X-Git-Url: http://git.maemo.org/git/?p=opencv;a=blobdiff_plain;f=ml%2Fsrc%2Fmlknearest.cpp;fp=ml%2Fsrc%2Fmlknearest.cpp;h=0000000000000000000000000000000000000000;hp=348d5b323c6aa7ad40f26c55e692df8c95afa216;hb=e4c14cdbdf2fe805e79cd96ded236f57e7b89060;hpb=454138ff8a20f6edb9b65a910101403d8b520643 diff --git a/ml/src/mlknearest.cpp b/ml/src/mlknearest.cpp deleted file mode 100644 index 348d5b3..0000000 --- a/ml/src/mlknearest.cpp +++ /dev/null @@ -1,401 +0,0 @@ -/*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" - -/****************************************************************************************\ -* K-Nearest Neighbors Classifier * -\****************************************************************************************/ - -// k Nearest Neighbors -CvKNearest::CvKNearest() -{ - samples = 0; - clear(); -} - - -CvKNearest::~CvKNearest() -{ - clear(); -} - - -CvKNearest::CvKNearest( const CvMat* _train_data, const CvMat* _responses, - const CvMat* _sample_idx, bool _is_regression, int _max_k ) -{ - samples = 0; - train( _train_data, _responses, _sample_idx, _is_regression, _max_k, false ); -} - - -void CvKNearest::clear() -{ - while( samples ) - { - CvVectors* next_samples = samples->next; - cvFree( &samples->data.fl ); - cvFree( &samples ); - samples = next_samples; - } - var_count = 0; - total = 0; - max_k = 0; -} - - -int CvKNearest::get_max_k() const { return max_k; } - -int CvKNearest::get_var_count() const { return var_count; } - -bool CvKNearest::is_regression() const { return regression; } - -int CvKNearest::get_sample_count() const { return total; } - -bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses, - const CvMat* _sample_idx, bool _is_regression, - int _max_k, bool _update_base ) -{ - bool ok = false; - CvMat* responses = 0; - - CV_FUNCNAME( "CvKNearest::train" ); - - __BEGIN__; - - CvVectors* _samples; - float** _data; - int _count, _dims, _dims_all, _rsize; - - if( !_update_base ) - clear(); - - // Prepare training data and related parameters. - // Treat categorical responses as ordered - to prevent class label compression and - // to enable entering new classes in the updates - CV_CALL( cvPrepareTrainData( "CvKNearest::train", _train_data, CV_ROW_SAMPLE, - _responses, CV_VAR_ORDERED, 0, _sample_idx, true, (const float***)&_data, - &_count, &_dims, &_dims_all, &responses, 0, 0 )); - - if( _update_base && _dims != var_count ) - CV_ERROR( CV_StsBadArg, "The newly added data have different dimensionality" ); - - if( !_update_base ) - { - if( _max_k < 1 ) - CV_ERROR( CV_StsOutOfRange, "max_k must be a positive number" ); - - regression = _is_regression; - var_count = _dims; - max_k = _max_k; - } - - _rsize = _count*sizeof(float); - CV_CALL( _samples = (CvVectors*)cvAlloc( sizeof(*_samples) + _rsize )); - _samples->next = samples; - _samples->type = CV_32F; - _samples->data.fl = _data; - _samples->count = _count; - total += _count; - - samples = _samples; - memcpy( _samples + 1, responses->data.fl, _rsize ); - - ok = true; - - __END__; - - return ok; -} - - - -void CvKNearest::find_neighbors_direct( const CvMat* _samples, int k, int start, int end, - float* neighbor_responses, const float** neighbors, float* dist ) const -{ - int i, j, count = end - start, k1 = 0, k2 = 0, d = var_count; - CvVectors* s = samples; - - for( ; s != 0; s = s->next ) - { - int n = s->count; - for( j = 0; j < n; j++ ) - { - for( i = 0; i < count; i++ ) - { - double sum = 0; - Cv32suf si; - const float* v = s->data.fl[j]; - const float* u = (float*)(_samples->data.ptr + _samples->step*(start + i)); - Cv32suf* dd = (Cv32suf*)(dist + i*k); - float* nr; - const float** nn; - int t, ii, ii1; - - for( t = 0; t <= d - 4; t += 4 ) - { - double t0 = u[t] - v[t], t1 = u[t+1] - v[t+1]; - double t2 = u[t+2] - v[t+2], t3 = u[t+3] - v[t+3]; - sum += t0*t0 + t1*t1 + t2*t2 + t3*t3; - } - - for( ; t < d; t++ ) - { - double t0 = u[t] - v[t]; - sum += t0*t0; - } - - si.f = (float)sum; - for( ii = k1-1; ii >= 0; ii-- ) - if( si.i > dd[ii].i ) - break; - if( ii >= k-1 ) - continue; - - nr = neighbor_responses + i*k; - nn = neighbors ? neighbors + (start + i)*k : 0; - for( ii1 = k2 - 1; ii1 > ii; ii1-- ) - { - dd[ii1+1].i = dd[ii1].i; - nr[ii1+1] = nr[ii1]; - if( nn ) nn[ii1+1] = nn[ii1]; - } - dd[ii+1].i = si.i; - nr[ii+1] = ((float*)(s + 1))[j]; - if( nn ) - nn[ii+1] = v; - } - k1 = MIN( k1+1, k ); - k2 = MIN( k1, k-1 ); - } - } -} - - -float CvKNearest::write_results( int k, int k1, int start, int end, - const float* neighbor_responses, const float* dist, - CvMat* _results, CvMat* _neighbor_responses, - CvMat* _dist, Cv32suf* sort_buf ) const -{ - float result = 0.f; - int i, j, j1, count = end - start; - double inv_scale = 1./k1; - int rstep = _results && !CV_IS_MAT_CONT(_results->type) ? _results->step/sizeof(result) : 1; - - for( i = 0; i < count; i++ ) - { - const Cv32suf* nr = (const Cv32suf*)(neighbor_responses + i*k); - float* dst; - float r; - if( _results || start+i == 0 ) - { - if( regression ) - { - double s = 0; - for( j = 0; j < k1; j++ ) - s += nr[j].f; - r = (float)(s*inv_scale); - } - else - { - int prev_start = 0, best_count = 0, cur_count; - Cv32suf best_val; - - for( j = 0; j < k1; j++ ) - sort_buf[j].i = nr[j].i; - - for( j = k1-1; j > 0; j-- ) - { - bool swap_fl = false; - for( j1 = 0; j1 < j; j1++ ) - if( sort_buf[j1].i > sort_buf[j1+1].i ) - { - int t; - CV_SWAP( sort_buf[j1].i, sort_buf[j1+1].i, t ); - swap_fl = true; - } - if( !swap_fl ) - break; - } - - best_val.i = 0; - for( j = 1; j <= k1; j++ ) - if( j == k1 || sort_buf[j].i != sort_buf[j-1].i ) - { - cur_count = j - prev_start; - if( best_count < cur_count ) - { - best_count = cur_count; - best_val.i = sort_buf[j-1].i; - } - prev_start = j; - } - r = best_val.f; - } - - if( start+i == 0 ) - result = r; - - if( _results ) - _results->data.fl[(start + i)*rstep] = r; - } - - if( _neighbor_responses ) - { - dst = (float*)(_neighbor_responses->data.ptr + - (start + i)*_neighbor_responses->step); - for( j = 0; j < k1; j++ ) - dst[j] = nr[j].f; - for( ; j < k; j++ ) - dst[j] = 0.f; - } - - if( _dist ) - { - dst = (float*)(_dist->data.ptr + (start + i)*_dist->step); - for( j = 0; j < k1; j++ ) - dst[j] = dist[j + i*k]; - for( ; j < k; j++ ) - dst[j] = 0.f; - } - } - - return result; -} - - - -float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results, - const float** _neighbors, CvMat* _neighbor_responses, CvMat* _dist ) const -{ - float result = 0.f; - bool local_alloc = false; - float* buf = 0; - const int max_blk_count = 128, max_buf_sz = 1 << 12; - - CV_FUNCNAME( "CvKNearest::find_nearest" ); - - __BEGIN__; - - int i, count, count_scale, blk_count0, blk_count = 0, buf_sz, k1; - - if( !samples ) - CV_ERROR( CV_StsError, "The search tree must be constructed first using train method" ); - - if( !CV_IS_MAT(_samples) || - CV_MAT_TYPE(_samples->type) != CV_32FC1 || - _samples->cols != var_count ) - CV_ERROR( CV_StsBadArg, "Input samples must be floating-point matrix (x)" ); - - if( _results && (!CV_IS_MAT(_results) || - _results->cols != 1 && _results->rows != 1 || - _results->cols + _results->rows - 1 != _samples->rows) ) - CV_ERROR( CV_StsBadArg, - "The results must be 1d vector containing as much elements as the number of samples" ); - - if( _results && CV_MAT_TYPE(_results->type) != CV_32FC1 && - (CV_MAT_TYPE(_results->type) != CV_32SC1 || regression)) - CV_ERROR( CV_StsUnsupportedFormat, - "The results must be floating-point or integer (in case of classification) vector" ); - - if( k < 1 || k > max_k ) - CV_ERROR( CV_StsOutOfRange, "k must be within 1..max_k range" ); - - if( _neighbor_responses ) - { - if( !CV_IS_MAT(_neighbor_responses) || CV_MAT_TYPE(_neighbor_responses->type) != CV_32FC1 || - _neighbor_responses->rows != _samples->rows || _neighbor_responses->cols != k ) - CV_ERROR( CV_StsBadArg, - "The neighbor responses (if present) must be floating-point matrix of x size" ); - } - - if( _dist ) - { - if( !CV_IS_MAT(_dist) || CV_MAT_TYPE(_dist->type) != CV_32FC1 || - _dist->rows != _samples->rows || _dist->cols != k ) - CV_ERROR( CV_StsBadArg, - "The distances from the neighbors (if present) must be floating-point matrix of x size" ); - } - - count = _samples->rows; - count_scale = k*2*sizeof(float); - blk_count0 = MIN( count, max_blk_count ); - buf_sz = MIN( blk_count0 * count_scale, max_buf_sz ); - blk_count0 = MAX( buf_sz/count_scale, 1 ); - blk_count0 += blk_count0 % 2; - blk_count0 = MIN( blk_count0, count ); - buf_sz = blk_count0 * count_scale + k*sizeof(float); - k1 = get_sample_count(); - k1 = MIN( k1, k ); - - if( buf_sz <= CV_MAX_LOCAL_SIZE ) - { - buf = (float*)cvStackAlloc( buf_sz ); - local_alloc = true; - } - else - CV_CALL( buf = (float*)cvAlloc( buf_sz )); - - for( i = 0; i < count; i += blk_count ) - { - blk_count = MIN( count - i, blk_count0 ); - float* neighbor_responses = buf; - float* dist = buf + blk_count*k; - Cv32suf* sort_buf = (Cv32suf*)(dist + blk_count*k); - - find_neighbors_direct( _samples, k, i, i + blk_count, - neighbor_responses, _neighbors, dist ); - - float r = write_results( k, k1, i, i + blk_count, neighbor_responses, dist, - _results, _neighbor_responses, _dist, sort_buf ); - if( i == 0 ) - result = r; - } - - __END__; - - if( !local_alloc ) - cvFree( &buf ); - - return result; -} - -/* End of file */ -