);
-double model_length_s(MODEL *, KERNEL_PARM *);
-void clear_vector_n(double *, long);
-void add_vector_ns(double *, SVECTOR *, double);
-double sprod_ns(double *, SVECTOR *);
-void add_weight_vector_to_linear_model(MODEL *);
-DOC *create_example(long, long, long, double, SVECTOR *);
-void free_example(DOC *, long);
-MODEL *read_model(char *);
-MODEL *copy_model(MODEL *);
-void free_model(MODEL *, int);
-void read_documents(char *, DOC ***, double **, long *, long *);
-int parse_document(char *, WORD *, double *, long *, long *, double *, long *, long, char **);
-double *read_alphas(char *,long);
-void nol_ll(char *, long *, long *, long *);
-long minl(long, long);
-long maxl(long, long);
-long get_runtime(void);
-int space_or_null(int);
-void *my_malloc(size_t);
-void copyright_notice(void);
-# ifdef _MSC_VER
- int isnan(double);
-# endif
-
-extern long verbosity; /* verbosity level (0-4) */
-extern long kernel_cache_statistic;
-
-#endif
diff --git a/trunk/svm_light/build/svm_light-tar/svm_hideo.c b/trunk/svm_light/build/svm_light-tar/svm_hideo.c
deleted file mode 100755
index ffad2d3c1c0ae41e63ff57f6cb40aad005952e05..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_hideo.c
+++ /dev/null
@@ -1,1062 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_hideo.c */
-/* */
-/* The Hildreth and D'Espo solver specialized for SVMs. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/***********************************************************************/
-
-# include <math.h>
-# include "svm_common.h"
-
-/*
- solve the quadratic programming problem
-
- minimize g0 * x + 1/2 x' * G * x
- subject to ce*x = ce0
- l <= x <= u
-
- The linear constraint vector ce can only have -1/+1 as entries
-*/
-
-/* Common Block Declarations */
-
-long verbosity;
-
-# define PRIMAL_OPTIMAL 1
-# define DUAL_OPTIMAL 2
-# define MAXITER_EXCEEDED 3
-# define NAN_SOLUTION 4
-# define ONLY_ONE_VARIABLE 5
-
-# define LARGEROUND 0
-# define SMALLROUND 1
-
-/* /////////////////////////////////////////////////////////////// */
-
-# define DEF_PRECISION 1E-5
-# define DEF_MAX_ITERATIONS 200
-# define DEF_LINDEP_SENSITIVITY 1E-8
-# define EPSILON_HIDEO 1E-20
-# define EPSILON_EQ 1E-5
-
-double *optimize_qp(QP *, double *, long, double *, LEARN_PARM *);
-double *primal=0,*dual=0;
-long precision_violations=0;
-double opt_precision=DEF_PRECISION;
-long maxiter=DEF_MAX_ITERATIONS;
-double lindep_sensitivity=DEF_LINDEP_SENSITIVITY;
-double *buffer;
-long *nonoptimal;
-
-long smallroundcount=0;
-long roundnumber=0;
-
-/* /////////////////////////////////////////////////////////////// */
-
-void *my_malloc();
-
-int optimize_hildreth_despo(long,long,double,double,double,long,long,long,double,double *,
- double *,double *,double *,double *,double *,
- double *,double *,double *,long *,double *,double *);
-int solve_dual(long,long,double,double,long,double *,double *,double *,
- double *,double *,double *,double *,double *,double *,
- double *,double *,double *,double *,long);
-
-void linvert_matrix(double *, long, double *, double, long *);
-void lprint_matrix(double *, long);
-void ladd_matrix(double *, long, double);
-void lcopy_matrix(double *, long, double *);
-void lswitch_rows_matrix(double *, long, long, long);
-void lswitchrk_matrix(double *, long, long, long);
-
-double calculate_qp_objective(long, double *, double *, double *);
-
-
-
-double *optimize_qp(qp,epsilon_crit,nx,threshold,learn_parm)
-QP *qp;
-double *epsilon_crit;
-long nx; /* Maximum number of variables in QP */
-double *threshold;
-LEARN_PARM *learn_parm;
-/* start the optimizer and return the optimal values */
-/* The HIDEO optimizer does not necessarily fully solve the problem. */
-/* Since it requires a strictly positive definite hessian, the solution */
-/* is restricted to a linear independent subset in case the matrix is */
-/* only semi-definite. */
-{
- long i,j;
- int result;
- double eq,progress;
-
- roundnumber++;
-
- if(!primal) { /* allocate memory at first call */
- primal=(double *)my_malloc(sizeof(double)*nx);
- dual=(double *)my_malloc(sizeof(double)*((nx+1)*2));
- nonoptimal=(long *)my_malloc(sizeof(long)*(nx));
- buffer=(double *)my_malloc(sizeof(double)*((nx+1)*2*(nx+1)*2+
- nx*nx+2*(nx+1)*2+2*nx+1+2*nx+
- nx+nx+nx*nx));
- (*threshold)=0;
- for(i=0;i<nx;i++) {
- primal[i]=0;
- }
- }
-
- if(verbosity>=4) { /* really verbose */
- printf("\n\n");
- eq=qp->opt_ce0[0];
- for(i=0;i<qp->opt_n;i++) {
- eq+=qp->opt_xinit[i]*qp->opt_ce[i];
- printf("%f: ",qp->opt_g0[i]);
- for(j=0;j<qp->opt_n;j++) {
- printf("%f ",qp->opt_g[i*qp->opt_n+j]);
- }
- printf(": a=%.10f < %f",qp->opt_xinit[i],qp->opt_up[i]);
- printf(": y=%f\n",qp->opt_ce[i]);
- }
- if(qp->opt_m) {
- printf("EQ: %f*x0",qp->opt_ce[0]);
- for(i=1;i<qp->opt_n;i++) {
- printf(" + %f*x%ld",qp->opt_ce[i],i);
- }
- printf(" = %f\n\n",-qp->opt_ce0[0]);
- }
- }
-
- result=optimize_hildreth_despo(qp->opt_n,qp->opt_m,
- opt_precision,(*epsilon_crit),
- learn_parm->epsilon_a,maxiter,
- /* (long)PRIMAL_OPTIMAL, */
- (long)0, (long)0,
- lindep_sensitivity,
- qp->opt_g,qp->opt_g0,qp->opt_ce,qp->opt_ce0,
- qp->opt_low,qp->opt_up,primal,qp->opt_xinit,
- dual,nonoptimal,buffer,&progress);
- if(verbosity>=3) {
- printf("return(%d)...",result);
- }
-
- if(learn_parm->totwords < learn_parm->svm_maxqpsize) {
- /* larger working sets will be linear dependent anyway */
- learn_parm->svm_maxqpsize=maxl(learn_parm->totwords,(long)2);
- }
-
- if(result == NAN_SOLUTION) {
- lindep_sensitivity*=2; /* throw out linear dependent examples more */
- /* generously */
- if(learn_parm->svm_maxqpsize>2) {
- learn_parm->svm_maxqpsize--; /* decrease size of qp-subproblems */
- }
- precision_violations++;
- }
-
- /* take one round of only two variable to get unstuck */
- if((result != PRIMAL_OPTIMAL) || (!(roundnumber % 31)) || (progress <= 0)) {
-
- smallroundcount++;
-
- result=optimize_hildreth_despo(qp->opt_n,qp->opt_m,
- opt_precision,(*epsilon_crit),
- learn_parm->epsilon_a,(long)maxiter,
- (long)PRIMAL_OPTIMAL,(long)SMALLROUND,
- lindep_sensitivity,
- qp->opt_g,qp->opt_g0,qp->opt_ce,qp->opt_ce0,
- qp->opt_low,qp->opt_up,primal,qp->opt_xinit,
- dual,nonoptimal,buffer,&progress);
- if(verbosity>=3) {
- printf("return_srd(%d)...",result);
- }
-
- if(result != PRIMAL_OPTIMAL) {
- if(result != ONLY_ONE_VARIABLE)
- precision_violations++;
- if(result == MAXITER_EXCEEDED)
- maxiter+=100;
- if(result == NAN_SOLUTION) {
- lindep_sensitivity*=2; /* throw out linear dependent examples more */
- /* generously */
- /* results not valid, so return inital values */
- for(i=0;i<qp->opt_n;i++) {
- primal[i]=qp->opt_xinit[i];
- }
- }
- }
- }
-
-
- if(precision_violations > 50) {
- precision_violations=0;
- (*epsilon_crit)*=10.0;
- if(verbosity>=1) {
- printf("\nWARNING: Relaxing epsilon on KT-Conditions (%f).\n",
- (*epsilon_crit));
- }
- }
-
- if((qp->opt_m>0) && (result != NAN_SOLUTION) && (!isnan(dual[1]-dual[0])))
- (*threshold)=dual[1]-dual[0];
- else
- (*threshold)=0;
-
- if(verbosity>=4) { /* really verbose */
- printf("\n\n");
- eq=qp->opt_ce0[0];
- for(i=0;i<qp->opt_n;i++) {
- eq+=primal[i]*qp->opt_ce[i];
- printf("%f: ",qp->opt_g0[i]);
- for(j=0;j<qp->opt_n;j++) {
- printf("%f ",qp->opt_g[i*qp->opt_n+j]);
- }
- printf(": a=%.30f",primal[i]);
- printf(": nonopti=%ld",nonoptimal[i]);
- printf(": y=%f\n",qp->opt_ce[i]);
- }
- printf("eq-constraint=%.30f\n",eq);
- printf("b=%f\n",(*threshold));
- printf(" smallroundcount=%ld ",smallroundcount);
- }
-
- return(primal);
-}
-
-
-
-int optimize_hildreth_despo(n,m,precision,epsilon_crit,epsilon_a,maxiter,goal,
- smallround,lindep_sensitivity,g,g0,ce,ce0,low,up,
- primal,init,dual,lin_dependent,buffer,progress)
- long n; /* number of variables */
- long m; /* number of linear equality constraints [0,1] */
- double precision; /* solve at least to this dual precision */
- double epsilon_crit; /* stop, if KT-Conditions approx fulfilled */
- double epsilon_a; /* precision of alphas at bounds */
- long maxiter; /* stop after this many iterations */
- long goal; /* keep going until goal fulfilled */
- long smallround; /* use only two variables of steepest descent */
- double lindep_sensitivity; /* epsilon for detecting linear dependent ex */
- double *g; /* hessian of objective */
- double *g0; /* linear part of objective */
- double *ce,*ce0; /* linear equality constraints */
- double *low,*up; /* box constraints */
- double *primal; /* primal variables */
- double *init; /* initial values of primal */
- double *dual; /* dual variables */
- long *lin_dependent;
- double *buffer;
- double *progress; /* delta in the objective function between
- before and after */
-{
- long i,j,k,from,to,n_indep,changed;
- double sum,bmin=0,bmax=0;
- double *d,*d0,*ig,*dual_old,*temp,*start;
- double *g0_new,*g_new,*ce_new,*ce0_new,*low_new,*up_new;
- double add,t;
- int result;
- double obj_before,obj_after;
- long b1,b2;
- double g0_b1,g0_b2,ce0_b;
-
- g0_new=&(buffer[0]); /* claim regions of buffer */
- d=&(buffer[n]);
- d0=&(buffer[n+(n+m)*2*(n+m)*2]);
- ce_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2]);
- ce0_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n]);
- ig=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m]);
- dual_old=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n]);
- low_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2]);
- up_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n]);
- start=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n+n]);
- g_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n+n+n]);
- temp=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n+n+n+n*n]);
-
- b1=-1;
- b2=-1;
- for(i=0;i<n;i++) { /* get variables with steepest feasible descent */
- sum=g0[i];
- for(j=0;j<n;j++)
- sum+=init[j]*g[i*n+j];
- sum=sum*ce[i];
- if(((b1==-1) || (sum<bmin))
- && (!((init[i]<=(low[i]+epsilon_a)) && (ce[i]<0.0)))
- && (!((init[i]>=( up[i]-epsilon_a)) && (ce[i]>0.0)))
- ) {
- bmin=sum;
- b1=i;
- }
- if(((b2==-1) || (sum>=bmax))
- && (!((init[i]<=(low[i]+epsilon_a)) && (ce[i]>0.0)))
- && (!((init[i]>=( up[i]-epsilon_a)) && (ce[i]<0.0)))
- ) {
- bmax=sum;
- b2=i;
- }
- }
- /* in case of unbiased hyperplane, the previous projection on */
- /* equality constraint can lead to b1 or b2 being -1. */
- if((b1 == -1) || (b2 == -1)) {
- b1=maxl(b1,b2);
- b2=maxl(b1,b2);
- }
-
- for(i=0;i<n;i++) {
- start[i]=init[i];
- }
-
- /* in case both example vectors are linearly dependent */
- /* WARNING: Assumes that ce[] in {-1,1} */
- add=0;
- changed=0;
- if((b1 != b2) && (m==1)) {
- for(i=0;i<n;i++) { /* fix other vectors */
- if(i==b1)
- g0_b1=g0[i];
- if(i==b2)
- g0_b2=g0[i];
- }
- ce0_b=ce0[0];
- for(i=0;i<n;i++) {
- if((i!=b1) && (i!=b2)) {
- for(j=0;j<n;j++) {
- if(j==b1)
- g0_b1+=start[i]*g[i*n+j];
- if(j==b2)
- g0_b2+=start[i]*g[i*n+j];
- }
- ce0_b-=(start[i]*ce[i]);
- }
- }
- if((g[b1*n+b2] == g[b1*n+b1]) && (g[b1*n+b2] == g[b2*n+b2])) {
- /* printf("euqal\n"); */
- if(ce[b1] == ce[b2]) {
- if(g0_b1 <= g0_b2) { /* set b1 to upper bound */
- /* printf("case +=<\n"); */
- changed=1;
- t=up[b1]-init[b1];
- if((init[b2]-low[b2]) < t) {
- t=init[b2]-low[b2];
- }
- start[b1]=init[b1]+t;
- start[b2]=init[b2]-t;
- }
- else if(g0_b1 > g0_b2) { /* set b2 to upper bound */
- /* printf("case +=>\n"); */
- changed=1;
- t=up[b2]-init[b2];
- if((init[b1]-low[b1]) < t) {
- t=init[b1]-low[b1];
- }
- start[b1]=init[b1]-t;
- start[b2]=init[b2]+t;
- }
- }
- else if(((g[b1*n+b1]>0) || (g[b2*n+b2]>0))) { /* (ce[b1] != ce[b2]) */
- /* printf("case +!\n"); */
- t=((ce[b2]/ce[b1])*g0[b1]-g0[b2]+ce0[0]*(g[b1*n+b1]*ce[b2]/ce[b1]-g[b1*n+b2]/ce[b1]))/((ce[b2]*ce[b2]/(ce[b1]*ce[b1]))*g[b1*n+b1]+g[b2*n+b2]-2*(g[b1*n+b2]*ce[b2]/ce[b1]))-init[b2];
- changed=1;
- if((up[b2]-init[b2]) < t) {
- t=up[b2]-init[b2];
- }
- if((init[b2]-low[b2]) < -t) {
- t=-(init[b2]-low[b2]);
- }
- if((up[b1]-init[b1]) < t) {
- t=(up[b1]-init[b1]);
- }
- if((init[b1]-low[b1]) < -t) {
- t=-(init[b1]-low[b1]);
- }
- start[b1]=init[b1]+t;
- start[b2]=init[b2]+t;
- }
- }
- if((-g[b1*n+b2] == g[b1*n+b1]) && (-g[b1*n+b2] == g[b2*n+b2])) {
- /* printf("diffeuqal\n"); */
- if(ce[b1] != ce[b2]) {
- if((g0_b1+g0_b2) < 0) { /* set b1 and b2 to upper bound */
- /* printf("case -!<\n"); */
- changed=1;
- t=up[b1]-init[b1];
- if((up[b2]-init[b2]) < t) {
- t=up[b2]-init[b2];
- }
- start[b1]=init[b1]+t;
- start[b2]=init[b2]+t;
- }
- else if((g0_b1+g0_b2) >= 0) { /* set b1 and b2 to lower bound */
- /* printf("case -!>\n"); */
- changed=1;
- t=init[b1]-low[b1];
- if((init[b2]-low[b2]) < t) {
- t=init[b2]-low[b2];
- }
- start[b1]=init[b1]-t;
- start[b2]=init[b2]-t;
- }
- }
- else if(((g[b1*n+b1]>0) || (g[b2*n+b2]>0))) { /* (ce[b1]==ce[b2]) */
- /* printf("case -=\n"); */
- t=((ce[b2]/ce[b1])*g0[b1]-g0[b2]+ce0[0]*(g[b1*n+b1]*ce[b2]/ce[b1]-g[b1*n+b2]/ce[b1]))/((ce[b2]*ce[b2]/(ce[b1]*ce[b1]))*g[b1*n+b1]+g[b2*n+b2]-2*(g[b1*n+b2]*ce[b2]/ce[b1]))-init[b2];
- changed=1;
- if((up[b2]-init[b2]) < t) {
- t=up[b2]-init[b2];
- }
- if((init[b2]-low[b2]) < -t) {
- t=-(init[b2]-low[b2]);
- }
- if((up[b1]-init[b1]) < -t) {
- t=-(up[b1]-init[b1]);
- }
- if((init[b1]-low[b1]) < t) {
- t=init[b1]-low[b1];
- }
- start[b1]=init[b1]-t;
- start[b2]=init[b2]+t;
- }
- }
- }
- /* if we have a biased hyperplane, then adding a constant to the */
- /* hessian does not change the solution. So that is done for examples */
- /* with zero diagonal entry, since HIDEO cannot handle them. */
- if((m>0)
- && ((fabs(g[b1*n+b1]) < lindep_sensitivity)
- || (fabs(g[b2*n+b2]) < lindep_sensitivity))) {
- /* printf("Case 0\n"); */
- add+=0.093274;
- }
- /* in case both examples are linear dependent */
- else if((m>0)
- && (g[b1*n+b2] != 0 && g[b2*n+b2] != 0)
- && (fabs(g[b1*n+b1]/g[b1*n+b2] - g[b1*n+b2]/g[b2*n+b2])
- < lindep_sensitivity)) {
- /* printf("Case lindep\n"); */
- add+=0.078274;
- }
-
- /* special case for zero diagonal entry on unbiased hyperplane */
- if((m==0) && (b1>=0)) {
- if(fabs(g[b1*n+b1]) < lindep_sensitivity) {
- /* printf("Case 0b1\n"); */
- for(i=0;i<n;i++) { /* fix other vectors */
- if(i==b1)
- g0_b1=g0[i];
- }
- for(i=0;i<n;i++) {
- if(i!=b1) {
- for(j=0;j<n;j++) {
- if(j==b1)
- g0_b1+=start[i]*g[i*n+j];
- }
- }
- }
- if(g0_b1<0)
- start[b1]=up[b1];
- if(g0_b1>=0)
- start[b1]=low[b1];
- }
- }
- if((m==0) && (b2>=0)) {
- if(fabs(g[b2*n+b2]) < lindep_sensitivity) {
- /* printf("Case 0b2\n"); */
- for(i=0;i<n;i++) { /* fix other vectors */
- if(i==b2)
- g0_b2=g0[i];
- }
- for(i=0;i<n;i++) {
- if(i!=b2) {
- for(j=0;j<n;j++) {
- if(j==b2)
- g0_b2+=start[i]*g[i*n+j];
- }
- }
- }
- if(g0_b2<0)
- start[b2]=up[b2];
- if(g0_b2>=0)
- start[b2]=low[b2];
- }
- }
-
- /* printf("b1=%ld,b2=%ld\n",b1,b2); */
-
- lcopy_matrix(g,n,d);
- if((m==1) && (add>0.0)) {
- for(j=0;j<n;j++) {
- for(k=0;k<n;k++) {
- d[j*n+k]+=add*ce[j]*ce[k];
- }
- }
- }
- else {
- add=0.0;
- }
-
- if(n>2) { /* switch, so that variables are better mixed */
- lswitchrk_matrix(d,n,b1,(long)0);
- if(b2 == 0)
- lswitchrk_matrix(d,n,b1,(long)1);
- else
- lswitchrk_matrix(d,n,b2,(long)1);
- }
- if(smallround == SMALLROUND) {
- for(i=2;i<n;i++) {
- lin_dependent[i]=1;
- }
- if(m>0) { /* for biased hyperplane, pick two variables */
- lin_dependent[0]=0;
- lin_dependent[1]=0;
- }
- else { /* for unbiased hyperplane, pick only one variable */
- lin_dependent[0]=smallroundcount % 2;
- lin_dependent[1]=(smallroundcount+1) % 2;
- }
- }
- else {
- for(i=0;i<n;i++) {
- lin_dependent[i]=0;
- }
- }
- linvert_matrix(d,n,ig,lindep_sensitivity,lin_dependent);
- if(n>2) { /* now switch back */
- if(b2 == 0) {
- lswitchrk_matrix(ig,n,b1,(long)1);
- i=lin_dependent[1];
- lin_dependent[1]=lin_dependent[b1];
- lin_dependent[b1]=i;
- }
- else {
- lswitchrk_matrix(ig,n,b2,(long)1);
- i=lin_dependent[1];
- lin_dependent[1]=lin_dependent[b2];
- lin_dependent[b2]=i;
- }
- lswitchrk_matrix(ig,n,b1,(long)0);
- i=lin_dependent[0];
- lin_dependent[0]=lin_dependent[b1];
- lin_dependent[b1]=i;
- }
- /* lprint_matrix(d,n); */
- /* lprint_matrix(ig,n); */
-
- lcopy_matrix(g,n,g_new); /* restore g_new matrix */
- if(add>0)
- for(j=0;j<n;j++) {
- for(k=0;k<n;k++) {
- g_new[j*n+k]+=add*ce[j]*ce[k];
- }
- }
-
- for(i=0;i<n;i++) { /* fix linear dependent vectors */
- g0_new[i]=g0[i]+add*ce0[0]*ce[i];
- }
- if(m>0) ce0_new[0]=-ce0[0];
- for(i=0;i<n;i++) { /* fix linear dependent vectors */
- if(lin_dependent[i]) {
- for(j=0;j<n;j++) {
- if(!lin_dependent[j]) {
- g0_new[j]+=start[i]*g_new[i*n+j];
- }
- }
- if(m>0) ce0_new[0]-=(start[i]*ce[i]);
- }
- }
- from=0; /* remove linear dependent vectors */
- to=0;
- n_indep=0;
- for(i=0;i<n;i++) {
- if(!lin_dependent[i]) {
- g0_new[n_indep]=g0_new[i];
- ce_new[n_indep]=ce[i];
- low_new[n_indep]=low[i];
- up_new[n_indep]=up[i];
- primal[n_indep]=start[i];
- n_indep++;
- }
- for(j=0;j<n;j++) {
- if((!lin_dependent[i]) && (!lin_dependent[j])) {
- ig[to]=ig[from];
- g_new[to]=g_new[from];
- to++;
- }
- from++;
- }
- }
-
- if(verbosity>=3) {
- printf("real_qp_size(%ld)...",n_indep);
- }
-
- /* cannot optimize with only one variable */
- if((n_indep<=1) && (m>0) && (!changed)) {
- for(i=n-1;i>=0;i--) {
- primal[i]=init[i];
- }
- return((int)ONLY_ONE_VARIABLE);
- }
-
- if((!changed) || (n_indep>1)) {
- result=solve_dual(n_indep,m,precision,epsilon_crit,maxiter,g_new,g0_new,
- ce_new,ce0_new,low_new,up_new,primal,d,d0,ig,
- dual,dual_old,temp,goal);
- }
- else {
- result=PRIMAL_OPTIMAL;
- }
-
- j=n_indep;
- for(i=n-1;i>=0;i--) {
- if(!lin_dependent[i]) {
- j--;
- primal[i]=primal[j];
- }
- else {
- primal[i]=start[i]; /* leave as is */
- }
- temp[i]=primal[i];
- }
-
- obj_before=calculate_qp_objective(n,g,g0,init);
- obj_after=calculate_qp_objective(n,g,g0,primal);
- (*progress)=obj_before-obj_after;
- if(verbosity>=3) {
- printf("before(%.30f)...after(%.30f)...result_sd(%d)...",
- obj_before,obj_after,result);
- }
-
- return((int)result);
-}
-
-
-int solve_dual(n,m,precision,epsilon_crit,maxiter,g,g0,ce,ce0,low,up,primal,
- d,d0,ig,dual,dual_old,temp,goal)
- /* Solves the dual using the method of Hildreth and D'Espo. */
- /* Can only handle problems with zero or exactly one */
- /* equality constraints. */
-
- long n; /* number of variables */
- long m; /* number of linear equality constraints */
- double precision; /* solve at least to this dual precision */
- double epsilon_crit; /* stop, if KT-Conditions approx fulfilled */
- long maxiter; /* stop after that many iterations */
- double *g;
- double *g0; /* linear part of objective */
- double *ce,*ce0; /* linear equality constraints */
- double *low,*up; /* box constraints */
- double *primal; /* variables (with initial values) */
- double *d,*d0,*ig,*dual,*dual_old,*temp; /* buffer */
- long goal;
-{
- long i,j,k,iter;
- double sum,w,maxviol,viol,temp1,temp2,isnantest;
- double model_b,dist;
- long retrain,maxfaktor,primal_optimal=0,at_bound,scalemaxiter;
- double epsilon_a=1E-15,epsilon_hideo;
- double eq;
-
- if((m<0) || (m>1))
- perror("SOLVE DUAL: inappropriate number of eq-constrains!");
-
- /*
- printf("\n");
- for(i=0;i<n;i++) {
- printf("%f: ",g0[i]);
- for(j=0;j<n;j++) {
- printf("%f ",g[i*n+j]);
- }
- printf(": a=%.30f",primal[i]);
- printf(": y=%f\n",ce[i]);
- }
- */
-
- for(i=0;i<2*(n+m);i++) {
- dual[i]=0;
- dual_old[i]=0;
- }
- for(i=0;i<n;i++) {
- for(j=0;j<n;j++) { /* dual hessian for box constraints */
- d[i*2*(n+m)+j]=ig[i*n+j];
- d[(i+n)*2*(n+m)+j]=-ig[i*n+j];
- d[i*2*(n+m)+j+n]=-ig[i*n+j];
- d[(i+n)*2*(n+m)+j+n]=ig[i*n+j];
- }
- if(m>0) {
- sum=0; /* dual hessian for eq constraints */
- for(j=0;j<n;j++) {
- sum+=(ce[j]*ig[i*n+j]);
- }
- d[i*2*(n+m)+2*n]=sum;
- d[i*2*(n+m)+2*n+1]=-sum;
- d[(n+i)*2*(n+m)+2*n]=-sum;
- d[(n+i)*2*(n+m)+2*n+1]=sum;
- d[(n+n)*2*(n+m)+i]=sum;
- d[(n+n+1)*2*(n+m)+i]=-sum;
- d[(n+n)*2*(n+m)+(n+i)]=-sum;
- d[(n+n+1)*2*(n+m)+(n+i)]=sum;
-
- sum=0;
- for(j=0;j<n;j++) {
- for(k=0;k<n;k++) {
- sum+=(ce[k]*ce[j]*ig[j*n+k]);
- }
- }
- d[(n+n)*2*(n+m)+2*n]=sum;
- d[(n+n)*2*(n+m)+2*n+1]=-sum;
- d[(n+n+1)*2*(n+m)+2*n]=-sum;
- d[(n+n+1)*2*(n+m)+2*n+1]=sum;
- }
- }
-
- for(i=0;i<n;i++) { /* dual linear component for the box constraints */
- w=0;
- for(j=0;j<n;j++) {
- w+=(ig[i*n+j]*g0[j]);
- }
- d0[i]=up[i]+w;
- d0[i+n]=-low[i]-w;
- }
-
- if(m>0) {
- sum=0; /* dual linear component for eq constraints */
- for(j=0;j<n;j++) {
- for(k=0;k<n;k++) {
- sum+=(ce[k]*ig[k*n+j]*g0[j]);
- }
- }
- d0[2*n]=ce0[0]+sum;
- d0[2*n+1]=-ce0[0]-sum;
- }
-
- maxviol=999999;
- iter=0;
- retrain=1;
- maxfaktor=1;
- scalemaxiter=maxiter/5;
- while((retrain) && (maxviol > 0) && (iter < (scalemaxiter*maxfaktor))) {
- iter++;
-
- while((maxviol > precision) && (iter < (scalemaxiter*maxfaktor))) {
- iter++;
- maxviol=0;
- for(i=0;i<2*(n+m);i++) {
- sum=d0[i];
- for(j=0;j<2*(n+m);j++) {
- sum+=d[i*2*(n+m)+j]*dual_old[j];
- }
- sum-=d[i*2*(n+m)+i]*dual_old[i];
- dual[i]=-sum/d[i*2*(n+m)+i];
- if(dual[i]<0) dual[i]=0;
-
- viol=fabs(dual[i]-dual_old[i]);
- if(viol>maxviol)
- maxviol=viol;
- dual_old[i]=dual[i];
- }
- /*
- printf("%d) maxviol=%20f precision=%f\n",iter,maxviol,precision);
- */
- }
-
- if(m>0) {
- for(i=0;i<n;i++) {
- temp[i]=dual[i]-dual[i+n]+ce[i]*(dual[n+n]-dual[n+n+1])+g0[i];
- }
- }
- else {
- for(i=0;i<n;i++) {
- temp[i]=dual[i]-dual[i+n]+g0[i];
- }
- }
- for(i=0;i<n;i++) {
- primal[i]=0; /* calc value of primal variables */
- for(j=0;j<n;j++) {
- primal[i]+=ig[i*n+j]*temp[j];
- }
- primal[i]*=-1.0;
- if(primal[i]<=(low[i])) { /* clip conservatively */
- primal[i]=low[i];
- }
- else if(primal[i]>=(up[i])) {
- primal[i]=up[i];
- }
- }
-
- if(m>0)
- model_b=dual[n+n+1]-dual[n+n];
- else
- model_b=0;
-
- epsilon_hideo=EPSILON_HIDEO;
- for(i=0;i<n;i++) { /* check precision of alphas */
- dist=-model_b*ce[i];
- dist+=(g0[i]+1.0);
- for(j=0;j<i;j++) {
- dist+=(primal[j]*g[j*n+i]);
- }
- for(j=i;j<n;j++) {
- dist+=(primal[j]*g[i*n+j]);
- }
- if((primal[i]<(up[i]-epsilon_hideo)) && (dist < (1.0-epsilon_crit))) {
- epsilon_hideo=(up[i]-primal[i])*2.0;
- }
- else if((primal[i]>(low[i]+epsilon_hideo)) &&(dist>(1.0+epsilon_crit))) {
- epsilon_hideo=(primal[i]-low[i])*2.0;
- }
- }
- /* printf("\nEPSILON_HIDEO=%.30f\n",epsilon_hideo); */
-
- for(i=0;i<n;i++) { /* clip alphas to bounds */
- if(primal[i]<=(low[i]+epsilon_hideo)) {
- primal[i]=low[i];
- }
- else if(primal[i]>=(up[i]-epsilon_hideo)) {
- primal[i]=up[i];
- }
- }
-
- retrain=0;
- primal_optimal=1;
- at_bound=0;
- for(i=0;(i<n);i++) { /* check primal KT-Conditions */
- dist=-model_b*ce[i];
- dist+=(g0[i]+1.0);
- for(j=0;j<i;j++) {
- dist+=(primal[j]*g[j*n+i]);
- }
- for(j=i;j<n;j++) {
- dist+=(primal[j]*g[i*n+j]);
- }
- if((primal[i]<(up[i]-epsilon_a)) && (dist < (1.0-epsilon_crit))) {
- retrain=1;
- primal_optimal=0;
- }
- else if((primal[i]>(low[i]+epsilon_a)) && (dist > (1.0+epsilon_crit))) {
- retrain=1;
- primal_optimal=0;
- }
- if((primal[i]<=(low[i]+epsilon_a)) || (primal[i]>=(up[i]-epsilon_a))) {
- at_bound++;
- }
- /* printf("HIDEOtemp: a[%ld]=%.30f, dist=%.6f, b=%f, at_bound=%ld\n",i,primal[i],dist,model_b,at_bound); */
- }
- if(m>0) {
- eq=-ce0[0]; /* check precision of eq-constraint */
- for(i=0;i<n;i++) {
- eq+=(ce[i]*primal[i]);
- }
- if((EPSILON_EQ < fabs(eq))
- /*
- && !((goal==PRIMAL_OPTIMAL)
- && (at_bound==n)) */
- ) {
- retrain=1;
- primal_optimal=0;
- }
- /* printf("\n eq=%.30f ce0=%f at-bound=%ld\n",eq,ce0[0],at_bound); */
- }
-
- if(retrain) {
- precision/=10;
- if(((goal == PRIMAL_OPTIMAL) && (maxfaktor < 50000))
- || (maxfaktor < 5)) {
- maxfaktor++;
- }
- }
- }
-
- if(!primal_optimal) {
- for(i=0;i<n;i++) {
- primal[i]=0; /* calc value of primal variables */
- for(j=0;j<n;j++) {
- primal[i]+=ig[i*n+j]*temp[j];
- }
- primal[i]*=-1.0;
- if(primal[i]<=(low[i]+epsilon_a)) { /* clip conservatively */
- primal[i]=low[i];
- }
- else if(primal[i]>=(up[i]-epsilon_a)) {
- primal[i]=up[i];
- }
- }
- }
-
- isnantest=0;
- for(i=0;i<n;i++) { /* check for isnan */
- isnantest+=primal[i];
- }
-
- if(m>0) {
- temp1=dual[n+n+1]; /* copy the dual variables for the eq */
- temp2=dual[n+n]; /* constraints to a handier location */
- for(i=n+n+1;i>=2;i--) {
- dual[i]=dual[i-2];
- }
- dual[0]=temp2;
- dual[1]=temp1;
- isnantest+=temp1+temp2;
- }
-
- if(isnan(isnantest)) {
- return((int)NAN_SOLUTION);
- }
- else if(primal_optimal) {
- return((int)PRIMAL_OPTIMAL);
- }
- else if(maxviol == 0.0) {
- return((int)DUAL_OPTIMAL);
- }
- else {
- return((int)MAXITER_EXCEEDED);
- }
-}
-
-
-void linvert_matrix(matrix,depth,inverse,lindep_sensitivity,lin_dependent)
-double *matrix;
-long depth;
-double *inverse,lindep_sensitivity;
-long *lin_dependent; /* indicates the active parts of matrix on
- input and output*/
-{
- long i,j,k;
- double factor;
-
- for(i=0;i<depth;i++) {
- /* lin_dependent[i]=0; */
- for(j=0;j<depth;j++) {
- inverse[i*depth+j]=0.0;
- }
- inverse[i*depth+i]=1.0;
- }
- for(i=0;i<depth;i++) {
- if(lin_dependent[i] || (fabs(matrix[i*depth+i])<lindep_sensitivity)) {
- lin_dependent[i]=1;
- }
- else {
- for(j=i+1;j<depth;j++) {
- factor=matrix[j*depth+i]/matrix[i*depth+i];
- for(k=i;k<depth;k++) {
- matrix[j*depth+k]-=(factor*matrix[i*depth+k]);
- }
- for(k=0;k<depth;k++) {
- inverse[j*depth+k]-=(factor*inverse[i*depth+k]);
- }
- }
- }
- }
- for(i=depth-1;i>=0;i--) {
- if(!lin_dependent[i]) {
- factor=1/matrix[i*depth+i];
- for(k=0;k<depth;k++) {
- inverse[i*depth+k]*=factor;
- }
- matrix[i*depth+i]=1;
- for(j=i-1;j>=0;j--) {
- factor=matrix[j*depth+i];
- matrix[j*depth+i]=0;
- for(k=0;k<depth;k++) {
- inverse[j*depth+k]-=(factor*inverse[i*depth+k]);
- }
- }
- }
- }
-}
-
-void lprint_matrix(matrix,depth)
-double *matrix;
-long depth;
-{
- long i,j;
- for(i=0;i<depth;i++) {
- for(j=0;j<depth;j++) {
- printf("%5.2f ",(double)(matrix[i*depth+j]));
- }
- printf("\n");
- }
- printf("\n");
-}
-
-void ladd_matrix(matrix,depth,scalar)
-double *matrix;
-long depth;
-double scalar;
-{
- long i,j;
- for(i=0;i<depth;i++) {
- for(j=0;j<depth;j++) {
- matrix[i*depth+j]+=scalar;
- }
- }
-}
-
-void lcopy_matrix(matrix,depth,matrix2)
-double *matrix;
-long depth;
-double *matrix2;
-{
- long i;
-
- for(i=0;i<(depth)*(depth);i++) {
- matrix2[i]=matrix[i];
- }
-}
-
-void lswitch_rows_matrix(matrix,depth,r1,r2)
-double *matrix;
-long depth,r1,r2;
-{
- long i;
- double temp;
-
- for(i=0;i<depth;i++) {
- temp=matrix[r1*depth+i];
- matrix[r1*depth+i]=matrix[r2*depth+i];
- matrix[r2*depth+i]=temp;
- }
-}
-
-void lswitchrk_matrix(matrix,depth,rk1,rk2)
-double *matrix;
-long depth,rk1,rk2;
-{
- long i;
- double temp;
-
- for(i=0;i<depth;i++) {
- temp=matrix[rk1*depth+i];
- matrix[rk1*depth+i]=matrix[rk2*depth+i];
- matrix[rk2*depth+i]=temp;
- }
- for(i=0;i<depth;i++) {
- temp=matrix[i*depth+rk1];
- matrix[i*depth+rk1]=matrix[i*depth+rk2];
- matrix[i*depth+rk2]=temp;
- }
-}
-
-double calculate_qp_objective(opt_n,opt_g,opt_g0,alpha)
-long opt_n;
-double *opt_g,*opt_g0,*alpha;
-{
- double obj;
- long i,j;
- obj=0; /* calculate objective */
- for(i=0;i<opt_n;i++) {
- obj+=(opt_g0[i]*alpha[i]);
- obj+=(0.5*alpha[i]*alpha[i]*opt_g[i*opt_n+i]);
- for(j=0;j<i;j++) {
- obj+=(alpha[j]*alpha[i]*opt_g[j*opt_n+i]);
- }
- }
- return(obj);
-}
diff --git a/trunk/svm_light/build/svm_light-tar/svm_learn.c b/trunk/svm_light/build/svm_light-tar/svm_learn.c
deleted file mode 100755
index d2b5a89b93dcdefb444bae5f306448bbf877f695..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_learn.c
+++ /dev/null
@@ -1,4650 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_learn.c */
-/* */
-/* Learning module of Support Vector Machine. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/***********************************************************************/
-
-
-# include "svm_common.h"
-# include "svm_learn.h"
-
-
-/* interface to QP-solver */
-double *optimize_qp(QP *, double *, long, double *, LEARN_PARM *);
-
-/*---------------------------------------------------------------------------*/
-
-void svm_learn_classification_extend(DOC **docs, double *class, long int
- totdoc, long int totwords,
- LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache,
- MODEL *model,
- double *alpha,
- int* nerrors,
- double* maxerror)
-{
- long *inconsistent, i, *label;
- long inconsistentnum;
- long misclassified, upsupvecnum;
- double loss, model_length, example_length;
- double maxdiff, *lin, *a, *c;
- long runtime_start, runtime_end;
- long iterations;
- long *unlabeled, transduction;
- long heldout;
- long loo_count = 0, loo_count_pos = 0, loo_count_neg = 0, trainpos = 0, trainneg = 0;
- long loocomputed = 0, runtime_start_loo = 0, runtime_start_xa = 0;
- double heldout_c = 0, r_delta_sq = 0, r_delta, r_delta_avg;
- long *index, *index2dnum;
- double *weights;
- CFLOAT *aicache; /* buffer to keep one row of hessian */
-
- double *xi_fullset; /* buffer for storing xi on full sample in loo */
- double *a_fullset; /* buffer for storing alpha on full sample in loo */
- TIMING timing_profile;
- SHRINK_STATE shrink_state;
-
- runtime_start = get_runtime();
- timing_profile.time_kernel = 0;
- timing_profile.time_opti = 0;
- timing_profile.time_shrink = 0;
- timing_profile.time_update = 0;
- timing_profile.time_model = 0;
- timing_profile.time_check = 0;
- timing_profile.time_select = 0;
- kernel_cache_statistic = 0;
-
- learn_parm->totwords = totwords;
-
- /* make sure -n value is reasonable */
- if ((learn_parm->svm_newvarsinqp < 2)
- || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize))
- {
- learn_parm->svm_newvarsinqp = learn_parm->svm_maxqpsize;
- }
-
- init_shrink_state(&shrink_state, totdoc, (long)MAXSHRINK);
-
- label = (long *)my_malloc(sizeof(long) * totdoc);
- inconsistent = (long *)my_malloc(sizeof(long) * totdoc);
- unlabeled = (long *)my_malloc(sizeof(long) * totdoc);
- c = (double *)my_malloc(sizeof(double) * totdoc);
- a = (double *)my_malloc(sizeof(double) * totdoc);
- a_fullset = (double *)my_malloc(sizeof(double) * totdoc);
- xi_fullset = (double *)my_malloc(sizeof(double) * totdoc);
- lin = (double *)my_malloc(sizeof(double) * totdoc);
- learn_parm->svm_cost = (double *)my_malloc(sizeof(double) * totdoc);
- model->supvec = (DOC **)my_malloc(sizeof(DOC *) * (totdoc + 2));
- model->alpha = (double *)my_malloc(sizeof(double) * (totdoc + 2));
- model->index = (long *)my_malloc(sizeof(long) * (totdoc + 2));
-
- model->at_upper_bound = 0;
- model->b = 0;
- model->supvec[0] = 0; /* element 0 reserved and empty for now */
- model->alpha[0] = 0;
- model->lin_weights = NULL;
- model->totwords = totwords;
- model->totdoc = totdoc;
- model->kernel_parm = (*kernel_parm);
- model->sv_num = 1;
- model->loo_error = -1;
- model->loo_recall = -1;
- model->loo_precision = -1;
- model->xa_error = -1;
- model->xa_recall = -1;
- model->xa_precision = -1;
- inconsistentnum = 0;
- transduction = 0;
-
- r_delta = estimate_r_delta(docs, totdoc, kernel_parm);
- r_delta_sq = r_delta * r_delta;
-
- r_delta_avg = estimate_r_delta_average(docs, totdoc, kernel_parm);
- if (learn_parm->svm_c == 0.0) /* default value for C */
- {
- learn_parm->svm_c = 1.0 / (r_delta_avg * r_delta_avg);
- if (verbosity >= 1)
- printf("Setting default regularization parameter C=%.4f\n",
- learn_parm->svm_c);
- }
-
- learn_parm->eps = -1.0; /* equivalent regression epsilon for
- classification */
-
- for (i = 0; i < totdoc; i++) /* various inits */
- {
- docs[i]->docnum = i;
- inconsistent[i] = 0;
- a[i] = 0;
- lin[i] = 0;
- c[i] = 0.0;
- unlabeled[i] = 0;
- if (class[i] == 0)
- {
- unlabeled[i] = 1;
- label[i] = 0;
- transduction = 1;
- }
- if (class[i] > 0)
- {
- learn_parm->svm_cost[i] = learn_parm->svm_c * learn_parm->svm_costratio *
- docs[i]->costfactor;
- label[i] = 1;
- trainpos++;
- }
- else if (class[i] < 0)
- {
- learn_parm->svm_cost[i] = learn_parm->svm_c * docs[i]->costfactor;
- label[i] = -1;
- trainneg++;
- }
- else
- {
- learn_parm->svm_cost[i] = 0;
- }
- }
- if (verbosity >= 2)
- {
- printf("%ld positive, %ld negative, and %ld unlabeled examples.\n", trainpos, trainneg, totdoc - trainpos - trainneg);
- fflush(stdout);
- }
-
- /* caching makes no sense for linear kernel */
- if (kernel_parm->kernel_type == LINEAR)
- {
- kernel_cache = NULL;
- }
-
- /* compute starting state for initial alpha values */
- if (alpha)
- {
- if (verbosity >= 1)
- {
- printf("Computing starting state...");
- fflush(stdout);
- }
- index = (long *)my_malloc(sizeof(long) * totdoc);
- index2dnum = (long *)my_malloc(sizeof(long) * (totdoc + 11));
- weights = (double *)my_malloc(sizeof(double) * (totwords + 1));
- aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT) * totdoc);
- for (i = 0; i < totdoc; i++) /* create full index and clip alphas */
- {
- index[i] = 1;
- alpha[i] = fabs(alpha[i]);
- if (alpha[i] < 0) alpha[i] = 0;
- if (alpha[i] > learn_parm->svm_cost[i]) alpha[i] = learn_parm->svm_cost[i];
- }
- if (kernel_parm->kernel_type != LINEAR)
- {
- for (i = 0; i < totdoc; i++) /* fill kernel cache with unbounded SV */
- if ((alpha[i] > 0) && (alpha[i] < learn_parm->svm_cost[i])
- && (kernel_cache_space_available(kernel_cache)))
- cache_kernel_row(kernel_cache, docs, i, kernel_parm);
- for (i = 0; i < totdoc; i++) /* fill rest of kernel cache with bounded SV */
- if ((alpha[i] == learn_parm->svm_cost[i])
- && (kernel_cache_space_available(kernel_cache)))
- cache_kernel_row(kernel_cache, docs, i, kernel_parm);
- }
- (void)compute_index(index, totdoc, index2dnum);
- update_linear_component(docs, label, index2dnum, alpha, a, index2dnum, totdoc,
- totwords, kernel_parm, kernel_cache, lin, aicache,
- weights);
- (void)calculate_svm_model(docs, label, unlabeled, lin, alpha, a, c,
- learn_parm, index2dnum, index2dnum, model);
- for (i = 0; i < totdoc; i++) /* copy initial alphas */
- {
- a[i] = alpha[i];
- }
- free(index);
- free(index2dnum);
- free(weights);
- free(aicache);
- if (verbosity >= 1)
- {
- printf("done.\n");
- fflush(stdout);
- }
- }
-
- if (transduction)
- {
- learn_parm->svm_iter_to_shrink = 99999999;
- if (verbosity >= 1)
- printf("\nDeactivating Shrinking due to an incompatibility with the transductive \nlearner in the current version.\n\n");
- }
-
-
- if (transduction && learn_parm->compute_loo)
- {
- learn_parm->compute_loo = 0;
- if (verbosity >= 1)
- printf("\nCannot compute leave-one-out estimates for transductive learner.\n\n");
- }
-
- if (learn_parm->remove_inconsistent && learn_parm->compute_loo)
- {
- learn_parm->compute_loo = 0;
- printf("\nCannot compute leave-one-out estimates when removing inconsistent examples.\n\n");
- }
-
- if (learn_parm->compute_loo && ((trainpos == 1) || (trainneg == 1)))
- {
- learn_parm->compute_loo = 0;
- printf("\nCannot compute leave-one-out with only one example in one class.\n\n");
- }
-
-
- if (verbosity == 1)
- {
- printf("Optimizing");
- fflush(stdout);
- }
-
- /* train the svm */
- iterations = optimize_to_convergence(docs, label, totdoc, totwords, learn_parm,
- kernel_parm, kernel_cache, &shrink_state, model,
- inconsistent, unlabeled, a, lin,
- c, &timing_profile,
- &maxdiff, (long) - 1,
- (long)1);
-
- misclassified = 0;
- double maxerror_ = 0;
- for (i = 0; (i < totdoc); i++) /* get final statistic */
- {
- if ((lin[i] - model->b)*(double)label[i] <= 0.0)
- {
- misclassified++;
- if(maxerror_ < -(lin[i] - model->b)*(double)label[i])
- maxerror_ = -(lin[i] - model->b)*(double)label[i];
- }
- }
-
- *nerrors = misclassified;
- *maxerror = maxerror_;
-
- if (verbosity >= 1)
- {
- if (verbosity == 1) printf("done. (%ld iterations)\n", iterations);
-
- misclassified = 0;
- for (i = 0; (i < totdoc); i++) /* get final statistic */
- {
- if ((lin[i] - model->b)*(double)label[i] <= 0.0)
- misclassified++;
- }
-
- printf("Optimization finished (%ld misclassified, maxdiff=%.5f).\n",
- misclassified, maxdiff);
-
- runtime_end = get_runtime();
- if (verbosity >= 2)
- {
- printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n",
- ((float)runtime_end - (float)runtime_start) / 100.0,
- (100.0*timing_profile.time_kernel) / (float)(runtime_end - runtime_start),
- (100.0*timing_profile.time_opti) / (float)(runtime_end - runtime_start),
- (100.0*timing_profile.time_shrink) / (float)(runtime_end - runtime_start),
- (100.0*timing_profile.time_update) / (float)(runtime_end - runtime_start),
- (100.0*timing_profile.time_model) / (float)(runtime_end - runtime_start),
- (100.0*timing_profile.time_check) / (float)(runtime_end - runtime_start),
- (100.0*timing_profile.time_select) / (float)(runtime_end - runtime_start));
- }
- else
- {
- printf("Runtime in cpu-seconds: %.2f\n",
- (runtime_end - runtime_start) / 100.0);
- }
-
- if (learn_parm->remove_inconsistent)
- {
- inconsistentnum = 0;
- for (i = 0; i < totdoc; i++)
- if (inconsistent[i])
- inconsistentnum++;
- printf("Number of SV: %ld (plus %ld inconsistent examples)\n",
- model->sv_num - 1, inconsistentnum);
- }
- else
- {
- upsupvecnum = 0;
- for (i = 1; i < model->sv_num; i++)
- {
- if (fabs(model->alpha[i]) >=
- (learn_parm->svm_cost[(model->supvec[i])->docnum] -
- learn_parm->epsilon_a))
- upsupvecnum++;
- }
- printf("Number of SV: %ld (including %ld at upper bound)\n",
- model->sv_num - 1, upsupvecnum);
- }
-
- if ((verbosity >= 1) && (!learn_parm->skip_final_opt_check))
- {
- loss = 0;
- model_length = 0;
- for (i = 0; i < totdoc; i++)
- {
- if ((lin[i] - model->b)*(double)label[i] < 1.0 - learn_parm->epsilon_crit)
- loss += 1.0 - (lin[i] - model->b) * (double)label[i];
- model_length += a[i] * label[i] * lin[i];
- }
- model_length = sqrt(model_length);
- fprintf(stdout, "L1 loss: loss=%.5f\n", loss);
- fprintf(stdout, "Norm of weight vector: |w|=%.5f\n", model_length);
- example_length = estimate_sphere(model, kernel_parm);
- fprintf(stdout, "Norm of longest example vector: |x|=%.5f\n",
- length_of_longest_document_vector(docs, totdoc, kernel_parm));
- fprintf(stdout, "Estimated VCdim of classifier: VCdim<=%.5f\n",
- estimate_margin_vcdim(model, model_length, example_length,
- kernel_parm));
- if ((!learn_parm->remove_inconsistent) && (!transduction))
- {
- runtime_start_xa = get_runtime();
- if (verbosity >= 1)
- {
- printf("Computing XiAlpha-estimates...");
- fflush(stdout);
- }
- compute_xa_estimates(model, label, unlabeled, totdoc, docs, lin, a,
- kernel_parm, learn_parm, &(model->xa_error),
- &(model->xa_recall), &(model->xa_precision));
- if (verbosity >= 1)
- {
- printf("done\n");
- }
- printf("Runtime for XiAlpha-estimates in cpu-seconds: %.2f\n",
- (get_runtime() - runtime_start_xa) / 100.0);
-
- fprintf(stdout, "XiAlpha-estimate of the error: error<=%.2f%% (rho=%.2f,depth=%ld)\n",
- model->xa_error, learn_parm->rho, learn_parm->xa_depth);
- fprintf(stdout, "XiAlpha-estimate of the recall: recall=>%.2f%% (rho=%.2f,depth=%ld)\n",
- model->xa_recall, learn_parm->rho, learn_parm->xa_depth);
- fprintf(stdout, "XiAlpha-estimate of the precision: precision=>%.2f%% (rho=%.2f,depth=%ld)\n",
- model->xa_precision, learn_parm->rho, learn_parm->xa_depth);
- }
- else if (!learn_parm->remove_inconsistent)
- {
- estimate_transduction_quality(model, label, unlabeled, totdoc, docs, lin);
- }
- }
- if (verbosity >= 1)
- {
- printf("Number of kernel evaluations: %ld\n", kernel_cache_statistic);
- }
- }
-
-
- /* leave-one-out testing starts now */
- if (learn_parm->compute_loo)
- {
- /* save results of training on full dataset for leave-one-out */
- runtime_start_loo = get_runtime();
- for (i = 0; i < totdoc; i++)
- {
- xi_fullset[i] = 1.0 - ((lin[i] - model->b) * (double)label[i]);
- if (xi_fullset[i] < 0) xi_fullset[i] = 0;
- a_fullset[i] = a[i];
- }
- if (verbosity >= 1)
- {
- printf("Computing leave-one-out");
- }
-
- /* repeat this loop for every held-out example */
- for (heldout = 0; (heldout < totdoc); heldout++)
- {
- if (learn_parm->rho*a_fullset[heldout]*r_delta_sq + xi_fullset[heldout]
- < 1.0)
- {
- /* guaranteed to not produce a leave-one-out error */
- if (verbosity == 1)
- {
- printf("+");
- fflush(stdout);
- }
- }
- else if (xi_fullset[heldout] > 1.0)
- {
- /* guaranteed to produce a leave-one-out error */
- loo_count++;
- if (label[heldout] > 0) loo_count_pos++;
- else loo_count_neg++;
- if (verbosity == 1)
- {
- printf("-");
- fflush(stdout);
- }
- }
- else
- {
- loocomputed++;
- heldout_c = learn_parm->svm_cost[heldout]; /* set upper bound to zero */
- learn_parm->svm_cost[heldout] = 0;
- /* make sure heldout example is not currently */
- /* shrunk away. Assumes that lin is up to date! */
- shrink_state.active[heldout] = 1;
- if (verbosity >= 2)
- printf("\nLeave-One-Out test on example %ld\n", heldout);
- if (verbosity >= 1)
- {
- printf("(?[%ld]", heldout);
- fflush(stdout);
- }
-
- optimize_to_convergence(docs, label, totdoc, totwords, learn_parm,
- kernel_parm,
- kernel_cache, &shrink_state, model, inconsistent, unlabeled,
- a, lin, c, &timing_profile,
- &maxdiff, heldout, (long)2);
-
- /* printf("%.20f\n",(lin[heldout]-model->b)*(double)label[heldout]); */
-
- if (((lin[heldout] - model->b)*(double)label[heldout]) <= 0.0)
- {
- loo_count++; /* there was a loo-error */
- if (label[heldout] > 0) loo_count_pos++;
- else loo_count_neg++;
- if (verbosity >= 1)
- {
- printf("-)");
- fflush(stdout);
- }
- }
- else
- {
- if (verbosity >= 1)
- {
- printf("+)");
- fflush(stdout);
- }
- }
- /* now we need to restore the original data set*/
- learn_parm->svm_cost[heldout] = heldout_c; /* restore upper bound */
- }
- } /* end of leave-one-out loop */
-
-
- if (verbosity >= 1)
- {
- printf("\nRetrain on full problem");
- fflush(stdout);
- }
- optimize_to_convergence(docs, label, totdoc, totwords, learn_parm,
- kernel_parm,
- kernel_cache, &shrink_state, model, inconsistent, unlabeled,
- a, lin, c, &timing_profile,
- &maxdiff, (long) - 1, (long)1);
- if (verbosity >= 1)
- printf("done.\n");
-
-
- /* after all leave-one-out computed */
- model->loo_error = 100.0 * loo_count / (double)totdoc;
-
- model->loo_recall = (1.0 - (double)loo_count_pos / (double)trainpos) * 100.0;
- model->loo_precision = (trainpos - loo_count_pos) /
- (double)(trainpos - loo_count_pos + loo_count_neg) * 100.0;
- if (verbosity >= 1)
- {
- fprintf(stdout, "Leave-one-out estimate of the error: error=%.2f%%\n",
- model->loo_error);
- fprintf(stdout, "Leave-one-out estimate of the recall: recall=%.2f%%\n",
- model->loo_recall);
- fprintf(stdout, "Leave-one-out estimate of the precision: precision=%.2f%%\n",
- model->loo_precision);
- fprintf(stdout, "Actual leave-one-outs computed: %ld (rho=%.2f)\n",
- loocomputed, learn_parm->rho);
- printf("Runtime for leave-one-out in cpu-seconds: %.2f\n",
- (double)(get_runtime() - runtime_start_loo) / 100.0);
- }
- }
-
- if (learn_parm->alphafile[0])
- write_alphas(learn_parm->alphafile, a, label, totdoc);
-
- shrink_state_cleanup(&shrink_state);
- free(label);
- free(inconsistent);
- free(unlabeled);
- free(c);
- free(a);
- free(a_fullset);
- free(xi_fullset);
- free(lin);
- free(learn_parm->svm_cost);
-}
-
-
-/* Learns an SVM classification model based on the training data in
- docs/label. The resulting model is returned in the structure
- model. */
-
-void svm_learn_classification(DOC **docs, double *class, long int
- totdoc, long int totwords,
- LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache,
- MODEL *model,
- double *alpha)
- /* docs: Training vectors (x-part) */
- /* class: Training labels (y-part, zero if test example for
- transduction) */
- /* totdoc: Number of examples in docs/label */
- /* totwords: Number of features (i.e. highest feature index) */
- /* learn_parm: Learning paramenters */
- /* kernel_parm: Kernel paramenters */
- /* kernel_cache:Initialized Cache of size totdoc, if using a kernel.
- NULL if linear.*/
- /* model: Returns learning result (assumed empty before called) */
- /* alpha: Start values for the alpha variables or NULL
- pointer. The new alpha values are returned after
- optimization if not NULL. Array must be of size totdoc. */
-{
- long *inconsistent,i,*label;
- long inconsistentnum;
- long misclassified,upsupvecnum;
- double loss,model_length,example_length;
- double maxdiff,*lin,*a,*c;
- long runtime_start,runtime_end;
- long iterations;
- long *unlabeled,transduction;
- long heldout;
- long loo_count=0,loo_count_pos=0,loo_count_neg=0,trainpos=0,trainneg=0;
- long loocomputed=0,runtime_start_loo=0,runtime_start_xa=0;
- double heldout_c=0,r_delta_sq=0,r_delta,r_delta_avg;
- long *index,*index2dnum;
- double *weights;
- CFLOAT *aicache; /* buffer to keep one row of hessian */
-
- double *xi_fullset; /* buffer for storing xi on full sample in loo */
- double *a_fullset; /* buffer for storing alpha on full sample in loo */
- TIMING timing_profile;
- SHRINK_STATE shrink_state;
-
- runtime_start=get_runtime();
- timing_profile.time_kernel=0;
- timing_profile.time_opti=0;
- timing_profile.time_shrink=0;
- timing_profile.time_update=0;
- timing_profile.time_model=0;
- timing_profile.time_check=0;
- timing_profile.time_select=0;
- kernel_cache_statistic=0;
-
- learn_parm->totwords=totwords;
-
- /* make sure -n value is reasonable */
- if((learn_parm->svm_newvarsinqp < 2)
- || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) {
- learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
- }
-
- init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK);
-
- label = (long *)my_malloc(sizeof(long)*totdoc);
- inconsistent = (long *)my_malloc(sizeof(long)*totdoc);
- unlabeled = (long *)my_malloc(sizeof(long)*totdoc);
- c = (double *)my_malloc(sizeof(double)*totdoc);
- a = (double *)my_malloc(sizeof(double)*totdoc);
- a_fullset = (double *)my_malloc(sizeof(double)*totdoc);
- xi_fullset = (double *)my_malloc(sizeof(double)*totdoc);
- lin = (double *)my_malloc(sizeof(double)*totdoc);
- learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc);
- model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2));
- model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2));
- model->index = (long *)my_malloc(sizeof(long)*(totdoc+2));
-
- model->at_upper_bound=0;
- model->b=0;
- model->supvec[0]=0; /* element 0 reserved and empty for now */
- model->alpha[0]=0;
- model->lin_weights=NULL;
- model->totwords=totwords;
- model->totdoc=totdoc;
- model->kernel_parm=(*kernel_parm);
- model->sv_num=1;
- model->loo_error=-1;
- model->loo_recall=-1;
- model->loo_precision=-1;
- model->xa_error=-1;
- model->xa_recall=-1;
- model->xa_precision=-1;
- inconsistentnum=0;
- transduction=0;
-
- r_delta=estimate_r_delta(docs,totdoc,kernel_parm);
- r_delta_sq=r_delta*r_delta;
-
- r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm);
- if(learn_parm->svm_c == 0.0) { /* default value for C */
- learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg);
- if(verbosity>=1)
- printf("Setting default regularization parameter C=%.4f\n",
- learn_parm->svm_c);
- }
-
- learn_parm->eps=-1.0; /* equivalent regression epsilon for
- classification */
-
- for(i=0;i<totdoc;i++) { /* various inits */
- docs[i]->docnum=i;
- inconsistent[i]=0;
- a[i]=0;
- lin[i]=0;
- c[i]=0.0;
- unlabeled[i]=0;
- if(class[i] == 0) {
- unlabeled[i]=1;
- label[i]=0;
- transduction=1;
- }
- if(class[i] > 0) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio*
- docs[i]->costfactor;
- label[i]=1;
- trainpos++;
- }
- else if(class[i] < 0) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*docs[i]->costfactor;
- label[i]=-1;
- trainneg++;
- }
- else {
- learn_parm->svm_cost[i]=0;
- }
- }
- if(verbosity>=2) {
- printf("%ld positive, %ld negative, and %ld unlabeled examples.\n",trainpos,trainneg,totdoc-trainpos-trainneg); fflush(stdout);
- }
-
- /* caching makes no sense for linear kernel */
- if(kernel_parm->kernel_type == LINEAR) {
- kernel_cache = NULL;
- }
-
- /* compute starting state for initial alpha values */
- if(alpha) {
- if(verbosity>=1) {
- printf("Computing starting state..."); fflush(stdout);
- }
- index = (long *)my_malloc(sizeof(long)*totdoc);
- index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- weights=(double *)my_malloc(sizeof(double)*(totwords+1));
- aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc);
- for(i=0;i<totdoc;i++) { /* create full index and clip alphas */
- index[i]=1;
- alpha[i]=fabs(alpha[i]);
- if(alpha[i]<0) alpha[i]=0;
- if(alpha[i]>learn_parm->svm_cost[i]) alpha[i]=learn_parm->svm_cost[i];
- }
- if(kernel_parm->kernel_type != LINEAR) {
- for(i=0;i<totdoc;i++) /* fill kernel cache with unbounded SV */
- if((alpha[i]>0) && (alpha[i]<learn_parm->svm_cost[i])
- && (kernel_cache_space_available(kernel_cache)))
- cache_kernel_row(kernel_cache,docs,i,kernel_parm);
- for(i=0;i<totdoc;i++) /* fill rest of kernel cache with bounded SV */
- if((alpha[i]==learn_parm->svm_cost[i])
- && (kernel_cache_space_available(kernel_cache)))
- cache_kernel_row(kernel_cache,docs,i,kernel_parm);
- }
- (void)compute_index(index,totdoc,index2dnum);
- update_linear_component(docs,label,index2dnum,alpha,a,index2dnum,totdoc,
- totwords,kernel_parm,kernel_cache,lin,aicache,
- weights);
- (void)calculate_svm_model(docs,label,unlabeled,lin,alpha,a,c,
- learn_parm,index2dnum,index2dnum,model);
- for(i=0;i<totdoc;i++) { /* copy initial alphas */
- a[i]=alpha[i];
- }
- free(index);
- free(index2dnum);
- free(weights);
- free(aicache);
- if(verbosity>=1) {
- printf("done.\n"); fflush(stdout);
- }
- }
-
- if(transduction) {
- learn_parm->svm_iter_to_shrink=99999999;
- if(verbosity >= 1)
- printf("\nDeactivating Shrinking due to an incompatibility with the transductive \nlearner in the current version.\n\n");
- }
-
- if(transduction && learn_parm->compute_loo) {
- learn_parm->compute_loo=0;
- if(verbosity >= 1)
- printf("\nCannot compute leave-one-out estimates for transductive learner.\n\n");
- }
-
- if(learn_parm->remove_inconsistent && learn_parm->compute_loo) {
- learn_parm->compute_loo=0;
- printf("\nCannot compute leave-one-out estimates when removing inconsistent examples.\n\n");
- }
-
- if(learn_parm->compute_loo && ((trainpos == 1) || (trainneg == 1))) {
- learn_parm->compute_loo=0;
- printf("\nCannot compute leave-one-out with only one example in one class.\n\n");
- }
-
-
- if(verbosity==1) {
- printf("Optimizing"); fflush(stdout);
- }
-
- /* train the svm */
- iterations=optimize_to_convergence(docs,label,totdoc,totwords,learn_parm,
- kernel_parm,kernel_cache,&shrink_state,model,
- inconsistent,unlabeled,a,lin,
- c,&timing_profile,
- &maxdiff,(long)-1,
- (long)1);
-
- if(verbosity>=1) {
- if(verbosity==1) printf("done. (%ld iterations)\n",iterations);
-
- misclassified=0;
- for(i=0;(i<totdoc);i++) { /* get final statistic */
- if((lin[i]-model->b)*(double)label[i] <= 0.0)
- misclassified++;
- }
-
- printf("Optimization finished (%ld misclassified, maxdiff=%.5f).\n",
- misclassified,maxdiff);
-
- runtime_end=get_runtime();
- if(verbosity>=2) {
- printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n",
- ((float)runtime_end-(float)runtime_start)/100.0,
- (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start));
- }
- else {
- printf("Runtime in cpu-seconds: %.2f\n",
- (runtime_end-runtime_start)/100.0);
- }
-
- if(learn_parm->remove_inconsistent) {
- inconsistentnum=0;
- for(i=0;i<totdoc;i++)
- if(inconsistent[i])
- inconsistentnum++;
- printf("Number of SV: %ld (plus %ld inconsistent examples)\n",
- model->sv_num-1,inconsistentnum);
- }
- else {
- upsupvecnum=0;
- for(i=1;i<model->sv_num;i++) {
- if(fabs(model->alpha[i]) >=
- (learn_parm->svm_cost[(model->supvec[i])->docnum]-
- learn_parm->epsilon_a))
- upsupvecnum++;
- }
- printf("Number of SV: %ld (including %ld at upper bound)\n",
- model->sv_num-1,upsupvecnum);
- }
-
- if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) {
- loss=0;
- model_length=0;
- for(i=0;i<totdoc;i++) {
- if((lin[i]-model->b)*(double)label[i] < 1.0-learn_parm->epsilon_crit)
- loss+=1.0-(lin[i]-model->b)*(double)label[i];
- model_length+=a[i]*label[i]*lin[i];
- }
- model_length=sqrt(model_length);
- fprintf(stdout,"L1 loss: loss=%.5f\n",loss);
- fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length);
- example_length=estimate_sphere(model,kernel_parm);
- fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n",
- length_of_longest_document_vector(docs,totdoc,kernel_parm));
- fprintf(stdout,"Estimated VCdim of classifier: VCdim<=%.5f\n",
- estimate_margin_vcdim(model,model_length,example_length,
- kernel_parm));
- if((!learn_parm->remove_inconsistent) && (!transduction)) {
- runtime_start_xa=get_runtime();
- if(verbosity>=1) {
- printf("Computing XiAlpha-estimates..."); fflush(stdout);
- }
- compute_xa_estimates(model,label,unlabeled,totdoc,docs,lin,a,
- kernel_parm,learn_parm,&(model->xa_error),
- &(model->xa_recall),&(model->xa_precision));
- if(verbosity>=1) {
- printf("done\n");
- }
- printf("Runtime for XiAlpha-estimates in cpu-seconds: %.2f\n",
- (get_runtime()-runtime_start_xa)/100.0);
-
- fprintf(stdout,"XiAlpha-estimate of the error: error<=%.2f%% (rho=%.2f,depth=%ld)\n",
- model->xa_error,learn_parm->rho,learn_parm->xa_depth);
- fprintf(stdout,"XiAlpha-estimate of the recall: recall=>%.2f%% (rho=%.2f,depth=%ld)\n",
- model->xa_recall,learn_parm->rho,learn_parm->xa_depth);
- fprintf(stdout,"XiAlpha-estimate of the precision: precision=>%.2f%% (rho=%.2f,depth=%ld)\n",
- model->xa_precision,learn_parm->rho,learn_parm->xa_depth);
- }
- else if(!learn_parm->remove_inconsistent) {
- estimate_transduction_quality(model,label,unlabeled,totdoc,docs,lin);
- }
- }
- if(verbosity>=1) {
- printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic);
- }
- }
-
-
- /* leave-one-out testing starts now */
- if(learn_parm->compute_loo) {
- /* save results of training on full dataset for leave-one-out */
- runtime_start_loo=get_runtime();
- for(i=0;i<totdoc;i++) {
- xi_fullset[i]=1.0-((lin[i]-model->b)*(double)label[i]);
- if(xi_fullset[i]<0) xi_fullset[i]=0;
- a_fullset[i]=a[i];
- }
- if(verbosity>=1) {
- printf("Computing leave-one-out");
- }
-
- /* repeat this loop for every held-out example */
- for(heldout=0;(heldout<totdoc);heldout++) {
- if(learn_parm->rho*a_fullset[heldout]*r_delta_sq+xi_fullset[heldout]
- < 1.0) {
- /* guaranteed to not produce a leave-one-out error */
- if(verbosity==1) {
- printf("+"); fflush(stdout);
- }
- }
- else if(xi_fullset[heldout] > 1.0) {
- /* guaranteed to produce a leave-one-out error */
- loo_count++;
- if(label[heldout] > 0) loo_count_pos++; else loo_count_neg++;
- if(verbosity==1) {
- printf("-"); fflush(stdout);
- }
- }
- else {
- loocomputed++;
- heldout_c=learn_parm->svm_cost[heldout]; /* set upper bound to zero */
- learn_parm->svm_cost[heldout]=0;
- /* make sure heldout example is not currently */
- /* shrunk away. Assumes that lin is up to date! */
- shrink_state.active[heldout]=1;
- if(verbosity>=2)
- printf("\nLeave-One-Out test on example %ld\n",heldout);
- if(verbosity>=1) {
- printf("(?[%ld]",heldout); fflush(stdout);
- }
-
- optimize_to_convergence(docs,label,totdoc,totwords,learn_parm,
- kernel_parm,
- kernel_cache,&shrink_state,model,inconsistent,unlabeled,
- a,lin,c,&timing_profile,
- &maxdiff,heldout,(long)2);
-
- /* printf("%.20f\n",(lin[heldout]-model->b)*(double)label[heldout]); */
-
- if(((lin[heldout]-model->b)*(double)label[heldout]) <= 0.0) {
- loo_count++; /* there was a loo-error */
- if(label[heldout] > 0) loo_count_pos++; else loo_count_neg++;
- if(verbosity>=1) {
- printf("-)"); fflush(stdout);
- }
- }
- else {
- if(verbosity>=1) {
- printf("+)"); fflush(stdout);
- }
- }
- /* now we need to restore the original data set*/
- learn_parm->svm_cost[heldout]=heldout_c; /* restore upper bound */
- }
- } /* end of leave-one-out loop */
-
-
- if(verbosity>=1) {
- printf("\nRetrain on full problem"); fflush(stdout);
- }
- optimize_to_convergence(docs,label,totdoc,totwords,learn_parm,
- kernel_parm,
- kernel_cache,&shrink_state,model,inconsistent,unlabeled,
- a,lin,c,&timing_profile,
- &maxdiff,(long)-1,(long)1);
- if(verbosity >= 1)
- printf("done.\n");
-
-
- /* after all leave-one-out computed */
- model->loo_error=100.0*loo_count/(double)totdoc;
- model->loo_recall=(1.0-(double)loo_count_pos/(double)trainpos)*100.0;
- model->loo_precision=(trainpos-loo_count_pos)/
- (double)(trainpos-loo_count_pos+loo_count_neg)*100.0;
- if(verbosity >= 1) {
- fprintf(stdout,"Leave-one-out estimate of the error: error=%.2f%%\n",
- model->loo_error);
- fprintf(stdout,"Leave-one-out estimate of the recall: recall=%.2f%%\n",
- model->loo_recall);
- fprintf(stdout,"Leave-one-out estimate of the precision: precision=%.2f%%\n",
- model->loo_precision);
- fprintf(stdout,"Actual leave-one-outs computed: %ld (rho=%.2f)\n",
- loocomputed,learn_parm->rho);
- printf("Runtime for leave-one-out in cpu-seconds: %.2f\n",
- (double)(get_runtime()-runtime_start_loo)/100.0);
- }
- }
-
- if(learn_parm->alphafile[0])
- write_alphas(learn_parm->alphafile,a,label,totdoc);
-
- shrink_state_cleanup(&shrink_state);
- free(label);
- free(inconsistent);
- free(unlabeled);
- free(c);
- free(a);
- free(a_fullset);
- free(xi_fullset);
- free(lin);
- free(learn_parm->svm_cost);
-}
-
-
-/* Learns an SVM regression model based on the training data in
- docs/label. The resulting model is returned in the structure
- model. */
-
-void svm_learn_regression(DOC **docs, double *value, long int totdoc,
- long int totwords, LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE **kernel_cache, MODEL *model)
- /* docs: Training vectors (x-part) */
- /* class: Training value (y-part) */
- /* totdoc: Number of examples in docs/label */
- /* totwords: Number of features (i.e. highest feature index) */
- /* learn_parm: Learning paramenters */
- /* kernel_parm: Kernel paramenters */
- /* kernel_cache:Initialized Cache, if using a kernel. NULL if
- linear. Note that it will be free'd and reassigned */
- /* model: Returns learning result (assumed empty before called) */
-{
- long *inconsistent,i,j;
- long inconsistentnum;
- long upsupvecnum;
- double loss,model_length,example_length;
- double maxdiff,*lin,*a,*c;
- long runtime_start,runtime_end;
- long iterations,kernel_cache_size;
- long *unlabeled;
- double r_delta_sq=0,r_delta,r_delta_avg;
- double *xi_fullset; /* buffer for storing xi on full sample in loo */
- double *a_fullset; /* buffer for storing alpha on full sample in loo */
- TIMING timing_profile;
- SHRINK_STATE shrink_state;
- DOC **docs_org;
- long *label;
-
- /* set up regression problem in standard form */
- docs_org=docs;
- docs = (DOC **)my_malloc(sizeof(DOC)*2*totdoc);
- label = (long *)my_malloc(sizeof(long)*2*totdoc);
- c = (double *)my_malloc(sizeof(double)*2*totdoc);
- for(i=0;i<totdoc;i++) {
- j=2*totdoc-1-i;
- docs[i]=create_example(i,0,0,docs_org[i]->costfactor,docs_org[i]->fvec);
- label[i]=+1;
- c[i]=value[i];
- docs[j]=create_example(j,0,0,docs_org[i]->costfactor,docs_org[i]->fvec);
- label[j]=-1;
- c[j]=value[i];
- }
- totdoc*=2;
-
- /* need to get a bigger kernel cache */
- if(*kernel_cache) {
- kernel_cache_size=(*kernel_cache)->buffsize*sizeof(CFLOAT)/(1024*1024);
- kernel_cache_cleanup(*kernel_cache);
- (*kernel_cache)=kernel_cache_init(totdoc,kernel_cache_size);
- }
-
- runtime_start=get_runtime();
- timing_profile.time_kernel=0;
- timing_profile.time_opti=0;
- timing_profile.time_shrink=0;
- timing_profile.time_update=0;
- timing_profile.time_model=0;
- timing_profile.time_check=0;
- timing_profile.time_select=0;
- kernel_cache_statistic=0;
-
- learn_parm->totwords=totwords;
-
- /* make sure -n value is reasonable */
- if((learn_parm->svm_newvarsinqp < 2)
- || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) {
- learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
- }
-
- init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK);
-
- inconsistent = (long *)my_malloc(sizeof(long)*totdoc);
- unlabeled = (long *)my_malloc(sizeof(long)*totdoc);
- a = (double *)my_malloc(sizeof(double)*totdoc);
- a_fullset = (double *)my_malloc(sizeof(double)*totdoc);
- xi_fullset = (double *)my_malloc(sizeof(double)*totdoc);
- lin = (double *)my_malloc(sizeof(double)*totdoc);
- learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc);
- model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2));
- model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2));
- model->index = (long *)my_malloc(sizeof(long)*(totdoc+2));
-
- model->at_upper_bound=0;
- model->b=0;
- model->supvec[0]=0; /* element 0 reserved and empty for now */
- model->alpha[0]=0;
- model->lin_weights=NULL;
- model->totwords=totwords;
- model->totdoc=totdoc;
- model->kernel_parm=(*kernel_parm);
- model->sv_num=1;
- model->loo_error=-1;
- model->loo_recall=-1;
- model->loo_precision=-1;
- model->xa_error=-1;
- model->xa_recall=-1;
- model->xa_precision=-1;
- inconsistentnum=0;
-
- r_delta=estimate_r_delta(docs,totdoc,kernel_parm);
- r_delta_sq=r_delta*r_delta;
-
- r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm);
- if(learn_parm->svm_c == 0.0) { /* default value for C */
- learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg);
- if(verbosity>=1)
- printf("Setting default regularization parameter C=%.4f\n",
- learn_parm->svm_c);
- }
-
- for(i=0;i<totdoc;i++) { /* various inits */
- inconsistent[i]=0;
- a[i]=0;
- lin[i]=0;
- unlabeled[i]=0;
- if(label[i] > 0) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio*
- docs[i]->costfactor;
- }
- else if(label[i] < 0) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*docs[i]->costfactor;
- }
- }
-
- /* caching makes no sense for linear kernel */
- if((kernel_parm->kernel_type == LINEAR) && (*kernel_cache)) {
- printf("WARNING: Using a kernel cache for linear case will slow optimization down!\n");
- }
-
- if(verbosity==1) {
- printf("Optimizing"); fflush(stdout);
- }
-
- /* train the svm */
- iterations=optimize_to_convergence(docs,label,totdoc,totwords,learn_parm,
- kernel_parm,*kernel_cache,&shrink_state,
- model,inconsistent,unlabeled,a,lin,c,
- &timing_profile,&maxdiff,(long)-1,
- (long)1);
-
- if(verbosity>=1) {
- if(verbosity==1) printf("done. (%ld iterations)\n",iterations);
-
- printf("Optimization finished (maxdiff=%.5f).\n",maxdiff);
-
- runtime_end=get_runtime();
- if(verbosity>=2) {
- printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n",
- ((float)runtime_end-(float)runtime_start)/100.0,
- (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start));
- }
- else {
- printf("Runtime in cpu-seconds: %.2f\n",
- (runtime_end-runtime_start)/100.0);
- }
-
- if(learn_parm->remove_inconsistent) {
- inconsistentnum=0;
- for(i=0;i<totdoc;i++)
- if(inconsistent[i])
- inconsistentnum++;
- printf("Number of SV: %ld (plus %ld inconsistent examples)\n",
- model->sv_num-1,inconsistentnum);
- }
- else {
- upsupvecnum=0;
- for(i=1;i<model->sv_num;i++) {
- if(fabs(model->alpha[i]) >=
- (learn_parm->svm_cost[(model->supvec[i])->docnum]-
- learn_parm->epsilon_a))
- upsupvecnum++;
- }
- printf("Number of SV: %ld (including %ld at upper bound)\n",
- model->sv_num-1,upsupvecnum);
- }
-
- if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) {
- loss=0;
- model_length=0;
- for(i=0;i<totdoc;i++) {
- if((lin[i]-model->b)*(double)label[i] < (-learn_parm->eps+(double)label[i]*c[i])-learn_parm->epsilon_crit)
- loss+=-learn_parm->eps+(double)label[i]*c[i]-(lin[i]-model->b)*(double)label[i];
- model_length+=a[i]*label[i]*lin[i];
- }
- model_length=sqrt(model_length);
- fprintf(stdout,"L1 loss: loss=%.5f\n",loss);
- fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length);
- example_length=estimate_sphere(model,kernel_parm);
- fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n",
- length_of_longest_document_vector(docs,totdoc,kernel_parm));
- }
- if(verbosity>=1) {
- printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic);
- }
- }
-
- if(learn_parm->alphafile[0])
- write_alphas(learn_parm->alphafile,a,label,totdoc);
-
- /* this makes sure the model we return does not contain pointers to the
- temporary documents */
- for(i=1;i<model->sv_num;i++) {
- j=model->supvec[i]->docnum;
- if(j >= (totdoc/2)) {
- j=totdoc-j-1;
- }
- model->supvec[i]=docs_org[j];
- }
-
- shrink_state_cleanup(&shrink_state);
- for(i=0;i<totdoc;i++)
- free_example(docs[i],0);
- free(docs);
- free(label);
- free(inconsistent);
- free(unlabeled);
- free(c);
- free(a);
- free(a_fullset);
- free(xi_fullset);
- free(lin);
- free(learn_parm->svm_cost);
-}
-
-void svm_learn_ranking(DOC **docs, double *rankvalue, long int totdoc,
- long int totwords, LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm, KERNEL_CACHE **kernel_cache,
- MODEL *model)
- /* docs: Training vectors (x-part) */
- /* rankvalue: Training target values that determine the ranking */
- /* totdoc: Number of examples in docs/label */
- /* totwords: Number of features (i.e. highest feature index) */
- /* learn_parm: Learning paramenters */
- /* kernel_parm: Kernel paramenters */
- /* kernel_cache:Initialized pointer to Cache of size 1*totdoc, if
- using a kernel. NULL if linear. NOTE: Cache is
- getting reinitialized in this function */
- /* model: Returns learning result (assumed empty before called) */
-{
- DOC **docdiff;
- long i,j,k,totpair,kernel_cache_size;
- double *target,*alpha,cost;
- long *greater,*lesser;
- MODEL *pairmodel;
- SVECTOR *flow,*fhigh;
-
- totpair=0;
- for(i=0;i<totdoc;i++) {
- for(j=i+1;j<totdoc;j++) {
- if((docs[i]->queryid==docs[j]->queryid) && (rankvalue[i] != rankvalue[j])) {
- totpair++;
- }
- }
- }
-
- printf("Constructing %ld rank constraints...",totpair); fflush(stdout);
- docdiff=(DOC **)my_malloc(sizeof(DOC)*totpair);
- target=(double *)my_malloc(sizeof(double)*totpair);
- greater=(long *)my_malloc(sizeof(long)*totpair);
- lesser=(long *)my_malloc(sizeof(long)*totpair);
-
- k=0;
- for(i=0;i<totdoc;i++) {
- for(j=i+1;j<totdoc;j++) {
- if(docs[i]->queryid == docs[j]->queryid) {
- cost=(docs[i]->costfactor+docs[j]->costfactor)/2.0;
- if(rankvalue[i] > rankvalue[j]) {
- if(kernel_parm->kernel_type == LINEAR)
- docdiff[k]=create_example(k,0,0,cost,
- sub_ss(docs[i]->fvec,docs[j]->fvec));
- else {
- flow=copy_svector(docs[j]->fvec);
- flow->factor=-1.0;
- flow->next=NULL;
- fhigh=copy_svector(docs[i]->fvec);
- fhigh->factor=1.0;
- fhigh->next=flow;
- docdiff[k]=create_example(k,0,0,cost,fhigh);
- }
- target[k]=1;
- greater[k]=i;
- lesser[k]=j;
- k++;
- }
- else if(rankvalue[i] < rankvalue[j]) {
- if(kernel_parm->kernel_type == LINEAR)
- docdiff[k]=create_example(k,0,0,cost,
- sub_ss(docs[i]->fvec,docs[j]->fvec));
- else {
- flow=copy_svector(docs[j]->fvec);
- flow->factor=-1.0;
- flow->next=NULL;
- fhigh=copy_svector(docs[i]->fvec);
- fhigh->factor=1.0;
- fhigh->next=flow;
- docdiff[k]=create_example(k,0,0,cost,fhigh);
- }
- target[k]=-1;
- greater[k]=i;
- lesser[k]=j;
- k++;
- }
- }
- }
- }
- printf("done.\n"); fflush(stdout);
-
- /* need to get a bigger kernel cache */
- if(*kernel_cache) {
- kernel_cache_size=(*kernel_cache)->buffsize*sizeof(CFLOAT)/(1024*1024);
- kernel_cache_cleanup(*kernel_cache);
- (*kernel_cache)=kernel_cache_init(totpair,kernel_cache_size);
- }
-
- /* must use unbiased hyperplane on difference vectors */
- learn_parm->biased_hyperplane=0;
- pairmodel=(MODEL *)my_malloc(sizeof(MODEL));
- svm_learn_classification(docdiff,target,totpair,totwords,learn_parm,
- kernel_parm,(*kernel_cache),pairmodel,NULL);
-
- /* Transfer the result into a more compact model. If you would like
- to output the original model on pairs of documents, see below. */
- alpha=(double *)my_malloc(sizeof(double)*totdoc);
- for(i=0;i<totdoc;i++) {
- alpha[i]=0;
- }
- for(i=1;i<pairmodel->sv_num;i++) {
- alpha[lesser[(pairmodel->supvec[i])->docnum]]-=pairmodel->alpha[i];
- alpha[greater[(pairmodel->supvec[i])->docnum]]+=pairmodel->alpha[i];
- }
- model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2));
- model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2));
- model->index = (long *)my_malloc(sizeof(long)*(totdoc+2));
- model->supvec[0]=0; /* element 0 reserved and empty for now */
- model->alpha[0]=0;
- model->sv_num=1;
- for(i=0;i<totdoc;i++) {
- if(alpha[i]) {
- model->supvec[model->sv_num]=docs[i];
- model->alpha[model->sv_num]=alpha[i];
- model->index[i]=model->sv_num;
- model->sv_num++;
- }
- else {
- model->index[i]=-1;
- }
- }
- model->at_upper_bound=0;
- model->b=0;
- model->lin_weights=NULL;
- model->totwords=totwords;
- model->totdoc=totdoc;
- model->kernel_parm=(*kernel_parm);
- model->loo_error=-1;
- model->loo_recall=-1;
- model->loo_precision=-1;
- model->xa_error=-1;
- model->xa_recall=-1;
- model->xa_precision=-1;
-
- free(alpha);
- free(greater);
- free(lesser);
- free(target);
-
- /* If you would like to output the original model on pairs of
- document, replace the following lines with '(*model)=(*pairmodel);' */
- for(i=0;i<totpair;i++)
- free_example(docdiff[i],1);
- free(docdiff);
- free_model(pairmodel,0);
-}
-
-
-/* The following solves a freely defined and given set of
- inequalities. The optimization problem is of the following form:
-
- min 0.5 w*w + C sum_i C_i \xi_i
- s.t. x_i * w > rhs_i - \xi_i
-
- This corresponds to the -z o option. */
-
-void svm_learn_optimization(DOC **docs, double *rhs, long int
- totdoc, long int totwords,
- LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache, MODEL *model,
- double *alpha)
- /* docs: Left-hand side of inequalities (x-part) */
- /* rhs: Right-hand side of inequalities */
- /* totdoc: Number of examples in docs/label */
- /* totwords: Number of features (i.e. highest feature index) */
- /* learn_parm: Learning paramenters */
- /* kernel_parm: Kernel paramenters */
- /* kernel_cache:Initialized Cache of size 1*totdoc, if using a kernel.
- NULL if linear.*/
- /* model: Returns solution as SV expansion (assumed empty before called) */
- /* alpha: Start values for the alpha variables or NULL
- pointer. The new alpha values are returned after
- optimization if not NULL. Array must be of size totdoc. */
-{
- long i,*label;
- long misclassified,upsupvecnum;
- double loss,model_length,example_length;
- double maxdiff,*lin,*a,*c;
- long runtime_start,runtime_end;
- long iterations,maxslackid,svsetnum;
- long *unlabeled,*inconsistent;
- double r_delta_sq=0,r_delta,r_delta_avg;
- long *index,*index2dnum;
- double *weights,*slack,*alphaslack;
- CFLOAT *aicache; /* buffer to keep one row of hessian */
-
- TIMING timing_profile;
- SHRINK_STATE shrink_state;
-
- runtime_start=get_runtime();
- timing_profile.time_kernel=0;
- timing_profile.time_opti=0;
- timing_profile.time_shrink=0;
- timing_profile.time_update=0;
- timing_profile.time_model=0;
- timing_profile.time_check=0;
- timing_profile.time_select=0;
- kernel_cache_statistic=0;
-
- learn_parm->totwords=totwords;
-
- /* make sure -n value is reasonable */
- if((learn_parm->svm_newvarsinqp < 2)
- || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) {
- learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
- }
-
- init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK);
-
- label = (long *)my_malloc(sizeof(long)*totdoc);
- unlabeled = (long *)my_malloc(sizeof(long)*totdoc);
- inconsistent = (long *)my_malloc(sizeof(long)*totdoc);
- c = (double *)my_malloc(sizeof(double)*totdoc);
- a = (double *)my_malloc(sizeof(double)*totdoc);
- lin = (double *)my_malloc(sizeof(double)*totdoc);
- learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc);
- model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2));
- model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2));
- model->index = (long *)my_malloc(sizeof(long)*(totdoc+2));
-
- model->at_upper_bound=0;
- model->b=0;
- model->supvec[0]=0; /* element 0 reserved and empty for now */
- model->alpha[0]=0;
- model->lin_weights=NULL;
- model->totwords=totwords;
- model->totdoc=totdoc;
- model->kernel_parm=(*kernel_parm);
- model->sv_num=1;
- model->loo_error=-1;
- model->loo_recall=-1;
- model->loo_precision=-1;
- model->xa_error=-1;
- model->xa_recall=-1;
- model->xa_precision=-1;
-
- r_delta=estimate_r_delta(docs,totdoc,kernel_parm);
- r_delta_sq=r_delta*r_delta;
-
- r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm);
- if(learn_parm->svm_c == 0.0) { /* default value for C */
- learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg);
- if(verbosity>=1)
- printf("Setting default regularization parameter C=%.4f\n",
- learn_parm->svm_c);
- }
-
- learn_parm->biased_hyperplane=0; /* learn an unbiased hyperplane */
-
- learn_parm->eps=0.0; /* No margin, unless explicitly handcoded
- in the right-hand side in the training
- set. */
-
- for(i=0;i<totdoc;i++) { /* various inits */
- docs[i]->docnum=i;
- a[i]=0;
- lin[i]=0;
- c[i]=rhs[i]; /* set right-hand side */
- unlabeled[i]=0;
- inconsistent[i]=0;
- learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio*
- docs[i]->costfactor;
- label[i]=1;
- }
- if(learn_parm->sharedslack) /* if shared slacks are used, they must */
- for(i=0;i<totdoc;i++) /* be used on every constraint */
- if(!docs[i]->slackid) {
- perror("Error: Missing shared slacks definitions in some of the examples.");
- exit(0);
- }
-
- /* compute starting state for initial alpha values */
- if(alpha) {
- if(verbosity>=1) {
- printf("Computing starting state..."); fflush(stdout);
- }
- index = (long *)my_malloc(sizeof(long)*totdoc);
- index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- weights=(double *)my_malloc(sizeof(double)*(totwords+1));
- aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc);
- for(i=0;i<totdoc;i++) { /* create full index and clip alphas */
- index[i]=1;
- alpha[i]=fabs(alpha[i]);
- if(alpha[i]<0) alpha[i]=0;
- if(alpha[i]>learn_parm->svm_cost[i]) alpha[i]=learn_parm->svm_cost[i];
- }
- if(kernel_parm->kernel_type != LINEAR) {
- for(i=0;i<totdoc;i++) /* fill kernel cache with unbounded SV */
- if((alpha[i]>0) && (alpha[i]<learn_parm->svm_cost[i])
- && (kernel_cache_space_available(kernel_cache)))
- cache_kernel_row(kernel_cache,docs,i,kernel_parm);
- for(i=0;i<totdoc;i++) /* fill rest of kernel cache with bounded SV */
- if((alpha[i]==learn_parm->svm_cost[i])
- && (kernel_cache_space_available(kernel_cache)))
- cache_kernel_row(kernel_cache,docs,i,kernel_parm);
- }
- (void)compute_index(index,totdoc,index2dnum);
- update_linear_component(docs,label,index2dnum,alpha,a,index2dnum,totdoc,
- totwords,kernel_parm,kernel_cache,lin,aicache,
- weights);
- (void)calculate_svm_model(docs,label,unlabeled,lin,alpha,a,c,
- learn_parm,index2dnum,index2dnum,model);
- for(i=0;i<totdoc;i++) { /* copy initial alphas */
- a[i]=alpha[i];
- }
- free(index);
- free(index2dnum);
- free(weights);
- free(aicache);
- if(verbosity>=1) {
- printf("done.\n"); fflush(stdout);
- }
- }
-
- /* removing inconsistent does not work for general optimization problem */
- if(learn_parm->remove_inconsistent) {
- learn_parm->remove_inconsistent = 0;
- printf("'remove inconsistent' not available in this mode. Switching option off!"); fflush(stdout);
- }
-
- /* caching makes no sense for linear kernel */
- if(kernel_parm->kernel_type == LINEAR) {
- kernel_cache = NULL;
- }
-
- if(verbosity==1) {
- printf("Optimizing"); fflush(stdout);
- }
-
- /* train the svm */
- if(learn_parm->sharedslack)
- iterations=optimize_to_convergence_sharedslack(docs,label,totdoc,
- totwords,learn_parm,kernel_parm,
- kernel_cache,&shrink_state,model,
- a,lin,c,&timing_profile,
- &maxdiff);
- else
- iterations=optimize_to_convergence(docs,label,totdoc,
- totwords,learn_parm,kernel_parm,
- kernel_cache,&shrink_state,model,
- inconsistent,unlabeled,
- a,lin,c,&timing_profile,
- &maxdiff,(long)-1,(long)1);
-
- if(verbosity>=1) {
- if(verbosity==1) printf("done. (%ld iterations)\n",iterations);
-
- misclassified=0;
- for(i=0;(i<totdoc);i++) { /* get final statistic */
- if((lin[i]-model->b)*(double)label[i] <= 0.0)
- misclassified++;
- }
-
- printf("Optimization finished (maxdiff=%.5f).\n",maxdiff);
-
- runtime_end=get_runtime();
- if(verbosity>=2) {
- printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n",
- ((float)runtime_end-(float)runtime_start)/100.0,
- (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start),
- (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start));
- }
- else {
- printf("Runtime in cpu-seconds: %.2f\n",
- (runtime_end-runtime_start)/100.0);
- }
- }
- if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) {
- loss=0;
- model_length=0;
- for(i=0;i<totdoc;i++) {
- if((lin[i]-model->b)*(double)label[i] < c[i]-learn_parm->epsilon_crit)
- loss+=c[i]-(lin[i]-model->b)*(double)label[i];
- model_length+=a[i]*label[i]*lin[i];
- }
- model_length=sqrt(model_length);
- fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length);
- }
-
- if(learn_parm->sharedslack) {
- index = (long *)my_malloc(sizeof(long)*totdoc);
- index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- maxslackid=0;
- for(i=0;i<totdoc;i++) { /* create full index */
- index[i]=1;
- if(maxslackid<docs[i]->slackid)
- maxslackid=docs[i]->slackid;
- }
- (void)compute_index(index,totdoc,index2dnum);
- slack=(double *)my_malloc(sizeof(double)*(maxslackid+1));
- alphaslack=(double *)my_malloc(sizeof(double)*(maxslackid+1));
- for(i=0;i<=maxslackid;i++) { /* init shared slacks */
- slack[i]=0;
- alphaslack[i]=0;
- }
- compute_shared_slacks(docs,label,a,lin,c,index2dnum,learn_parm,
- slack,alphaslack);
- loss=0;
- model->at_upper_bound=0;
- svsetnum=0;
- for(i=0;i<=maxslackid;i++) { /* create full index */
- loss+=slack[i];
- if(alphaslack[i] > (learn_parm->svm_c - learn_parm->epsilon_a))
- model->at_upper_bound++;
- if(alphaslack[i] > learn_parm->epsilon_a)
- svsetnum++;
- }
- free(index);
- free(index2dnum);
- free(slack);
- free(alphaslack);
- }
-
- if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) {
- if(learn_parm->sharedslack) {
- printf("Number of SV: %ld\n",
- model->sv_num-1);
- printf("Number of non-zero slack variables: %ld (out of %ld)\n",
- model->at_upper_bound,svsetnum);
- fprintf(stdout,"L1 loss: loss=%.5f\n",loss);
- }
- else {
- upsupvecnum=0;
- for(i=1;i<model->sv_num;i++) {
- if(fabs(model->alpha[i]) >=
- (learn_parm->svm_cost[(model->supvec[i])->docnum]-
- learn_parm->epsilon_a))
- upsupvecnum++;
- }
- printf("Number of SV: %ld (including %ld at upper bound)\n",
- model->sv_num-1,upsupvecnum);
- fprintf(stdout,"L1 loss: loss=%.5f\n",loss);
- }
- example_length=estimate_sphere(model,kernel_parm);
- fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n",
- length_of_longest_document_vector(docs,totdoc,kernel_parm));
- }
- if(verbosity>=1) {
- printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic);
- }
-
- if(alpha) {
- for(i=0;i<totdoc;i++) { /* copy final alphas */
- alpha[i]=a[i];
- }
- }
-
- if(learn_parm->alphafile[0])
- write_alphas(learn_parm->alphafile,a,label,totdoc);
-
- shrink_state_cleanup(&shrink_state);
- free(label);
- free(unlabeled);
- free(inconsistent);
- free(c);
- free(a);
- free(lin);
- free(learn_parm->svm_cost);
-}
-
-
-long optimize_to_convergence(DOC **docs, long int *label, long int totdoc,
- long int totwords, LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache,
- SHRINK_STATE *shrink_state, MODEL *model,
- long int *inconsistent, long int *unlabeled,
- double *a, double *lin, double *c,
- TIMING *timing_profile, double *maxdiff,
- long int heldout, long int retrain)
- /* docs: Training vectors (x-part) */
- /* label: Training labels/value (y-part, zero if test example for
- transduction) */
- /* totdoc: Number of examples in docs/label */
- /* totwords: Number of features (i.e. highest feature index) */
- /* laern_parm: Learning paramenters */
- /* kernel_parm: Kernel paramenters */
- /* kernel_cache: Initialized/partly filled Cache, if using a kernel.
- NULL if linear. */
- /* shrink_state: State of active variables */
- /* model: Returns learning result */
- /* inconsistent: examples thrown out as inconstistent */
- /* unlabeled: test examples for transduction */
- /* a: alphas */
- /* lin: linear component of gradient */
- /* c: right hand side of inequalities (margin) */
- /* maxdiff: returns maximum violation of KT-conditions */
- /* heldout: marks held-out example for leave-one-out (or -1) */
- /* retrain: selects training mode (1=regular / 2=holdout) */
-{
- long *chosen,*key,i,j,jj,*last_suboptimal_at,noshrink;
- long inconsistentnum,choosenum,already_chosen=0,iteration;
- long misclassified,supvecnum=0,*active2dnum,inactivenum;
- long *working2dnum,*selexam;
- long activenum;
- double criterion,eq;
- double *a_old;
- long t0=0,t1=0,t2=0,t3=0,t4=0,t5=0,t6=0; /* timing */
- long transductcycle;
- long transduction;
- double epsilon_crit_org;
- double bestmaxdiff;
- long bestmaxdiffiter,terminate;
-
- double *selcrit; /* buffer for sorting */
- CFLOAT *aicache; /* buffer to keep one row of hessian */
- double *weights; /* buffer for weight vector in linear case */
- QP qp; /* buffer for one quadratic program */
-
- epsilon_crit_org=learn_parm->epsilon_crit; /* save org */
- if(kernel_parm->kernel_type == LINEAR) {
- learn_parm->epsilon_crit=2.0;
- kernel_cache=NULL; /* caching makes no sense for linear kernel */
- }
- learn_parm->epsilon_shrink=2;
- (*maxdiff)=1;
-
- learn_parm->totwords=totwords;
-
- chosen = (long *)my_malloc(sizeof(long)*totdoc);
- last_suboptimal_at = (long *)my_malloc(sizeof(long)*totdoc);
- key = (long *)my_malloc(sizeof(long)*(totdoc+11));
- selcrit = (double *)my_malloc(sizeof(double)*totdoc);
- selexam = (long *)my_malloc(sizeof(long)*totdoc);
- a_old = (double *)my_malloc(sizeof(double)*totdoc);
- aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc);
- working2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- active2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- qp.opt_ce = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_ce0 = (double *)my_malloc(sizeof(double));
- qp.opt_g = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize
- *learn_parm->svm_maxqpsize);
- qp.opt_g0 = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_xinit = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_low=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_up=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- weights=(double *)my_malloc(sizeof(double)*(totwords+1));
-
- choosenum=0;
- inconsistentnum=0;
- transductcycle=0;
- transduction=0;
- if(!retrain) retrain=1;
- iteration=1;
- bestmaxdiffiter=1;
- bestmaxdiff=999999999;
- terminate=0;
-
- if(kernel_cache) {
- kernel_cache->time=iteration; /* for lru cache */
- kernel_cache_reset_lru(kernel_cache);
- }
-
- for(i=0;i<totdoc;i++) { /* various inits */
- chosen[i]=0;
- a_old[i]=a[i];
- last_suboptimal_at[i]=1;
- if(inconsistent[i])
- inconsistentnum++;
- if(unlabeled[i]) {
- transduction=1;
- }
- }
- activenum=compute_index(shrink_state->active,totdoc,active2dnum);
- inactivenum=totdoc-activenum;
- clear_index(working2dnum);
-
- /* repeat this loop until we have convergence */
- for(;retrain && (!terminate);iteration++) {
-
- if(kernel_cache)
- kernel_cache->time=iteration; /* for lru cache */
- if(verbosity>=2) {
- printf(
- "Iteration %ld: ",iteration); fflush(stdout);
- }
- else if(verbosity==1) {
- printf("."); fflush(stdout);
- }
-
- if(verbosity>=2) t0=get_runtime();
- if(verbosity>=3) {
- printf("\nSelecting working set... "); fflush(stdout);
- }
-
- if(learn_parm->svm_newvarsinqp>learn_parm->svm_maxqpsize)
- learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
-
- i=0;
- for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* clear working set */
- if((chosen[j]>=(learn_parm->svm_maxqpsize/
- minl(learn_parm->svm_maxqpsize,
- learn_parm->svm_newvarsinqp)))
- || (inconsistent[j])
- || (j == heldout)) {
- chosen[j]=0;
- choosenum--;
- }
- else {
- chosen[j]++;
- working2dnum[i++]=j;
- }
- }
- working2dnum[i]=-1;
-
- if(retrain == 2) {
- choosenum=0;
- for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* fully clear working set */
- chosen[j]=0;
- }
- clear_index(working2dnum);
- for(i=0;i<totdoc;i++) { /* set inconsistent examples to zero (-i 1) */
- if((inconsistent[i] || (heldout==i)) && (a[i] != 0.0)) {
- chosen[i]=99999;
- choosenum++;
- a[i]=0;
- }
- }
- if(learn_parm->biased_hyperplane) {
- eq=0;
- for(i=0;i<totdoc;i++) { /* make sure we fulfill equality constraint */
- eq+=a[i]*label[i];
- }
- for(i=0;(i<totdoc) && (fabs(eq) > learn_parm->epsilon_a);i++) {
- if((eq*label[i] > 0) && (a[i] > 0)) {
- chosen[i]=88888;
- choosenum++;
- if((eq*label[i]) > a[i]) {
- eq-=(a[i]*label[i]);
- a[i]=0;
- }
- else {
- a[i]-=(eq*label[i]);
- eq=0;
- }
- }
- }
- }
- compute_index(chosen,totdoc,working2dnum);
- }
- else { /* select working set according to steepest gradient */
- if(iteration % 101) {
- already_chosen=0;
- if((minl(learn_parm->svm_newvarsinqp,
- learn_parm->svm_maxqpsize-choosenum)>=4)
- && (kernel_parm->kernel_type != LINEAR)) {
- /* select part of the working set from cache */
- already_chosen=select_next_qp_subproblem_grad(
- label,unlabeled,a,lin,c,totdoc,
- (long)(minl(learn_parm->svm_maxqpsize-choosenum,
- learn_parm->svm_newvarsinqp)
- /2),
- learn_parm,inconsistent,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,1,
- key,chosen);
- choosenum+=already_chosen;
- }
- choosenum+=select_next_qp_subproblem_grad(
- label,unlabeled,a,lin,c,totdoc,
- minl(learn_parm->svm_maxqpsize-choosenum,
- learn_parm->svm_newvarsinqp-already_chosen),
- learn_parm,inconsistent,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,0,key,
- chosen);
- }
- else { /* once in a while, select a somewhat random working set
- to get unlocked of infinite loops due to numerical
- inaccuracies in the core qp-solver */
- choosenum+=select_next_qp_subproblem_rand(
- label,unlabeled,a,lin,c,totdoc,
- minl(learn_parm->svm_maxqpsize-choosenum,
- learn_parm->svm_newvarsinqp),
- learn_parm,inconsistent,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,key,
- chosen,iteration);
- }
- }
-
- if(verbosity>=2) {
- printf(" %ld vectors chosen\n",choosenum); fflush(stdout);
- }
-
- if(verbosity>=2) t1=get_runtime();
-
- if(kernel_cache)
- cache_multiple_kernel_rows(kernel_cache,docs,working2dnum,
- choosenum,kernel_parm);
-
- if(verbosity>=2) t2=get_runtime();
- if(retrain != 2) {
- optimize_svm(docs,label,unlabeled,inconsistent,0.0,chosen,active2dnum,
- model,totdoc,working2dnum,choosenum,a,lin,c,learn_parm,
- aicache,kernel_parm,&qp,&epsilon_crit_org);
- }
-
- if(verbosity>=2) t3=get_runtime();
- update_linear_component(docs,label,active2dnum,a,a_old,working2dnum,totdoc,
- totwords,kernel_parm,kernel_cache,lin,aicache,
- weights);
-
- if(verbosity>=2) t4=get_runtime();
- supvecnum=calculate_svm_model(docs,label,unlabeled,lin,a,a_old,c,
- learn_parm,working2dnum,active2dnum,model);
-
- if(verbosity>=2) t5=get_runtime();
-
- /* The following computation of the objective function works only */
- /* relative to the active variables */
- if(verbosity>=3) {
- criterion=compute_objective_function(a,lin,c,learn_parm->eps,label,
- active2dnum);
- printf("Objective function (over active variables): %.16f\n",criterion);
- fflush(stdout);
- }
-
- for(jj=0;(i=working2dnum[jj])>=0;jj++) {
- a_old[i]=a[i];
- }
-
- if(retrain == 2) { /* reset inconsistent unlabeled examples */
- for(i=0;(i<totdoc);i++) {
- if(inconsistent[i] && unlabeled[i]) {
- inconsistent[i]=0;
- label[i]=0;
- }
- }
- }
-
- retrain=check_optimality(model,label,unlabeled,a,lin,c,totdoc,learn_parm,
- maxdiff,epsilon_crit_org,&misclassified,
- inconsistent,active2dnum,last_suboptimal_at,
- iteration,kernel_parm);
-
- if(verbosity>=2) {
- t6=get_runtime();
- timing_profile->time_select+=t1-t0;
- timing_profile->time_kernel+=t2-t1;
- timing_profile->time_opti+=t3-t2;
- timing_profile->time_update+=t4-t3;
- timing_profile->time_model+=t5-t4;
- timing_profile->time_check+=t6-t5;
- }
-
- /* checking whether optimizer got stuck */
- if((*maxdiff) < bestmaxdiff) {
- bestmaxdiff=(*maxdiff);
- bestmaxdiffiter=iteration;
- }
- if(iteration > (bestmaxdiffiter+learn_parm->maxiter)) {
- /* long time no progress? */
- terminate=1;
- retrain=0;
- if(verbosity>=1)
- printf("\nWARNING: Relaxing KT-Conditions due to slow progress! Terminating!\n");
- }
-
- noshrink=0;
- if((!retrain) && (inactivenum>0)
- && ((!learn_parm->skip_final_opt_check)
- || (kernel_parm->kernel_type == LINEAR))) {
- if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR))
- || (verbosity>=2)) {
- if(verbosity==1) {
- printf("\n");
- }
- printf(" Checking optimality of inactive variables...");
- fflush(stdout);
- }
- t1=get_runtime();
- reactivate_inactive_examples(label,unlabeled,a,shrink_state,lin,c,totdoc,
- totwords,iteration,learn_parm,inconsistent,
- docs,kernel_parm,kernel_cache,model,aicache,
- weights,maxdiff);
- /* Update to new active variables. */
- activenum=compute_index(shrink_state->active,totdoc,active2dnum);
- inactivenum=totdoc-activenum;
- /* reset watchdog */
- bestmaxdiff=(*maxdiff);
- bestmaxdiffiter=iteration;
- /* termination criterion */
- noshrink=1;
- retrain=0;
- if((*maxdiff) > learn_parm->epsilon_crit)
- retrain=1;
- timing_profile->time_shrink+=get_runtime()-t1;
- if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR))
- || (verbosity>=2)) {
- printf("done.\n"); fflush(stdout);
- printf(" Number of inactive variables = %ld\n",inactivenum);
- }
- }
-
- if((!retrain) && (learn_parm->epsilon_crit>(*maxdiff)))
- learn_parm->epsilon_crit=(*maxdiff);
- if((!retrain) && (learn_parm->epsilon_crit>epsilon_crit_org)) {
- learn_parm->epsilon_crit/=2.0;
- retrain=1;
- noshrink=1;
- }
- if(learn_parm->epsilon_crit<epsilon_crit_org)
- learn_parm->epsilon_crit=epsilon_crit_org;
-
- if(verbosity>=2) {
- printf(" => (%ld SV (incl. %ld SV at u-bound), max violation=%.5f)\n",
- supvecnum,model->at_upper_bound,(*maxdiff));
- fflush(stdout);
- }
- if(verbosity>=3) {
- printf("\n");
- }
-
- if((!retrain) && (transduction)) {
- for(i=0;(i<totdoc);i++) {
- shrink_state->active[i]=1;
- }
- activenum=compute_index(shrink_state->active,totdoc,active2dnum);
- inactivenum=0;
- if(verbosity==1) printf("done\n");
- retrain=incorporate_unlabeled_examples(model,label,inconsistent,
- unlabeled,a,lin,totdoc,
- selcrit,selexam,key,
- transductcycle,kernel_parm,
- learn_parm);
- epsilon_crit_org=learn_parm->epsilon_crit;
- if(kernel_parm->kernel_type == LINEAR)
- learn_parm->epsilon_crit=1;
- transductcycle++;
- /* reset watchdog */
- bestmaxdiff=(*maxdiff);
- bestmaxdiffiter=iteration;
- }
- else if(((iteration % 10) == 0) && (!noshrink)) {
- activenum=shrink_problem(docs,learn_parm,shrink_state,kernel_parm,
- active2dnum,last_suboptimal_at,iteration,totdoc,
- maxl((long)(activenum/10),
- maxl((long)(totdoc/500),100)),
- a,inconsistent);
- inactivenum=totdoc-activenum;
- if((kernel_cache)
- && (supvecnum>kernel_cache->max_elems)
- && ((kernel_cache->activenum-activenum)>maxl((long)(activenum/10),500))) {
- kernel_cache_shrink(kernel_cache,totdoc,
- minl((kernel_cache->activenum-activenum),
- (kernel_cache->activenum-supvecnum)),
- shrink_state->active);
- }
- }
-
- if((!retrain) && learn_parm->remove_inconsistent) {
- if(verbosity>=1) {
- printf(" Moving training errors to inconsistent examples...");
- fflush(stdout);
- }
- if(learn_parm->remove_inconsistent == 1) {
- retrain=identify_inconsistent(a,label,unlabeled,totdoc,learn_parm,
- &inconsistentnum,inconsistent);
- }
- else if(learn_parm->remove_inconsistent == 2) {
- retrain=identify_misclassified(lin,label,unlabeled,totdoc,
- model,&inconsistentnum,inconsistent);
- }
- else if(learn_parm->remove_inconsistent == 3) {
- retrain=identify_one_misclassified(lin,label,unlabeled,totdoc,
- model,&inconsistentnum,inconsistent);
- }
- if(retrain) {
- if(kernel_parm->kernel_type == LINEAR) { /* reinit shrinking */
- learn_parm->epsilon_crit=2.0;
- }
- }
- if(verbosity>=1) {
- printf("done.\n");
- if(retrain) {
- printf(" Now %ld inconsistent examples.\n",inconsistentnum);
- }
- }
- }
- } /* end of loop */
-
- free(chosen);
- free(last_suboptimal_at);
- free(key);
- free(selcrit);
- free(selexam);
- free(a_old);
- free(aicache);
- free(working2dnum);
- free(active2dnum);
- free(qp.opt_ce);
- free(qp.opt_ce0);
- free(qp.opt_g);
- free(qp.opt_g0);
- free(qp.opt_xinit);
- free(qp.opt_low);
- free(qp.opt_up);
- free(weights);
-
- learn_parm->epsilon_crit=epsilon_crit_org; /* restore org */
- model->maxdiff=(*maxdiff);
-
- return(iteration);
-}
-
-long optimize_to_convergence_sharedslack(DOC **docs, long int *label,
- long int totdoc,
- long int totwords, LEARN_PARM *learn_parm,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache,
- SHRINK_STATE *shrink_state, MODEL *model,
- double *a, double *lin, double *c,
- TIMING *timing_profile, double *maxdiff)
- /* docs: Training vectors (x-part) */
- /* label: Training labels/value (y-part, zero if test example for
- transduction) */
- /* totdoc: Number of examples in docs/label */
- /* totwords: Number of features (i.e. highest feature index) */
- /* learn_parm: Learning paramenters */
- /* kernel_parm: Kernel paramenters */
- /* kernel_cache: Initialized/partly filled Cache, if using a kernel.
- NULL if linear. */
- /* shrink_state: State of active variables */
- /* model: Returns learning result */
- /* a: alphas */
- /* lin: linear component of gradient */
- /* c: right hand side of inequalities (margin) */
- /* maxdiff: returns maximum violation of KT-conditions */
-{
- long *chosen,*key,i,j,jj,*last_suboptimal_at,noshrink,*unlabeled;
- long *inconsistent,choosenum,already_chosen=0,iteration;
- long misclassified,supvecnum=0,*active2dnum,inactivenum;
- long *working2dnum,*selexam,*ignore;
- long activenum,retrain,maxslackid,slackset,jointstep;
- double criterion,eq_target;
- double *a_old,*alphaslack;
- long t0=0,t1=0,t2=0,t3=0,t4=0,t5=0,t6=0; /* timing */
- double epsilon_crit_org,maxsharedviol;
- double bestmaxdiff;
- long bestmaxdiffiter,terminate;
-
- double *selcrit; /* buffer for sorting */
- CFLOAT *aicache; /* buffer to keep one row of hessian */
- double *weights; /* buffer for weight vector in linear case */
- QP qp; /* buffer for one quadratic program */
- double *slack; /* vector of slack variables for optimization with
- shared slacks */
-
- epsilon_crit_org=learn_parm->epsilon_crit; /* save org */
- if(kernel_parm->kernel_type == LINEAR) {
- learn_parm->epsilon_crit=2.0;
- kernel_cache=NULL; /* caching makes no sense for linear kernel */
- }
- learn_parm->epsilon_shrink=2;
- (*maxdiff)=1;
-
- learn_parm->totwords=totwords;
-
- chosen = (long *)my_malloc(sizeof(long)*totdoc);
- unlabeled = (long *)my_malloc(sizeof(long)*totdoc);
- inconsistent = (long *)my_malloc(sizeof(long)*totdoc);
- ignore = (long *)my_malloc(sizeof(long)*totdoc);
- key = (long *)my_malloc(sizeof(long)*(totdoc+11));
- selcrit = (double *)my_malloc(sizeof(double)*totdoc);
- selexam = (long *)my_malloc(sizeof(long)*totdoc);
- a_old = (double *)my_malloc(sizeof(double)*totdoc);
- aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc);
- working2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- active2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- qp.opt_ce = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_ce0 = (double *)my_malloc(sizeof(double));
- qp.opt_g = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize
- *learn_parm->svm_maxqpsize);
- qp.opt_g0 = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_xinit = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_low=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- qp.opt_up=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize);
- weights=(double *)my_malloc(sizeof(double)*(totwords+1));
- maxslackid=0;
- for(i=0;i<totdoc;i++) { /* determine size of slack array */
- if(maxslackid<docs[i]->slackid)
- maxslackid=docs[i]->slackid;
- }
- slack=(double *)my_malloc(sizeof(double)*(maxslackid+1));
- alphaslack=(double *)my_malloc(sizeof(double)*(maxslackid+1));
- last_suboptimal_at = (long *)my_malloc(sizeof(long)*(maxslackid+1));
- for(i=0;i<=maxslackid;i++) { /* init shared slacks */
- slack[i]=0;
- alphaslack[i]=0;
- last_suboptimal_at[i]=1;
- }
-
- choosenum=0;
- retrain=1;
- iteration=1;
- bestmaxdiffiter=1;
- bestmaxdiff=999999999;
- terminate=0;
-
- if(kernel_cache) {
- kernel_cache->time=iteration; /* for lru cache */
- kernel_cache_reset_lru(kernel_cache);
- }
-
- for(i=0;i<totdoc;i++) { /* various inits */
- chosen[i]=0;
- unlabeled[i]=0;
- inconsistent[i]=0;
- ignore[i]=0;
- a_old[i]=a[i];
- }
- activenum=compute_index(shrink_state->active,totdoc,active2dnum);
- inactivenum=totdoc-activenum;
- clear_index(working2dnum);
-
- /* call to init slack and alphaslack */
- compute_shared_slacks(docs,label,a,lin,c,active2dnum,learn_parm,
- slack,alphaslack);
-
- /* repeat this loop until we have convergence */
- for(;retrain && (!terminate);iteration++) {
-
- if(kernel_cache)
- kernel_cache->time=iteration; /* for lru cache */
- if(verbosity>=2) {
- printf(
- "Iteration %ld: ",iteration); fflush(stdout);
- }
- else if(verbosity==1) {
- printf("."); fflush(stdout);
- }
-
- if(verbosity>=2) t0=get_runtime();
- if(verbosity>=3) {
- printf("\nSelecting working set... "); fflush(stdout);
- }
-
- if(learn_parm->svm_newvarsinqp>learn_parm->svm_maxqpsize)
- learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
-
- /* select working set according to steepest gradient */
- jointstep=0;
- eq_target=0;
- if(iteration % 101) {
- slackset=select_next_qp_slackset(docs,label,a,lin,slack,alphaslack,c,
- learn_parm,active2dnum,&maxsharedviol);
- if((iteration % 2)
- || (!slackset) || (maxsharedviol<learn_parm->epsilon_crit)){
- /* do a step with examples from different slack sets */
- if(verbosity >= 2) {
- printf("(i-step)"); fflush(stdout);
- }
- i=0;
- for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* clear old part of working set */
- if((chosen[j]>=(learn_parm->svm_maxqpsize/
- minl(learn_parm->svm_maxqpsize,
- learn_parm->svm_newvarsinqp)))) {
- chosen[j]=0;
- choosenum--;
- }
- else {
- chosen[j]++;
- working2dnum[i++]=j;
- }
- }
- working2dnum[i]=-1;
-
- already_chosen=0;
- if((minl(learn_parm->svm_newvarsinqp,
- learn_parm->svm_maxqpsize-choosenum)>=4)
- && (kernel_parm->kernel_type != LINEAR)) {
- /* select part of the working set from cache */
- already_chosen=select_next_qp_subproblem_grad(
- label,unlabeled,a,lin,c,totdoc,
- (long)(minl(learn_parm->svm_maxqpsize-choosenum,
- learn_parm->svm_newvarsinqp)
- /2),
- learn_parm,inconsistent,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,
- (long)1,key,chosen);
- choosenum+=already_chosen;
- }
- choosenum+=select_next_qp_subproblem_grad(
- label,unlabeled,a,lin,c,totdoc,
- minl(learn_parm->svm_maxqpsize-choosenum,
- learn_parm->svm_newvarsinqp-already_chosen),
- learn_parm,inconsistent,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,
- (long)0,key,chosen);
- }
- else { /* do a step with all examples from same slack set */
- if(verbosity >= 2) {
- printf("(j-step on %ld)",slackset); fflush(stdout);
- }
- jointstep=1;
- for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* clear working set */
- chosen[j]=0;
- }
- working2dnum[0]=-1;
- eq_target=alphaslack[slackset];
- for(j=0;j<totdoc;j++) { /* mask all but slackset */
- /* for(jj=0;(j=active2dnum[jj])>=0;jj++) { */
- if(docs[j]->slackid != slackset)
- ignore[j]=1;
- else {
- ignore[j]=0;
- learn_parm->svm_cost[j]=learn_parm->svm_c;
- /* printf("Inslackset(%ld,%ld)",j,shrink_state->active[j]); */
- }
- }
- learn_parm->biased_hyperplane=1;
- choosenum=select_next_qp_subproblem_grad(
- label,unlabeled,a,lin,c,totdoc,
- learn_parm->svm_maxqpsize,
- learn_parm,ignore,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,
- (long)0,key,chosen);
- learn_parm->biased_hyperplane=0;
- }
- }
- else { /* once in a while, select a somewhat random working set
- to get unlocked of infinite loops due to numerical
- inaccuracies in the core qp-solver */
- choosenum+=select_next_qp_subproblem_rand(
- label,unlabeled,a,lin,c,totdoc,
- minl(learn_parm->svm_maxqpsize-choosenum,
- learn_parm->svm_newvarsinqp),
- learn_parm,inconsistent,active2dnum,
- working2dnum,selcrit,selexam,kernel_cache,key,
- chosen,iteration);
- }
-
- if(verbosity>=2) {
- printf(" %ld vectors chosen\n",choosenum); fflush(stdout);
- }
-
- if(verbosity>=2) t1=get_runtime();
-
- if(kernel_cache)
- cache_multiple_kernel_rows(kernel_cache,docs,working2dnum,
- choosenum,kernel_parm);
-
- if(verbosity>=2) t2=get_runtime();
- if(jointstep) learn_parm->biased_hyperplane=1;
- optimize_svm(docs,label,unlabeled,ignore,eq_target,chosen,active2dnum,
- model,totdoc,working2dnum,choosenum,a,lin,c,learn_parm,
- aicache,kernel_parm,&qp,&epsilon_crit_org);
- learn_parm->biased_hyperplane=0;
-
- for(jj=0;(i=working2dnum[jj])>=0;jj++) /* recompute sums of alphas */
- alphaslack[docs[i]->slackid]+=(a[i]-a_old[i]);
- for(jj=0;(i=working2dnum[jj])>=0;jj++) { /* reduce alpha to fulfill
- constraints */
- if(alphaslack[docs[i]->slackid] > learn_parm->svm_c) {
- if(a[i] < (alphaslack[docs[i]->slackid]-learn_parm->svm_c)) {
- alphaslack[docs[i]->slackid]-=a[i];
- a[i]=0;
- }
- else {
- a[i]-=(alphaslack[docs[i]->slackid]-learn_parm->svm_c);
- alphaslack[docs[i]->slackid]=learn_parm->svm_c;
- }
- }
- }
- for(jj=0;(i=active2dnum[jj])>=0;jj++)
- learn_parm->svm_cost[i]=a[i]+(learn_parm->svm_c
- -alphaslack[docs[i]->slackid]);
-
- if(verbosity>=2) t3=get_runtime();
- update_linear_component(docs,label,active2dnum,a,a_old,working2dnum,totdoc,
- totwords,kernel_parm,kernel_cache,lin,aicache,
- weights);
- compute_shared_slacks(docs,label,a,lin,c,active2dnum,learn_parm,
- slack,alphaslack);
-
- if(verbosity>=2) t4=get_runtime();
- supvecnum=calculate_svm_model(docs,label,unlabeled,lin,a,a_old,c,
- learn_parm,working2dnum,active2dnum,model);
-
- if(verbosity>=2) t5=get_runtime();
-
- /* The following computation of the objective function works only */
- /* relative to the active variables */
- if(verbosity>=3) {
- criterion=compute_objective_function(a,lin,c,learn_parm->eps,label,
- active2dnum);
- printf("Objective function (over active variables): %.16f\n",criterion);
- fflush(stdout);
- }
-
- for(jj=0;(i=working2dnum[jj])>=0;jj++) {
- a_old[i]=a[i];
- }
-
- retrain=check_optimality_sharedslack(docs,model,label,a,lin,c,
- slack,alphaslack,totdoc,learn_parm,
- maxdiff,epsilon_crit_org,&misclassified,
- active2dnum,last_suboptimal_at,
- iteration,kernel_parm);
-
- if(verbosity>=2) {
- t6=get_runtime();
- timing_profile->time_select+=t1-t0;
- timing_profile->time_kernel+=t2-t1;
- timing_profile->time_opti+=t3-t2;
- timing_profile->time_update+=t4-t3;
- timing_profile->time_model+=t5-t4;
- timing_profile->time_check+=t6-t5;
- }
-
- /* checking whether optimizer got stuck */
- if((*maxdiff) < bestmaxdiff) {
- bestmaxdiff=(*maxdiff);
- bestmaxdiffiter=iteration;
- }
- if(iteration > (bestmaxdiffiter+learn_parm->maxiter)) {
- /* long time no progress? */
- terminate=1;
- retrain=0;
- if(verbosity>=1)
- printf("\nWARNING: Relaxing KT-Conditions due to slow progress! Terminating!\n");
- }
-
- noshrink=0;
-
- if((!retrain) && (inactivenum>0)
- && ((!learn_parm->skip_final_opt_check)
- || (kernel_parm->kernel_type == LINEAR))) {
- if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR))
- || (verbosity>=2)) {
- if(verbosity==1) {
- printf("\n");
- }
- printf(" Checking optimality of inactive variables...");
- fflush(stdout);
- }
- t1=get_runtime();
- reactivate_inactive_examples(label,unlabeled,a,shrink_state,lin,c,totdoc,
- totwords,iteration,learn_parm,inconsistent,
- docs,kernel_parm,kernel_cache,model,aicache,
- weights,maxdiff);
- /* Update to new active variables. */
- activenum=compute_index(shrink_state->active,totdoc,active2dnum);
- inactivenum=totdoc-activenum;
- /* check optimality, since check in reactivate does not work for
- sharedslacks */
- retrain=check_optimality_sharedslack(docs,model,label,a,lin,c,
- slack,alphaslack,totdoc,learn_parm,
- maxdiff,epsilon_crit_org,&misclassified,
- active2dnum,last_suboptimal_at,
- iteration,kernel_parm);
-
- /* reset watchdog */
- bestmaxdiff=(*maxdiff);
- bestmaxdiffiter=iteration;
- /* termination criterion */
- noshrink=1;
- retrain=0;
- if((*maxdiff) > learn_parm->epsilon_crit)
- retrain=1;
- timing_profile->time_shrink+=get_runtime()-t1;
- if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR))
- || (verbosity>=2)) {
- printf("done.\n"); fflush(stdout);
- printf(" Number of inactive variables = %ld\n",inactivenum);
- }
- }
-
- if((!retrain) && (learn_parm->epsilon_crit>(*maxdiff)))
- learn_parm->epsilon_crit=(*maxdiff);
- if((!retrain) && (learn_parm->epsilon_crit>epsilon_crit_org)) {
- learn_parm->epsilon_crit/=2.0;
- retrain=1;
- noshrink=1;
- }
- if(learn_parm->epsilon_crit<epsilon_crit_org)
- learn_parm->epsilon_crit=epsilon_crit_org;
-
- if(verbosity>=2) {
- printf(" => (%ld SV (incl. %ld SV at u-bound), max violation=%.5f)\n",
- supvecnum,model->at_upper_bound,(*maxdiff));
- fflush(stdout);
- }
- if(verbosity>=3) {
- printf("\n");
- }
-
- if(((iteration % 10) == 0) && (!noshrink)) {
- activenum=shrink_problem(docs,learn_parm,shrink_state,
- kernel_parm,active2dnum,
- last_suboptimal_at,iteration,totdoc,
- maxl((long)(activenum/10),
- maxl((long)(totdoc/500),100)),
- a,inconsistent);
- inactivenum=totdoc-activenum;
- if((kernel_cache)
- && (supvecnum>kernel_cache->max_elems)
- && ((kernel_cache->activenum-activenum)>maxl((long)(activenum/10),500))) {
- kernel_cache_shrink(kernel_cache,totdoc,
- minl((kernel_cache->activenum-activenum),
- (kernel_cache->activenum-supvecnum)),
- shrink_state->active);
- }
- }
-
- } /* end of loop */
-
-
- free(alphaslack);
- free(slack);
- free(chosen);
- free(unlabeled);
- free(inconsistent);
- free(ignore);
- free(last_suboptimal_at);
- free(key);
- free(selcrit);
- free(selexam);
- free(a_old);
- free(aicache);
- free(working2dnum);
- free(active2dnum);
- free(qp.opt_ce);
- free(qp.opt_ce0);
- free(qp.opt_g);
- free(qp.opt_g0);
- free(qp.opt_xinit);
- free(qp.opt_low);
- free(qp.opt_up);
- free(weights);
-
- learn_parm->epsilon_crit=epsilon_crit_org; /* restore org */
- model->maxdiff=(*maxdiff);
-
- return(iteration);
-}
-
-
-double compute_objective_function(double *a, double *lin, double *c,
- double eps, long int *label,
- long int *active2dnum)
- /* Return value of objective function. */
- /* Works only relative to the active variables! */
-{
- long i,ii;
- double criterion;
- /* calculate value of objective function */
- criterion=0;
- for(ii=0;active2dnum[ii]>=0;ii++) {
- i=active2dnum[ii];
- criterion=criterion+(eps-(double)label[i]*c[i])*a[i]+0.5*a[i]*label[i]*lin[i];
- }
- return(criterion);
-}
-
-void clear_index(long int *index)
- /* initializes and empties index */
-{
- index[0]=-1;
-}
-
-void add_to_index(long int *index, long int elem)
- /* initializes and empties index */
-{
- register long i;
- for(i=0;index[i] != -1;i++);
- index[i]=elem;
- index[i+1]=-1;
-}
-
-long compute_index(long int *binfeature, long int range, long int *index)
- /* create an inverted index of binfeature */
-{
- register long i,ii;
-
- ii=0;
- for(i=0;i<range;i++) {
- if(binfeature[i]) {
- index[ii]=i;
- ii++;
- }
- }
- for(i=0;i<4;i++) {
- index[ii+i]=-1;
- }
- return(ii);
-}
-
-
-void optimize_svm(DOC **docs, long int *label, long int *unlabeled,
- long int *exclude_from_eq_const, double eq_target,
- long int *chosen, long int *active2dnum, MODEL *model,
- long int totdoc, long int *working2dnum, long int varnum,
- double *a, double *lin, double *c, LEARN_PARM *learn_parm,
- CFLOAT *aicache, KERNEL_PARM *kernel_parm, QP *qp,
- double *epsilon_crit_target)
- /* Do optimization on the working set. */
-{
- long i;
- double *a_v;
-
- compute_matrices_for_optimization(docs,label,unlabeled,
- exclude_from_eq_const,eq_target,chosen,
- active2dnum,working2dnum,model,a,lin,c,
- varnum,totdoc,learn_parm,aicache,
- kernel_parm,qp);
-
- if(verbosity>=3) {
- printf("Running optimizer..."); fflush(stdout);
- }
- /* call the qp-subsolver */
- a_v=optimize_qp(qp,epsilon_crit_target,
- learn_parm->svm_maxqpsize,
- &(model->b), /* in case the optimizer gives us */
- /* the threshold for free. otherwise */
- /* b is calculated in calculate_model. */
- learn_parm);
- if(verbosity>=3) {
- printf("done\n");
- }
-
- for(i=0;i<varnum;i++) {
- a[working2dnum[i]]=a_v[i];
- /*
- if(a_v[i]<=(0+learn_parm->epsilon_a)) {
- a[working2dnum[i]]=0;
- }
- else if(a_v[i]>=(learn_parm->svm_cost[working2dnum[i]]-learn_parm->epsilon_a)) {
- a[working2dnum[i]]=learn_parm->svm_cost[working2dnum[i]];
- }
- */
- }
-}
-
-void compute_matrices_for_optimization(DOC **docs, long int *label,
- long int *unlabeled, long *exclude_from_eq_const, double eq_target,
- long int *chosen, long int *active2dnum,
- long int *key, MODEL *model, double *a, double *lin, double *c,
- long int varnum, long int totdoc, LEARN_PARM *learn_parm,
- CFLOAT *aicache, KERNEL_PARM *kernel_parm, QP *qp)
-{
- register long ki,kj,i,j;
- register double kernel_temp;
-
- if(verbosity>=3) {
- fprintf(stdout,"Computing qp-matrices (type %ld kernel [degree %ld, rbf_gamma %f, coef_lin %f, coef_const %f])...",kernel_parm->kernel_type,kernel_parm->poly_degree,kernel_parm->rbf_gamma,kernel_parm->coef_lin,kernel_parm->coef_const);
- fflush(stdout);
- }
-
- qp->opt_n=varnum;
- qp->opt_ce0[0]=-eq_target; /* compute the constant for equality constraint */
- for(j=1;j<model->sv_num;j++) { /* start at 1 */
- if((!chosen[(model->supvec[j])->docnum])
- && (!exclude_from_eq_const[(model->supvec[j])->docnum])) {
- qp->opt_ce0[0]+=model->alpha[j];
- }
- }
- if(learn_parm->biased_hyperplane)
- qp->opt_m=1;
- else
- qp->opt_m=0; /* eq-constraint will be ignored */
-
- /* init linear part of objective function */
- for(i=0;i<varnum;i++) {
- qp->opt_g0[i]=lin[key[i]];
- }
-
- for(i=0;i<varnum;i++) {
- ki=key[i];
-
- /* Compute the matrix for equality constraints */
- qp->opt_ce[i]=label[ki];
- qp->opt_low[i]=0;
- qp->opt_up[i]=learn_parm->svm_cost[ki];
-
- kernel_temp=(double)kernel(kernel_parm,docs[ki],docs[ki]);
- /* compute linear part of objective function */
- qp->opt_g0[i]-=(kernel_temp*a[ki]*(double)label[ki]);
- /* compute quadratic part of objective function */
- qp->opt_g[varnum*i+i]=kernel_temp;
- for(j=i+1;j<varnum;j++) {
- kj=key[j];
- kernel_temp=(double)kernel(kernel_parm,docs[ki],docs[kj]);
- /* compute linear part of objective function */
- qp->opt_g0[i]-=(kernel_temp*a[kj]*(double)label[kj]);
- qp->opt_g0[j]-=(kernel_temp*a[ki]*(double)label[ki]);
- /* compute quadratic part of objective function */
- qp->opt_g[varnum*i+j]=(double)label[ki]*(double)label[kj]*kernel_temp;
- qp->opt_g[varnum*j+i]=(double)label[ki]*(double)label[kj]*kernel_temp;
- }
-
- if(verbosity>=3) {
- if(i % 20 == 0) {
- fprintf(stdout,"%ld..",i); fflush(stdout);
- }
- }
- }
-
- for(i=0;i<varnum;i++) {
- /* assure starting at feasible point */
- qp->opt_xinit[i]=a[key[i]];
- /* set linear part of objective function */
- qp->opt_g0[i]=(learn_parm->eps-(double)label[key[i]]*c[key[i]])+qp->opt_g0[i]*(double)label[key[i]];
- }
-
- if(verbosity>=3) {
- fprintf(stdout,"done\n");
- }
-}
-
-long calculate_svm_model(DOC **docs, long int *label, long int *unlabeled,
- double *lin, double *a, double *a_old, double *c,
- LEARN_PARM *learn_parm, long int *working2dnum,
- long int *active2dnum, MODEL *model)
- /* Compute decision function based on current values */
- /* of alpha. */
-{
- long i,ii,pos,b_calculated=0,first_low,first_high;
- double ex_c,b_temp,b_low,b_high;
-
- if(verbosity>=3) {
- printf("Calculating model..."); fflush(stdout);
- }
-
- if(!learn_parm->biased_hyperplane) {
- model->b=0;
- b_calculated=1;
- }
-
- for(ii=0;(i=working2dnum[ii])>=0;ii++) {
- if((a_old[i]>0) && (a[i]==0)) { /* remove from model */
- pos=model->index[i];
- model->index[i]=-1;
- (model->sv_num)--;
- model->supvec[pos]=model->supvec[model->sv_num];
- model->alpha[pos]=model->alpha[model->sv_num];
- model->index[(model->supvec[pos])->docnum]=pos;
- }
- else if((a_old[i]==0) && (a[i]>0)) { /* add to model */
- model->supvec[model->sv_num]=docs[i];
- model->alpha[model->sv_num]=a[i]*(double)label[i];
- model->index[i]=model->sv_num;
- (model->sv_num)++;
- }
- else if(a_old[i]==a[i]) { /* nothing to do */
- }
- else { /* just update alpha */
- model->alpha[model->index[i]]=a[i]*(double)label[i];
- }
-
- ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a;
- if((a_old[i]>=ex_c) && (a[i]<ex_c)) {
- (model->at_upper_bound)--;
- }
- else if((a_old[i]<ex_c) && (a[i]>=ex_c)) {
- (model->at_upper_bound)++;
- }
-
- if((!b_calculated)
- && (a[i]>learn_parm->epsilon_a) && (a[i]<ex_c)) { /* calculate b */
- model->b=((double)label[i]*learn_parm->eps-c[i]+lin[i]);
- /* model->b=(-(double)label[i]+lin[i]); */
- b_calculated=1;
- }
- }
-
- /* No alpha in the working set not at bounds, so b was not
- calculated in the usual way. The following handles this special
- case. */
- if(learn_parm->biased_hyperplane
- && (!b_calculated)
- && (model->sv_num-1 == model->at_upper_bound)) {
- first_low=1;
- first_high=1;
- b_low=0;
- b_high=0;
- for(ii=0;(i=active2dnum[ii])>=0;ii++) {
- ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a;
- if(a[i]<ex_c) {
- if(label[i]>0) {
- b_temp=-(learn_parm->eps-c[i]+lin[i]);
- if((b_temp>b_low) || (first_low)) {
- b_low=b_temp;
- first_low=0;
- }
- }
- else {
- b_temp=-(-learn_parm->eps-c[i]+lin[i]);
- if((b_temp<b_high) || (first_high)) {
- b_high=b_temp;
- first_high=0;
- }
- }
- }
- else {
- if(label[i]<0) {
- b_temp=-(-learn_parm->eps-c[i]+lin[i]);
- if((b_temp>b_low) || (first_low)) {
- b_low=b_temp;
- first_low=0;
- }
- }
- else {
- b_temp=-(learn_parm->eps-c[i]+lin[i]);
- if((b_temp<b_high) || (first_high)) {
- b_high=b_temp;
- first_high=0;
- }
- }
- }
- }
- if(first_high) {
- model->b=-b_low;
- }
- else if(first_low) {
- model->b=-b_high;
- }
- else {
- model->b=-(b_high+b_low)/2.0; /* select b as the middle of range */
- /* printf("\nb_low=%f, b_high=%f,b=%f\n",b_low,b_high,model->b); */
- }
- }
-
- if(verbosity>=3) {
- printf("done\n"); fflush(stdout);
- }
-
- return(model->sv_num-1); /* have to substract one, since element 0 is empty*/
-}
-
-long check_optimality(MODEL *model, long int *label, long int *unlabeled,
- double *a, double *lin, double *c, long int totdoc,
- LEARN_PARM *learn_parm, double *maxdiff,
- double epsilon_crit_org, long int *misclassified,
- long int *inconsistent, long int *active2dnum,
- long int *last_suboptimal_at,
- long int iteration, KERNEL_PARM *kernel_parm)
- /* Check KT-conditions */
-{
- long i,ii,retrain;
- double dist,ex_c,target;
-
- if(kernel_parm->kernel_type == LINEAR) { /* be optimistic */
- learn_parm->epsilon_shrink=-learn_parm->epsilon_crit+epsilon_crit_org;
- }
- else { /* be conservative */
- learn_parm->epsilon_shrink=learn_parm->epsilon_shrink*0.7+(*maxdiff)*0.3;
- }
- retrain=0;
- (*maxdiff)=0;
- (*misclassified)=0;
- for(ii=0;(i=active2dnum[ii])>=0;ii++) {
- if((!inconsistent[i]) && label[i]) {
- dist=(lin[i]-model->b)*(double)label[i];/* 'distance' from
- hyperplane*/
- target=-(learn_parm->eps-(double)label[i]*c[i]);
- ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a;
- if(dist <= 0) {
- (*misclassified)++; /* does not work due to deactivation of var */
- }
- if((a[i]>learn_parm->epsilon_a) && (dist > target)) {
- if((dist-target)>(*maxdiff)) /* largest violation */
- (*maxdiff)=dist-target;
- }
- else if((a[i]<ex_c) && (dist < target)) {
- if((target-dist)>(*maxdiff)) /* largest violation */
- (*maxdiff)=target-dist;
- }
- /* Count how long a variable was at lower/upper bound (and optimal).*/
- /* Variables, which were at the bound and optimal for a long */
- /* time are unlikely to become support vectors. In case our */
- /* cache is filled up, those variables are excluded to save */
- /* kernel evaluations. (See chapter 'Shrinking').*/
- if((a[i]>(learn_parm->epsilon_a))
- && (a[i]<ex_c)) {
- last_suboptimal_at[i]=iteration; /* not at bound */
- }
- else if((a[i]<=(learn_parm->epsilon_a))
- && (dist < (target+learn_parm->epsilon_shrink))) {
- last_suboptimal_at[i]=iteration; /* not likely optimal */
- }
- else if((a[i]>=ex_c)
- && (dist > (target-learn_parm->epsilon_shrink))) {
- last_suboptimal_at[i]=iteration; /* not likely optimal */
- }
- }
- }
- /* termination criterion */
- if((!retrain) && ((*maxdiff) > learn_parm->epsilon_crit)) {
- retrain=1;
- }
- return(retrain);
-}
-
-long check_optimality_sharedslack(DOC **docs, MODEL *model, long int *label,
- double *a, double *lin, double *c, double *slack,
- double *alphaslack,
- long int totdoc,
- LEARN_PARM *learn_parm, double *maxdiff,
- double epsilon_crit_org, long int *misclassified,
- long int *active2dnum,
- long int *last_suboptimal_at,
- long int iteration, KERNEL_PARM *kernel_parm)
- /* Check KT-conditions */
-{
- long i,ii,retrain;
- double dist,ex_c=0,target;
-
- if(kernel_parm->kernel_type == LINEAR) { /* be optimistic */
- learn_parm->epsilon_shrink=-learn_parm->epsilon_crit+epsilon_crit_org;
- }
- else { /* be conservative */
- learn_parm->epsilon_shrink=learn_parm->epsilon_shrink*0.7+(*maxdiff)*0.3;
- }
-
- retrain=0;
- (*maxdiff)=0;
- (*misclassified)=0;
- for(ii=0;(i=active2dnum[ii])>=0;ii++) {
- /* 'distance' from hyperplane*/
- dist=(lin[i]-model->b)*(double)label[i]+slack[docs[i]->slackid];
- target=-(learn_parm->eps-(double)label[i]*c[i]);
- ex_c=learn_parm->svm_c-learn_parm->epsilon_a;
- if((a[i]>learn_parm->epsilon_a) && (dist > target)) {
- if((dist-target)>(*maxdiff)) { /* largest violation */
- (*maxdiff)=dist-target;
- if(verbosity>=5) printf("sid %ld: dist=%.2f, target=%.2f, slack=%.2f, a=%f, alphaslack=%f\n",docs[i]->slackid,dist,target,slack[docs[i]->slackid],a[i],alphaslack[docs[i]->slackid]);
- if(verbosity>=5) printf(" (single %f)\n",(*maxdiff));
- }
- }
- if((alphaslack[docs[i]->slackid]<ex_c) && (slack[docs[i]->slackid]>0)) {
- if((slack[docs[i]->slackid])>(*maxdiff)) { /* largest violation */
- (*maxdiff)=slack[docs[i]->slackid];
- if(verbosity>=5) printf("sid %ld: dist=%.2f, target=%.2f, slack=%.2f, a=%f, alphaslack=%f\n",docs[i]->slackid,dist,target,slack[docs[i]->slackid],a[i],alphaslack[docs[i]->slackid]);
- if(verbosity>=5) printf(" (joint %f)\n",(*maxdiff));
- }
- }
- /* Count how long a variable was at lower/upper bound (and optimal).*/
- /* Variables, which were at the bound and optimal for a long */
- /* time are unlikely to become support vectors. In case our */
- /* cache is filled up, those variables are excluded to save */
- /* kernel evaluations. (See chapter 'Shrinking').*/
- if((a[i]>(learn_parm->epsilon_a))
- && (a[i]<ex_c)) {
- last_suboptimal_at[docs[i]->slackid]=iteration; /* not at bound */
- }
- else if((a[i]<=(learn_parm->epsilon_a))
- && (dist < (target+learn_parm->epsilon_shrink))) {
- last_suboptimal_at[docs[i]->slackid]=iteration; /* not likely optimal */
- }
- else if((a[i]>=ex_c)
- && (slack[docs[i]->slackid] < learn_parm->epsilon_shrink)) {
- last_suboptimal_at[docs[i]->slackid]=iteration; /* not likely optimal */
- }
- }
- /* termination criterion */
- if((!retrain) && ((*maxdiff) > learn_parm->epsilon_crit)) {
- retrain=1;
- }
- return(retrain);
-}
-
-void compute_shared_slacks(DOC **docs, long int *label,
- double *a, double *lin,
- double *c, long int *active2dnum,
- LEARN_PARM *learn_parm,
- double *slack, double *alphaslack)
- /* compute the value of shared slacks and the joint alphas */
-{
- long jj,i;
- double dist,target;
-
- for(jj=0;(i=active2dnum[jj])>=0;jj++) { /* clear slack variables */
- slack[docs[i]->slackid]=0.0;
- alphaslack[docs[i]->slackid]=0.0;
- }
- for(jj=0;(i=active2dnum[jj])>=0;jj++) { /* recompute slack variables */
- dist=(lin[i])*(double)label[i];
- target=-(learn_parm->eps-(double)label[i]*c[i]);
- if((target-dist) > slack[docs[i]->slackid])
- slack[docs[i]->slackid]=target-dist;
- alphaslack[docs[i]->slackid]+=a[i];
- }
-}
-
-
-long identify_inconsistent(double *a, long int *label,
- long int *unlabeled, long int totdoc,
- LEARN_PARM *learn_parm,
- long int *inconsistentnum, long int *inconsistent)
-{
- long i,retrain;
-
- /* Throw out examples with multipliers at upper bound. This */
- /* corresponds to the -i 1 option. */
- /* ATTENTION: this is just a heuristic for finding a close */
- /* to minimum number of examples to exclude to */
- /* make the problem separable with desired margin */
- retrain=0;
- for(i=0;i<totdoc;i++) {
- if((!inconsistent[i]) && (!unlabeled[i])
- && (a[i]>=(learn_parm->svm_cost[i]-learn_parm->epsilon_a))) {
- (*inconsistentnum)++;
- inconsistent[i]=1; /* never choose again */
- retrain=2; /* start over */
- if(verbosity>=3) {
- printf("inconsistent(%ld)..",i); fflush(stdout);
- }
- }
- }
- return(retrain);
-}
-
-long identify_misclassified(double *lin, long int *label,
- long int *unlabeled, long int totdoc,
- MODEL *model, long int *inconsistentnum,
- long int *inconsistent)
-{
- long i,retrain;
- double dist;
-
- /* Throw out misclassified examples. This */
- /* corresponds to the -i 2 option. */
- /* ATTENTION: this is just a heuristic for finding a close */
- /* to minimum number of examples to exclude to */
- /* make the problem separable with desired margin */
- retrain=0;
- for(i=0;i<totdoc;i++) {
- dist=(lin[i]-model->b)*(double)label[i]; /* 'distance' from hyperplane*/
- if((!inconsistent[i]) && (!unlabeled[i]) && (dist <= 0)) {
- (*inconsistentnum)++;
- inconsistent[i]=1; /* never choose again */
- retrain=2; /* start over */
- if(verbosity>=3) {
- printf("inconsistent(%ld)..",i); fflush(stdout);
- }
- }
- }
- return(retrain);
-}
-
-long identify_one_misclassified(double *lin, long int *label,
- long int *unlabeled,
- long int totdoc, MODEL *model,
- long int *inconsistentnum,
- long int *inconsistent)
-{
- long i,retrain,maxex=-1;
- double dist,maxdist=0;
-
- /* Throw out the 'most misclassified' example. This */
- /* corresponds to the -i 3 option. */
- /* ATTENTION: this is just a heuristic for finding a close */
- /* to minimum number of examples to exclude to */
- /* make the problem separable with desired margin */
- retrain=0;
- for(i=0;i<totdoc;i++) {
- if((!inconsistent[i]) && (!unlabeled[i])) {
- dist=(lin[i]-model->b)*(double)label[i];/* 'distance' from hyperplane*/
- if(dist<maxdist) {
- maxdist=dist;
- maxex=i;
- }
- }
- }
- if(maxex>=0) {
- (*inconsistentnum)++;
- inconsistent[maxex]=1; /* never choose again */
- retrain=2; /* start over */
- if(verbosity>=3) {
- printf("inconsistent(%ld)..",i); fflush(stdout);
- }
- }
- return(retrain);
-}
-
-void update_linear_component(DOC **docs, long int *label,
- long int *active2dnum, double *a,
- double *a_old, long int *working2dnum,
- long int totdoc, long int totwords,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache,
- double *lin, CFLOAT *aicache, double *weights)
- /* keep track of the linear component */
- /* lin of the gradient etc. by updating */
- /* based on the change of the variables */
- /* in the current working set */
-{
- register long i,ii,j,jj;
- register double tec;
- SVECTOR *f;
-
- if(kernel_parm->kernel_type==0) { /* special linear case */
- clear_vector_n(weights,totwords);
- for(ii=0;(i=working2dnum[ii])>=0;ii++) {
- if(a[i] != a_old[i]) {
- for(f=docs[i]->fvec;f;f=f->next)
- add_vector_ns(weights,f,
- f->factor*((a[i]-a_old[i])*(double)label[i]));
- }
- }
- for(jj=0;(j=active2dnum[jj])>=0;jj++) {
- for(f=docs[j]->fvec;f;f=f->next)
- lin[j]+=f->factor*sprod_ns(weights,f);
- }
- }
- else { /* general case */
- for(jj=0;(i=working2dnum[jj])>=0;jj++) {
- if(a[i] != a_old[i]) {
- get_kernel_row(kernel_cache,docs,i,totdoc,active2dnum,aicache,
- kernel_parm);
- for(ii=0;(j=active2dnum[ii])>=0;ii++) {
- tec=aicache[j];
- lin[j]+=(((a[i]*tec)-(a_old[i]*tec))*(double)label[i]);
- }
- }
- }
- }
-}
-
-
-long incorporate_unlabeled_examples(MODEL *model, long int *label,
- long int *inconsistent,
- long int *unlabeled,
- double *a, double *lin,
- long int totdoc, double *selcrit,
- long int *select, long int *key,
- long int transductcycle,
- KERNEL_PARM *kernel_parm,
- LEARN_PARM *learn_parm)
-{
- long i,j,k,j1,j2,j3,j4,unsupaddnum1=0,unsupaddnum2=0;
- long pos,neg,upos,uneg,orgpos,orgneg,nolabel,newpos,newneg,allunlab;
- double dist,model_length,posratio,negratio;
- long check_every=2;
- double loss;
- static double switchsens=0.0,switchsensorg=0.0;
- double umin,umax,sumalpha;
- long imin=0,imax=0;
- static long switchnum=0;
-
- switchsens/=1.2;
-
- /* assumes that lin[] is up to date -> no inactive vars */
-
- orgpos=0;
- orgneg=0;
- newpos=0;
- newneg=0;
- nolabel=0;
- allunlab=0;
- for(i=0;i<totdoc;i++) {
- if(!unlabeled[i]) {
- if(label[i] > 0) {
- orgpos++;
- }
- else {
- orgneg++;
- }
- }
- else {
- allunlab++;
- if(unlabeled[i]) {
- if(label[i] > 0) {
- newpos++;
- }
- else if(label[i] < 0) {
- newneg++;
- }
- }
- }
- if(label[i]==0) {
- nolabel++;
- }
- }
-
- if(learn_parm->transduction_posratio >= 0) {
- posratio=learn_parm->transduction_posratio;
- }
- else {
- posratio=(double)orgpos/(double)(orgpos+orgneg); /* use ratio of pos/neg */
- } /* in training data */
- negratio=1.0-posratio;
-
- learn_parm->svm_costratio=1.0; /* global */
- if(posratio>0) {
- learn_parm->svm_costratio_unlab=negratio/posratio;
- }
- else {
- learn_parm->svm_costratio_unlab=1.0;
- }
-
- pos=0;
- neg=0;
- upos=0;
- uneg=0;
- for(i=0;i<totdoc;i++) {
- dist=(lin[i]-model->b); /* 'distance' from hyperplane*/
- if(dist>0) {
- pos++;
- }
- else {
- neg++;
- }
- if(unlabeled[i]) {
- if(dist>0) {
- upos++;
- }
- else {
- uneg++;
- }
- }
- if((!unlabeled[i]) && (a[i]>(learn_parm->svm_cost[i]-learn_parm->epsilon_a))) {
- /* printf("Ubounded %ld (class %ld, unlabeled %ld)\n",i,label[i],unlabeled[i]); */
- }
- }
- if(verbosity>=2) {
- printf("POS=%ld, ORGPOS=%ld, ORGNEG=%ld\n",pos,orgpos,orgneg);
- printf("POS=%ld, NEWPOS=%ld, NEWNEG=%ld\n",pos,newpos,newneg);
- printf("pos ratio = %f (%f).\n",(double)(upos)/(double)(allunlab),posratio);
- fflush(stdout);
- }
-
- if(transductcycle == 0) {
- j1=0;
- j2=0;
- j4=0;
- for(i=0;i<totdoc;i++) {
- dist=(lin[i]-model->b); /* 'distance' from hyperplane*/
- if((label[i]==0) && (unlabeled[i])) {
- selcrit[j4]=dist;
- key[j4]=i;
- j4++;
- }
- }
- unsupaddnum1=0;
- unsupaddnum2=0;
- select_top_n(selcrit,j4,select,(long)(allunlab*posratio+0.5));
- for(k=0;(k<(long)(allunlab*posratio+0.5));k++) {
- i=key[select[k]];
- label[i]=1;
- unsupaddnum1++;
- j1++;
- }
- for(i=0;i<totdoc;i++) {
- if((label[i]==0) && (unlabeled[i])) {
- label[i]=-1;
- j2++;
- unsupaddnum2++;
- }
- }
- for(i=0;i<totdoc;i++) { /* set upper bounds on vars */
- if(unlabeled[i]) {
- if(label[i] == 1) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*
- learn_parm->svm_costratio_unlab*learn_parm->svm_unlabbound;
- }
- else if(label[i] == -1) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*
- learn_parm->svm_unlabbound;
- }
- }
- }
- if(verbosity>=1) {
- /* printf("costratio %f, costratio_unlab %f, unlabbound %f\n",
- learn_parm->svm_costratio,learn_parm->svm_costratio_unlab,
- learn_parm->svm_unlabbound); */
- printf("Classifying unlabeled data as %ld POS / %ld NEG.\n",
- unsupaddnum1,unsupaddnum2);
- fflush(stdout);
- }
- if(verbosity >= 1)
- printf("Retraining.");
- if(verbosity >= 2) printf("\n");
- return((long)3);
- }
- if((transductcycle % check_every) == 0) {
- if(verbosity >= 1)
- printf("Retraining.");
- if(verbosity >= 2) printf("\n");
- j1=0;
- j2=0;
- unsupaddnum1=0;
- unsupaddnum2=0;
- for(i=0;i<totdoc;i++) {
- if((unlabeled[i] == 2)) {
- unlabeled[i]=1;
- label[i]=1;
- j1++;
- unsupaddnum1++;
- }
- else if((unlabeled[i] == 3)) {
- unlabeled[i]=1;
- label[i]=-1;
- j2++;
- unsupaddnum2++;
- }
- }
- for(i=0;i<totdoc;i++) { /* set upper bounds on vars */
- if(unlabeled[i]) {
- if(label[i] == 1) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*
- learn_parm->svm_costratio_unlab*learn_parm->svm_unlabbound;
- }
- else if(label[i] == -1) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*
- learn_parm->svm_unlabbound;
- }
- }
- }
-
- if(verbosity>=2) {
- /* printf("costratio %f, costratio_unlab %f, unlabbound %f\n",
- learn_parm->svm_costratio,learn_parm->svm_costratio_unlab,
- learn_parm->svm_unlabbound); */
- printf("%ld positive -> Added %ld POS / %ld NEG unlabeled examples.\n",
- upos,unsupaddnum1,unsupaddnum2);
- fflush(stdout);
- }
-
- if(learn_parm->svm_unlabbound == 1) {
- learn_parm->epsilon_crit=0.001; /* do the last run right */
- }
- else {
- learn_parm->epsilon_crit=0.01; /* otherwise, no need to be so picky */
- }
-
- return((long)3);
- }
- else if(((transductcycle % check_every) < check_every)) {
- model_length=0;
- sumalpha=0;
- loss=0;
- for(i=0;i<totdoc;i++) {
- model_length+=a[i]*label[i]*lin[i];
- sumalpha+=a[i];
- dist=(lin[i]-model->b); /* 'distance' from hyperplane*/
- if((label[i]*dist)<(1.0-learn_parm->epsilon_crit)) {
- loss+=(1.0-(label[i]*dist))*learn_parm->svm_cost[i];
- }
- }
- model_length=sqrt(model_length);
- if(verbosity>=2) {
- printf("Model-length = %f (%f), loss = %f, objective = %f\n",
- model_length,sumalpha,loss,loss+0.5*model_length*model_length);
- fflush(stdout);
- }
- j1=0;
- j2=0;
- j3=0;
- j4=0;
- unsupaddnum1=0;
- unsupaddnum2=0;
- umin=99999;
- umax=-99999;
- j4=1;
- while(j4) {
- umin=99999;
- umax=-99999;
- for(i=0;(i<totdoc);i++) {
- dist=(lin[i]-model->b);
- if((label[i]>0) && (unlabeled[i]) && (!inconsistent[i])
- && (dist<umin)) {
- umin=dist;
- imin=i;
- }
- if((label[i]<0) && (unlabeled[i]) && (!inconsistent[i])
- && (dist>umax)) {
- umax=dist;
- imax=i;
- }
- }
- if((umin < (umax+switchsens-1E-4))) {
- j1++;
- j2++;
- unsupaddnum1++;
- unlabeled[imin]=3;
- inconsistent[imin]=1;
- unsupaddnum2++;
- unlabeled[imax]=2;
- inconsistent[imax]=1;
- }
- else
- j4=0;
- j4=0;
- }
- for(j=0;(j<totdoc);j++) {
- if(unlabeled[j] && (!inconsistent[j])) {
- if(label[j]>0) {
- unlabeled[j]=2;
- }
- else if(label[j]<0) {
- unlabeled[j]=3;
- }
- /* inconsistent[j]=1; */
- j3++;
- }
- }
- switchnum+=unsupaddnum1+unsupaddnum2;
-
- /* stop and print out current margin
- printf("switchnum %ld %ld\n",switchnum,kernel_parm->poly_degree);
- if(switchnum == 2*kernel_parm->poly_degree) {
- learn_parm->svm_unlabbound=1;
- }
- */
-
- if((!unsupaddnum1) && (!unsupaddnum2)) {
- if((learn_parm->svm_unlabbound>=1) && ((newpos+newneg) == allunlab)) {
- for(j=0;(j<totdoc);j++) {
- inconsistent[j]=0;
- if(unlabeled[j]) unlabeled[j]=1;
- }
- write_prediction(learn_parm->predfile,model,lin,a,unlabeled,label,
- totdoc,learn_parm);
- if(verbosity>=1)
- printf("Number of switches: %ld\n",switchnum);
- return((long)0);
- }
- switchsens=switchsensorg;
- learn_parm->svm_unlabbound*=1.5;
- if(learn_parm->svm_unlabbound>1) {
- learn_parm->svm_unlabbound=1;
- }
- model->at_upper_bound=0; /* since upper bound increased */
- if(verbosity>=1)
- printf("Increasing influence of unlabeled examples to %f%% .",
- learn_parm->svm_unlabbound*100.0);
- }
- else if(verbosity>=1) {
- printf("%ld positive -> Switching labels of %ld POS / %ld NEG unlabeled examples.",
- upos,unsupaddnum1,unsupaddnum2);
- fflush(stdout);
- }
-
- if(verbosity >= 2) printf("\n");
-
- learn_parm->epsilon_crit=0.5; /* don't need to be so picky */
-
- for(i=0;i<totdoc;i++) { /* set upper bounds on vars */
- if(unlabeled[i]) {
- if(label[i] == 1) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*
- learn_parm->svm_costratio_unlab*learn_parm->svm_unlabbound;
- }
- else if(label[i] == -1) {
- learn_parm->svm_cost[i]=learn_parm->svm_c*
- learn_parm->svm_unlabbound;
- }
- }
- }
-
- return((long)2);
- }
-
- return((long)0);
-}
-
-/*************************** Working set selection ***************************/
-
-long select_next_qp_subproblem_grad(long int *label,
- long int *unlabeled,
- double *a, double *lin,
- double *c, long int totdoc,
- long int qp_size,
- LEARN_PARM *learn_parm,
- long int *inconsistent,
- long int *active2dnum,
- long int *working2dnum,
- double *selcrit,
- long int *select,
- KERNEL_CACHE *kernel_cache,
- long int cache_only,
- long int *key, long int *chosen)
- /* Use the feasible direction approach to select the next
- qp-subproblem (see chapter 'Selecting a good working set'). If
- 'cache_only' is true, then the variables are selected only among
- those for which the kernel evaluations are cached. */
-{
- long choosenum,i,j,k,activedoc,inum,valid;
- double s;
-
- for(inum=0;working2dnum[inum]>=0;inum++); /* find end of index */
- choosenum=0;
- activedoc=0;
- for(i=0;(j=active2dnum[i])>=0;i++) {
- s=-label[j];
- if(kernel_cache && cache_only)
- valid=(kernel_cache->index[j]>=0);
- else
- valid=1;
- if(valid
- && (!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0)))
- && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a))
- && (s>0)))
- && (!chosen[j])
- && (label[j])
- && (!inconsistent[j]))
- {
- selcrit[activedoc]=(double)label[j]*(learn_parm->eps-(double)label[j]*c[j]+(double)label[j]*lin[j]);
- /* selcrit[activedoc]=(double)label[j]*(-1.0+(double)label[j]*lin[j]); */
- key[activedoc]=j;
- activedoc++;
- }
- }
- select_top_n(selcrit,activedoc,select,(long)(qp_size/2));
- for(k=0;(choosenum<(qp_size/2)) && (k<(qp_size/2)) && (k<activedoc);k++) {
- /* if(learn_parm->biased_hyperplane || (selcrit[select[k]] > 0)) { */
- i=key[select[k]];
- chosen[i]=1;
- working2dnum[inum+choosenum]=i;
- choosenum+=1;
- if(kernel_cache)
- kernel_cache_touch(kernel_cache,i); /* make sure it does not get
- kicked out of cache */
- /* } */
- }
-
- activedoc=0;
- for(i=0;(j=active2dnum[i])>=0;i++) {
- s=label[j];
- if(kernel_cache && cache_only)
- valid=(kernel_cache->index[j]>=0);
- else
- valid=1;
- if(valid
- && (!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0)))
- && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a))
- && (s>0)))
- && (!chosen[j])
- && (label[j])
- && (!inconsistent[j]))
- {
- selcrit[activedoc]=-(double)label[j]*(learn_parm->eps-(double)label[j]*c[j]+(double)label[j]*lin[j]);
- /* selcrit[activedoc]=-(double)(label[j]*(-1.0+(double)label[j]*lin[j])); */
- key[activedoc]=j;
- activedoc++;
- }
- }
- select_top_n(selcrit,activedoc,select,(long)(qp_size/2));
- for(k=0;(choosenum<qp_size) && (k<(qp_size/2)) && (k<activedoc);k++) {
- /* if(learn_parm->biased_hyperplane || (selcrit[select[k]] > 0)) { */
- i=key[select[k]];
- chosen[i]=1;
- working2dnum[inum+choosenum]=i;
- choosenum+=1;
- if(kernel_cache)
- kernel_cache_touch(kernel_cache,i); /* make sure it does not get
- kicked out of cache */
- /* } */
- }
- working2dnum[inum+choosenum]=-1; /* complete index */
- return(choosenum);
-}
-
-long select_next_qp_subproblem_rand(long int *label,
- long int *unlabeled,
- double *a, double *lin,
- double *c, long int totdoc,
- long int qp_size,
- LEARN_PARM *learn_parm,
- long int *inconsistent,
- long int *active2dnum,
- long int *working2dnum,
- double *selcrit,
- long int *select,
- KERNEL_CACHE *kernel_cache,
- long int *key,
- long int *chosen,
- long int iteration)
-/* Use the feasible direction approach to select the next
- qp-subproblem (see section 'Selecting a good working set'). Chooses
- a feasible direction at (pseudo) random to help jump over numerical
- problem. */
-{
- long choosenum,i,j,k,activedoc,inum;
- double s;
-
- for(inum=0;working2dnum[inum]>=0;inum++); /* find end of index */
- choosenum=0;
- activedoc=0;
- for(i=0;(j=active2dnum[i])>=0;i++) {
- s=-label[j];
- if((!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0)))
- && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a))
- && (s>0)))
- && (!inconsistent[j])
- && (label[j])
- && (!chosen[j])) {
- selcrit[activedoc]=(j+iteration) % totdoc;
- key[activedoc]=j;
- activedoc++;
- }
- }
- select_top_n(selcrit,activedoc,select,(long)(qp_size/2));
- for(k=0;(choosenum<(qp_size/2)) && (k<(qp_size/2)) && (k<activedoc);k++) {
- i=key[select[k]];
- chosen[i]=1;
- working2dnum[inum+choosenum]=i;
- choosenum+=1;
- kernel_cache_touch(kernel_cache,i); /* make sure it does not get kicked */
- /* out of cache */
- }
-
- activedoc=0;
- for(i=0;(j=active2dnum[i])>=0;i++) {
- s=label[j];
- if((!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0)))
- && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a))
- && (s>0)))
- && (!inconsistent[j])
- && (label[j])
- && (!chosen[j])) {
- selcrit[activedoc]=(j+iteration) % totdoc;
- key[activedoc]=j;
- activedoc++;
- }
- }
- select_top_n(selcrit,activedoc,select,(long)(qp_size/2));
- for(k=0;(choosenum<qp_size) && (k<(qp_size/2)) && (k<activedoc);k++) {
- i=key[select[k]];
- chosen[i]=1;
- working2dnum[inum+choosenum]=i;
- choosenum+=1;
- kernel_cache_touch(kernel_cache,i); /* make sure it does not get kicked */
- /* out of cache */
- }
- working2dnum[inum+choosenum]=-1; /* complete index */
- return(choosenum);
-}
-
-long select_next_qp_slackset(DOC **docs, long int *label,
- double *a, double *lin,
- double *slack, double *alphaslack,
- double *c,
- LEARN_PARM *learn_parm,
- long int *active2dnum, double *maxviol)
- /* returns the slackset with the largest internal violation */
-{
- long i,ii,maxdiffid;
- double dist,target,maxdiff,ex_c;
-
- maxdiff=0;
- maxdiffid=0;
- for(ii=0;(i=active2dnum[ii])>=0;ii++) {
- ex_c=learn_parm->svm_c-learn_parm->epsilon_a;
- if(alphaslack[docs[i]->slackid] >= ex_c) {
- dist=(lin[i])*(double)label[i]+slack[docs[i]->slackid]; /* distance */
- target=-(learn_parm->eps-(double)label[i]*c[i]); /* rhs of constraint */
- if((a[i]>learn_parm->epsilon_a) && (dist > target)) {
- if((dist-target)>maxdiff) { /* largest violation */
- maxdiff=dist-target;
- maxdiffid=docs[i]->slackid;
- }
- }
- }
- }
- (*maxviol)=maxdiff;
- return(maxdiffid);
-}
-
-
-void select_top_n(double *selcrit, long int range, long int *select,
- long int n)
-{
- register long i,j;
-
- for(i=0;(i<n) && (i<range);i++) { /* Initialize with the first n elements */
- for(j=i;j>=0;j--) {
- if((j>0) && (selcrit[select[j-1]]<selcrit[i])){
- select[j]=select[j-1];
- }
- else {
- select[j]=i;
- j=-1;
- }
- }
- }
- if(n>0) {
- for(i=n;i<range;i++) {
- if(selcrit[i]>selcrit[select[n-1]]) {
- for(j=n-1;j>=0;j--) {
- if((j>0) && (selcrit[select[j-1]]<selcrit[i])) {
- select[j]=select[j-1];
- }
- else {
- select[j]=i;
- j=-1;
- }
- }
- }
- }
- }
-}
-
-
-/******************************** Shrinking *********************************/
-
-void init_shrink_state(SHRINK_STATE *shrink_state, long int totdoc,
- long int maxhistory)
-{
- long i;
-
- shrink_state->deactnum=0;
- shrink_state->active = (long *)my_malloc(sizeof(long)*totdoc);
- shrink_state->inactive_since = (long *)my_malloc(sizeof(long)*totdoc);
- shrink_state->a_history = (double **)my_malloc(sizeof(double *)*maxhistory);
- shrink_state->maxhistory=maxhistory;
- shrink_state->last_lin = (double *)my_malloc(sizeof(double)*totdoc);
- shrink_state->last_a = (double *)my_malloc(sizeof(double)*totdoc);
-
- for(i=0;i<totdoc;i++) {
- shrink_state->active[i]=1;
- shrink_state->inactive_since[i]=0;
- shrink_state->last_a[i]=0;
- shrink_state->last_lin[i]=0;
- }
-}
-
-void shrink_state_cleanup(SHRINK_STATE *shrink_state)
-{
- free(shrink_state->active);
- free(shrink_state->inactive_since);
- if(shrink_state->deactnum > 0)
- free(shrink_state->a_history[shrink_state->deactnum-1]);
- free(shrink_state->a_history);
- free(shrink_state->last_a);
- free(shrink_state->last_lin);
-}
-
-long shrink_problem(DOC **docs,
- LEARN_PARM *learn_parm,
- SHRINK_STATE *shrink_state,
- KERNEL_PARM *kernel_parm,
- long int *active2dnum,
- long int *last_suboptimal_at,
- long int iteration,
- long int totdoc,
- long int minshrink,
- double *a,
- long int *inconsistent)
- /* Shrink some variables away. Do the shrinking only if at least
- minshrink variables can be removed. */
-{
- long i,ii,change,activenum,lastiter;
- double *a_old;
-
- activenum=0;
- change=0;
- for(ii=0;active2dnum[ii]>=0;ii++) {
- i=active2dnum[ii];
- activenum++;
- if(learn_parm->sharedslack)
- lastiter=last_suboptimal_at[docs[i]->slackid];
- else
- lastiter=last_suboptimal_at[i];
- if(((iteration-lastiter) > learn_parm->svm_iter_to_shrink)
- || (inconsistent[i])) {
- change++;
- }
- }
- if((change>=minshrink) /* shrink only if sufficiently many candidates */
- && (shrink_state->deactnum<shrink_state->maxhistory)) { /* and enough memory */
- /* Shrink problem by removing those variables which are */
- /* optimal at a bound for a minimum number of iterations */
- if(verbosity>=2) {
- printf(" Shrinking..."); fflush(stdout);
- }
- if(kernel_parm->kernel_type != LINEAR) { /* non-linear case save alphas */
- a_old=(double *)my_malloc(sizeof(double)*totdoc);
- shrink_state->a_history[shrink_state->deactnum]=a_old;
- for(i=0;i<totdoc;i++) {
- a_old[i]=a[i];
- }
- }
- for(ii=0;active2dnum[ii]>=0;ii++) {
- i=active2dnum[ii];
- if(learn_parm->sharedslack)
- lastiter=last_suboptimal_at[docs[i]->slackid];
- else
- lastiter=last_suboptimal_at[i];
- if(((iteration-lastiter) > learn_parm->svm_iter_to_shrink)
- || (inconsistent[i])) {
- shrink_state->active[i]=0;
- shrink_state->inactive_since[i]=shrink_state->deactnum;
- }
- }
- activenum=compute_index(shrink_state->active,totdoc,active2dnum);
- shrink_state->deactnum++;
- if(kernel_parm->kernel_type == LINEAR) {
- shrink_state->deactnum=0;
- }
- if(verbosity>=2) {
- printf("done.\n"); fflush(stdout);
- printf(" Number of inactive variables = %ld\n",totdoc-activenum);
- }
- }
- return(activenum);
-}
-
-
-void reactivate_inactive_examples(long int *label,
- long int *unlabeled,
- double *a,
- SHRINK_STATE *shrink_state,
- double *lin,
- double *c,
- long int totdoc,
- long int totwords,
- long int iteration,
- LEARN_PARM *learn_parm,
- long int *inconsistent,
- DOC **docs,
- KERNEL_PARM *kernel_parm,
- KERNEL_CACHE *kernel_cache,
- MODEL *model,
- CFLOAT *aicache,
- double *weights,
- double *maxdiff)
- /* Make all variables active again which had been removed by
- shrinking. */
- /* Computes lin for those variables from scratch. */
-{
- register long i,j,ii,jj,t,*changed2dnum,*inactive2dnum;
- long *changed,*inactive;
- register double kernel_val,*a_old,dist;
- double ex_c,target;
- SVECTOR *f;
-
- if(kernel_parm->kernel_type == LINEAR) { /* special linear case */
- a_old=shrink_state->last_a;
- clear_vector_n(weights,totwords);
- for(i=0;i<totdoc;i++) {
- if(a[i] != a_old[i]) {
- for(f=docs[i]->fvec;f;f=f->next)
- add_vector_ns(weights,f,
- f->factor*((a[i]-a_old[i])*(double)label[i]));
- a_old[i]=a[i];
- }
- }
- for(i=0;i<totdoc;i++) {
- if(!shrink_state->active[i]) {
- for(f=docs[i]->fvec;f;f=f->next)
- lin[i]=shrink_state->last_lin[i]+f->factor*sprod_ns(weights,f);
- }
- shrink_state->last_lin[i]=lin[i];
- }
- }
- else {
- changed=(long *)my_malloc(sizeof(long)*totdoc);
- changed2dnum=(long *)my_malloc(sizeof(long)*(totdoc+11));
- inactive=(long *)my_malloc(sizeof(long)*totdoc);
- inactive2dnum=(long *)my_malloc(sizeof(long)*(totdoc+11));
- for(t=shrink_state->deactnum-1;(t>=0) && shrink_state->a_history[t];t--) {
- if(verbosity>=2) {
- printf("%ld..",t); fflush(stdout);
- }
- a_old=shrink_state->a_history[t];
- for(i=0;i<totdoc;i++) {
- inactive[i]=((!shrink_state->active[i])
- && (shrink_state->inactive_since[i] == t));
- changed[i]= (a[i] != a_old[i]);
- }
- compute_index(inactive,totdoc,inactive2dnum);
- compute_index(changed,totdoc,changed2dnum);
-
- for(ii=0;(i=changed2dnum[ii])>=0;ii++) {
- get_kernel_row(kernel_cache,docs,i,totdoc,inactive2dnum,aicache,
- kernel_parm);
- for(jj=0;(j=inactive2dnum[jj])>=0;jj++) {
- kernel_val=aicache[j];
- lin[j]+=(((a[i]*kernel_val)-(a_old[i]*kernel_val))*(double)label[i]);
- }
- }
- }
- free(changed);
- free(changed2dnum);
- free(inactive);
- free(inactive2dnum);
- }
- (*maxdiff)=0;
- for(i=0;i<totdoc;i++) {
- shrink_state->inactive_since[i]=shrink_state->deactnum-1;
- if(!inconsistent[i]) {
- dist=(lin[i]-model->b)*(double)label[i];
- target=-(learn_parm->eps-(double)label[i]*c[i]);
- ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a;
- if((a[i]>learn_parm->epsilon_a) && (dist > target)) {
- if((dist-target)>(*maxdiff)) /* largest violation */
- (*maxdiff)=dist-target;
- }
- else if((a[i]<ex_c) && (dist < target)) {
- if((target-dist)>(*maxdiff)) /* largest violation */
- (*maxdiff)=target-dist;
- }
- if((a[i]>(0+learn_parm->epsilon_a))
- && (a[i]<ex_c)) {
- shrink_state->active[i]=1; /* not at bound */
- }
- else if((a[i]<=(0+learn_parm->epsilon_a)) && (dist < (target+learn_parm->epsilon_shrink))) {
- shrink_state->active[i]=1;
- }
- else if((a[i]>=ex_c)
- && (dist > (target-learn_parm->epsilon_shrink))) {
- shrink_state->active[i]=1;
- }
- else if(learn_parm->sharedslack) { /* make all active when sharedslack */
- shrink_state->active[i]=1;
- }
- }
- }
- if(kernel_parm->kernel_type != LINEAR) { /* update history for non-linear */
- for(i=0;i<totdoc;i++) {
- (shrink_state->a_history[shrink_state->deactnum-1])[i]=a[i];
- }
- for(t=shrink_state->deactnum-2;(t>=0) && shrink_state->a_history[t];t--) {
- free(shrink_state->a_history[t]);
- shrink_state->a_history[t]=0;
- }
- }
-}
-
-/****************************** Cache handling *******************************/
-
-void get_kernel_row(KERNEL_CACHE *kernel_cache, DOC **docs,
- long int docnum, long int totdoc,
- long int *active2dnum, CFLOAT *buffer,
- KERNEL_PARM *kernel_parm)
- /* Get's a row of the matrix of kernel values This matrix has the
- same form as the Hessian, just that the elements are not
- multiplied by */
- /* y_i * y_j * a_i * a_j */
- /* Takes the values from the cache if available. */
-{
- register long i,j,start;
- DOC *ex;
-
- ex=docs[docnum];
-
- if(kernel_cache->index[docnum] != -1) { /* row is cached? */
- kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */
- start=kernel_cache->activenum*kernel_cache->index[docnum];
- for(i=0;(j=active2dnum[i])>=0;i++) {
- if(kernel_cache->totdoc2active[j] >= 0) { /* column is cached? */
- buffer[j]=kernel_cache->buffer[start+kernel_cache->totdoc2active[j]];
- }
- else {
- buffer[j]=(CFLOAT)kernel(kernel_parm,ex,docs[j]);
- }
- }
- }
- else {
- for(i=0;(j=active2dnum[i])>=0;i++) {
- buffer[j]=(CFLOAT)kernel(kernel_parm,ex,docs[j]);
- }
- }
-}
-
-
-void cache_kernel_row(KERNEL_CACHE *kernel_cache, DOC **docs,
- long int m, KERNEL_PARM *kernel_parm)
- /* Fills cache for the row m */
-{
- register DOC *ex;
- register long j,k,l;
- register CFLOAT *cache;
-
- if(!kernel_cache_check(kernel_cache,m)) { /* not cached yet*/
- cache = kernel_cache_clean_and_malloc(kernel_cache,m);
- if(cache) {
- l=kernel_cache->totdoc2active[m];
- ex=docs[m];
- for(j=0;j<kernel_cache->activenum;j++) { /* fill cache */
- k=kernel_cache->active2totdoc[j];
- if((kernel_cache->index[k] != -1) && (l != -1) && (k != m)) {
- cache[j]=kernel_cache->buffer[kernel_cache->activenum
- *kernel_cache->index[k]+l];
- }
- else {
- cache[j]=kernel(kernel_parm,ex,docs[k]);
- }
- }
- }
- else {
- perror("Error: Kernel cache full! => increase cache size");
- }
- }
-}
-
-
-void cache_multiple_kernel_rows(KERNEL_CACHE *kernel_cache, DOC **docs,
- long int *key, long int varnum,
- KERNEL_PARM *kernel_parm)
- /* Fills cache for the rows in key */
-{
- register long i;
-
- for(i=0;i<varnum;i++) { /* fill up kernel cache */
- cache_kernel_row(kernel_cache,docs,key[i],kernel_parm);
- }
-}
-
-
-void kernel_cache_shrink(KERNEL_CACHE *kernel_cache, long int totdoc,
- long int numshrink, long int *after)
- /* Remove numshrink columns in the cache which correspond to
- examples marked 0 in after. */
-{
- register long i,j,jj,from=0,to=0,scount;
- long *keep;
-
- if(verbosity>=2) {
- printf(" Reorganizing cache..."); fflush(stdout);
- }
-
- keep=(long *)my_malloc(sizeof(long)*totdoc);
- for(j=0;j<totdoc;j++) {
- keep[j]=1;
- }
- scount=0;
- for(jj=0;(jj<kernel_cache->activenum) && (scount<numshrink);jj++) {
- j=kernel_cache->active2totdoc[jj];
- if(!after[j]) {
- scount++;
- keep[j]=0;
- }
- }
-
- for(i=0;i<kernel_cache->max_elems;i++) {
- for(jj=0;jj<kernel_cache->activenum;jj++) {
- j=kernel_cache->active2totdoc[jj];
- if(!keep[j]) {
- from++;
- }
- else {
- kernel_cache->buffer[to]=kernel_cache->buffer[from];
- to++;
- from++;
- }
- }
- }
-
- kernel_cache->activenum=0;
- for(j=0;j<totdoc;j++) {
- if((keep[j]) && (kernel_cache->totdoc2active[j] != -1)) {
- kernel_cache->active2totdoc[kernel_cache->activenum]=j;
- kernel_cache->totdoc2active[j]=kernel_cache->activenum;
- kernel_cache->activenum++;
- }
- else {
- kernel_cache->totdoc2active[j]=-1;
- }
- }
-
- kernel_cache->max_elems=(long)(kernel_cache->buffsize/kernel_cache->activenum);
- if(kernel_cache->max_elems>totdoc) {
- kernel_cache->max_elems=totdoc;
- }
-
- free(keep);
-
- if(verbosity>=2) {
- printf("done.\n"); fflush(stdout);
- printf(" Cache-size in rows = %ld\n",kernel_cache->max_elems);
- }
-}
-
-KERNEL_CACHE *kernel_cache_init(long int totdoc, long int buffsize)
-{
- long i;
- KERNEL_CACHE *kernel_cache;
-
- kernel_cache=(KERNEL_CACHE *)my_malloc(sizeof(KERNEL_CACHE));
- kernel_cache->index = (long *)my_malloc(sizeof(long)*totdoc);
- kernel_cache->occu = (long *)my_malloc(sizeof(long)*totdoc);
- kernel_cache->lru = (long *)my_malloc(sizeof(long)*totdoc);
- kernel_cache->invindex = (long *)my_malloc(sizeof(long)*totdoc);
- kernel_cache->active2totdoc = (long *)my_malloc(sizeof(long)*totdoc);
- kernel_cache->totdoc2active = (long *)my_malloc(sizeof(long)*totdoc);
- kernel_cache->buffer = (CFLOAT *)my_malloc((size_t)(buffsize)*1024*1024);
-
- kernel_cache->buffsize=(long)(buffsize/sizeof(CFLOAT)*1024*1024);
-
- kernel_cache->max_elems=(long)(kernel_cache->buffsize/totdoc);
- if(kernel_cache->max_elems>totdoc) {
- kernel_cache->max_elems=totdoc;
- }
-
- if(verbosity>=2) {
- printf(" Cache-size in rows = %ld\n",kernel_cache->max_elems);
- printf(" Kernel evals so far: %ld\n",kernel_cache_statistic);
- }
-
- kernel_cache->elems=0; /* initialize cache */
- for(i=0;i<totdoc;i++) {
- kernel_cache->index[i]=-1;
- kernel_cache->lru[i]=0;
- }
- for(i=0;i<totdoc;i++) {
- kernel_cache->occu[i]=0;
- kernel_cache->invindex[i]=-1;
- }
-
- kernel_cache->activenum=totdoc;;
- for(i=0;i<totdoc;i++) {
- kernel_cache->active2totdoc[i]=i;
- kernel_cache->totdoc2active[i]=i;
- }
-
- kernel_cache->time=0;
-
- return(kernel_cache);
-}
-
-void kernel_cache_reset_lru(KERNEL_CACHE *kernel_cache)
-{
- long maxlru=0,k;
-
- for(k=0;k<kernel_cache->max_elems;k++) {
- if(maxlru < kernel_cache->lru[k])
- maxlru=kernel_cache->lru[k];
- }
- for(k=0;k<kernel_cache->max_elems;k++) {
- kernel_cache->lru[k]-=maxlru;
- }
-}
-
-void kernel_cache_cleanup(KERNEL_CACHE *kernel_cache)
-{
- free(kernel_cache->index);
- free(kernel_cache->occu);
- free(kernel_cache->lru);
- free(kernel_cache->invindex);
- free(kernel_cache->active2totdoc);
- free(kernel_cache->totdoc2active);
- free(kernel_cache->buffer);
- free(kernel_cache);
-}
-
-long kernel_cache_malloc(KERNEL_CACHE *kernel_cache)
-{
- long i;
-
- if(kernel_cache_space_available(kernel_cache)) {
- for(i=0;i<kernel_cache->max_elems;i++) {
- if(!kernel_cache->occu[i]) {
- kernel_cache->occu[i]=1;
- kernel_cache->elems++;
- return(i);
- }
- }
- }
- return(-1);
-}
-
-void kernel_cache_free(KERNEL_CACHE *kernel_cache, long int i)
-{
- kernel_cache->occu[i]=0;
- kernel_cache->elems--;
-}
-
-long kernel_cache_free_lru(KERNEL_CACHE *kernel_cache)
- /* remove least recently used cache element */
-{
- register long k,least_elem=-1,least_time;
-
- least_time=kernel_cache->time+1;
- for(k=0;k<kernel_cache->max_elems;k++) {
- if(kernel_cache->invindex[k] != -1) {
- if(kernel_cache->lru[k]<least_time) {
- least_time=kernel_cache->lru[k];
- least_elem=k;
- }
- }
- }
- if(least_elem != -1) {
- kernel_cache_free(kernel_cache,least_elem);
- kernel_cache->index[kernel_cache->invindex[least_elem]]=-1;
- kernel_cache->invindex[least_elem]=-1;
- return(1);
- }
- return(0);
-}
-
-
-CFLOAT *kernel_cache_clean_and_malloc(KERNEL_CACHE *kernel_cache,
- long int docnum)
- /* Get a free cache entry. In case cache is full, the lru element
- is removed. */
-{
- long result;
- if((result = kernel_cache_malloc(kernel_cache)) == -1) {
- if(kernel_cache_free_lru(kernel_cache)) {
- result = kernel_cache_malloc(kernel_cache);
- }
- }
- kernel_cache->index[docnum]=result;
- if(result == -1) {
- return(0);
- }
- kernel_cache->invindex[result]=docnum;
- kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */
- return((CFLOAT *)((long)kernel_cache->buffer
- +(kernel_cache->activenum*sizeof(CFLOAT)*
- kernel_cache->index[docnum])));
-}
-
-long kernel_cache_touch(KERNEL_CACHE *kernel_cache, long int docnum)
- /* Update lru time to avoid removal from cache. */
-{
- if(kernel_cache && kernel_cache->index[docnum] != -1) {
- kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */
- return(1);
- }
- return(0);
-}
-
-long kernel_cache_check(KERNEL_CACHE *kernel_cache, long int docnum)
- /* Is that row cached? */
-{
- return(kernel_cache->index[docnum] != -1);
-}
-
-long kernel_cache_space_available(KERNEL_CACHE *kernel_cache)
- /* Is there room for one more row? */
-{
- return(kernel_cache->elems < kernel_cache->max_elems);
-}
-
-/************************** Compute estimates ******************************/
-
-void compute_xa_estimates(MODEL *model, long int *label,
- long int *unlabeled, long int totdoc,
- DOC **docs, double *lin, double *a,
- KERNEL_PARM *kernel_parm,
- LEARN_PARM *learn_parm, double *error,
- double *recall, double *precision)
- /* Computes xa-estimate of error rate, recall, and precision. See
- T. Joachims, Estimating the Generalization Performance of an SVM
- Efficiently, IMCL, 2000. */
-{
- long i,looerror,looposerror,loonegerror;
- long totex,totposex;
- double xi,r_delta,r_delta_sq,sim=0;
- long *sv2dnum=NULL,*sv=NULL,svnum;
-
- r_delta=estimate_r_delta(docs,totdoc,kernel_parm);
- r_delta_sq=r_delta*r_delta;
-
- looerror=0;
- looposerror=0;
- loonegerror=0;
- totex=0;
- totposex=0;
- svnum=0;
-
- if(learn_parm->xa_depth > 0) {
- sv = (long *)my_malloc(sizeof(long)*(totdoc+11));
- for(i=0;i<totdoc;i++)
- sv[i]=0;
- for(i=1;i<model->sv_num;i++)
- if(a[model->supvec[i]->docnum]
- < (learn_parm->svm_cost[model->supvec[i]->docnum]
- -learn_parm->epsilon_a)) {
- sv[model->supvec[i]->docnum]=1;
- svnum++;
- }
- sv2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
- clear_index(sv2dnum);
- compute_index(sv,totdoc,sv2dnum);
- }
-
- for(i=0;i<totdoc;i++) {
- if(unlabeled[i]) {
- /* ignore it */
- }
- else {
- xi=1.0-((lin[i]-model->b)*(double)label[i]);
- if(xi<0) xi=0;
- if(label[i]>0) {
- totposex++;
- }
- if((learn_parm->rho*a[i]*r_delta_sq+xi) >= 1.0) {
- if(learn_parm->xa_depth > 0) { /* makes assumptions */
- sim=distribute_alpha_t_greedily(sv2dnum,svnum,docs,a,i,label,
- kernel_parm,learn_parm,
- (double)((1.0-xi-a[i]*r_delta_sq)/(2.0*a[i])));
- }
- if((learn_parm->xa_depth == 0) ||
- ((a[i]*kernel(kernel_parm,docs[i],docs[i])+a[i]*2.0*sim+xi) >= 1.0)) {
- looerror++;
- if(label[i]>0) {
- looposerror++;
- }
- else {
- loonegerror++;
- }
- }
- }
- totex++;
- }
- }
-
- (*error)=((double)looerror/(double)totex)*100.0;
- (*recall)=(1.0-(double)looposerror/(double)totposex)*100.0;
- (*precision)=(((double)totposex-(double)looposerror)
- /((double)totposex-(double)looposerror+(double)loonegerror))*100.0;
-
- free(sv);
- free(sv2dnum);
-}
-
-
-double distribute_alpha_t_greedily(long int *sv2dnum, long int svnum,
- DOC **docs, double *a,
- long int docnum,
- long int *label,
- KERNEL_PARM *kernel_parm,
- LEARN_PARM *learn_parm, double thresh)
- /* Experimental Code improving plain XiAlpha Estimates by
- computing a better bound using a greedy optimzation strategy. */
-{
- long best_depth=0;
- long i,j,k,d,skip,allskip;
- double best,best_val[101],val,init_val_sq,init_val_lin;
- long best_ex[101];
- CFLOAT *cache,*trow;
-
- cache=(CFLOAT *)my_malloc(sizeof(CFLOAT)*learn_parm->xa_depth*svnum);
- trow = (CFLOAT *)my_malloc(sizeof(CFLOAT)*svnum);
-
- for(k=0;k<svnum;k++) {
- trow[k]=kernel(kernel_parm,docs[docnum],docs[sv2dnum[k]]);
- }
-
- init_val_sq=0;
- init_val_lin=0;
- best=0;
-
- for(d=0;d<learn_parm->xa_depth;d++) {
- allskip=1;
- if(d>=1) {
- init_val_sq+=cache[best_ex[d-1]+svnum*(d-1)];
- for(k=0;k<d-1;k++) {
- init_val_sq+=2.0*cache[best_ex[k]+svnum*(d-1)];
- }
- init_val_lin+=trow[best_ex[d-1]];
- }
- for(i=0;i<svnum;i++) {
- skip=0;
- if(sv2dnum[i] == docnum) skip=1;
- for(j=0;j<d;j++) {
- if(i == best_ex[j]) skip=1;
- }
-
- if(!skip) {
- val=init_val_sq;
- if(kernel_parm->kernel_type == LINEAR)
- val+=docs[sv2dnum[i]]->fvec->twonorm_sq;
- else
- val+=kernel(kernel_parm,docs[sv2dnum[i]],docs[sv2dnum[i]]);
- for(j=0;j<d;j++) {
- val+=2.0*cache[i+j*svnum];
- }
- val*=(1.0/(2.0*(d+1.0)*(d+1.0)));
- val-=((init_val_lin+trow[i])/(d+1.0));
-
- if(allskip || (val < best_val[d])) {
- best_val[d]=val;
- best_ex[d]=i;
- }
- allskip=0;
- if(val < thresh) {
- i=svnum;
- /* printf("EARLY"); */
- }
- }
- }
- if(!allskip) {
- for(k=0;k<svnum;k++) {
- cache[d*svnum+k]=kernel(kernel_parm,
- docs[sv2dnum[best_ex[d]]],
- docs[sv2dnum[k]]);
- }
- }
- if((!allskip) && ((best_val[d] < best) || (d == 0))) {
- best=best_val[d];
- best_depth=d;
- }
- if(allskip || (best < thresh)) {
- d=learn_parm->xa_depth;
- }
- }
-
- free(cache);
- free(trow);
-
- /* printf("Distribute[%ld](%ld)=%f, ",docnum,best_depth,best); */
- return(best);
-}
-
-
-void estimate_transduction_quality(MODEL *model, long int *label,
- long int *unlabeled,
- long int totdoc, DOC **docs, double *lin)
- /* Loo-bound based on observation that loo-errors must have an
- equal distribution in both training and test examples, given
- that the test examples are classified correctly. Compare
- chapter "Constraints on the Transductive Hyperplane" in my
- Dissertation. */
-{
- long i,j,l=0,ulab=0,lab=0,labpos=0,labneg=0,ulabpos=0,ulabneg=0,totulab=0;
- double totlab=0,totlabpos=0,totlabneg=0,labsum=0,ulabsum=0;
- double r_delta,r_delta_sq,xi,xisum=0,asum=0;
-
- r_delta=estimate_r_delta(docs,totdoc,&(model->kernel_parm));
- r_delta_sq=r_delta*r_delta;
-
- for(j=0;j<totdoc;j++) {
- if(unlabeled[j]) {
- totulab++;
- }
- else {
- totlab++;
- if(label[j] > 0)
- totlabpos++;
- else
- totlabneg++;
- }
- }
- for(j=1;j<model->sv_num;j++) {
- i=model->supvec[j]->docnum;
- xi=1.0-((lin[i]-model->b)*(double)label[i]);
- if(xi<0) xi=0;
-
- xisum+=xi;
- asum+=fabs(model->alpha[j]);
- if(unlabeled[i]) {
- ulabsum+=(fabs(model->alpha[j])*r_delta_sq+xi);
- }
- else {
- labsum+=(fabs(model->alpha[j])*r_delta_sq+xi);
- }
- if((fabs(model->alpha[j])*r_delta_sq+xi) >= 1) {
- l++;
- if(unlabeled[model->supvec[j]->docnum]) {
- ulab++;
- if(model->alpha[j] > 0)
- ulabpos++;
- else
- ulabneg++;
- }
- else {
- lab++;
- if(model->alpha[j] > 0)
- labpos++;
- else
- labneg++;
- }
- }
- }
- printf("xacrit>=1: labeledpos=%.5f labeledneg=%.5f default=%.5f\n",(double)labpos/(double)totlab*100.0,(double)labneg/(double)totlab*100.0,(double)totlabpos/(double)(totlab)*100.0);
- printf("xacrit>=1: unlabelpos=%.5f unlabelneg=%.5f\n",(double)ulabpos/(double)totulab*100.0,(double)ulabneg/(double)totulab*100.0);
- printf("xacrit>=1: labeled=%.5f unlabled=%.5f all=%.5f\n",(double)lab/(double)totlab*100.0,(double)ulab/(double)totulab*100.0,(double)l/(double)(totdoc)*100.0);
- printf("xacritsum: labeled=%.5f unlabled=%.5f all=%.5f\n",(double)labsum/(double)totlab*100.0,(double)ulabsum/(double)totulab*100.0,(double)(labsum+ulabsum)/(double)(totdoc)*100.0);
- printf("r_delta_sq=%.5f xisum=%.5f asum=%.5f\n",r_delta_sq,xisum,asum);
-}
-
-double estimate_margin_vcdim(MODEL *model, double w, double R,
- KERNEL_PARM *kernel_parm)
- /* optional: length of model vector in feature space */
- /* optional: radius of ball containing the data */
-{
- double h;
-
- /* follows chapter 5.6.4 in [Vapnik/95] */
-
- if(w<0) {
- w=model_length_s(model,kernel_parm);
- }
- if(R<0) {
- R=estimate_sphere(model,kernel_parm);
- }
- h = w*w * R*R +1;
- return(h);
-}
-
-double estimate_sphere(MODEL *model, KERNEL_PARM *kernel_parm)
- /* Approximates the radius of the ball containing */
- /* the support vectors by bounding it with the */
-{ /* length of the longest support vector. This is */
- register long j; /* pretty good for text categorization, since all */
- double xlen,maxxlen=0; /* documents have feature vectors of length 1. It */
- DOC *nulldoc; /* assumes that the center of the ball is at the */
- WORD nullword; /* origin of the space. */
-
- nullword.wnum=0;
- nulldoc=create_example(-2,0,0,0.0,create_svector(&nullword,"",1.0));
-
- for(j=1;j<model->sv_num;j++) {
- xlen=sqrt(kernel(kernel_parm,model->supvec[j],model->supvec[j])
- -2*kernel(kernel_parm,model->supvec[j],nulldoc)
- +kernel(kernel_parm,nulldoc,nulldoc));
- if(xlen>maxxlen) {
- maxxlen=xlen;
- }
- }
-
- free_example(nulldoc,1);
- return(maxxlen);
-}
-
-double estimate_r_delta(DOC **docs, long int totdoc, KERNEL_PARM *kernel_parm)
-{
- long i;
- double maxxlen,xlen;
- DOC *nulldoc; /* assumes that the center of the ball is at the */
- WORD nullword; /* origin of the space. */
-
- nullword.wnum=0;
- nulldoc=create_example(-2,0,0,0.0,create_svector(&nullword,"",1.0));
-
- maxxlen=0;
- for(i=0;i<totdoc;i++) {
- xlen=sqrt(kernel(kernel_parm,docs[i],docs[i])
- -2*kernel(kernel_parm,docs[i],nulldoc)
- +kernel(kernel_parm,nulldoc,nulldoc));
- if(xlen>maxxlen) {
- maxxlen=xlen;
- }
- }
-
- free_example(nulldoc,1);
- return(maxxlen);
-}
-
-double estimate_r_delta_average(DOC **docs, long int totdoc,
- KERNEL_PARM *kernel_parm)
-{
- long i;
- double avgxlen;
- DOC *nulldoc; /* assumes that the center of the ball is at the */
- WORD nullword; /* origin of the space. */
-
- nullword.wnum=0;
- nulldoc=create_example(-2,0,0,0.0,create_svector(&nullword,"",1.0));
-
- avgxlen=0;
- for(i=0;i<totdoc;i++) {
- avgxlen+=sqrt(kernel(kernel_parm,docs[i],docs[i])
- -2*kernel(kernel_parm,docs[i],nulldoc)
- +kernel(kernel_parm,nulldoc,nulldoc));
- }
-
- free_example(nulldoc,1);
- return(avgxlen/totdoc);
-}
-
-double length_of_longest_document_vector(DOC **docs, long int totdoc,
- KERNEL_PARM *kernel_parm)
-{
- long i;
- double maxxlen,xlen;
-
- maxxlen=0;
- for(i=0;i<totdoc;i++) {
- xlen=sqrt(kernel(kernel_parm,docs[i],docs[i]));
- if(xlen>maxxlen) {
- maxxlen=xlen;
- }
- }
-
- return(maxxlen);
-}
-
-/****************************** IO-handling **********************************/
-
-void write_prediction(char *predfile, MODEL *model, double *lin,
- double *a, long int *unlabeled,
- long int *label, long int totdoc,
- LEARN_PARM *learn_parm)
-{
- FILE *predfl;
- long i;
- double dist,a_max;
-
- if(verbosity>=1) {
- printf("Writing prediction file..."); fflush(stdout);
- }
- if ((predfl = fopen (predfile, "w")) == NULL)
- { perror (predfile); exit (1); }
- a_max=learn_parm->epsilon_a;
- for(i=0;i<totdoc;i++) {
- if((unlabeled[i]) && (a[i]>a_max)) {
- a_max=a[i];
- }
- }
- for(i=0;i<totdoc;i++) {
- if(unlabeled[i]) {
- if((a[i]>(learn_parm->epsilon_a))) {
- dist=(double)label[i]*(1.0-learn_parm->epsilon_crit-a[i]/(a_max*2.0));
- }
- else {
- dist=(lin[i]-model->b);
- }
- if(dist>0) {
- fprintf(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist);
- }
- else {
- fprintf(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist);
- }
- }
- }
- fclose(predfl);
- if(verbosity>=1) {
- printf("done\n");
- }
-}
-
-void write_alphas(char *alphafile, double *a,
- long int *label, long int totdoc)
-{
- FILE *alphafl;
- long i;
-
- if(verbosity>=1) {
- printf("Writing alpha file..."); fflush(stdout);
- }
- if ((alphafl = fopen (alphafile, "w")) == NULL)
- { perror (alphafile); exit (1); }
- for(i=0;i<totdoc;i++) {
- fprintf(alphafl,"%.18g\n",a[i]*(double)label[i]);
- }
- fclose(alphafl);
- if(verbosity>=1) {
- printf("done\n");
- }
-}
-
diff --git a/trunk/svm_light/build/svm_light-tar/svm_learn.h b/trunk/svm_light/build/svm_light-tar/svm_learn.h
deleted file mode 100755
index 8a1edf7b75c6b2b6ea6952062a61ee7e7b4f8fbd..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_learn.h
+++ /dev/null
@@ -1,173 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_learn.h */
-/* */
-/* Declarations for learning module of Support Vector Machine. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/***********************************************************************/
-
-#ifndef SVM_LEARN
-#define SVM_LEARN
-
-void svm_learn_classification_extend(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *, int *, double *);
-void svm_learn_classification(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *);
-void svm_learn_regression(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE **, MODEL *);
-void svm_learn_ranking(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE **, MODEL *);
-void svm_learn_optimization(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *);
-long optimize_to_convergence(DOC **, long *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, SHRINK_STATE *,
- MODEL *, long *, long *, double *,
- double *, double *,
- TIMING *, double *, long, long);
-long optimize_to_convergence_sharedslack(DOC **, long *, long, long,
- LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, SHRINK_STATE *,
- MODEL *, double *, double *, double *,
- TIMING *, double *);
-double compute_objective_function(double *, double *, double *, double,
- long *, long *);
-void clear_index(long *);
-void add_to_index(long *, long);
-long compute_index(long *,long, long *);
-void optimize_svm(DOC **, long *, long *, long *, double, long *, long *,
- MODEL *,
- long, long *, long, double *, double *, double *,
- LEARN_PARM *, CFLOAT *, KERNEL_PARM *, QP *, double *);
-void compute_matrices_for_optimization(DOC **, long *, long *, long *, double,
- long *,
- long *, long *, MODEL *, double *,
- double *, double *, long, long, LEARN_PARM *,
- CFLOAT *, KERNEL_PARM *, QP *);
-long calculate_svm_model(DOC **, long *, long *, double *, double *,
- double *, double *, LEARN_PARM *, long *,
- long *, MODEL *);
-long check_optimality(MODEL *, long *, long *, double *, double *,
- double *, long,
- LEARN_PARM *,double *, double, long *, long *, long *,
- long *, long, KERNEL_PARM *);
-long check_optimality_sharedslack(DOC **docs, MODEL *model, long int *label,
- double *a, double *lin, double *c, double *slack,
- double *alphaslack, long int totdoc,
- LEARN_PARM *learn_parm, double *maxdiff,
- double epsilon_crit_org, long int *misclassified,
- long int *active2dnum,
- long int *last_suboptimal_at,
- long int iteration, KERNEL_PARM *kernel_parm);
-void compute_shared_slacks(DOC **docs, long int *label, double *a,
- double *lin, double *c, long int *active2dnum,
- LEARN_PARM *learn_parm,
- double *slack, double *alphaslack);
-long identify_inconsistent(double *, long *, long *, long, LEARN_PARM *,
- long *, long *);
-long identify_misclassified(double *, long *, long *, long,
- MODEL *, long *, long *);
-long identify_one_misclassified(double *, long *, long *, long,
- MODEL *, long *, long *);
-long incorporate_unlabeled_examples(MODEL *, long *,long *, long *,
- double *, double *, long, double *,
- long *, long *, long, KERNEL_PARM *,
- LEARN_PARM *);
-void update_linear_component(DOC **, long *, long *, double *, double *,
- long *, long, long, KERNEL_PARM *,
- KERNEL_CACHE *, double *,
- CFLOAT *, double *);
-long select_next_qp_subproblem_grad(long *, long *, double *,
- double *, double *, long,
- long, LEARN_PARM *, long *, long *,
- long *, double *, long *, KERNEL_CACHE *,
- long, long *, long *);
-long select_next_qp_subproblem_rand(long *, long *, double *,
- double *, double *, long,
- long, LEARN_PARM *, long *, long *,
- long *, double *, long *, KERNEL_CACHE *,
- long *, long *, long);
-long select_next_qp_slackset(DOC **docs, long int *label, double *a,
- double *lin, double *slack, double *alphaslack,
- double *c, LEARN_PARM *learn_parm,
- long int *active2dnum, double *maxviol);
-void select_top_n(double *, long, long *, long);
-void init_shrink_state(SHRINK_STATE *, long, long);
-void shrink_state_cleanup(SHRINK_STATE *);
-long shrink_problem(DOC **, LEARN_PARM *, SHRINK_STATE *, KERNEL_PARM *,
- long *, long *, long, long, long, double *, long *);
-void reactivate_inactive_examples(long *, long *, double *, SHRINK_STATE *,
- double *, double*, long, long, long, LEARN_PARM *,
- long *, DOC **, KERNEL_PARM *,
- KERNEL_CACHE *, MODEL *, CFLOAT *,
- double *, double *);
-
-/* cache kernel evalutations to improve speed */
-KERNEL_CACHE *kernel_cache_init(long, long);
-void kernel_cache_cleanup(KERNEL_CACHE *);
-void get_kernel_row(KERNEL_CACHE *,DOC **, long, long, long *, CFLOAT *,
- KERNEL_PARM *);
-void cache_kernel_row(KERNEL_CACHE *,DOC **, long, KERNEL_PARM *);
-void cache_multiple_kernel_rows(KERNEL_CACHE *,DOC **, long *, long,
- KERNEL_PARM *);
-void kernel_cache_shrink(KERNEL_CACHE *,long, long, long *);
-void kernel_cache_reset_lru(KERNEL_CACHE *);
-long kernel_cache_malloc(KERNEL_CACHE *);
-void kernel_cache_free(KERNEL_CACHE *,long);
-long kernel_cache_free_lru(KERNEL_CACHE *);
-CFLOAT *kernel_cache_clean_and_malloc(KERNEL_CACHE *,long);
-long kernel_cache_touch(KERNEL_CACHE *,long);
-long kernel_cache_check(KERNEL_CACHE *,long);
-long kernel_cache_space_available(KERNEL_CACHE *);
-
-void compute_xa_estimates(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *, double *, double *, double *);
-double xa_estimate_error(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *);
-double xa_estimate_recall(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *);
-double xa_estimate_precision(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *);
-void avg_similarity_of_sv_of_one_class(MODEL *, DOC **, double *, long *, KERNEL_PARM *, double *, double *);
-double most_similar_sv_of_same_class(MODEL *, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *);
-double distribute_alpha_t_greedily(long *, long, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *, double);
-double distribute_alpha_t_greedily_noindex(MODEL *, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *, double);
-void estimate_transduction_quality(MODEL *, long *, long *, long, DOC **, double *);
-double estimate_margin_vcdim(MODEL *, double, double, KERNEL_PARM *);
-double estimate_sphere(MODEL *, KERNEL_PARM *);
-double estimate_r_delta_average(DOC **, long, KERNEL_PARM *);
-double estimate_r_delta(DOC **, long, KERNEL_PARM *);
-double length_of_longest_document_vector(DOC **, long, KERNEL_PARM *);
-
-
-void write_model(char *, MODEL *);
-void write_prediction(char *, MODEL *, double *, double *, long *, long *,
- long, LEARN_PARM *);
-void write_alphas(char *, double *, long *, long);
-
-typedef struct cache_parm_s {
- KERNEL_CACHE *kernel_cache;
- CFLOAT *cache;
- DOC **docs;
- long m;
- KERNEL_PARM *kernel_parm;
- long offset,stepsize;
-} cache_parm_t;
-
-#endif
diff --git a/trunk/svm_light/build/svm_light-tar/svm_learn_main.c b/trunk/svm_light/build/svm_light-tar/svm_learn_main.c
deleted file mode 100755
index e2a157dad12a25097bbf335d603497ac09534d9a..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_learn_main.c
+++ /dev/null
@@ -1,397 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_learn_main.c */
-/* */
-/* Command line interface to the learning module of the */
-/* Support Vector Machine. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2000 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/***********************************************************************/
-
-
-/* if svm-learn is used out of C++, define it as extern "C" */
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-# include "svm_common.h"
-# include "svm_learn.h"
-
-#ifdef __cplusplus
-}
-#endif
-
-char docfile[200]; /* file with training examples */
-char modelfile[200]; /* file for resulting classifier */
-char restartfile[200]; /* file with initial alphas */
-
-void read_input_parameters(int, char **, char *, char *, char *, long *,
- LEARN_PARM *, KERNEL_PARM *);
-void wait_any_key();
-void print_help();
-
-
-
-int main (int argc, char* argv[])
-{
- DOC **docs; /* training examples */
- long totwords,totdoc,i;
- double *target;
- double *alpha_in=NULL;
- KERNEL_CACHE *kernel_cache;
- LEARN_PARM learn_parm;
- KERNEL_PARM kernel_parm;
- MODEL *model=(MODEL *)my_malloc(sizeof(MODEL));
-
- read_input_parameters(argc,argv,docfile,modelfile,restartfile,&verbosity,
- &learn_parm,&kernel_parm);
- read_documents(docfile,&docs,&target,&totwords,&totdoc);
- if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc);
-
- if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
- kernel_cache=NULL;
- }
- else {
- /* Always get a new kernel cache. It is not possible to use the
- same cache for two different training runs */
- kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size);
- }
-
- if(learn_parm.type == CLASSIFICATION) {
- svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
- &kernel_parm,kernel_cache,model,alpha_in);
- }
- else if(learn_parm.type == REGRESSION) {
- svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
- &kernel_parm,&kernel_cache,model);
- }
- else if(learn_parm.type == RANKING) {
- svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
- &kernel_parm,&kernel_cache,model);
- }
- else if(learn_parm.type == OPTIMIZATION) {
- svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm,
- &kernel_parm,kernel_cache,model,alpha_in);
- }
-
- if(kernel_cache) {
- /* Free the memory used for the cache. */
- kernel_cache_cleanup(kernel_cache);
- }
-
- /* Warning: The model contains references to the original data 'docs'.
- If you want to free the original data, and only keep the model, you
- have to make a deep copy of 'model'. */
- /* deep_copy_of_model=copy_model(model); */
- write_model(modelfile,model);
-
- free(alpha_in);
- free_model(model,0);
- for(i=0;i<totdoc;i++)
- free_example(docs[i],1);
- free(docs);
- free(target);
-
- return(0);
-}
-
-/*---------------------------------------------------------------------------*/
-
-void read_input_parameters(int argc,char *argv[],char *docfile,char *modelfile,
- char *restartfile,long *verbosity,
- LEARN_PARM *learn_parm,KERNEL_PARM *kernel_parm)
-{
- long i;
- char type[100];
-
- /* set default */
- strcpy (modelfile, "svm_model");
- strcpy (learn_parm->predfile, "trans_predictions");
- strcpy (learn_parm->alphafile, "");
- strcpy (restartfile, "");
- (*verbosity)=1;
- learn_parm->biased_hyperplane=1;
- learn_parm->sharedslack=0;
- learn_parm->remove_inconsistent=0;
- learn_parm->skip_final_opt_check=0;
- learn_parm->svm_maxqpsize=10;
- learn_parm->svm_newvarsinqp=0;
- learn_parm->svm_iter_to_shrink=-9999;
- learn_parm->maxiter=100000;
- learn_parm->kernel_cache_size=40;
- learn_parm->svm_c=0.0;
- learn_parm->eps=0.1;
- learn_parm->transduction_posratio=-1.0;
- learn_parm->svm_costratio=1.0;
- learn_parm->svm_costratio_unlab=1.0;
- learn_parm->svm_unlabbound=1E-5;
- learn_parm->epsilon_crit=0.001;
- learn_parm->epsilon_a=1E-15;
- learn_parm->compute_loo=0;
- learn_parm->rho=1.0;
- learn_parm->xa_depth=0;
- kernel_parm->kernel_type=0;
- kernel_parm->poly_degree=3;
- kernel_parm->rbf_gamma=1.0;
- kernel_parm->coef_lin=1;
- kernel_parm->coef_const=1;
- strcpy(kernel_parm->custom,"empty");
- strcpy(type,"c");
-
- for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
- switch ((argv[i])[1])
- {
- case '?': print_help(); exit(0);
- case 'z': i++; strcpy(type,argv[i]); break;
- case 'v': i++; (*verbosity)=atol(argv[i]); break;
- case 'b': i++; learn_parm->biased_hyperplane=atol(argv[i]); break;
- case 'i': i++; learn_parm->remove_inconsistent=atol(argv[i]); break;
- case 'f': i++; learn_parm->skip_final_opt_check=!atol(argv[i]); break;
- case 'q': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break;
- case 'n': i++; learn_parm->svm_newvarsinqp=atol(argv[i]); break;
- case '#': i++; learn_parm->maxiter=atol(argv[i]); break;
- case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break;
- case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break;
- case 'c': i++; learn_parm->svm_c=atof(argv[i]); break;
- case 'w': i++; learn_parm->eps=atof(argv[i]); break;
- case 'p': i++; learn_parm->transduction_posratio=atof(argv[i]); break;
- case 'j': i++; learn_parm->svm_costratio=atof(argv[i]); break;
- case 'e': i++; learn_parm->epsilon_crit=atof(argv[i]); break;
- case 'o': i++; learn_parm->rho=atof(argv[i]); break;
- case 'k': i++; learn_parm->xa_depth=atol(argv[i]); break;
- case 'x': i++; learn_parm->compute_loo=atol(argv[i]); break;
- case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break;
- case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break;
- case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break;
- case 's': i++; kernel_parm->coef_lin=atof(argv[i]); break;
- case 'r': i++; kernel_parm->coef_const=atof(argv[i]); break;
- case 'u': i++; strcpy(kernel_parm->custom,argv[i]); break;
- case 'l': i++; strcpy(learn_parm->predfile,argv[i]); break;
- case 'a': i++; strcpy(learn_parm->alphafile,argv[i]); break;
- case 'y': i++; strcpy(restartfile,argv[i]); break;
- default: printf("\nUnrecognized option %s!\n\n",argv[i]);
- print_help();
- exit(0);
- }
- }
- if(i>=argc) {
- printf("\nNot enough input parameters!\n\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- strcpy (docfile, argv[i]);
- if((i+1)<argc) {
- strcpy (modelfile, argv[i+1]);
- }
- if(learn_parm->svm_iter_to_shrink == -9999) {
- if(kernel_parm->kernel_type == LINEAR)
- learn_parm->svm_iter_to_shrink=2;
- else
- learn_parm->svm_iter_to_shrink=100;
- }
- if(strcmp(type,"c")==0) {
- learn_parm->type=CLASSIFICATION;
- }
- else if(strcmp(type,"r")==0) {
- learn_parm->type=REGRESSION;
- }
- else if(strcmp(type,"p")==0) {
- learn_parm->type=RANKING;
- }
- else if(strcmp(type,"o")==0) {
- learn_parm->type=OPTIMIZATION;
- }
- else if(strcmp(type,"s")==0) {
- learn_parm->type=OPTIMIZATION;
- learn_parm->sharedslack=1;
- }
- else {
- printf("\nUnknown type '%s': Valid types are 'c' (classification), 'r' regession, and 'p' preference ranking.\n",type);
- wait_any_key();
- print_help();
- exit(0);
- }
- if((learn_parm->skip_final_opt_check)
- && (kernel_parm->kernel_type == LINEAR)) {
- printf("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n");
- learn_parm->skip_final_opt_check=0;
- }
- if((learn_parm->skip_final_opt_check)
- && (learn_parm->remove_inconsistent)) {
- printf("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- if((learn_parm->svm_maxqpsize<2)) {
- printf("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize);
- wait_any_key();
- print_help();
- exit(0);
- }
- if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) {
- printf("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize);
- printf("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp);
- wait_any_key();
- print_help();
- exit(0);
- }
- if(learn_parm->svm_iter_to_shrink<1) {
- printf("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink);
- wait_any_key();
- print_help();
- exit(0);
- }
- if(learn_parm->svm_c<0) {
- printf("\nThe C parameter must be greater than zero!\n\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- if(learn_parm->transduction_posratio>1) {
- printf("\nThe fraction of unlabeled examples to classify as positives must\n");
- printf("be less than 1.0 !!!\n\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- if(learn_parm->svm_costratio<=0) {
- printf("\nThe COSTRATIO parameter must be greater than zero!\n\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- if(learn_parm->epsilon_crit<=0) {
- printf("\nThe epsilon parameter must be greater than zero!\n\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- if(learn_parm->rho<0) {
- printf("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n");
- printf("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n");
- printf("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n");
- wait_any_key();
- print_help();
- exit(0);
- }
- if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) {
- printf("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n");
- printf("for switching to the conventional xa/estimates described in T. Joachims,\n");
- printf("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n");
- wait_any_key();
- print_help();
- exit(0);
- }
-}
-
-void wait_any_key()
-{
- printf("\n(more)\n");
- (void)getc(stdin);
-}
-
-void print_help()
-{
- printf("\nSVM-light %s: Support Vector Machine, learning module %s\n",VERSION,VERSION_DATE);
- copyright_notice();
- printf(" usage: svm_learn [options] example_file model_file\n\n");
- printf("Arguments:\n");
- printf(" example_file-> file with training data\n");
- printf(" model_file -> file to store learned decision rule in\n");
-
- printf("General options:\n");
- printf(" -? -> this help\n");
- printf(" -v [0..3] -> verbosity level (default 1)\n");
- printf("Learning options:\n");
- printf(" -z {c,r,p} -> select between classification (c), regression (r),\n");
- printf(" and preference ranking (p) (default classification)\n");
- printf(" -c float -> C: trade-off between training error\n");
- printf(" and margin (default [avg. x*x]^-1)\n");
- printf(" -w [0..] -> epsilon width of tube for regression\n");
- printf(" (default 0.1)\n");
- printf(" -j float -> Cost: cost-factor, by which training errors on\n");
- printf(" positive examples outweight errors on negative\n");
- printf(" examples (default 1) (see [4])\n");
- printf(" -b [0,1] -> use biased hyperplane (i.e. x*w+b>0) instead\n");
- printf(" of unbiased hyperplane (i.e. x*w>0) (default 1)\n");
- printf(" -i [0,1] -> remove inconsistent training examples\n");
- printf(" and retrain (default 0)\n");
- printf("Performance estimation options:\n");
- printf(" -x [0,1] -> compute leave-one-out estimates (default 0)\n");
- printf(" (see [5])\n");
- printf(" -o ]0..2] -> value of rho for XiAlpha-estimator and for pruning\n");
- printf(" leave-one-out computation (default 1.0) (see [2])\n");
- printf(" -k [0..100] -> search depth for extended XiAlpha-estimator \n");
- printf(" (default 0)\n");
- printf("Transduction options (see [3]):\n");
- printf(" -p [0..1] -> fraction of unlabeled examples to be classified\n");
- printf(" into the positive class (default is the ratio of\n");
- printf(" positive and negative examples in the training data)\n");
- printf("Kernel options:\n");
- printf(" -t int -> type of kernel function:\n");
- printf(" 0: linear (default)\n");
- printf(" 1: polynomial (s a*b+c)^d\n");
- printf(" 2: radial basis function exp(-gamma ||a-b||^2)\n");
- printf(" 3: sigmoid tanh(s a*b + c)\n");
- printf(" 4: user defined kernel from kernel.h\n");
- printf(" -d int -> parameter d in polynomial kernel\n");
- printf(" -g float -> parameter gamma in rbf kernel\n");
- printf(" -s float -> parameter s in sigmoid/poly kernel\n");
- printf(" -r float -> parameter c in sigmoid/poly kernel\n");
- printf(" -u string -> parameter of user defined kernel\n");
- printf("Optimization options (see [1]):\n");
- printf(" -q [2..] -> maximum size of QP-subproblems (default 10)\n");
- printf(" -n [2..q] -> number of new variables entering the working set\n");
- printf(" in each iteration (default n = q). Set n<q to prevent\n");
- printf(" zig-zagging.\n");
- printf(" -m [5..] -> size of cache for kernel evaluations in MB (default 40)\n");
- printf(" The larger the faster...\n");
- printf(" -e float -> eps: Allow that error for termination criterion\n");
- printf(" [y [w*x+b] - 1] >= eps (default 0.001)\n");
- printf(" -y [0,1] -> restart the optimization from alpha values in file\n");
- printf(" specified by -a option. (default 0)\n");
- printf(" -h [5..] -> number of iterations a variable needs to be\n");
- printf(" optimal before considered for shrinking (default 100)\n");
- printf(" -f [0,1] -> do final optimality check for variables removed\n");
- printf(" by shrinking. Although this test is usually \n");
- printf(" positive, there is no guarantee that the optimum\n");
- printf(" was found if the test is omitted. (default 1)\n");
- printf(" -y string -> if option is given, reads alphas from file with given\n");
- printf(" and uses them as starting point. (default 'disabled')\n");
- printf(" -# int -> terminate optimization, if no progress after this\n");
- printf(" number of iterations. (default 100000)\n");
- printf("Output options:\n");
- printf(" -l string -> file to write predicted labels of unlabeled\n");
- printf(" examples into after transductive learning\n");
- printf(" -a string -> write all alphas to this file after learning\n");
- printf(" (in the same order as in the training set)\n");
- wait_any_key();
- printf("\nMore details in:\n");
- printf("[1] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in\n");
- printf(" Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and\n");
- printf(" A. Smola (ed.), MIT Press, 1999.\n");
- printf("[2] T. Joachims, Estimating the Generalization performance of an SVM\n");
- printf(" Efficiently. International Conference on Machine Learning (ICML), 2000.\n");
- printf("[3] T. Joachims, Transductive Inference for Text Classification using Support\n");
- printf(" Vector Machines. International Conference on Machine Learning (ICML),\n");
- printf(" 1999.\n");
- printf("[4] K. Morik, P. Brockhausen, and T. Joachims, Combining statistical learning\n");
- printf(" with a knowledge-based approach - A case study in intensive care \n");
- printf(" monitoring. International Conference on Machine Learning (ICML), 1999.\n");
- printf("[5] T. Joachims, Learning to Classify Text Using Support Vector\n");
- printf(" Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,\n");
- printf(" 2002.\n\n");
-}
-
-
diff --git a/trunk/svm_light/build/svm_light-tar/svm_loqo.c b/trunk/svm_light/build/svm_light-tar/svm_loqo.c
deleted file mode 100755
index ff31a655f26948a95a3ef0537d8a8b95fd1148c0..0000000000000000000000000000000000000000
--- a/trunk/svm_light/build/svm_light-tar/svm_loqo.c
+++ /dev/null
@@ -1,211 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_loqo.c */
-/* */
-/* Interface to the PR_LOQO optimization package for SVM. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 19.07.99 */
-/* */
-/* Copyright (c) 1999 Universitaet Dortmund - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/***********************************************************************/
-
-# include <math.h>
-# include "pr_loqo/pr_loqo.h"
-# include "svm_common.h"
-
-/* Common Block Declarations */
-
-long verbosity;
-
-/* /////////////////////////////////////////////////////////////// */
-
-# define DEF_PRECISION_LINEAR 1E-8
-# define DEF_PRECISION_NONLINEAR 1E-14
-
-double *optimize_qp();
-double *primal=0,*dual=0;
-double init_margin=0.15;
-long init_iter=500,precision_violations=0;
-double model_b;
-double opt_precision=DEF_PRECISION_LINEAR;
-
-/* /////////////////////////////////////////////////////////////// */
-
-void *my_malloc();
-
-double *optimize_qp(qp,epsilon_crit,nx,threshold,learn_parm)
-QP *qp;
-double *epsilon_crit;
-long nx; /* Maximum number of variables in QP */
-double *threshold;
-LEARN_PARM *learn_parm;
-/* start the optimizer and return the optimal values */
-{
- register long i,j,result;
- double margin,obj_before,obj_after;
- double sigdig,dist,epsilon_loqo;
- int iter;
-
- if(!primal) { /* allocate memory at first call */
- primal=(double *)my_malloc(sizeof(double)*nx*3);
- dual=(double *)my_malloc(sizeof(double)*(nx*2+1));
- }
-
- if(verbosity>=4) { /* really verbose */
- printf("\n\n");
- for(i=0;i<qp->opt_n;i++) {
- printf("%f: ",qp->opt_g0[i]);
- for(j=0;j<qp->opt_n;j++) {
- printf("%f ",qp->opt_g[i*qp->opt_n+j]);
- }
- printf(": a%ld=%.10f < %f",i,qp->opt_xinit[i],qp->opt_up[i]);
- printf(": y=%f\n",qp->opt_ce[i]);
- }
- for(j=0;j<qp->opt_m;j++) {
- printf("EQ-%ld: %f*a0",j,qp->opt_ce[j]);
- for(i=1;i<qp->opt_n;i++) {
- printf(" + %f*a%ld",qp->opt_ce[i],i);
- }
- printf(" = %f\n\n",-qp->opt_ce0[0]);
- }
-}
-
- obj_before=0; /* calculate objective before optimization */
- for(i=0;i<qp->opt_n;i++) {
- obj_before+=(qp->opt_g0[i]*qp->opt_xinit[i]);
- obj_before+=(0.5*qp->opt_xinit[i]*qp->opt_xinit[i]*qp->opt_g[i*qp->opt_n+i]);
- for(j=0;j<i;j++) {
- obj_before+=(qp->opt_xinit[j]*qp->opt_xinit[i]*qp->opt_g[j*qp->opt_n+i]);
- }
- }
-
- result=STILL_RUNNING;
- qp->opt_ce0[0]*=(-1.0);
- /* Run pr_loqo. If a run fails, try again with parameters which lead */
- /* to a slower, but more robust setting. */
- for(margin=init_margin,iter=init_iter;
- (margin<=0.9999999) && (result!=OPTIMAL_SOLUTION);) {
- sigdig=-log10(opt_precision);
-
- result=pr_loqo((int)qp->opt_n,(int)qp->opt_m,
- (double *)qp->opt_g0,(double *)qp->opt_g,
- (double *)qp->opt_ce,(double *)qp->opt_ce0,
- (double *)qp->opt_low,(double *)qp->opt_up,
- (double *)primal,(double *)dual,
- (int)(verbosity-2),
- (double)sigdig,(int)iter,
- (double)margin,(double)(qp->opt_up[0])/4.0,(int)0);
-
- if(isnan(dual[0])) { /* check for choldc problem */
- if(verbosity>=2) {
- printf("NOTICE: Restarting PR_LOQO with more conservative parameters.\n");
- }
- if(init_margin<0.80) { /* become more conservative in general */
- init_margin=(4.0*margin+1.0)/5.0;
- }
- margin=(margin+1.0)/2.0;
- (opt_precision)*=10.0; /* reduce precision */
- if(verbosity>=2) {
- printf("NOTICE: Reducing precision of PR_LOQO.\n");
- }
- }
- else if(result!=OPTIMAL_SOLUTION) {
- iter+=2000;
- init_iter+=10;
- (opt_precision)*=10.0; /* reduce precision */
- if(verbosity>=2) {
- printf("NOTICE: Reducing precision of PR_LOQO due to (%ld).\n",result);
- }
- }
- }
-
- if(qp->opt_m) /* Thanks to Alex Smola for this hint */
- model_b=dual[0];
- else
- model_b=0;
-
- /* Check the precision of the alphas. If results of current optimization */
- /* violate KT-Conditions, relax the epsilon on the bounds on alphas. */
- epsilon_loqo=1E-10;
- for(i=0;i<qp->opt_n;i++) {
- dist=-model_b*qp->opt_ce[i];
- dist+=(qp->opt_g0[i]+1.0);
- for(j=0;j<i;j++) {
- dist+=(primal[j]*qp->opt_g[j*qp->opt_n+i]);
- }
- for(j=i;j<qp->opt_n;j++) {
- dist+=(primal[j]*qp->opt_g[i*qp->opt_n+j]);
- }
- /* printf("LOQO: a[%d]=%f, dist=%f, b=%f\n",i,primal[i],dist,dual[0]); */
- if((primal[i]<(qp->opt_up[i]-epsilon_loqo)) && (dist < (1.0-(*epsilon_crit)))) {
- epsilon_loqo=(qp->opt_up[i]-primal[i])*2.0;
- }
- else if((primal[i]>(0+epsilon_loqo)) && (dist > (1.0+(*epsilon_crit)))) {
- epsilon_loqo=primal[i]*2.0;
- }
- }
-
- for(i=0;i<qp->opt_n;i++) { /* clip alphas to bounds */
- if(primal[i]<=(0+epsilon_loqo)) {
- primal[i]=0;
- }
- else if(primal[i]>=(qp->opt_up[i]-epsilon_loqo)) {
- primal[i]=qp->opt_up[i];
- }
- }
-
- obj_after=0; /* calculate objective after optimization */
- for(i=0;i<qp->opt_n;i++) {
- obj_after+=(qp->opt_g0[i]*primal[i]);
- obj_after+=(0.5*primal[i]*primal[i]*qp->opt_g[i*qp->opt_n+i]);
- for(j=0;j<i;j++) {
- obj_after+=(primal[j]*primal[i]*qp->opt_g[j*qp->opt_n+i]);
- }
- }
-
- /* if optimizer returned NAN values, reset and retry with smaller */
- /* working set. */
- if(isnan(obj_after) || isnan(model_b)) {
- for(i=0;i<qp->opt_n;i++) {
- primal[i]=qp->opt_xinit[i];
- }
- model_b=0;
- if(learn_parm->svm_maxqpsize>2) {
- learn_parm->svm_maxqpsize--; /* decrease size of qp-subproblems */
- }
- }
-
- if(obj_after >= obj_before) { /* check whether there was progress */
- (opt_precision)/=100.0;
- precision_violations++;
- if(verbosity>=2) {
- printf("NOTICE: Increasing Precision of PR_LOQO.\n");
- }
- }
-
- if(precision_violations > 500) {
- (*epsilon_crit)*=10.0;
- precision_violations=0;
- if(verbosity>=1) {
- printf("\nWARNING: Relaxing epsilon on KT-Conditions.\n");
- }
- }
-
- (*threshold)=model_b;
-
- if(result!=OPTIMAL_SOLUTION) {
- printf("\nERROR: PR_LOQO did not converge. \n");
- return(qp->opt_xinit);
- }
- else {
- return(primal);
- }
-}
-
diff --git a/trunk/svm_light/build/svm_light-tar/unpacked b/trunk/svm_light/build/svm_light-tar/unpacked
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/trunk/svm_light/build/svm_light.tar.gz b/trunk/svm_light/build/svm_light.tar.gz
deleted file mode 100644
index 8c57097c83079c08b5b70bee20fb4a74ae09d608..0000000000000000000000000000000000000000
Binary files a/trunk/svm_light/build/svm_light.tar.gz and /dev/null differ
diff --git a/trunk/svm_light/installed b/trunk/svm_light/installed
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/trunk/svm_light/mainpage.dox b/trunk/svm_light/mainpage.dox
deleted file mode 100644
index c09c7e54e44ded6627795401c90abd3ed89b4713..0000000000000000000000000000000000000000
--- a/trunk/svm_light/mainpage.dox
+++ /dev/null
@@ -1,26 +0,0 @@
-/**
-\mainpage
-\htmlinclude manifest.html
-
-\b svm_light is ...
-
-<!--
-Provide an overview of your package.
--->
-
-
-\section codeapi Code API
-
-<!--
-Provide links to specific auto-generated API documentation within your
-package that is of particular interest to a reader. Doxygen will
-document pretty much every part of your code, so do your best here to
-point the reader to the actual API.
-
-If your codebase is fairly large or has different sets of APIs, you
-should use the doxygen 'group' tag to keep these APIs together. For
-example, the roscpp documentation has 'libros' group.
--->
-
-
-*/
diff --git a/trunk/svm_light/manifest.xml b/trunk/svm_light/manifest.xml
deleted file mode 100644
index 00e23c6afd98bcfcd309b60c5ff322848cafda7b..0000000000000000000000000000000000000000
--- a/trunk/svm_light/manifest.xml
+++ /dev/null
@@ -1,15 +0,0 @@
-<package>
- <description brief="svm_light">
- This package is a wrapper on the svm_light library available from <a href="http://svmlight.joachims.org/">here</a>. This package does not modify the contents of the original library in any manner and only wraps it for easy distribution with the ROS packaging system. svm_light is not under BSD license and is optional for FCL. Users can choose to use it by setting flag USE_SVM_LIGHT=1 in FCL.
- </description>
- <author>Maintained by Jia Pan and Sachin Chitta</author>
- <license>BSD</license>
- <review status="unreviewed" notes=""/>
- <url>http://ros.org/wiki/svm_light</url>
- <export>
- <cpp cflags="-I${prefix}/svm_light/include" lflags="-L${prefix}/svm_light/lib -Wl,-rpath,${prefix}/svm_light/lib -lsvmlight"/>
- </export>
-
-</package>
-
-
diff --git a/trunk/svm_light/svm_light.diff b/trunk/svm_light/svm_light.diff
deleted file mode 100644
index 86410f3fd1729f18794f39243276aebbc4dda45f..0000000000000000000000000000000000000000
--- a/trunk/svm_light/svm_light.diff
+++ /dev/null
@@ -1,543 +0,0 @@
---- Makefile 2011-09-06 22:14:22.363126903 -0400
-+++ Makefile 2011-09-06 22:14:53.859177783 -0400
-@@ -11,7 +11,7 @@ LD = gcc
- #Uncomment the following line to make CYGWIN produce stand-alone Windows executables
- #SFLAGS= -mno-cygwin
-
--CFLAGS= $(SFLAGS) -O3 # release C-Compiler flags
-+CFLAGS= $(SFLAGS) -fPIC -O3 # release C-Compiler flags
- LFLAGS= $(SFLAGS) -O3 # release linker flags
- #CFLAGS= $(SFLAGS) -pg -Wall -pedantic # debugging C-Compiler flags
- #LFLAGS= $(SFLAGS) -pg # debugging linker flags
---- svm_learn.c 2011-09-06 22:14:22.363126903 -0400
-+++ svm_learn.c 2011-09-06 22:49:10.011383409 -0400
-@@ -26,6 +26,509 @@ double *optimize_qp(QP *, double *, long
-
- /*---------------------------------------------------------------------------*/
-
-+void svm_learn_classification_extend(DOC **docs, double *class, long int
-+ totdoc, long int totwords,
-+ LEARN_PARM *learn_parm,
-+ KERNEL_PARM *kernel_parm,
-+ KERNEL_CACHE *kernel_cache,
-+ MODEL *model,
-+ double *alpha,
-+ int* nerrors,
-+ double* maxerror)
-+{
-+ long *inconsistent, i, *label;
-+ long inconsistentnum;
-+ long misclassified, upsupvecnum;
-+ double loss, model_length, example_length;
-+ double maxdiff, *lin, *a, *c;
-+ long runtime_start, runtime_end;
-+ long iterations;
-+ long *unlabeled, transduction;
-+ long heldout;
-+ long loo_count = 0, loo_count_pos = 0, loo_count_neg = 0, trainpos = 0, trainneg = 0;
-+ long loocomputed = 0, runtime_start_loo = 0, runtime_start_xa = 0;
-+ double heldout_c = 0, r_delta_sq = 0, r_delta, r_delta_avg;
-+ long *index, *index2dnum;
-+ double *weights;
-+ CFLOAT *aicache; /* buffer to keep one row of hessian */
-+
-+ double *xi_fullset; /* buffer for storing xi on full sample in loo */
-+ double *a_fullset; /* buffer for storing alpha on full sample in loo */
-+ TIMING timing_profile;
-+ SHRINK_STATE shrink_state;
-+
-+ runtime_start = get_runtime();
-+ timing_profile.time_kernel = 0;
-+ timing_profile.time_opti = 0;
-+ timing_profile.time_shrink = 0;
-+ timing_profile.time_update = 0;
-+ timing_profile.time_model = 0;
-+ timing_profile.time_check = 0;
-+ timing_profile.time_select = 0;
-+ kernel_cache_statistic = 0;
-+
-+ learn_parm->totwords = totwords;
-+
-+ /* make sure -n value is reasonable */
-+ if ((learn_parm->svm_newvarsinqp < 2)
-+ || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize))
-+ {
-+ learn_parm->svm_newvarsinqp = learn_parm->svm_maxqpsize;
-+ }
-+
-+ init_shrink_state(&shrink_state, totdoc, (long)MAXSHRINK);
-+
-+ label = (long *)my_malloc(sizeof(long) * totdoc);
-+ inconsistent = (long *)my_malloc(sizeof(long) * totdoc);
-+ unlabeled = (long *)my_malloc(sizeof(long) * totdoc);
-+ c = (double *)my_malloc(sizeof(double) * totdoc);
-+ a = (double *)my_malloc(sizeof(double) * totdoc);
-+ a_fullset = (double *)my_malloc(sizeof(double) * totdoc);
-+ xi_fullset = (double *)my_malloc(sizeof(double) * totdoc);
-+ lin = (double *)my_malloc(sizeof(double) * totdoc);
-+ learn_parm->svm_cost = (double *)my_malloc(sizeof(double) * totdoc);
-+ model->supvec = (DOC **)my_malloc(sizeof(DOC *) * (totdoc + 2));
-+ model->alpha = (double *)my_malloc(sizeof(double) * (totdoc + 2));
-+ model->index = (long *)my_malloc(sizeof(long) * (totdoc + 2));
-+
-+ model->at_upper_bound = 0;
-+ model->b = 0;
-+ model->supvec[0] = 0; /* element 0 reserved and empty for now */
-+ model->alpha[0] = 0;
-+ model->lin_weights = NULL;
-+ model->totwords = totwords;
-+ model->totdoc = totdoc;
-+ model->kernel_parm = (*kernel_parm);
-+ model->sv_num = 1;
-+ model->loo_error = -1;
-+ model->loo_recall = -1;
-+ model->loo_precision = -1;
-+ model->xa_error = -1;
-+ model->xa_recall = -1;
-+ model->xa_precision = -1;
-+ inconsistentnum = 0;
-+ transduction = 0;
-+
-+ r_delta = estimate_r_delta(docs, totdoc, kernel_parm);
-+ r_delta_sq = r_delta * r_delta;
-+
-+ r_delta_avg = estimate_r_delta_average(docs, totdoc, kernel_parm);
-+ if (learn_parm->svm_c == 0.0) /* default value for C */
-+ {
-+ learn_parm->svm_c = 1.0 / (r_delta_avg * r_delta_avg);
-+ if (verbosity >= 1)
-+ printf("Setting default regularization parameter C=%.4f\n",
-+ learn_parm->svm_c);
-+ }
-+
-+ learn_parm->eps = -1.0; /* equivalent regression epsilon for
-+ classification */
-+
-+ for (i = 0; i < totdoc; i++) /* various inits */
-+ {
-+ docs[i]->docnum = i;
-+ inconsistent[i] = 0;
-+ a[i] = 0;
-+ lin[i] = 0;
-+ c[i] = 0.0;
-+ unlabeled[i] = 0;
-+ if (class[i] == 0)
-+ {
-+ unlabeled[i] = 1;
-+ label[i] = 0;
-+ transduction = 1;
-+ }
-+ if (class[i] > 0)
-+ {
-+ learn_parm->svm_cost[i] = learn_parm->svm_c * learn_parm->svm_costratio *
-+ docs[i]->costfactor;
-+ label[i] = 1;
-+ trainpos++;
-+ }
-+ else if (class[i] < 0)
-+ {
-+ learn_parm->svm_cost[i] = learn_parm->svm_c * docs[i]->costfactor;
-+ label[i] = -1;
-+ trainneg++;
-+ }
-+ else
-+ {
-+ learn_parm->svm_cost[i] = 0;
-+ }
-+ }
-+ if (verbosity >= 2)
-+ {
-+ printf("%ld positive, %ld negative, and %ld unlabeled examples.\n", trainpos, trainneg, totdoc - trainpos - trainneg);
-+ fflush(stdout);
-+ }
-+
-+ /* caching makes no sense for linear kernel */
-+ if (kernel_parm->kernel_type == LINEAR)
-+ {
-+ kernel_cache = NULL;
-+ }
-+
-+ /* compute starting state for initial alpha values */
-+ if (alpha)
-+ {
-+ if (verbosity >= 1)
-+ {
-+ printf("Computing starting state...");
-+ fflush(stdout);
-+ }
-+ index = (long *)my_malloc(sizeof(long) * totdoc);
-+ index2dnum = (long *)my_malloc(sizeof(long) * (totdoc + 11));
-+ weights = (double *)my_malloc(sizeof(double) * (totwords + 1));
-+ aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT) * totdoc);
-+ for (i = 0; i < totdoc; i++) /* create full index and clip alphas */
-+ {
-+ index[i] = 1;
-+ alpha[i] = fabs(alpha[i]);
-+ if (alpha[i] < 0) alpha[i] = 0;
-+ if (alpha[i] > learn_parm->svm_cost[i]) alpha[i] = learn_parm->svm_cost[i];
-+ }
-+ if (kernel_parm->kernel_type != LINEAR)
-+ {
-+ for (i = 0; i < totdoc; i++) /* fill kernel cache with unbounded SV */
-+ if ((alpha[i] > 0) && (alpha[i] < learn_parm->svm_cost[i])
-+ && (kernel_cache_space_available(kernel_cache)))
-+ cache_kernel_row(kernel_cache, docs, i, kernel_parm);
-+ for (i = 0; i < totdoc; i++) /* fill rest of kernel cache with bounded SV */
-+ if ((alpha[i] == learn_parm->svm_cost[i])
-+ && (kernel_cache_space_available(kernel_cache)))
-+ cache_kernel_row(kernel_cache, docs, i, kernel_parm);
-+ }
-+ (void)compute_index(index, totdoc, index2dnum);
-+ update_linear_component(docs, label, index2dnum, alpha, a, index2dnum, totdoc,
-+ totwords, kernel_parm, kernel_cache, lin, aicache,
-+ weights);
-+ (void)calculate_svm_model(docs, label, unlabeled, lin, alpha, a, c,
-+ learn_parm, index2dnum, index2dnum, model);
-+ for (i = 0; i < totdoc; i++) /* copy initial alphas */
-+ {
-+ a[i] = alpha[i];
-+ }
-+ free(index);
-+ free(index2dnum);
-+ free(weights);
-+ free(aicache);
-+ if (verbosity >= 1)
-+ {
-+ printf("done.\n");
-+ fflush(stdout);
-+ }
-+ }
-+
-+ if (transduction)
-+ {
-+ learn_parm->svm_iter_to_shrink = 99999999;
-+ if (verbosity >= 1)
-+ printf("\nDeactivating Shrinking due to an incompatibility with the transductive \nlearner in the current version.\n\n");
-+ }
-+
-+
-+ if (transduction && learn_parm->compute_loo)
-+ {
-+ learn_parm->compute_loo = 0;
-+ if (verbosity >= 1)
-+ printf("\nCannot compute leave-one-out estimates for transductive learner.\n\n");
-+ }
-+
-+ if (learn_parm->remove_inconsistent && learn_parm->compute_loo)
-+ {
-+ learn_parm->compute_loo = 0;
-+ printf("\nCannot compute leave-one-out estimates when removing inconsistent examples.\n\n");
-+ }
-+
-+ if (learn_parm->compute_loo && ((trainpos == 1) || (trainneg == 1)))
-+ {
-+ learn_parm->compute_loo = 0;
-+ printf("\nCannot compute leave-one-out with only one example in one class.\n\n");
-+ }
-+
-+
-+ if (verbosity == 1)
-+ {
-+ printf("Optimizing");
-+ fflush(stdout);
-+ }
-+
-+ /* train the svm */
-+ iterations = optimize_to_convergence(docs, label, totdoc, totwords, learn_parm,
-+ kernel_parm, kernel_cache, &shrink_state, model,
-+ inconsistent, unlabeled, a, lin,
-+ c, &timing_profile,
-+ &maxdiff, (long) - 1,
-+ (long)1);
-+
-+ misclassified = 0;
-+ double maxerror_ = 0;
-+ for (i = 0; (i < totdoc); i++) /* get final statistic */
-+ {
-+ if ((lin[i] - model->b)*(double)label[i] <= 0.0)
-+ {
-+ misclassified++;
-+ if(maxerror_ < -(lin[i] - model->b)*(double)label[i])
-+ maxerror_ = -(lin[i] - model->b)*(double)label[i];
-+ }
-+ }
-+
-+ *nerrors = misclassified;
-+ *maxerror = maxerror_;
-+
-+ if (verbosity >= 1)
-+ {
-+ if (verbosity == 1) printf("done. (%ld iterations)\n", iterations);
-+
-+ misclassified = 0;
-+ for (i = 0; (i < totdoc); i++) /* get final statistic */
-+ {
-+ if ((lin[i] - model->b)*(double)label[i] <= 0.0)
-+ misclassified++;
-+ }
-+
-+ printf("Optimization finished (%ld misclassified, maxdiff=%.5f).\n",
-+ misclassified, maxdiff);
-+
-+ runtime_end = get_runtime();
-+ if (verbosity >= 2)
-+ {
-+ printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n",
-+ ((float)runtime_end - (float)runtime_start) / 100.0,
-+ (100.0*timing_profile.time_kernel) / (float)(runtime_end - runtime_start),
-+ (100.0*timing_profile.time_opti) / (float)(runtime_end - runtime_start),
-+ (100.0*timing_profile.time_shrink) / (float)(runtime_end - runtime_start),
-+ (100.0*timing_profile.time_update) / (float)(runtime_end - runtime_start),
-+ (100.0*timing_profile.time_model) / (float)(runtime_end - runtime_start),
-+ (100.0*timing_profile.time_check) / (float)(runtime_end - runtime_start),
-+ (100.0*timing_profile.time_select) / (float)(runtime_end - runtime_start));
-+ }
-+ else
-+ {
-+ printf("Runtime in cpu-seconds: %.2f\n",
-+ (runtime_end - runtime_start) / 100.0);
-+ }
-+
-+ if (learn_parm->remove_inconsistent)
-+ {
-+ inconsistentnum = 0;
-+ for (i = 0; i < totdoc; i++)
-+ if (inconsistent[i])
-+ inconsistentnum++;
-+ printf("Number of SV: %ld (plus %ld inconsistent examples)\n",
-+ model->sv_num - 1, inconsistentnum);
-+ }
-+ else
-+ {
-+ upsupvecnum = 0;
-+ for (i = 1; i < model->sv_num; i++)
-+ {
-+ if (fabs(model->alpha[i]) >=
-+ (learn_parm->svm_cost[(model->supvec[i])->docnum] -
-+ learn_parm->epsilon_a))
-+ upsupvecnum++;
-+ }
-+ printf("Number of SV: %ld (including %ld at upper bound)\n",
-+ model->sv_num - 1, upsupvecnum);
-+ }
-+
-+ if ((verbosity >= 1) && (!learn_parm->skip_final_opt_check))
-+ {
-+ loss = 0;
-+ model_length = 0;
-+ for (i = 0; i < totdoc; i++)
-+ {
-+ if ((lin[i] - model->b)*(double)label[i] < 1.0 - learn_parm->epsilon_crit)
-+ loss += 1.0 - (lin[i] - model->b) * (double)label[i];
-+ model_length += a[i] * label[i] * lin[i];
-+ }
-+ model_length = sqrt(model_length);
-+ fprintf(stdout, "L1 loss: loss=%.5f\n", loss);
-+ fprintf(stdout, "Norm of weight vector: |w|=%.5f\n", model_length);
-+ example_length = estimate_sphere(model, kernel_parm);
-+ fprintf(stdout, "Norm of longest example vector: |x|=%.5f\n",
-+ length_of_longest_document_vector(docs, totdoc, kernel_parm));
-+ fprintf(stdout, "Estimated VCdim of classifier: VCdim<=%.5f\n",
-+ estimate_margin_vcdim(model, model_length, example_length,
-+ kernel_parm));
-+ if ((!learn_parm->remove_inconsistent) && (!transduction))
-+ {
-+ runtime_start_xa = get_runtime();
-+ if (verbosity >= 1)
-+ {
-+ printf("Computing XiAlpha-estimates...");
-+ fflush(stdout);
-+ }
-+ compute_xa_estimates(model, label, unlabeled, totdoc, docs, lin, a,
-+ kernel_parm, learn_parm, &(model->xa_error),
-+ &(model->xa_recall), &(model->xa_precision));
-+ if (verbosity >= 1)
-+ {
-+ printf("done\n");
-+ }
-+ printf("Runtime for XiAlpha-estimates in cpu-seconds: %.2f\n",
-+ (get_runtime() - runtime_start_xa) / 100.0);
-+
-+ fprintf(stdout, "XiAlpha-estimate of the error: error<=%.2f%% (rho=%.2f,depth=%ld)\n",
-+ model->xa_error, learn_parm->rho, learn_parm->xa_depth);
-+ fprintf(stdout, "XiAlpha-estimate of the recall: recall=>%.2f%% (rho=%.2f,depth=%ld)\n",
-+ model->xa_recall, learn_parm->rho, learn_parm->xa_depth);
-+ fprintf(stdout, "XiAlpha-estimate of the precision: precision=>%.2f%% (rho=%.2f,depth=%ld)\n",
-+ model->xa_precision, learn_parm->rho, learn_parm->xa_depth);
-+ }
-+ else if (!learn_parm->remove_inconsistent)
-+ {
-+ estimate_transduction_quality(model, label, unlabeled, totdoc, docs, lin);
-+ }
-+ }
-+ if (verbosity >= 1)
-+ {
-+ printf("Number of kernel evaluations: %ld\n", kernel_cache_statistic);
-+ }
-+ }
-+
-+
-+ /* leave-one-out testing starts now */
-+ if (learn_parm->compute_loo)
-+ {
-+ /* save results of training on full dataset for leave-one-out */
-+ runtime_start_loo = get_runtime();
-+ for (i = 0; i < totdoc; i++)
-+ {
-+ xi_fullset[i] = 1.0 - ((lin[i] - model->b) * (double)label[i]);
-+ if (xi_fullset[i] < 0) xi_fullset[i] = 0;
-+ a_fullset[i] = a[i];
-+ }
-+ if (verbosity >= 1)
-+ {
-+ printf("Computing leave-one-out");
-+ }
-+
-+ /* repeat this loop for every held-out example */
-+ for (heldout = 0; (heldout < totdoc); heldout++)
-+ {
-+ if (learn_parm->rho*a_fullset[heldout]*r_delta_sq + xi_fullset[heldout]
-+ < 1.0)
-+ {
-+ /* guaranteed to not produce a leave-one-out error */
-+ if (verbosity == 1)
-+ {
-+ printf("+");
-+ fflush(stdout);
-+ }
-+ }
-+ else if (xi_fullset[heldout] > 1.0)
-+ {
-+ /* guaranteed to produce a leave-one-out error */
-+ loo_count++;
-+ if (label[heldout] > 0) loo_count_pos++;
-+ else loo_count_neg++;
-+ if (verbosity == 1)
-+ {
-+ printf("-");
-+ fflush(stdout);
-+ }
-+ }
-+ else
-+ {
-+ loocomputed++;
-+ heldout_c = learn_parm->svm_cost[heldout]; /* set upper bound to zero */
-+ learn_parm->svm_cost[heldout] = 0;
-+ /* make sure heldout example is not currently */
-+ /* shrunk away. Assumes that lin is up to date! */
-+ shrink_state.active[heldout] = 1;
-+ if (verbosity >= 2)
-+ printf("\nLeave-One-Out test on example %ld\n", heldout);
-+ if (verbosity >= 1)
-+ {
-+ printf("(?[%ld]", heldout);
-+ fflush(stdout);
-+ }
-+
-+ optimize_to_convergence(docs, label, totdoc, totwords, learn_parm,
-+ kernel_parm,
-+ kernel_cache, &shrink_state, model, inconsistent, unlabeled,
-+ a, lin, c, &timing_profile,
-+ &maxdiff, heldout, (long)2);
-+
-+ /* printf("%.20f\n",(lin[heldout]-model->b)*(double)label[heldout]); */
-+
-+ if (((lin[heldout] - model->b)*(double)label[heldout]) <= 0.0)
-+ {
-+ loo_count++; /* there was a loo-error */
-+ if (label[heldout] > 0) loo_count_pos++;
-+ else loo_count_neg++;
-+ if (verbosity >= 1)
-+ {
-+ printf("-)");
-+ fflush(stdout);
-+ }
-+ }
-+ else
-+ {
-+ if (verbosity >= 1)
-+ {
-+ printf("+)");
-+ fflush(stdout);
-+ }
-+ }
-+ /* now we need to restore the original data set*/
-+ learn_parm->svm_cost[heldout] = heldout_c; /* restore upper bound */
-+ }
-+ } /* end of leave-one-out loop */
-+
-+
-+ if (verbosity >= 1)
-+ {
-+ printf("\nRetrain on full problem");
-+ fflush(stdout);
-+ }
-+ optimize_to_convergence(docs, label, totdoc, totwords, learn_parm,
-+ kernel_parm,
-+ kernel_cache, &shrink_state, model, inconsistent, unlabeled,
-+ a, lin, c, &timing_profile,
-+ &maxdiff, (long) - 1, (long)1);
-+ if (verbosity >= 1)
-+ printf("done.\n");
-+
-+
-+ /* after all leave-one-out computed */
-+ model->loo_error = 100.0 * loo_count / (double)totdoc;
-+
-+ model->loo_recall = (1.0 - (double)loo_count_pos / (double)trainpos) * 100.0;
-+ model->loo_precision = (trainpos - loo_count_pos) /
-+ (double)(trainpos - loo_count_pos + loo_count_neg) * 100.0;
-+ if (verbosity >= 1)
-+ {
-+ fprintf(stdout, "Leave-one-out estimate of the error: error=%.2f%%\n",
-+ model->loo_error);
-+ fprintf(stdout, "Leave-one-out estimate of the recall: recall=%.2f%%\n",
-+ model->loo_recall);
-+ fprintf(stdout, "Leave-one-out estimate of the precision: precision=%.2f%%\n",
-+ model->loo_precision);
-+ fprintf(stdout, "Actual leave-one-outs computed: %ld (rho=%.2f)\n",
-+ loocomputed, learn_parm->rho);
-+ printf("Runtime for leave-one-out in cpu-seconds: %.2f\n",
-+ (double)(get_runtime() - runtime_start_loo) / 100.0);
-+ }
-+ }
-+
-+ if (learn_parm->alphafile[0])
-+ write_alphas(learn_parm->alphafile, a, label, totdoc);
-+
-+ shrink_state_cleanup(&shrink_state);
-+ free(label);
-+ free(inconsistent);
-+ free(unlabeled);
-+ free(c);
-+ free(a);
-+ free(a_fullset);
-+ free(xi_fullset);
-+ free(lin);
-+ free(learn_parm->svm_cost);
-+}
-+
-+
- /* Learns an SVM classification model based on the training data in
- docs/label. The resulting model is returned in the structure
- model. */
---- svm_learn.h 2011-09-06 22:14:22.363126903 -0400
-+++ svm_learn.h 2011-09-06 22:49:59.247171372 -0400
-@@ -19,6 +19,9 @@
- #ifndef SVM_LEARN
- #define SVM_LEARN
-
-+void svm_learn_classification_extend(DOC **, double *, long, long, LEARN_PARM *,
-+ KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
-+ double *, int *, double *);
- void svm_learn_classification(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *);
-@@ -152,6 +155,7 @@ double estimate_r_delta_average(DOC **,
- double estimate_r_delta(DOC **, long, KERNEL_PARM *);
- double length_of_longest_document_vector(DOC **, long, KERNEL_PARM *);
-
-+
- void write_model(char *, MODEL *);
- void write_prediction(char *, MODEL *, double *, double *, long *, long *,
- long, LEARN_PARM *);
diff --git a/trunk/svm_light/svm_light/include/svm_light/kernel.h b/trunk/svm_light/svm_light/include/svm_light/kernel.h
deleted file mode 100755
index 0133b00661fe990c832eabe2f110166c991b814e..0000000000000000000000000000000000000000
--- a/trunk/svm_light/svm_light/include/svm_light/kernel.h
+++ /dev/null
@@ -1,40 +0,0 @@
-/************************************************************************/
-/* */
-/* kernel.h */
-/* */
-/* User defined kernel function. Feel free to plug in your own. */
-/* */
-/* Copyright: Thorsten Joachims */
-/* Date: 16.12.97 */
-/* */
-/************************************************************************/
-
-/* KERNEL_PARM is defined in svm_common.h The field 'custom' is reserved for */
-/* parameters of the user defined kernel. You can also access and use */
-/* the parameters of the other kernels. Just replace the line
- return((double)(1.0));
- with your own kernel. */
-
- /* Example: The following computes the polynomial kernel. sprod_ss
- computes the inner product between two sparse vectors.
-
- return((CFLOAT)pow(kernel_parm->coef_lin*sprod_ss(a->words,b->words)
- +kernel_parm->coef_const,(double)kernel_parm->poly_degree));
- */
-
-/* If you are implementing a kernel that is not based on a
- feature/value representation, you might want to make use of the
- field "userdefined" in SVECTOR. By default, this field will contain
- whatever string you put behind a # sign in the example file. So, if
- a line in your training file looks like
-
- -1 1:3 5:6 #abcdefg
-
- then the SVECTOR field "words" will contain the vector 1:3 5:6, and
- "userdefined" will contain the string "abcdefg". */
-
-double custom_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b)
- /* plug in you favorite kernel */
-{
- return((double)(1.0));
-}
diff --git a/trunk/svm_light/svm_light/include/svm_light/svm_common.h b/trunk/svm_light/svm_light/include/svm_light/svm_common.h
deleted file mode 100755
index 6487fa1d1017d9fcb3b32d7e66cc514b4af9bddf..0000000000000000000000000000000000000000
--- a/trunk/svm_light/svm_light/include/svm_light/svm_common.h
+++ /dev/null
@@ -1,301 +0,0 @@
-/************************************************************************/
-/* */
-/* svm_common.h */
-/* */
-/* Definitions and functions used in both svm_learn and svm_classify. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/************************************************************************/
-
-#ifndef SVM_COMMON
-#define SVM_COMMON
-
-# include <stdio.h>
-# include <ctype.h>
-# include <math.h>
-# include <string.h>
-# include <stdlib.h>
-# include <time.h>
-# include <float.h>
-
-# define VERSION "V6.02"
-# define VERSION_DATE "14.08.08"
-
-# define CFLOAT float /* the type of float to use for caching */
- /* kernel evaluations. Using float saves */
- /* us some memory, but you can use double, too */
-# define FNUM long /* the type used for storing feature ids */
-# define FVAL float /* the type used for storing feature values */
-# define MAXFEATNUM 99999999 /* maximum feature number (must be in
- valid range of FNUM type and long int!) */
-
-# define LINEAR 0 /* linear kernel type */
-# define POLY 1 /* polynoial kernel type */
-# define RBF 2 /* rbf kernel type */
-# define SIGMOID 3 /* sigmoid kernel type */
-
-# define CLASSIFICATION 1 /* train classification model */
-# define REGRESSION 2 /* train regression model */
-# define RANKING 3 /* train ranking model */
-# define OPTIMIZATION 4 /* train on general set of constraints */
-
-# define MAXSHRINK 50000 /* maximum number of shrinking rounds */
-
-typedef struct word {
- FNUM wnum; /* word number */
- FVAL weight; /* word weight */
-} WORD;
-
-typedef struct svector {
- WORD *words; /* The features/values in the vector by
- increasing feature-number. Feature
- numbers that are skipped are
- interpreted as having value zero. */
- double twonorm_sq; /* The squared euclidian length of the
- vector. Used to speed up the RBF kernel. */
- char *userdefined; /* You can put additional information
- here. This can be useful, if you are
- implementing your own kernel that
- does not work with feature/values
- representations (for example a
- string kernel). By default,
- svm-light will put here the string
- after the # sign from each line of
- the input file. */
- long kernel_id; /* Feature vectors with different
- kernel_id's are orthogonal (ie. the
- feature number do not match). This
- is used for computing component
- kernels for linear constraints which
- are a sum of several different
- weight vectors. (currently not
- implemented). */
- struct svector *next; /* Let's you set up a list of SVECTOR's
- for linear constraints which are a
- sum of multiple feature
- vectors. List is terminated by
- NULL. */
- double factor; /* Factor by which this feature vector
- is multiplied in the sum. */
-} SVECTOR;
-
-typedef struct doc {
- long docnum; /* Document ID. This has to be the position of
- the document in the training set array. */
- long queryid; /* for learning rankings, constraints are
- generated for documents with the same
- queryID. */
- double costfactor; /* Scales the cost of misclassifying this
- document by this factor. The effect of this
- value is, that the upper bound on the alpha
- for this example is scaled by this factor.
- The factors are set by the feature
- 'cost:<val>' in the training data. */
- long slackid; /* Index of the slack variable
- corresponding to this
- constraint. All constraints with the
- same slackid share the same slack
- variable. This can only be used for
- svm_learn_optimization. */
- SVECTOR *fvec; /* Feature vector of the example. The
- feature vector can actually be a
- list of feature vectors. For
- example, the list will have two
- elements, if this DOC is a
- preference constraint. The one
- vector that is supposed to be ranked
- higher, will have a factor of +1,
- the lower ranked one should have a
- factor of -1. */
-} DOC;
-
-typedef struct learn_parm {
- long type; /* selects between regression and
- classification */
- double svm_c; /* upper bound C on alphas */
- double eps; /* regression epsilon (eps=1.0 for
- classification */
- double svm_costratio; /* factor to multiply C for positive examples */
- double transduction_posratio;/* fraction of unlabeled examples to be */
- /* classified as positives */
- long biased_hyperplane; /* if nonzero, use hyperplane w*x+b=0
- otherwise w*x=0 */
- long sharedslack; /* if nonzero, it will use the shared
- slack variable mode in
- svm_learn_optimization. It requires
- that the slackid is set for every
- training example */
- long svm_maxqpsize; /* size q of working set */
- long svm_newvarsinqp; /* new variables to enter the working set
- in each iteration */
- long kernel_cache_size; /* size of kernel cache in megabytes */
- double epsilon_crit; /* tolerable error for distances used
- in stopping criterion */
- double epsilon_shrink; /* how much a multiplier should be above
- zero for shrinking */
- long svm_iter_to_shrink; /* iterations h after which an example can
- be removed by shrinking */
- long maxiter; /* number of iterations after which the
- optimizer terminates, if there was
- no progress in maxdiff */
- long remove_inconsistent; /* exclude examples with alpha at C and
- retrain */
- long skip_final_opt_check; /* do not check KT-Conditions at the end of
- optimization for examples removed by
- shrinking. WARNING: This might lead to
- sub-optimal solutions! */
- long compute_loo; /* if nonzero, computes leave-one-out
- estimates */
- double rho; /* parameter in xi/alpha-estimates and for
- pruning leave-one-out range [1..2] */
- long xa_depth; /* parameter in xi/alpha-estimates upper
- bounding the number of SV the current
- alpha_t is distributed over */
- char predfile[200]; /* file for predicitions on unlabeled examples
- in transduction */
- char alphafile[200]; /* file to store optimal alphas in. use
- empty string if alphas should not be
- output */
-
- /* you probably do not want to touch the following */
- double epsilon_const; /* tolerable error on eq-constraint */
- double epsilon_a; /* tolerable error on alphas at bounds */
- double opt_precision; /* precision of solver, set to e.g. 1e-21
- if you get convergence problems */
-
- /* the following are only for internal use */
- long svm_c_steps; /* do so many steps for finding optimal C */
- double svm_c_factor; /* increase C by this factor every step */
- double svm_costratio_unlab;
- double svm_unlabbound;
- double *svm_cost; /* individual upper bounds for each var */
- long totwords; /* number of features */
-} LEARN_PARM;
-
-typedef struct kernel_parm {
- long kernel_type; /* 0=linear, 1=poly, 2=rbf, 3=sigmoid, 4=custom */
- long poly_degree;
- double rbf_gamma;
- double coef_lin;
- double coef_const;
- char custom[50]; /* for user supplied kernel */
-} KERNEL_PARM;
-
-typedef struct model {
- long sv_num;
- long at_upper_bound;
- double b;
- DOC **supvec;
- double *alpha;
- long *index; /* index from docnum to position in model */
- long totwords; /* number of features */
- long totdoc; /* number of training documents */
- KERNEL_PARM kernel_parm; /* kernel */
-
- /* the following values are not written to file */
- double loo_error,loo_recall,loo_precision; /* leave-one-out estimates */
- double xa_error,xa_recall,xa_precision; /* xi/alpha estimates */
- double *lin_weights; /* weights for linear case using
- folding */
- double maxdiff; /* precision, up to which this
- model is accurate */
-} MODEL;
-
-typedef struct quadratic_program {
- long opt_n; /* number of variables */
- long opt_m; /* number of linear equality constraints */
- double *opt_ce,*opt_ce0; /* linear equality constraints */
- double *opt_g; /* hessian of objective */
- double *opt_g0; /* linear part of objective */
- double *opt_xinit; /* initial value for variables */
- double *opt_low,*opt_up; /* box constraints */
-} QP;
-
-typedef struct kernel_cache {
- long *index; /* cache some kernel evalutations */
- CFLOAT *buffer; /* to improve speed */
- long *invindex;
- long *active2totdoc;
- long *totdoc2active;
- long *lru;
- long *occu;
- long elems;
- long max_elems;
- long time;
- long activenum;
- long buffsize;
-} KERNEL_CACHE;
-
-
-typedef struct timing_profile {
- long time_kernel;
- long time_opti;
- long time_shrink;
- long time_update;
- long time_model;
- long time_check;
- long time_select;
-} TIMING;
-
-typedef struct shrink_state {
- long *active;
- long *inactive_since;
- long deactnum;
- double **a_history; /* for shrinking with non-linear kernel */
- long maxhistory;
- double *last_a; /* for shrinking with linear kernel */
- double *last_lin; /* for shrinking with linear kernel */
-} SHRINK_STATE;
-
-double classify_example(MODEL *, DOC *);
-double classify_example_linear(MODEL *, DOC *);
-double kernel(KERNEL_PARM *, DOC *, DOC *);
-double single_kernel(KERNEL_PARM *, SVECTOR *, SVECTOR *);
-double custom_kernel(KERNEL_PARM *, SVECTOR *, SVECTOR *);
-SVECTOR *create_svector(WORD *, char *, double);
-SVECTOR *copy_svector(SVECTOR *);
-void free_svector(SVECTOR *);
-double sprod_ss(SVECTOR *, SVECTOR *);
-SVECTOR* sub_ss(SVECTOR *, SVECTOR *);
-SVECTOR* add_ss(SVECTOR *, SVECTOR *);
-SVECTOR* add_list_ss(SVECTOR *);
-void append_svector_list(SVECTOR *a, SVECTOR *b);
-SVECTOR* smult_s(SVECTOR *, double);
-int featvec_eq(SVECTOR *, SVECTOR *);
-double model_length_s(MODEL *, KERNEL_PARM *);
-void clear_vector_n(double *, long);
-void add_vector_ns(double *, SVECTOR *, double);
-double sprod_ns(double *, SVECTOR *);
-void add_weight_vector_to_linear_model(MODEL *);
-DOC *create_example(long, long, long, double, SVECTOR *);
-void free_example(DOC *, long);
-MODEL *read_model(char *);
-MODEL *copy_model(MODEL *);
-void free_model(MODEL *, int);
-void read_documents(char *, DOC ***, double **, long *, long *);
-int parse_document(char *, WORD *, double *, long *, long *, double *, long *, long, char **);
-double *read_alphas(char *,long);
-void nol_ll(char *, long *, long *, long *);
-long minl(long, long);
-long maxl(long, long);
-long get_runtime(void);
-int space_or_null(int);
-void *my_malloc(size_t);
-void copyright_notice(void);
-# ifdef _MSC_VER
- int isnan(double);
-# endif
-
-extern long verbosity; /* verbosity level (0-4) */
-extern long kernel_cache_statistic;
-
-#endif
diff --git a/trunk/svm_light/svm_light/include/svm_light/svm_learn.h b/trunk/svm_light/svm_light/include/svm_light/svm_learn.h
deleted file mode 100755
index 8a1edf7b75c6b2b6ea6952062a61ee7e7b4f8fbd..0000000000000000000000000000000000000000
--- a/trunk/svm_light/svm_light/include/svm_light/svm_learn.h
+++ /dev/null
@@ -1,173 +0,0 @@
-/***********************************************************************/
-/* */
-/* svm_learn.h */
-/* */
-/* Declarations for learning module of Support Vector Machine. */
-/* */
-/* Author: Thorsten Joachims */
-/* Date: 02.07.02 */
-/* */
-/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */
-/* */
-/* This software is available for non-commercial use only. It must */
-/* not be modified and distributed without prior permission of the */
-/* author. The author is not responsible for implications from the */
-/* use of this software. */
-/* */
-/***********************************************************************/
-
-#ifndef SVM_LEARN
-#define SVM_LEARN
-
-void svm_learn_classification_extend(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *, int *, double *);
-void svm_learn_classification(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *);
-void svm_learn_regression(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE **, MODEL *);
-void svm_learn_ranking(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE **, MODEL *);
-void svm_learn_optimization(DOC **, double *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, MODEL *,
- double *);
-long optimize_to_convergence(DOC **, long *, long, long, LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, SHRINK_STATE *,
- MODEL *, long *, long *, double *,
- double *, double *,
- TIMING *, double *, long, long);
-long optimize_to_convergence_sharedslack(DOC **, long *, long, long,
- LEARN_PARM *,
- KERNEL_PARM *, KERNEL_CACHE *, SHRINK_STATE *,
- MODEL *, double *, double *, double *,
- TIMING *, double *);
-double compute_objective_function(double *, double *, double *, double,
- long *, long *);
-void clear_index(long *);
-void add_to_index(long *, long);
-long compute_index(long *,long, long *);
-void optimize_svm(DOC **, long *, long *, long *, double, long *, long *,
- MODEL *,
- long, long *, long, double *, double *, double *,
- LEARN_PARM *, CFLOAT *, KERNEL_PARM *, QP *, double *);
-void compute_matrices_for_optimization(DOC **, long *, long *, long *, double,
- long *,
- long *, long *, MODEL *, double *,
- double *, double *, long, long, LEARN_PARM *,
- CFLOAT *, KERNEL_PARM *, QP *);
-long calculate_svm_model(DOC **, long *, long *, double *, double *,
- double *, double *, LEARN_PARM *, long *,
- long *, MODEL *);
-long check_optimality(MODEL *, long *, long *, double *, double *,
- double *, long,
- LEARN_PARM *,double *, double, long *, long *, long *,
- long *, long, KERNEL_PARM *);
-long check_optimality_sharedslack(DOC **docs, MODEL *model, long int *label,
- double *a, double *lin, double *c, double *slack,
- double *alphaslack, long int totdoc,
- LEARN_PARM *learn_parm, double *maxdiff,
- double epsilon_crit_org, long int *misclassified,
- long int *active2dnum,
- long int *last_suboptimal_at,
- long int iteration, KERNEL_PARM *kernel_parm);
-void compute_shared_slacks(DOC **docs, long int *label, double *a,
- double *lin, double *c, long int *active2dnum,
- LEARN_PARM *learn_parm,
- double *slack, double *alphaslack);
-long identify_inconsistent(double *, long *, long *, long, LEARN_PARM *,
- long *, long *);
-long identify_misclassified(double *, long *, long *, long,
- MODEL *, long *, long *);
-long identify_one_misclassified(double *, long *, long *, long,
- MODEL *, long *, long *);
-long incorporate_unlabeled_examples(MODEL *, long *,long *, long *,
- double *, double *, long, double *,
- long *, long *, long, KERNEL_PARM *,
- LEARN_PARM *);
-void update_linear_component(DOC **, long *, long *, double *, double *,
- long *, long, long, KERNEL_PARM *,
- KERNEL_CACHE *, double *,
- CFLOAT *, double *);
-long select_next_qp_subproblem_grad(long *, long *, double *,
- double *, double *, long,
- long, LEARN_PARM *, long *, long *,
- long *, double *, long *, KERNEL_CACHE *,
- long, long *, long *);
-long select_next_qp_subproblem_rand(long *, long *, double *,
- double *, double *, long,
- long, LEARN_PARM *, long *, long *,
- long *, double *, long *, KERNEL_CACHE *,
- long *, long *, long);
-long select_next_qp_slackset(DOC **docs, long int *label, double *a,
- double *lin, double *slack, double *alphaslack,
- double *c, LEARN_PARM *learn_parm,
- long int *active2dnum, double *maxviol);
-void select_top_n(double *, long, long *, long);
-void init_shrink_state(SHRINK_STATE *, long, long);
-void shrink_state_cleanup(SHRINK_STATE *);
-long shrink_problem(DOC **, LEARN_PARM *, SHRINK_STATE *, KERNEL_PARM *,
- long *, long *, long, long, long, double *, long *);
-void reactivate_inactive_examples(long *, long *, double *, SHRINK_STATE *,
- double *, double*, long, long, long, LEARN_PARM *,
- long *, DOC **, KERNEL_PARM *,
- KERNEL_CACHE *, MODEL *, CFLOAT *,
- double *, double *);
-
-/* cache kernel evalutations to improve speed */
-KERNEL_CACHE *kernel_cache_init(long, long);
-void kernel_cache_cleanup(KERNEL_CACHE *);
-void get_kernel_row(KERNEL_CACHE *,DOC **, long, long, long *, CFLOAT *,
- KERNEL_PARM *);
-void cache_kernel_row(KERNEL_CACHE *,DOC **, long, KERNEL_PARM *);
-void cache_multiple_kernel_rows(KERNEL_CACHE *,DOC **, long *, long,
- KERNEL_PARM *);
-void kernel_cache_shrink(KERNEL_CACHE *,long, long, long *);
-void kernel_cache_reset_lru(KERNEL_CACHE *);
-long kernel_cache_malloc(KERNEL_CACHE *);
-void kernel_cache_free(KERNEL_CACHE *,long);
-long kernel_cache_free_lru(KERNEL_CACHE *);
-CFLOAT *kernel_cache_clean_and_malloc(KERNEL_CACHE *,long);
-long kernel_cache_touch(KERNEL_CACHE *,long);
-long kernel_cache_check(KERNEL_CACHE *,long);
-long kernel_cache_space_available(KERNEL_CACHE *);
-
-void compute_xa_estimates(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *, double *, double *, double *);
-double xa_estimate_error(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *);
-double xa_estimate_recall(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *);
-double xa_estimate_precision(MODEL *, long *, long *, long, DOC **,
- double *, double *, KERNEL_PARM *,
- LEARN_PARM *);
-void avg_similarity_of_sv_of_one_class(MODEL *, DOC **, double *, long *, KERNEL_PARM *, double *, double *);
-double most_similar_sv_of_same_class(MODEL *, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *);
-double distribute_alpha_t_greedily(long *, long, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *, double);
-double distribute_alpha_t_greedily_noindex(MODEL *, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *, double);
-void estimate_transduction_quality(MODEL *, long *, long *, long, DOC **, double *);
-double estimate_margin_vcdim(MODEL *, double, double, KERNEL_PARM *);
-double estimate_sphere(MODEL *, KERNEL_PARM *);
-double estimate_r_delta_average(DOC **, long, KERNEL_PARM *);
-double estimate_r_delta(DOC **, long, KERNEL_PARM *);
-double length_of_longest_document_vector(DOC **, long, KERNEL_PARM *);
-
-
-void write_model(char *, MODEL *);
-void write_prediction(char *, MODEL *, double *, double *, long *, long *,
- long, LEARN_PARM *);
-void write_alphas(char *, double *, long *, long);
-
-typedef struct cache_parm_s {
- KERNEL_CACHE *kernel_cache;
- CFLOAT *cache;
- DOC **docs;
- long m;
- KERNEL_PARM *kernel_parm;
- long offset,stepsize;
-} cache_parm_t;
-
-#endif
diff --git a/trunk/svm_light/wiped b/trunk/svm_light/wiped
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000