483 lines
16 KiB
C++
483 lines
16 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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/****************************************************************************************\
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* K-Nearest Neighbors Classifier *
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\****************************************************************************************/
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// k Nearest Neighbors
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CvKNearest::CvKNearest()
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{
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samples = 0;
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clear();
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}
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CvKNearest::~CvKNearest()
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{
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clear();
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}
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CvKNearest::CvKNearest( const CvMat* _train_data, const CvMat* _responses,
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const CvMat* _sample_idx, bool _is_regression, int _max_k )
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{
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samples = 0;
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train( _train_data, _responses, _sample_idx, _is_regression, _max_k, false );
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}
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void CvKNearest::clear()
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{
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while( samples )
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{
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CvVectors* next_samples = samples->next;
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cvFree( &samples->data.fl );
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cvFree( &samples );
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samples = next_samples;
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}
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var_count = 0;
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total = 0;
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max_k = 0;
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}
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int CvKNearest::get_max_k() const { return max_k; }
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int CvKNearest::get_var_count() const { return var_count; }
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bool CvKNearest::is_regression() const { return regression; }
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int CvKNearest::get_sample_count() const { return total; }
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bool CvKNearest::train( const CvMat* _train_data, const CvMat* _responses,
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const CvMat* _sample_idx, bool _is_regression,
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int _max_k, bool _update_base )
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{
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bool ok = false;
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CvMat* responses = 0;
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CV_FUNCNAME( "CvKNearest::train" );
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__BEGIN__;
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CvVectors* _samples = 0;
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float** _data = 0;
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int _count = 0, _dims = 0, _dims_all = 0, _rsize = 0;
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if( !_update_base )
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clear();
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// Prepare training data and related parameters.
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// Treat categorical responses as ordered - to prevent class label compression and
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// to enable entering new classes in the updates
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CV_CALL( cvPrepareTrainData( "CvKNearest::train", _train_data, CV_ROW_SAMPLE,
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_responses, CV_VAR_ORDERED, 0, _sample_idx, true, (const float***)&_data,
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&_count, &_dims, &_dims_all, &responses, 0, 0 ));
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if( !responses )
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CV_ERROR( CV_StsNoMem, "Could not allocate memory for responses" );
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if( _update_base && _dims != var_count )
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CV_ERROR( CV_StsBadArg, "The newly added data have different dimensionality" );
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if( !_update_base )
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{
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if( _max_k < 1 )
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CV_ERROR( CV_StsOutOfRange, "max_k must be a positive number" );
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regression = _is_regression;
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var_count = _dims;
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max_k = _max_k;
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}
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_rsize = _count*sizeof(float);
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CV_CALL( _samples = (CvVectors*)cvAlloc( sizeof(*_samples) + _rsize ));
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_samples->next = samples;
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_samples->type = CV_32F;
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_samples->data.fl = _data;
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_samples->count = _count;
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total += _count;
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samples = _samples;
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memcpy( _samples + 1, responses->data.fl, _rsize );
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ok = true;
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__END__;
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if( responses && responses->data.ptr != _responses->data.ptr )
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cvReleaseMat(&responses);
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return ok;
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}
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void CvKNearest::find_neighbors_direct( const CvMat* _samples, int k, int start, int end,
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float* neighbor_responses, const float** neighbors, float* dist ) const
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{
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int i, j, count = end - start, k1 = 0, k2 = 0, d = var_count;
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CvVectors* s = samples;
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for( ; s != 0; s = s->next )
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{
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int n = s->count;
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for( j = 0; j < n; j++ )
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{
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for( i = 0; i < count; i++ )
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{
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double sum = 0;
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Cv32suf si;
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const float* v = s->data.fl[j];
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const float* u = (float*)(_samples->data.ptr + _samples->step*(start + i));
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Cv32suf* dd = (Cv32suf*)(dist + i*k);
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float* nr;
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const float** nn;
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int t, ii, ii1;
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for( t = 0; t <= d - 4; t += 4 )
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{
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double t0 = u[t] - v[t], t1 = u[t+1] - v[t+1];
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double t2 = u[t+2] - v[t+2], t3 = u[t+3] - v[t+3];
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sum += t0*t0 + t1*t1 + t2*t2 + t3*t3;
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}
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for( ; t < d; t++ )
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{
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double t0 = u[t] - v[t];
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sum += t0*t0;
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}
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si.f = (float)sum;
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for( ii = k1-1; ii >= 0; ii-- )
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if( si.i > dd[ii].i )
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break;
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if( ii >= k-1 )
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continue;
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nr = neighbor_responses + i*k;
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nn = neighbors ? neighbors + (start + i)*k : 0;
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for( ii1 = k2 - 1; ii1 > ii; ii1-- )
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{
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dd[ii1+1].i = dd[ii1].i;
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nr[ii1+1] = nr[ii1];
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if( nn ) nn[ii1+1] = nn[ii1];
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}
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dd[ii+1].i = si.i;
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nr[ii+1] = ((float*)(s + 1))[j];
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if( nn )
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nn[ii+1] = v;
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}
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k1 = MIN( k1+1, k );
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k2 = MIN( k1, k-1 );
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}
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}
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}
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float CvKNearest::write_results( int k, int k1, int start, int end,
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const float* neighbor_responses, const float* dist,
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CvMat* _results, CvMat* _neighbor_responses,
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CvMat* _dist, Cv32suf* sort_buf ) const
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{
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float result = 0.f;
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int i, j, j1, count = end - start;
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double inv_scale = 1./k1;
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int rstep = _results && !CV_IS_MAT_CONT(_results->type) ? _results->step/sizeof(result) : 1;
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for( i = 0; i < count; i++ )
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{
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const Cv32suf* nr = (const Cv32suf*)(neighbor_responses + i*k);
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float* dst;
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float r;
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if( _results || start+i == 0 )
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{
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if( regression )
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{
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double s = 0;
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for( j = 0; j < k1; j++ )
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s += nr[j].f;
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r = (float)(s*inv_scale);
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}
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else
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{
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int prev_start = 0, best_count = 0, cur_count;
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Cv32suf best_val;
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for( j = 0; j < k1; j++ )
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sort_buf[j].i = nr[j].i;
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for( j = k1-1; j > 0; j-- )
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{
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bool swap_fl = false;
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for( j1 = 0; j1 < j; j1++ )
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if( sort_buf[j1].i > sort_buf[j1+1].i )
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{
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int t;
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CV_SWAP( sort_buf[j1].i, sort_buf[j1+1].i, t );
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swap_fl = true;
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}
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if( !swap_fl )
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break;
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}
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best_val.i = 0;
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for( j = 1; j <= k1; j++ )
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if( j == k1 || sort_buf[j].i != sort_buf[j-1].i )
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{
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cur_count = j - prev_start;
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if( best_count < cur_count )
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{
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best_count = cur_count;
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best_val.i = sort_buf[j-1].i;
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}
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prev_start = j;
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}
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r = best_val.f;
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}
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if( start+i == 0 )
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result = r;
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if( _results )
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_results->data.fl[(start + i)*rstep] = r;
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}
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if( _neighbor_responses )
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{
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dst = (float*)(_neighbor_responses->data.ptr +
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(start + i)*_neighbor_responses->step);
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for( j = 0; j < k1; j++ )
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dst[j] = nr[j].f;
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for( ; j < k; j++ )
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dst[j] = 0.f;
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}
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if( _dist )
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{
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dst = (float*)(_dist->data.ptr + (start + i)*_dist->step);
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for( j = 0; j < k1; j++ )
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dst[j] = dist[j + i*k];
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for( ; j < k; j++ )
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dst[j] = 0.f;
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}
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}
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return result;
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}
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struct P1 : cv::ParallelLoopBody {
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P1(const CvKNearest* _pointer, int _buf_sz, int _k, const CvMat* __samples, const float** __neighbors,
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int _k1, CvMat* __results, CvMat* __neighbor_responses, CvMat* __dist, float* _result)
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{
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pointer = _pointer;
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k = _k;
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_samples = __samples;
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_neighbors = __neighbors;
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k1 = _k1;
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_results = __results;
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_neighbor_responses = __neighbor_responses;
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_dist = __dist;
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result = _result;
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buf_sz = _buf_sz;
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}
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const CvKNearest* pointer;
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int k;
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const CvMat* _samples;
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const float** _neighbors;
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int k1;
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CvMat* _results;
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CvMat* _neighbor_responses;
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CvMat* _dist;
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float* result;
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int buf_sz;
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void operator()( const cv::Range& range ) const
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{
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cv::AutoBuffer<float> buf(buf_sz);
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for(int i = range.start; i < range.end; i += 1 )
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{
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float* neighbor_responses = &buf[0];
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float* dist = neighbor_responses + 1*k;
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Cv32suf* sort_buf = (Cv32suf*)(dist + 1*k);
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pointer->find_neighbors_direct( _samples, k, i, i + 1,
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neighbor_responses, _neighbors, dist );
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float r = pointer->write_results( k, k1, i, i + 1, neighbor_responses, dist,
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_results, _neighbor_responses, _dist, sort_buf );
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if( i == 0 )
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*result = r;
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}
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}
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};
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float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
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const float** _neighbors, CvMat* _neighbor_responses, CvMat* _dist ) const
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{
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float result = 0.f;
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const int max_blk_count = 128, max_buf_sz = 1 << 12;
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if( !samples )
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CV_Error( CV_StsError, "The search tree must be constructed first using train method" );
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if( !CV_IS_MAT(_samples) ||
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CV_MAT_TYPE(_samples->type) != CV_32FC1 ||
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_samples->cols != var_count )
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CV_Error( CV_StsBadArg, "Input samples must be floating-point matrix (<num_samples>x<var_count>)" );
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if( _results && (!CV_IS_MAT(_results) ||
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(_results->cols != 1 && _results->rows != 1) ||
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_results->cols + _results->rows - 1 != _samples->rows) )
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CV_Error( CV_StsBadArg,
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"The results must be 1d vector containing as much elements as the number of samples" );
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if( _results && CV_MAT_TYPE(_results->type) != CV_32FC1 &&
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(CV_MAT_TYPE(_results->type) != CV_32SC1 || regression))
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CV_Error( CV_StsUnsupportedFormat,
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"The results must be floating-point or integer (in case of classification) vector" );
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if( k < 1 || k > max_k )
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CV_Error( CV_StsOutOfRange, "k must be within 1..max_k range" );
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if( _neighbor_responses )
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{
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if( !CV_IS_MAT(_neighbor_responses) || CV_MAT_TYPE(_neighbor_responses->type) != CV_32FC1 ||
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_neighbor_responses->rows != _samples->rows || _neighbor_responses->cols != k )
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CV_Error( CV_StsBadArg,
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"The neighbor responses (if present) must be floating-point matrix of <num_samples> x <k> size" );
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}
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if( _dist )
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{
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if( !CV_IS_MAT(_dist) || CV_MAT_TYPE(_dist->type) != CV_32FC1 ||
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_dist->rows != _samples->rows || _dist->cols != k )
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CV_Error( CV_StsBadArg,
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"The distances from the neighbors (if present) must be floating-point matrix of <num_samples> x <k> size" );
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}
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int count = _samples->rows;
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int count_scale = k*2;
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int blk_count0 = MIN( count, max_blk_count );
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int buf_sz = MIN( blk_count0 * count_scale, max_buf_sz );
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blk_count0 = MAX( buf_sz/count_scale, 1 );
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blk_count0 += blk_count0 % 2;
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blk_count0 = MIN( blk_count0, count );
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buf_sz = blk_count0 * count_scale + k;
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int k1 = get_sample_count();
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k1 = MIN( k1, k );
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cv::parallel_for_(cv::Range(0, count), P1(this, buf_sz, k, _samples, _neighbors, k1,
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_results, _neighbor_responses, _dist, &result)
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);
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return result;
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}
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using namespace cv;
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CvKNearest::CvKNearest( const Mat& _train_data, const Mat& _responses,
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const Mat& _sample_idx, bool _is_regression, int _max_k )
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{
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samples = 0;
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train(_train_data, _responses, _sample_idx, _is_regression, _max_k, false );
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}
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bool CvKNearest::train( const Mat& _train_data, const Mat& _responses,
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const Mat& _sample_idx, bool _is_regression,
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int _max_k, bool _update_base )
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{
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CvMat tdata = _train_data, responses = _responses, sidx = _sample_idx;
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return train(&tdata, &responses, sidx.data.ptr ? &sidx : 0, _is_regression, _max_k, _update_base );
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}
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float CvKNearest::find_nearest( const Mat& _samples, int k, Mat* _results,
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const float** _neighbors, Mat* _neighbor_responses,
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Mat* _dist ) const
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{
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CvMat s = _samples, results, *presults = 0, nresponses, *pnresponses = 0, dist, *pdist = 0;
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if( _results )
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{
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if(!(_results->data && (_results->type() == CV_32F ||
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(_results->type() == CV_32S && regression)) &&
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(_results->cols == 1 || _results->rows == 1) &&
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_results->cols + _results->rows - 1 == _samples.rows) )
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_results->create(_samples.rows, 1, CV_32F);
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presults = &(results = *_results);
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}
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if( _neighbor_responses )
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{
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if(!(_neighbor_responses->data && _neighbor_responses->type() == CV_32F &&
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_neighbor_responses->cols == k && _neighbor_responses->rows == _samples.rows) )
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_neighbor_responses->create(_samples.rows, k, CV_32F);
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pnresponses = &(nresponses = *_neighbor_responses);
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}
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if( _dist )
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{
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if(!(_dist->data && _dist->type() == CV_32F &&
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_dist->cols == k && _dist->rows == _samples.rows) )
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_dist->create(_samples.rows, k, CV_32F);
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pdist = &(dist = *_dist);
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}
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return find_nearest(&s, k, presults, _neighbors, pnresponses, pdist );
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}
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float CvKNearest::find_nearest( const cv::Mat& _samples, int k, CV_OUT cv::Mat& results,
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CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const
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{
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return find_nearest(_samples, k, &results, 0, &neighborResponses, &dists);
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}
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/* End of file */
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