a big patch; use special proxy types (Input/OutputArray, Input/OutputArrayOfArrays) for passing in vectors, matrices etc.
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@@ -1228,7 +1228,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
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__BEGIN__;
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int i, ivcount, ovcount, l_count, total = 0, max_iter, buf_sz, dcount0, dcount=0;
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int i, ivcount, ovcount, l_count, total = 0, max_iter, buf_sz, dcount0;
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double *buf_ptr;
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double prev_E = DBL_MAX*0.5, epsilon;
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double dw_plus, dw_minus, dw_min, dw_max;
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@@ -1294,10 +1294,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
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*/
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for( iter = 0; iter < max_iter; iter++ )
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{
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int n1, n2, si, j, k;
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double* w;
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CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df;
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CvMat *x1, *x2, *grad1, *grad2, *temp;
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int n1, n2, j, k;
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double E = 0;
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// first, iterate through all the samples and compute dEdw
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@@ -355,8 +355,6 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
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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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int i, count, count_scale, blk_count0, blk_count = 0, buf_sz, k1;
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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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@@ -395,15 +393,15 @@ float CvKNearest::find_nearest( const CvMat* _samples, int k, CvMat* _results,
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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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count = _samples->rows;
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count_scale = k*2;
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blk_count0 = MIN( count, max_blk_count );
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buf_sz = MIN( blk_count0 * count_scale, max_buf_sz );
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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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k1 = get_sample_count();
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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::BlockedRange(0, count), P1(this, buf_sz, k, _samples, _neighbors, k1,
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