--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// Intel License Agreement
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of Intel Corporation may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "_ml.h"
+
+
+CvStatModel::CvStatModel()
+{
+ default_model_name = "my_stat_model";
+}
+
+
+CvStatModel::~CvStatModel()
+{
+ clear();
+}
+
+
+void CvStatModel::clear()
+{
+}
+
+
+void CvStatModel::save( const char* filename, const char* name ) const
+{
+ CvFileStorage* fs = 0;
+
+ CV_FUNCNAME( "CvStatModel::save" );
+
+ __BEGIN__;
+
+ CV_CALL( fs = cvOpenFileStorage( filename, 0, CV_STORAGE_WRITE ));
+ if( !fs )
+ CV_ERROR( CV_StsError, "Could not open the file storage. Check the path and permissions" );
+
+ write( fs, name ? name : default_model_name );
+
+ __END__;
+
+ cvReleaseFileStorage( &fs );
+}
+
+
+void CvStatModel::load( const char* filename, const char* name )
+{
+ CvFileStorage* fs = 0;
+
+ CV_FUNCNAME( "CvStatModel::load" );
+
+ __BEGIN__;
+
+ CvFileNode* model_node = 0;
+
+ CV_CALL( fs = cvOpenFileStorage( filename, 0, CV_STORAGE_READ ));
+ if( !fs )
+ EXIT;
+
+ if( name )
+ model_node = cvGetFileNodeByName( fs, 0, name );
+ else
+ {
+ CvFileNode* root = cvGetRootFileNode( fs );
+ if( root->data.seq->total > 0 )
+ model_node = (CvFileNode*)cvGetSeqElem( root->data.seq, 0 );
+ }
+
+ read( fs, model_node );
+
+ __END__;
+
+ cvReleaseFileStorage( &fs );
+}
+
+
+void CvStatModel::write( CvFileStorage*, const char* ) const
+{
+ OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::write", "" );
+}
+
+
+void CvStatModel::read( CvFileStorage*, CvFileNode* )
+{
+ OPENCV_ERROR( CV_StsNotImplemented, "CvStatModel::read", "" );
+}
+
+
+/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
+CV_IMPL void cvChol( CvMat* A, CvMat* S )
+{
+ int dim = A->rows;
+
+ int i, j, k;
+ float sum;
+
+ for( i = 0; i < dim; i++ )
+ {
+ for( j = 0; j < i; j++ )
+ CV_MAT_ELEM(*S, float, i, j) = 0;
+
+ sum = 0;
+ for( k = 0; k < i; k++ )
+ sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, i);
+
+ CV_MAT_ELEM(*S, float, i, i) = (float)sqrt(CV_MAT_ELEM(*A, float, i, i) - sum);
+
+ for( j = i + 1; j < dim; j++ )
+ {
+ sum = 0;
+ for( k = 0; k < i; k++ )
+ sum += CV_MAT_ELEM(*S, float, k, i) * CV_MAT_ELEM(*S, float, k, j);
+
+ CV_MAT_ELEM(*S, float, i, j) =
+ (CV_MAT_ELEM(*A, float, i, j) - sum) / CV_MAT_ELEM(*S, float, i, i);
+
+ }
+ }
+}
+
+/* Generates <sample> from multivariate normal distribution, where <mean> - is an
+ average row vector, <cov> - symmetric covariation matrix */
+CV_IMPL void cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample, CvRNG* rng )
+{
+ int dim = sample->cols;
+ int amount = sample->rows;
+
+ CvRNG state = rng ? *rng : cvRNG( cvGetTickCount() );
+ cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1) );
+
+ CvMat* utmat = cvCreateMat(dim, dim, sample->type);
+ CvMat* vect = cvCreateMatHeader(1, dim, sample->type);
+
+ cvChol(cov, utmat);
+
+ int i;
+ for( i = 0; i < amount; i++ )
+ {
+ cvGetRow(sample, vect, i);
+ cvMatMulAdd(vect, utmat, mean, vect);
+ }
+
+ cvReleaseMat(&vect);
+ cvReleaseMat(&utmat);
+}
+
+
+/* Generates <sample> of <amount> points from a discrete variate xi,
+ where Pr{xi = k} == probs[k], 0 < k < len - 1. */
+CV_IMPL void cvRandSeries( float probs[], int len, int sample[], int amount )
+{
+ CvMat* univals = cvCreateMat(1, amount, CV_32FC1);
+ float* knots = (float*)cvAlloc( len * sizeof(float) );
+
+ int i, j;
+
+ CvRNG state = cvRNG(-1);
+ cvRandArr(&state, univals, CV_RAND_UNI, cvScalarAll(0), cvScalarAll(1) );
+
+ knots[0] = probs[0];
+ for( i = 1; i < len; i++ )
+ knots[i] = knots[i - 1] + probs[i];
+
+ for( i = 0; i < amount; i++ )
+ for( j = 0; j < len; j++ )
+ {
+ if ( CV_MAT_ELEM(*univals, float, 0, i) <= knots[j] )
+ {
+ sample[i] = j;
+ break;
+ }
+ }
+
+ cvFree(&knots);
+}
+
+/* Generates <sample> from gaussian mixture distribution */
+CV_IMPL void cvRandGaussMixture( CvMat* means[],
+ CvMat* covs[],
+ float weights[],
+ int clsnum,
+ CvMat* sample,
+ CvMat* sampClasses )
+{
+ int dim = sample->cols;
+ int amount = sample->rows;
+
+ int i, clss;
+
+ int* sample_clsnum = (int*)cvAlloc( amount * sizeof(int) );
+ CvMat** utmats = (CvMat**)cvAlloc( clsnum * sizeof(CvMat*) );
+ CvMat* vect = cvCreateMatHeader(1, dim, CV_32FC1);
+
+ CvMat* classes;
+ if( sampClasses )
+ classes = sampClasses;
+ else
+ classes = cvCreateMat(1, amount, CV_32FC1);
+
+ CvRNG state = cvRNG(-1);
+ cvRandArr(&state, sample, CV_RAND_NORMAL, cvScalarAll(0), cvScalarAll(1));
+
+ cvRandSeries(weights, clsnum, sample_clsnum, amount);
+
+ for( i = 0; i < clsnum; i++ )
+ {
+ utmats[i] = cvCreateMat(dim, dim, CV_32FC1);
+ cvChol(covs[i], utmats[i]);
+ }
+
+ for( i = 0; i < amount; i++ )
+ {
+ CV_MAT_ELEM(*classes, float, 0, i) = (float)sample_clsnum[i];
+ cvGetRow(sample, vect, i);
+ clss = sample_clsnum[i];
+ cvMatMulAdd(vect, utmats[clss], means[clss], vect);
+ }
+
+ if( !sampClasses )
+ cvReleaseMat(&classes);
+ for( i = 0; i < clsnum; i++ )
+ cvReleaseMat(&utmats[i]);
+ cvFree(&utmats);
+ cvFree(&sample_clsnum);
+ cvReleaseMat(&vect);
+}
+
+
+CvMat* icvGenerateRandomClusterCenters ( int seed, const CvMat* data,
+ int num_of_clusters, CvMat* _centers )
+{
+ CvMat* centers = _centers;
+
+ CV_FUNCNAME("icvGenerateRandomClusterCenters");
+ __BEGIN__;
+
+ CvRNG rng;
+ CvMat data_comp, centers_comp;
+ CvPoint minLoc, maxLoc; // Not used, just for function "cvMinMaxLoc"
+ double minVal, maxVal;
+ int i;
+ int dim = data ? data->cols : 0;
+
+ if( ICV_IS_MAT_OF_TYPE(data, CV_32FC1) )
+ {
+ if( _centers && !ICV_IS_MAT_OF_TYPE (_centers, CV_32FC1) )
+ {
+ CV_ERROR(CV_StsBadArg,"");
+ }
+ else if( !_centers )
+ CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_32FC1));
+ }
+ else if( ICV_IS_MAT_OF_TYPE(data, CV_64FC1) )
+ {
+ if( _centers && !ICV_IS_MAT_OF_TYPE (_centers, CV_64FC1) )
+ {
+ CV_ERROR(CV_StsBadArg,"");
+ }
+ else if( !_centers )
+ CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_64FC1));
+ }
+ else
+ CV_ERROR (CV_StsBadArg,"");
+
+ if( num_of_clusters < 1 )
+ CV_ERROR (CV_StsBadArg,"");
+
+ rng = cvRNG(seed);
+ for (i = 0; i < dim; i++)
+ {
+ CV_CALL(cvGetCol (data, &data_comp, i));
+ CV_CALL(cvMinMaxLoc (&data_comp, &minVal, &maxVal, &minLoc, &maxLoc));
+ CV_CALL(cvGetCol (centers, ¢ers_comp, i));
+ CV_CALL(cvRandArr (&rng, ¢ers_comp, CV_RAND_UNI, cvScalarAll(minVal), cvScalarAll(maxVal)));
+ }
+
+ __END__;
+
+ if( (cvGetErrStatus () < 0) || (centers != _centers) )
+ cvReleaseMat (¢ers);
+
+ return _centers ? _centers : centers;
+} // end of icvGenerateRandomClusterCenters
+
+// By S. Dilman - begin -
+
+#define ICV_RAND_MAX 4294967296 // == 2^32
+
+CV_IMPL void cvRandRoundUni (CvMat* center,
+ float radius_small,
+ float radius_large,
+ CvMat* desired_matrix,
+ CvRNG* rng_state_ptr)
+{
+ float rad, norm, coefficient;
+ int dim, size, i, j;
+ CvMat *cov, sample;
+ CvRNG rng_local;
+
+ CV_FUNCNAME("cvRandRoundUni");
+ __BEGIN__
+
+ rng_local = *rng_state_ptr;
+
+ CV_ASSERT ((radius_small >= 0) &&
+ (radius_large > 0) &&
+ (radius_small <= radius_large));
+ CV_ASSERT (center && desired_matrix && rng_state_ptr);
+ CV_ASSERT (center->rows == 1);
+ CV_ASSERT (center->cols == desired_matrix->cols);
+
+ dim = desired_matrix->cols;
+ size = desired_matrix->rows;
+ cov = cvCreateMat (dim, dim, CV_32FC1);
+ cvSetIdentity (cov);
+ cvRandMVNormal (center, cov, desired_matrix, &rng_local);
+
+ for (i = 0; i < size; i++)
+ {
+ rad = (float)(cvRandReal(&rng_local)*(radius_large - radius_small) + radius_small);
+ cvGetRow (desired_matrix, &sample, i);
+ norm = (float) cvNorm (&sample, 0, CV_L2);
+ coefficient = rad / norm;
+ for (j = 0; j < dim; j++)
+ CV_MAT_ELEM (sample, float, 0, j) *= coefficient;
+ }
+
+ __END__
+
+}
+
+// By S. Dilman - end -
+
+static int CV_CDECL
+icvCmpIntegers( const void* a, const void* b )
+{
+ return *(const int*)a - *(const int*)b;
+}
+
+
+static int CV_CDECL
+icvCmpIntegersPtr( const void* _a, const void* _b )
+{
+ int a = **(const int**)_a;
+ int b = **(const int**)_b;
+ return (a < b ? -1 : 0)|(a > b);
+}
+
+
+static int icvCmpSparseVecElems( const void* a, const void* b )
+{
+ return ((CvSparseVecElem32f*)a)->idx - ((CvSparseVecElem32f*)b)->idx;
+}
+
+
+CvMat*
+cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates )
+{
+ CvMat* idx = 0;
+
+ CV_FUNCNAME( "cvPreprocessIndexArray" );
+
+ __BEGIN__;
+
+ int i, idx_total, idx_selected = 0, step, type, prev = INT_MIN, is_sorted = 1;
+ uchar* srcb = 0;
+ int* srci = 0;
+ int* dsti;
+
+ if( !CV_IS_MAT(idx_arr) )
+ CV_ERROR( CV_StsBadArg, "Invalid index array" );
+
+ if( idx_arr->rows != 1 && idx_arr->cols != 1 )
+ CV_ERROR( CV_StsBadSize, "the index array must be 1-dimensional" );
+
+ idx_total = idx_arr->rows + idx_arr->cols - 1;
+ srcb = idx_arr->data.ptr;
+ srci = idx_arr->data.i;
+
+ type = CV_MAT_TYPE(idx_arr->type);
+ step = CV_IS_MAT_CONT(idx_arr->type) ? 1 : idx_arr->step/CV_ELEM_SIZE(type);
+
+ switch( type )
+ {
+ case CV_8UC1:
+ case CV_8SC1:
+ // idx_arr is array of 1's and 0's -
+ // i.e. it is a mask of the selected components
+ if( idx_total != data_arr_size )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "Component mask should contain as many elements as the total number of input variables" );
+
+ for( i = 0; i < idx_total; i++ )
+ idx_selected += srcb[i*step] != 0;
+
+ if( idx_selected == 0 )
+ CV_ERROR( CV_StsOutOfRange, "No components/input_variables is selected!" );
+
+ if( idx_selected == idx_total )
+ EXIT;
+ break;
+ case CV_32SC1:
+ // idx_arr is array of integer indices of selected components
+ if( idx_total > data_arr_size )
+ CV_ERROR( CV_StsOutOfRange,
+ "index array may not contain more elements than the total number of input variables" );
+ idx_selected = idx_total;
+ // check if sorted already
+ for( i = 0; i < idx_total; i++ )
+ {
+ int val = srci[i*step];
+ if( val >= prev )
+ {
+ is_sorted = 0;
+ break;
+ }
+ prev = val;
+ }
+ break;
+ default:
+ CV_ERROR( CV_StsUnsupportedFormat, "Unsupported index array data type "
+ "(it should be 8uC1, 8sC1 or 32sC1)" );
+ }
+
+ CV_CALL( idx = cvCreateMat( 1, idx_selected, CV_32SC1 ));
+ dsti = idx->data.i;
+
+ if( type < CV_32SC1 )
+ {
+ for( i = 0; i < idx_total; i++ )
+ if( srcb[i*step] )
+ *dsti++ = i;
+ }
+ else
+ {
+ for( i = 0; i < idx_total; i++ )
+ dsti[i] = srci[i*step];
+
+ if( !is_sorted )
+ qsort( dsti, idx_total, sizeof(dsti[0]), icvCmpIntegers );
+
+ if( dsti[0] < 0 || dsti[idx_total-1] >= data_arr_size )
+ CV_ERROR( CV_StsOutOfRange, "the index array elements are out of range" );
+
+ if( check_for_duplicates )
+ {
+ for( i = 1; i < idx_total; i++ )
+ if( dsti[i] <= dsti[i-1] )
+ CV_ERROR( CV_StsBadArg, "There are duplicated index array elements" );
+ }
+ }
+
+ __END__;
+
+ if( cvGetErrStatus() < 0 )
+ cvReleaseMat( &idx );
+
+ return idx;
+}
+
+
+CvMat*
+cvPreprocessVarType( const CvMat* var_type, const CvMat* var_idx,
+ int var_count, int* response_type )
+{
+ CvMat* out_var_type = 0;
+ CV_FUNCNAME( "cvPreprocessVarType" );
+
+ if( response_type )
+ *response_type = -1;
+
+ __BEGIN__;
+
+ int i, tm_size, tm_step;
+ //int* map = 0;
+ const uchar* src;
+ uchar* dst;
+
+ if( !CV_IS_MAT(var_type) )
+ CV_ERROR( var_type ? CV_StsBadArg : CV_StsNullPtr, "Invalid or absent var_type array" );
+
+ if( var_type->rows != 1 && var_type->cols != 1 )
+ CV_ERROR( CV_StsBadSize, "var_type array must be 1-dimensional" );
+
+ if( !CV_IS_MASK_ARR(var_type))
+ CV_ERROR( CV_StsUnsupportedFormat, "type mask must be 8uC1 or 8sC1 array" );
+
+ tm_size = var_type->rows + var_type->cols - 1;
+ tm_step = var_type->rows == 1 ? 1 : var_type->step/CV_ELEM_SIZE(var_type->type);
+
+ if( /*tm_size != var_count &&*/ tm_size != var_count + 1 )
+ CV_ERROR( CV_StsBadArg,
+ "type mask must be of <input var count> + 1 size" );
+
+ if( response_type && tm_size > var_count )
+ *response_type = var_type->data.ptr[var_count*tm_step] != 0;
+
+ if( var_idx )
+ {
+ if( !CV_IS_MAT(var_idx) || CV_MAT_TYPE(var_idx->type) != CV_32SC1 ||
+ (var_idx->rows != 1 && var_idx->cols != 1) || !CV_IS_MAT_CONT(var_idx->type) )
+ CV_ERROR( CV_StsBadArg, "var index array should be continuous 1-dimensional integer vector" );
+ if( var_idx->rows + var_idx->cols - 1 > var_count )
+ CV_ERROR( CV_StsBadSize, "var index array is too large" );
+ //map = var_idx->data.i;
+ var_count = var_idx->rows + var_idx->cols - 1;
+ }
+
+ CV_CALL( out_var_type = cvCreateMat( 1, var_count, CV_8UC1 ));
+ src = var_type->data.ptr;
+ dst = out_var_type->data.ptr;
+
+ for( i = 0; i < var_count; i++ )
+ {
+ //int idx = map ? map[i] : i;
+ assert( (unsigned)/*idx*/i < (unsigned)tm_size );
+ dst[i] = (uchar)(src[/*idx*/i*tm_step] != 0);
+ }
+
+ __END__;
+
+ return out_var_type;
+}
+
+
+CvMat*
+cvPreprocessOrderedResponses( const CvMat* responses, const CvMat* sample_idx, int sample_all )
+{
+ CvMat* out_responses = 0;
+
+ CV_FUNCNAME( "cvPreprocessOrderedResponses" );
+
+ __BEGIN__;
+
+ int i, r_type, r_step;
+ const int* map = 0;
+ float* dst;
+ int sample_count = sample_all;
+
+ if( !CV_IS_MAT(responses) )
+ CV_ERROR( CV_StsBadArg, "Invalid response array" );
+
+ if( responses->rows != 1 && responses->cols != 1 )
+ CV_ERROR( CV_StsBadSize, "Response array must be 1-dimensional" );
+
+ if( responses->rows + responses->cols - 1 != sample_count )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "Response array must contain as many elements as the total number of samples" );
+
+ r_type = CV_MAT_TYPE(responses->type);
+ if( r_type != CV_32FC1 && r_type != CV_32SC1 )
+ CV_ERROR( CV_StsUnsupportedFormat, "Unsupported response type" );
+
+ r_step = responses->step ? responses->step / CV_ELEM_SIZE(responses->type) : 1;
+
+ if( r_type == CV_32FC1 && CV_IS_MAT_CONT(responses->type) && !sample_idx )
+ {
+ out_responses = (CvMat*)responses;
+ EXIT;
+ }
+
+ if( sample_idx )
+ {
+ if( !CV_IS_MAT(sample_idx) || CV_MAT_TYPE(sample_idx->type) != CV_32SC1 ||
+ (sample_idx->rows != 1 && sample_idx->cols != 1) || !CV_IS_MAT_CONT(sample_idx->type) )
+ CV_ERROR( CV_StsBadArg, "sample index array should be continuous 1-dimensional integer vector" );
+ if( sample_idx->rows + sample_idx->cols - 1 > sample_count )
+ CV_ERROR( CV_StsBadSize, "sample index array is too large" );
+ map = sample_idx->data.i;
+ sample_count = sample_idx->rows + sample_idx->cols - 1;
+ }
+
+ CV_CALL( out_responses = cvCreateMat( 1, sample_count, CV_32FC1 ));
+
+ dst = out_responses->data.fl;
+ if( r_type == CV_32FC1 )
+ {
+ const float* src = responses->data.fl;
+ for( i = 0; i < sample_count; i++ )
+ {
+ int idx = map ? map[i] : i;
+ assert( (unsigned)idx < (unsigned)sample_all );
+ dst[i] = src[idx*r_step];
+ }
+ }
+ else
+ {
+ const int* src = responses->data.i;
+ for( i = 0; i < sample_count; i++ )
+ {
+ int idx = map ? map[i] : i;
+ assert( (unsigned)idx < (unsigned)sample_all );
+ dst[i] = (float)src[idx*r_step];
+ }
+ }
+
+ __END__;
+
+ return out_responses;
+}
+
+CvMat*
+cvPreprocessCategoricalResponses( const CvMat* responses,
+ const CvMat* sample_idx, int sample_all,
+ CvMat** out_response_map, CvMat** class_counts )
+{
+ CvMat* out_responses = 0;
+ int** response_ptr = 0;
+
+ CV_FUNCNAME( "cvPreprocessCategoricalResponses" );
+
+ if( out_response_map )
+ *out_response_map = 0;
+
+ if( class_counts )
+ *class_counts = 0;
+
+ __BEGIN__;
+
+ int i, r_type, r_step;
+ int cls_count = 1, prev_cls, prev_i;
+ const int* map = 0;
+ const int* srci;
+ const float* srcfl;
+ int* dst;
+ int* cls_map;
+ int* cls_counts = 0;
+ int sample_count = sample_all;
+
+ if( !CV_IS_MAT(responses) )
+ CV_ERROR( CV_StsBadArg, "Invalid response array" );
+
+ if( responses->rows != 1 && responses->cols != 1 )
+ CV_ERROR( CV_StsBadSize, "Response array must be 1-dimensional" );
+
+ if( responses->rows + responses->cols - 1 != sample_count )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "Response array must contain as many elements as the total number of samples" );
+
+ r_type = CV_MAT_TYPE(responses->type);
+ if( r_type != CV_32FC1 && r_type != CV_32SC1 )
+ CV_ERROR( CV_StsUnsupportedFormat, "Unsupported response type" );
+
+ r_step = responses->step ? responses->step / CV_ELEM_SIZE(responses->type) : 1;
+
+ if( sample_idx )
+ {
+ if( !CV_IS_MAT(sample_idx) || CV_MAT_TYPE(sample_idx->type) != CV_32SC1 ||
+ (sample_idx->rows != 1 && sample_idx->cols != 1) || !CV_IS_MAT_CONT(sample_idx->type) )
+ CV_ERROR( CV_StsBadArg, "sample index array should be continuous 1-dimensional integer vector" );
+ if( sample_idx->rows + sample_idx->cols - 1 > sample_count )
+ CV_ERROR( CV_StsBadSize, "sample index array is too large" );
+ map = sample_idx->data.i;
+ sample_count = sample_idx->rows + sample_idx->cols - 1;
+ }
+
+ CV_CALL( out_responses = cvCreateMat( 1, sample_count, CV_32SC1 ));
+
+ if( !out_response_map )
+ CV_ERROR( CV_StsNullPtr, "out_response_map pointer is NULL" );
+
+ CV_CALL( response_ptr = (int**)cvAlloc( sample_count*sizeof(response_ptr[0])));
+
+ srci = responses->data.i;
+ srcfl = responses->data.fl;
+ dst = out_responses->data.i;
+
+ for( i = 0; i < sample_count; i++ )
+ {
+ int idx = map ? map[i] : i;
+ assert( (unsigned)idx < (unsigned)sample_all );
+ if( r_type == CV_32SC1 )
+ dst[i] = srci[idx*r_step];
+ else
+ {
+ float rf = srcfl[idx*r_step];
+ int ri = cvRound(rf);
+ if( ri != rf )
+ {
+ char buf[100];
+ sprintf( buf, "response #%d is not integral", idx );
+ CV_ERROR( CV_StsBadArg, buf );
+ }
+ dst[i] = ri;
+ }
+ response_ptr[i] = dst + i;
+ }
+
+ qsort( response_ptr, sample_count, sizeof(int*), icvCmpIntegersPtr );
+
+ // count the classes
+ for( i = 1; i < sample_count; i++ )
+ cls_count += *response_ptr[i] != *response_ptr[i-1];
+
+ if( cls_count < 2 )
+ CV_ERROR( CV_StsBadArg, "There is only a single class" );
+
+ CV_CALL( *out_response_map = cvCreateMat( 1, cls_count, CV_32SC1 ));
+
+ if( class_counts )
+ {
+ CV_CALL( *class_counts = cvCreateMat( 1, cls_count, CV_32SC1 ));
+ cls_counts = (*class_counts)->data.i;
+ }
+
+ // compact the class indices and build the map
+ prev_cls = ~*response_ptr[0];
+ cls_count = -1;
+ cls_map = (*out_response_map)->data.i;
+
+ for( i = 0, prev_i = -1; i < sample_count; i++ )
+ {
+ int cur_cls = *response_ptr[i];
+ if( cur_cls != prev_cls )
+ {
+ if( cls_counts && cls_count >= 0 )
+ cls_counts[cls_count] = i - prev_i;
+ cls_map[++cls_count] = prev_cls = cur_cls;
+ prev_i = i;
+ }
+ *response_ptr[i] = cls_count;
+ }
+
+ if( cls_counts )
+ cls_counts[cls_count] = i - prev_i;
+
+ __END__;
+
+ cvFree( &response_ptr );
+
+ return out_responses;
+}
+
+
+const float**
+cvGetTrainSamples( const CvMat* train_data, int tflag,
+ const CvMat* var_idx, const CvMat* sample_idx,
+ int* _var_count, int* _sample_count,
+ bool always_copy_data )
+{
+ float** samples = 0;
+
+ CV_FUNCNAME( "cvGetTrainSamples" );
+
+ __BEGIN__;
+
+ int i, j, var_count, sample_count, s_step, v_step;
+ bool copy_data;
+ const float* data;
+ const int *s_idx, *v_idx;
+
+ if( !CV_IS_MAT(train_data) )
+ CV_ERROR( CV_StsBadArg, "Invalid or NULL training data matrix" );
+
+ var_count = var_idx ? var_idx->cols + var_idx->rows - 1 :
+ tflag == CV_ROW_SAMPLE ? train_data->cols : train_data->rows;
+ sample_count = sample_idx ? sample_idx->cols + sample_idx->rows - 1 :
+ tflag == CV_ROW_SAMPLE ? train_data->rows : train_data->cols;
+
+ if( _var_count )
+ *_var_count = var_count;
+
+ if( _sample_count )
+ *_sample_count = sample_count;
+
+ copy_data = tflag != CV_ROW_SAMPLE || var_idx || always_copy_data;
+
+ CV_CALL( samples = (float**)cvAlloc(sample_count*sizeof(samples[0]) +
+ (copy_data ? 1 : 0)*var_count*sample_count*sizeof(samples[0][0])) );
+ data = train_data->data.fl;
+ s_step = train_data->step / sizeof(samples[0][0]);
+ v_step = 1;
+ s_idx = sample_idx ? sample_idx->data.i : 0;
+ v_idx = var_idx ? var_idx->data.i : 0;
+
+ if( !copy_data )
+ {
+ for( i = 0; i < sample_count; i++ )
+ samples[i] = (float*)(data + (s_idx ? s_idx[i] : i)*s_step);
+ }
+ else
+ {
+ samples[0] = (float*)(samples + sample_count);
+ if( tflag != CV_ROW_SAMPLE )
+ CV_SWAP( s_step, v_step, i );
+
+ for( i = 0; i < sample_count; i++ )
+ {
+ float* dst = samples[i] = samples[0] + i*var_count;
+ const float* src = data + (s_idx ? s_idx[i] : i)*s_step;
+
+ if( !v_idx )
+ for( j = 0; j < var_count; j++ )
+ dst[j] = src[j*v_step];
+ else
+ for( j = 0; j < var_count; j++ )
+ dst[j] = src[v_idx[j]*v_step];
+ }
+ }
+
+ __END__;
+
+ return (const float**)samples;
+}
+
+
+void
+cvCheckTrainData( const CvMat* train_data, int tflag,
+ const CvMat* missing_mask,
+ int* var_all, int* sample_all )
+{
+ CV_FUNCNAME( "cvCheckTrainData" );
+
+ if( var_all )
+ *var_all = 0;
+
+ if( sample_all )
+ *sample_all = 0;
+
+ __BEGIN__;
+
+ // check parameter types and sizes
+ if( !CV_IS_MAT(train_data) || CV_MAT_TYPE(train_data->type) != CV_32FC1 )
+ CV_ERROR( CV_StsBadArg, "train data must be floating-point matrix" );
+
+ if( missing_mask )
+ {
+ if( !CV_IS_MAT(missing_mask) || !CV_IS_MASK_ARR(missing_mask) ||
+ !CV_ARE_SIZES_EQ(train_data, missing_mask) )
+ CV_ERROR( CV_StsBadArg,
+ "missing value mask must be 8-bit matrix of the same size as training data" );
+ }
+
+ if( tflag != CV_ROW_SAMPLE && tflag != CV_COL_SAMPLE )
+ CV_ERROR( CV_StsBadArg,
+ "Unknown training data layout (must be CV_ROW_SAMPLE or CV_COL_SAMPLE)" );
+
+ if( var_all )
+ *var_all = tflag == CV_ROW_SAMPLE ? train_data->cols : train_data->rows;
+
+ if( sample_all )
+ *sample_all = tflag == CV_ROW_SAMPLE ? train_data->rows : train_data->cols;
+
+ __END__;
+}
+
+
+int
+cvPrepareTrainData( const char* /*funcname*/,
+ const CvMat* train_data, int tflag,
+ const CvMat* responses, int response_type,
+ const CvMat* var_idx,
+ const CvMat* sample_idx,
+ bool always_copy_data,
+ const float*** out_train_samples,
+ int* _sample_count,
+ int* _var_count,
+ int* _var_all,
+ CvMat** out_responses,
+ CvMat** out_response_map,
+ CvMat** out_var_idx,
+ CvMat** out_sample_idx )
+{
+ int ok = 0;
+ CvMat* _var_idx = 0;
+ CvMat* _sample_idx = 0;
+ CvMat* _responses = 0;
+ int sample_all = 0, sample_count = 0, var_all = 0, var_count = 0;
+
+ CV_FUNCNAME( "cvPrepareTrainData" );
+
+ // step 0. clear all the output pointers to ensure we do not try
+ // to call free() with uninitialized pointers
+ if( out_responses )
+ *out_responses = 0;
+
+ if( out_response_map )
+ *out_response_map = 0;
+
+ if( out_var_idx )
+ *out_var_idx = 0;
+
+ if( out_sample_idx )
+ *out_sample_idx = 0;
+
+ if( out_train_samples )
+ *out_train_samples = 0;
+
+ if( _sample_count )
+ *_sample_count = 0;
+
+ if( _var_count )
+ *_var_count = 0;
+
+ if( _var_all )
+ *_var_all = 0;
+
+ __BEGIN__;
+
+ if( !out_train_samples )
+ CV_ERROR( CV_StsBadArg, "output pointer to train samples is NULL" );
+
+ CV_CALL( cvCheckTrainData( train_data, tflag, 0, &var_all, &sample_all ));
+
+ if( sample_idx )
+ CV_CALL( _sample_idx = cvPreprocessIndexArray( sample_idx, sample_all ));
+ if( var_idx )
+ CV_CALL( _var_idx = cvPreprocessIndexArray( var_idx, var_all ));
+
+ if( responses )
+ {
+ if( !out_responses )
+ CV_ERROR( CV_StsNullPtr, "output response pointer is NULL" );
+
+ if( response_type == CV_VAR_NUMERICAL )
+ {
+ CV_CALL( _responses = cvPreprocessOrderedResponses( responses,
+ _sample_idx, sample_all ));
+ }
+ else
+ {
+ CV_CALL( _responses = cvPreprocessCategoricalResponses( responses,
+ _sample_idx, sample_all, out_response_map, 0 ));
+ }
+ }
+
+ CV_CALL( *out_train_samples =
+ cvGetTrainSamples( train_data, tflag, _var_idx, _sample_idx,
+ &var_count, &sample_count, always_copy_data ));
+
+ ok = 1;
+
+ __END__;
+
+ if( ok )
+ {
+ if( out_responses )
+ *out_responses = _responses, _responses = 0;
+
+ if( out_var_idx )
+ *out_var_idx = _var_idx, _var_idx = 0;
+
+ if( out_sample_idx )
+ *out_sample_idx = _sample_idx, _sample_idx = 0;
+
+ if( _sample_count )
+ *_sample_count = sample_count;
+
+ if( _var_count )
+ *_var_count = var_count;
+
+ if( _var_all )
+ *_var_all = var_all;
+ }
+ else
+ {
+ if( out_response_map )
+ cvReleaseMat( out_response_map );
+ cvFree( out_train_samples );
+ }
+
+ if( _responses != responses )
+ cvReleaseMat( &_responses );
+ cvReleaseMat( &_var_idx );
+ cvReleaseMat( &_sample_idx );
+
+ return ok;
+}
+
+
+typedef struct CvSampleResponsePair
+{
+ const float* sample;
+ const uchar* mask;
+ int response;
+ int index;
+}
+CvSampleResponsePair;
+
+
+static int
+CV_CDECL icvCmpSampleResponsePairs( const void* a, const void* b )
+{
+ int ra = ((const CvSampleResponsePair*)a)->response;
+ int rb = ((const CvSampleResponsePair*)b)->response;
+ int ia = ((const CvSampleResponsePair*)a)->index;
+ int ib = ((const CvSampleResponsePair*)b)->index;
+
+ return ra < rb ? -1 : ra > rb ? 1 : ia - ib;
+ //return (ra > rb ? -1 : 0)|(ra < rb);
+}
+
+
+void
+cvSortSamplesByClasses( const float** samples, const CvMat* classes,
+ int* class_ranges, const uchar** mask )
+{
+ CvSampleResponsePair* pairs = 0;
+ CV_FUNCNAME( "cvSortSamplesByClasses" );
+
+ __BEGIN__;
+
+ int i, k = 0, sample_count;
+
+ if( !samples || !classes || !class_ranges )
+ CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: some of the args are NULL pointers" );
+
+ if( classes->rows != 1 || CV_MAT_TYPE(classes->type) != CV_32SC1 )
+ CV_ERROR( CV_StsBadArg, "classes array must be a single row of integers" );
+
+ sample_count = classes->cols;
+ CV_CALL( pairs = (CvSampleResponsePair*)cvAlloc( (sample_count+1)*sizeof(pairs[0])));
+
+ for( i = 0; i < sample_count; i++ )
+ {
+ pairs[i].sample = samples[i];
+ pairs[i].mask = (mask) ? (mask[i]) : 0;
+ pairs[i].response = classes->data.i[i];
+ pairs[i].index = i;
+ assert( classes->data.i[i] >= 0 );
+ }
+
+ qsort( pairs, sample_count, sizeof(pairs[0]), icvCmpSampleResponsePairs );
+ pairs[sample_count].response = -1;
+ class_ranges[0] = 0;
+
+ for( i = 0; i < sample_count; i++ )
+ {
+ samples[i] = pairs[i].sample;
+ if (mask)
+ mask[i] = pairs[i].mask;
+ classes->data.i[i] = pairs[i].response;
+
+ if( pairs[i].response != pairs[i+1].response )
+ class_ranges[++k] = i+1;
+ }
+
+ __END__;
+
+ cvFree( &pairs );
+}
+
+
+void
+cvPreparePredictData( const CvArr* _sample, int dims_all,
+ const CvMat* comp_idx, int class_count,
+ const CvMat* prob, float** _row_sample,
+ int as_sparse )
+{
+ float* row_sample = 0;
+ int* inverse_comp_idx = 0;
+
+ CV_FUNCNAME( "cvPreparePredictData" );
+
+ __BEGIN__;
+
+ const CvMat* sample = (const CvMat*)_sample;
+ float* sample_data;
+ int sample_step;
+ int is_sparse = CV_IS_SPARSE_MAT(sample);
+ int d, sizes[CV_MAX_DIM];
+ int i, dims_selected;
+ int vec_size;
+
+ if( !is_sparse && !CV_IS_MAT(sample) )
+ CV_ERROR( !sample ? CV_StsNullPtr : CV_StsBadArg, "The sample is not a valid vector" );
+
+ if( cvGetElemType( sample ) != CV_32FC1 )
+ CV_ERROR( CV_StsUnsupportedFormat, "Input sample must have 32fC1 type" );
+
+ CV_CALL( d = cvGetDims( sample, sizes ));
+
+ if( !((is_sparse && d == 1) || (!is_sparse && d == 2 && (sample->rows == 1 || sample->cols == 1))) )
+ CV_ERROR( CV_StsBadSize, "Input sample must be 1-dimensional vector" );
+
+ if( d == 1 )
+ sizes[1] = 1;
+
+ if( sizes[0] + sizes[1] - 1 != dims_all )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "The sample size is different from what has been used for training" );
+
+ if( !_row_sample )
+ CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: The row_sample pointer is NULL" );
+
+ if( comp_idx && (!CV_IS_MAT(comp_idx) || comp_idx->rows != 1 ||
+ CV_MAT_TYPE(comp_idx->type) != CV_32SC1) )
+ CV_ERROR( CV_StsBadArg, "INTERNAL ERROR: invalid comp_idx" );
+
+ dims_selected = comp_idx ? comp_idx->cols : dims_all;
+
+ if( prob )
+ {
+ if( !CV_IS_MAT(prob) )
+ CV_ERROR( CV_StsBadArg, "The output matrix of probabilities is invalid" );
+
+ if( (prob->rows != 1 && prob->cols != 1) ||
+ (CV_MAT_TYPE(prob->type) != CV_32FC1 &&
+ CV_MAT_TYPE(prob->type) != CV_64FC1) )
+ CV_ERROR( CV_StsBadSize,
+ "The matrix of probabilities must be 1-dimensional vector of 32fC1 type" );
+
+ if( prob->rows + prob->cols - 1 != class_count )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "The vector of probabilities must contain as many elements as "
+ "the number of classes in the training set" );
+ }
+
+ vec_size = !as_sparse ? dims_selected*sizeof(row_sample[0]) :
+ (dims_selected + 1)*sizeof(CvSparseVecElem32f);
+
+ if( CV_IS_MAT(sample) )
+ {
+ sample_data = sample->data.fl;
+ sample_step = sample->step / sizeof(row_sample[0]);
+
+ if( !comp_idx && CV_IS_MAT_CONT(sample->type) && !as_sparse )
+ *_row_sample = sample_data;
+ else
+ {
+ CV_CALL( row_sample = (float*)cvAlloc( vec_size ));
+
+ if( !comp_idx )
+ for( i = 0; i < dims_selected; i++ )
+ row_sample[i] = sample_data[sample_step*i];
+ else
+ {
+ int* comp = comp_idx->data.i;
+ if( !sample_step )
+ for( i = 0; i < dims_selected; i++ )
+ row_sample[i] = sample_data[comp[i]];
+ else
+ for( i = 0; i < dims_selected; i++ )
+ row_sample[i] = sample_data[sample_step*comp[i]];
+ }
+
+ *_row_sample = row_sample;
+ }
+
+ if( as_sparse )
+ {
+ const float* src = (const float*)row_sample;
+ CvSparseVecElem32f* dst = (CvSparseVecElem32f*)row_sample;
+
+ dst[dims_selected].idx = -1;
+ for( i = dims_selected - 1; i >= 0; i-- )
+ {
+ dst[i].idx = i;
+ dst[i].val = src[i];
+ }
+ }
+ }
+ else
+ {
+ CvSparseNode* node;
+ CvSparseMatIterator mat_iterator;
+ const CvSparseMat* sparse = (const CvSparseMat*)sample;
+ assert( is_sparse );
+
+ node = cvInitSparseMatIterator( sparse, &mat_iterator );
+ CV_CALL( row_sample = (float*)cvAlloc( vec_size ));
+
+ if( comp_idx )
+ {
+ CV_CALL( inverse_comp_idx = (int*)cvAlloc( dims_all*sizeof(int) ));
+ memset( inverse_comp_idx, -1, dims_all*sizeof(int) );
+ for( i = 0; i < dims_selected; i++ )
+ inverse_comp_idx[comp_idx->data.i[i]] = i;
+ }
+
+ if( !as_sparse )
+ {
+ memset( row_sample, 0, vec_size );
+
+ for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) )
+ {
+ int idx = *CV_NODE_IDX( sparse, node );
+ if( inverse_comp_idx )
+ {
+ idx = inverse_comp_idx[idx];
+ if( idx < 0 )
+ continue;
+ }
+ row_sample[idx] = *(float*)CV_NODE_VAL( sparse, node );
+ }
+ }
+ else
+ {
+ CvSparseVecElem32f* ptr = (CvSparseVecElem32f*)row_sample;
+
+ for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) )
+ {
+ int idx = *CV_NODE_IDX( sparse, node );
+ if( inverse_comp_idx )
+ {
+ idx = inverse_comp_idx[idx];
+ if( idx < 0 )
+ continue;
+ }
+ ptr->idx = idx;
+ ptr->val = *(float*)CV_NODE_VAL( sparse, node );
+ ptr++;
+ }
+
+ qsort( row_sample, ptr - (CvSparseVecElem32f*)row_sample,
+ sizeof(ptr[0]), icvCmpSparseVecElems );
+ ptr->idx = -1;
+ }
+
+ *_row_sample = row_sample;
+ }
+
+ __END__;
+
+ if( inverse_comp_idx )
+ cvFree( &inverse_comp_idx );
+
+ if( cvGetErrStatus() < 0 && _row_sample )
+ {
+ cvFree( &row_sample );
+ *_row_sample = 0;
+ }
+}
+
+
+static void
+icvConvertDataToSparse( const uchar* src, int src_step, int src_type,
+ uchar* dst, int dst_step, int dst_type,
+ CvSize size, int* idx )
+{
+ CV_FUNCNAME( "icvConvertDataToSparse" );
+
+ __BEGIN__;
+
+ int i, j;
+ src_type = CV_MAT_TYPE(src_type);
+ dst_type = CV_MAT_TYPE(dst_type);
+
+ if( CV_MAT_CN(src_type) != 1 || CV_MAT_CN(dst_type) != 1 )
+ CV_ERROR( CV_StsUnsupportedFormat, "The function supports only single-channel arrays" );
+
+ if( src_step == 0 )
+ src_step = CV_ELEM_SIZE(src_type);
+
+ if( dst_step == 0 )
+ dst_step = CV_ELEM_SIZE(dst_type);
+
+ // if there is no "idx" and if both arrays are continuous,
+ // do the whole processing (copying or conversion) in a single loop
+ if( !idx && CV_ELEM_SIZE(src_type)*size.width == src_step &&
+ CV_ELEM_SIZE(dst_type)*size.width == dst_step )
+ {
+ size.width *= size.height;
+ size.height = 1;
+ }
+
+ if( src_type == dst_type )
+ {
+ int full_width = CV_ELEM_SIZE(dst_type)*size.width;
+
+ if( full_width == sizeof(int) ) // another common case: copy int's or float's
+ for( i = 0; i < size.height; i++, src += src_step )
+ *(int*)(dst + dst_step*(idx ? idx[i] : i)) = *(int*)src;
+ else
+ for( i = 0; i < size.height; i++, src += src_step )
+ memcpy( dst + dst_step*(idx ? idx[i] : i), src, full_width );
+ }
+ else if( src_type == CV_32SC1 && (dst_type == CV_32FC1 || dst_type == CV_64FC1) )
+ for( i = 0; i < size.height; i++, src += src_step )
+ {
+ uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
+ if( dst_type == CV_32FC1 )
+ for( j = 0; j < size.width; j++ )
+ ((float*)_dst)[j] = (float)((int*)src)[j];
+ else
+ for( j = 0; j < size.width; j++ )
+ ((double*)_dst)[j] = ((int*)src)[j];
+ }
+ else if( (src_type == CV_32FC1 || src_type == CV_64FC1) && dst_type == CV_32SC1 )
+ for( i = 0; i < size.height; i++, src += src_step )
+ {
+ uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
+ if( src_type == CV_32FC1 )
+ for( j = 0; j < size.width; j++ )
+ ((int*)_dst)[j] = cvRound(((float*)src)[j]);
+ else
+ for( j = 0; j < size.width; j++ )
+ ((int*)_dst)[j] = cvRound(((double*)src)[j]);
+ }
+ else if( (src_type == CV_32FC1 && dst_type == CV_64FC1) ||
+ (src_type == CV_64FC1 && dst_type == CV_32FC1) )
+ for( i = 0; i < size.height; i++, src += src_step )
+ {
+ uchar* _dst = dst + dst_step*(idx ? idx[i] : i);
+ if( src_type == CV_32FC1 )
+ for( j = 0; j < size.width; j++ )
+ ((double*)_dst)[j] = ((float*)src)[j];
+ else
+ for( j = 0; j < size.width; j++ )
+ ((float*)_dst)[j] = (float)((double*)src)[j];
+ }
+ else
+ CV_ERROR( CV_StsUnsupportedFormat, "Unsupported combination of input and output vectors" );
+
+ __END__;
+}
+
+
+void
+cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
+ const CvMat* centers, CvMat* dst_centers,
+ const CvMat* probs, CvMat* dst_probs,
+ const CvMat* sample_idx, int samples_all,
+ const CvMat* comp_idx, int dims_all )
+{
+ CV_FUNCNAME( "cvWritebackLabels" );
+
+ __BEGIN__;
+
+ int samples_selected = samples_all, dims_selected = dims_all;
+
+ if( dst_labels && !CV_IS_MAT(dst_labels) )
+ CV_ERROR( CV_StsBadArg, "Array of output labels is not a valid matrix" );
+
+ if( dst_centers )
+ if( !ICV_IS_MAT_OF_TYPE(dst_centers, CV_32FC1) &&
+ !ICV_IS_MAT_OF_TYPE(dst_centers, CV_64FC1) )
+ CV_ERROR( CV_StsBadArg, "Array of cluster centers is not a valid matrix" );
+
+ if( dst_probs && !CV_IS_MAT(dst_probs) )
+ CV_ERROR( CV_StsBadArg, "Probability matrix is not valid" );
+
+ if( sample_idx )
+ {
+ CV_ASSERT( sample_idx->rows == 1 && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 );
+ samples_selected = sample_idx->cols;
+ }
+
+ if( comp_idx )
+ {
+ CV_ASSERT( comp_idx->rows == 1 && CV_MAT_TYPE(comp_idx->type) == CV_32SC1 );
+ dims_selected = comp_idx->cols;
+ }
+
+ if( dst_labels && (!labels || labels->data.ptr != dst_labels->data.ptr) )
+ {
+ if( !labels )
+ CV_ERROR( CV_StsNullPtr, "NULL labels" );
+
+ CV_ASSERT( labels->rows == 1 );
+
+ if( dst_labels->rows != 1 && dst_labels->cols != 1 )
+ CV_ERROR( CV_StsBadSize, "Array of output labels should be 1d vector" );
+
+ if( dst_labels->rows + dst_labels->cols - 1 != samples_all )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "Size of vector of output labels is not equal to the total number of input samples" );
+
+ CV_ASSERT( labels->cols == samples_selected );
+
+ CV_CALL( icvConvertDataToSparse( labels->data.ptr, labels->step, labels->type,
+ dst_labels->data.ptr, dst_labels->step, dst_labels->type,
+ cvSize( 1, samples_selected ), sample_idx ? sample_idx->data.i : 0 ));
+ }
+
+ if( dst_centers && (!centers || centers->data.ptr != dst_centers->data.ptr) )
+ {
+ int i;
+
+ if( !centers )
+ CV_ERROR( CV_StsNullPtr, "NULL centers" );
+
+ if( centers->rows != dst_centers->rows )
+ CV_ERROR( CV_StsUnmatchedSizes, "Invalid number of rows in matrix of output centers" );
+
+ if( dst_centers->cols != dims_all )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "Number of columns in matrix of output centers is "
+ "not equal to the total number of components in the input samples" );
+
+ CV_ASSERT( centers->cols == dims_selected );
+
+ for( i = 0; i < centers->rows; i++ )
+ CV_CALL( icvConvertDataToSparse( centers->data.ptr + i*centers->step, 0, centers->type,
+ dst_centers->data.ptr + i*dst_centers->step, 0, dst_centers->type,
+ cvSize( 1, dims_selected ), comp_idx ? comp_idx->data.i : 0 ));
+ }
+
+ if( dst_probs && (!probs || probs->data.ptr != dst_probs->data.ptr) )
+ {
+ if( !probs )
+ CV_ERROR( CV_StsNullPtr, "NULL probs" );
+
+ if( probs->cols != dst_probs->cols )
+ CV_ERROR( CV_StsUnmatchedSizes, "Invalid number of columns in output probability matrix" );
+
+ if( dst_probs->rows != samples_all )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "Number of rows in output probability matrix is "
+ "not equal to the total number of input samples" );
+
+ CV_ASSERT( probs->rows == samples_selected );
+
+ CV_CALL( icvConvertDataToSparse( probs->data.ptr, probs->step, probs->type,
+ dst_probs->data.ptr, dst_probs->step, dst_probs->type,
+ cvSize( probs->cols, samples_selected ),
+ sample_idx ? sample_idx->data.i : 0 ));
+ }
+
+ __END__;
+}
+
+#if 0
+CV_IMPL void
+cvStatModelMultiPredict( const CvStatModel* stat_model,
+ const CvArr* predict_input,
+ int flags, CvMat* predict_output,
+ CvMat* probs, const CvMat* sample_idx )
+{
+ CvMemStorage* storage = 0;
+ CvMat* sample_idx_buffer = 0;
+ CvSparseMat** sparse_rows = 0;
+ int samples_selected = 0;
+
+ CV_FUNCNAME( "cvStatModelMultiPredict" );
+
+ __BEGIN__;
+
+ int i;
+ int predict_output_step = 1, sample_idx_step = 1;
+ int type;
+ int d, sizes[CV_MAX_DIM];
+ int tflag = flags == CV_COL_SAMPLE;
+ int samples_all, dims_all;
+ int is_sparse = CV_IS_SPARSE_MAT(predict_input);
+ CvMat predict_input_part;
+ CvArr* sample = &predict_input_part;
+ CvMat probs_part;
+ CvMat* probs1 = probs ? &probs_part : 0;
+
+ if( !CV_IS_STAT_MODEL(stat_model) )
+ CV_ERROR( !stat_model ? CV_StsNullPtr : CV_StsBadArg, "Invalid statistical model" );
+
+ if( !stat_model->predict )
+ CV_ERROR( CV_StsNotImplemented, "There is no \"predict\" method" );
+
+ if( !predict_input || !predict_output )
+ CV_ERROR( CV_StsNullPtr, "NULL input or output matrices" );
+
+ if( !is_sparse && !CV_IS_MAT(predict_input) )
+ CV_ERROR( CV_StsBadArg, "predict_input should be a matrix or a sparse matrix" );
+
+ if( !CV_IS_MAT(predict_output) )
+ CV_ERROR( CV_StsBadArg, "predict_output should be a matrix" );
+
+ type = cvGetElemType( predict_input );
+ if( type != CV_32FC1 ||
+ (CV_MAT_TYPE(predict_output->type) != CV_32FC1 &&
+ CV_MAT_TYPE(predict_output->type) != CV_32SC1 ))
+ CV_ERROR( CV_StsUnsupportedFormat, "The input or output matrix has unsupported format" );
+
+ CV_CALL( d = cvGetDims( predict_input, sizes ));
+ if( d > 2 )
+ CV_ERROR( CV_StsBadSize, "The input matrix should be 1- or 2-dimensional" );
+
+ if( !tflag )
+ {
+ samples_all = samples_selected = sizes[0];
+ dims_all = sizes[1];
+ }
+ else
+ {
+ samples_all = samples_selected = sizes[1];
+ dims_all = sizes[0];
+ }
+
+ if( sample_idx )
+ {
+ if( !CV_IS_MAT(sample_idx) )
+ CV_ERROR( CV_StsBadArg, "Invalid sample_idx matrix" );
+
+ if( sample_idx->cols != 1 && sample_idx->rows != 1 )
+ CV_ERROR( CV_StsBadSize, "sample_idx must be 1-dimensional matrix" );
+
+ samples_selected = sample_idx->rows + sample_idx->cols - 1;
+
+ if( CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
+ {
+ if( samples_selected > samples_all )
+ CV_ERROR( CV_StsBadSize, "sample_idx is too large vector" );
+ }
+ else if( samples_selected != samples_all )
+ CV_ERROR( CV_StsUnmatchedSizes, "sample_idx has incorrect size" );
+
+ sample_idx_step = sample_idx->step ?
+ sample_idx->step / CV_ELEM_SIZE(sample_idx->type) : 1;
+ }
+
+ if( predict_output->rows != 1 && predict_output->cols != 1 )
+ CV_ERROR( CV_StsBadSize, "predict_output should be a 1-dimensional matrix" );
+
+ if( predict_output->rows + predict_output->cols - 1 != samples_all )
+ CV_ERROR( CV_StsUnmatchedSizes, "predict_output and predict_input have uncoordinated sizes" );
+
+ predict_output_step = predict_output->step ?
+ predict_output->step / CV_ELEM_SIZE(predict_output->type) : 1;
+
+ if( probs )
+ {
+ if( !CV_IS_MAT(probs) )
+ CV_ERROR( CV_StsBadArg, "Invalid matrix of probabilities" );
+
+ if( probs->rows != samples_all )
+ CV_ERROR( CV_StsUnmatchedSizes,
+ "matrix of probabilities must have as many rows as the total number of samples" );
+
+ if( CV_MAT_TYPE(probs->type) != CV_32FC1 )
+ CV_ERROR( CV_StsUnsupportedFormat, "matrix of probabilities must have 32fC1 type" );
+ }
+
+ if( is_sparse )
+ {
+ CvSparseNode* node;
+ CvSparseMatIterator mat_iterator;
+ CvSparseMat* sparse = (CvSparseMat*)predict_input;
+
+ if( sample_idx && CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
+ {
+ CV_CALL( sample_idx_buffer = cvCreateMat( 1, samples_all, CV_8UC1 ));
+ cvZero( sample_idx_buffer );
+ for( i = 0; i < samples_selected; i++ )
+ sample_idx_buffer->data.ptr[sample_idx->data.i[i*sample_idx_step]] = 1;
+ samples_selected = samples_all;
+ sample_idx = sample_idx_buffer;
+ sample_idx_step = 1;
+ }
+
+ CV_CALL( sparse_rows = (CvSparseMat**)cvAlloc( samples_selected*sizeof(sparse_rows[0])));
+ for( i = 0; i < samples_selected; i++ )
+ {
+ if( sample_idx && sample_idx->data.ptr[i*sample_idx_step] == 0 )
+ continue;
+ CV_CALL( sparse_rows[i] = cvCreateSparseMat( 1, &dims_all, type ));
+ if( !storage )
+ storage = sparse_rows[i]->heap->storage;
+ else
+ {
+ // hack: to decrease memory footprint, make all the sparse matrices
+ // reside in the same storage
+ int elem_size = sparse_rows[i]->heap->elem_size;
+ cvReleaseMemStorage( &sparse_rows[i]->heap->storage );
+ sparse_rows[i]->heap = cvCreateSet( 0, sizeof(CvSet), elem_size, storage );
+ }
+ }
+
+ // put each row (or column) of predict_input into separate sparse matrix.
+ node = cvInitSparseMatIterator( sparse, &mat_iterator );
+ for( ; node != 0; node = cvGetNextSparseNode( &mat_iterator ))
+ {
+ int* idx = CV_NODE_IDX( sparse, node );
+ int idx0 = idx[tflag ^ 1];
+ int idx1 = idx[tflag];
+
+ if( sample_idx && sample_idx->data.ptr[idx0*sample_idx_step] == 0 )
+ continue;
+
+ assert( sparse_rows[idx0] != 0 );
+ *(float*)cvPtrND( sparse, &idx1, 0, 1, 0 ) = *(float*)CV_NODE_VAL( sparse, node );
+ }
+ }
+
+ for( i = 0; i < samples_selected; i++ )
+ {
+ int idx = i;
+ float response;
+
+ if( sample_idx )
+ {
+ if( CV_MAT_TYPE(sample_idx->type) == CV_32SC1 )
+ {
+ idx = sample_idx->data.i[i*sample_idx_step];
+ if( (unsigned)idx >= (unsigned)samples_all )
+ CV_ERROR( CV_StsOutOfRange, "Some of sample_idx elements are out of range" );
+ }
+ else if( CV_MAT_TYPE(sample_idx->type) == CV_8UC1 &&
+ sample_idx->data.ptr[i*sample_idx_step] == 0 )
+ continue;
+ }
+
+ if( !is_sparse )
+ {
+ if( !tflag )
+ cvGetRow( predict_input, &predict_input_part, idx );
+ else
+ {
+ cvGetCol( predict_input, &predict_input_part, idx );
+ }
+ }
+ else
+ sample = sparse_rows[idx];
+
+ if( probs )
+ cvGetRow( probs, probs1, idx );
+
+ CV_CALL( response = stat_model->predict( stat_model, (CvMat*)sample, probs1 ));
+
+ if( CV_MAT_TYPE(predict_output->type) == CV_32FC1 )
+ predict_output->data.fl[idx*predict_output_step] = response;
+ else
+ {
+ CV_ASSERT( cvRound(response) == response );
+ predict_output->data.i[idx*predict_output_step] = cvRound(response);
+ }
+ }
+
+ __END__;
+
+ if( sparse_rows )
+ {
+ int i;
+ for( i = 0; i < samples_selected; i++ )
+ if( sparse_rows[i] )
+ {
+ sparse_rows[i]->heap->storage = 0;
+ cvReleaseSparseMat( &sparse_rows[i] );
+ }
+ cvFree( &sparse_rows );
+ }
+
+ cvReleaseMat( &sample_idx_buffer );
+ cvReleaseMemStorage( &storage );
+}
+#endif
+
+// By P. Yarykin - begin -
+
+void cvCombineResponseMaps (CvMat* _responses,
+ const CvMat* old_response_map,
+ CvMat* new_response_map,
+ CvMat** out_response_map)
+{
+ int** old_data = NULL;
+ int** new_data = NULL;
+
+ CV_FUNCNAME ("cvCombineResponseMaps");
+ __BEGIN__
+
+ int i,j;
+ int old_n, new_n, out_n;
+ int samples, free_response;
+ int* first;
+ int* responses;
+ int* out_data;
+
+ if( out_response_map )
+ *out_response_map = 0;
+
+// Check input data.
+ if ((!ICV_IS_MAT_OF_TYPE (_responses, CV_32SC1)) ||
+ (!ICV_IS_MAT_OF_TYPE (old_response_map, CV_32SC1)) ||
+ (!ICV_IS_MAT_OF_TYPE (new_response_map, CV_32SC1)))
+ {
+ CV_ERROR (CV_StsBadArg, "Some of input arguments is not the CvMat")
+ }
+
+// Prepare sorted responses.
+ first = new_response_map->data.i;
+ new_n = new_response_map->cols;
+ CV_CALL (new_data = (int**)cvAlloc (new_n * sizeof (new_data[0])));
+ for (i = 0; i < new_n; i++)
+ new_data[i] = first + i;
+ qsort (new_data, new_n, sizeof(int*), icvCmpIntegersPtr);
+
+ first = old_response_map->data.i;
+ old_n = old_response_map->cols;
+ CV_CALL (old_data = (int**)cvAlloc (old_n * sizeof (old_data[0])));
+ for (i = 0; i < old_n; i++)
+ old_data[i] = first + i;
+ qsort (old_data, old_n, sizeof(int*), icvCmpIntegersPtr);
+
+// Count the number of different responses.
+ for (i = 0, j = 0, out_n = 0; i < old_n && j < new_n; out_n++)
+ {
+ if (*old_data[i] == *new_data[j])
+ {
+ i++;
+ j++;
+ }
+ else if (*old_data[i] < *new_data[j])
+ i++;
+ else
+ j++;
+ }
+ out_n += old_n - i + new_n - j;
+
+// Create and fill the result response maps.
+ CV_CALL (*out_response_map = cvCreateMat (1, out_n, CV_32SC1));
+ out_data = (*out_response_map)->data.i;
+ memcpy (out_data, first, old_n * sizeof (int));
+
+ free_response = old_n;
+ for (i = 0, j = 0; i < old_n && j < new_n; )
+ {
+ if (*old_data[i] == *new_data[j])
+ {
+ *new_data[j] = (int)(old_data[i] - first);
+ i++;
+ j++;
+ }
+ else if (*old_data[i] < *new_data[j])
+ i++;
+ else
+ {
+ out_data[free_response] = *new_data[j];
+ *new_data[j] = free_response++;
+ j++;
+ }
+ }
+ for (; j < new_n; j++)
+ {
+ out_data[free_response] = *new_data[j];
+ *new_data[j] = free_response++;
+ }
+ CV_ASSERT (free_response == out_n);
+
+// Change <responses> according to out response map.
+ samples = _responses->cols + _responses->rows - 1;
+ responses = _responses->data.i;
+ first = new_response_map->data.i;
+ for (i = 0; i < samples; i++)
+ {
+ responses[i] = first[responses[i]];
+ }
+
+ __END__
+
+ cvFree(&old_data);
+ cvFree(&new_data);
+
+}
+
+
+int icvGetNumberOfCluster( double* prob_vector, int num_of_clusters, float r,
+ float outlier_thresh, int normalize_probs )
+{
+ int max_prob_loc = 0;
+
+ CV_FUNCNAME("icvGetNumberOfCluster");
+ __BEGIN__;
+
+ double prob, maxprob, sum;
+ int i;
+
+ CV_ASSERT(prob_vector);
+ CV_ASSERT(num_of_clusters >= 0);
+
+ maxprob = prob_vector[0];
+ max_prob_loc = 0;
+ sum = maxprob;
+ for( i = 1; i < num_of_clusters; i++ )
+ {
+ prob = prob_vector[i];
+ sum += prob;
+ if( prob > maxprob )
+ {
+ max_prob_loc = i;
+ maxprob = prob;
+ }
+ }
+ if( normalize_probs && fabs(sum - 1.) > FLT_EPSILON )
+ {
+ for( i = 0; i < num_of_clusters; i++ )
+ prob_vector[i] /= sum;
+ }
+ if( fabs(r - 1.) > FLT_EPSILON && fabs(sum - 1.) < outlier_thresh )
+ max_prob_loc = -1;
+
+ __END__;
+
+ return max_prob_loc;
+
+} // End of icvGetNumberOfCluster
+
+
+void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
+ const CvMat* labels )
+{
+ CvMat* counts = 0;
+
+ CV_FUNCNAME("icvFindClusterLabels");
+ __BEGIN__;
+
+ int nclusters, nsamples;
+ int i, j;
+ double* probs_data;
+
+ CV_ASSERT( ICV_IS_MAT_OF_TYPE(probs, CV_64FC1) );
+ CV_ASSERT( ICV_IS_MAT_OF_TYPE(labels, CV_32SC1) );
+
+ nclusters = probs->cols;
+ nsamples = probs->rows;
+ CV_ASSERT( nsamples == labels->cols );
+
+ CV_CALL( counts = cvCreateMat( 1, nclusters + 1, CV_32SC1 ) );
+ CV_CALL( cvSetZero( counts ));
+ for( i = 0; i < nsamples; i++ )
+ {
+ labels->data.i[i] = icvGetNumberOfCluster( probs->data.db + i*probs->cols,
+ nclusters, r, outlier_thresh, 1 );
+ counts->data.i[labels->data.i[i] + 1]++;
+ }
+ CV_ASSERT((int)cvSum(counts).val[0] == nsamples);
+ // Filling empty clusters with the vector, that has the maximal probability
+ for( j = 0; j < nclusters; j++ ) // outliers are ignored
+ {
+ int maxprob_loc = -1;
+ double maxprob = 0;
+
+ if( counts->data.i[j+1] ) // j-th class is not empty
+ continue;
+ // look for the presentative, which is not lonely in it's cluster
+ // and that has a maximal probability among all these vectors
+ probs_data = probs->data.db;
+ for( i = 0; i < nsamples; i++, probs_data++ )
+ {
+ int label = labels->data.i[i];
+ double prob;
+ if( counts->data.i[label+1] == 0 ||
+ (counts->data.i[label+1] <= 1 && label != -1) )
+ continue;
+ prob = *probs_data;
+ if( prob >= maxprob )
+ {
+ maxprob = prob;
+ maxprob_loc = i;
+ }
+ }
+ // maxprob_loc == 0 <=> number of vectors less then number of clusters
+ CV_ASSERT( maxprob_loc >= 0 );
+ counts->data.i[labels->data.i[maxprob_loc] + 1]--;
+ labels->data.i[maxprob_loc] = j;
+ counts->data.i[j + 1]++;
+ }
+
+ __END__;
+
+ cvReleaseMat( &counts );
+} // End of icvFindClusterLabels
+
+/* End of file */
projects / opencv / blobdiff