+++ /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"
-
-/****************************************************************************************\
-* 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 (<num_samples>x<var_count>)" );
-
- 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 <num_samples> x <k> 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 <num_samples> x <k> 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 */
-
projects / opencv / blobdiff