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inner_functions.cpp
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1880 lines (1495 loc) · 56.4 KB
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/*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 "precomp.hpp"
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 */
static 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. */
static 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
// static 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!" );
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 = cvCloneMat( 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->rows == 1 ? 1 : responses->step / CV_ELEM_SIZE(responses->type);
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;
size_t s_step, v_step, s;
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, s );
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 );