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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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//
// Intel License Agreement
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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//
// 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.
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// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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//M*/
#include "precomp.hpp"
namespace cv { namespace ml {
struct PairDI
{
double d;
int i;
};
struct CmpPairDI
{
bool operator ()(const PairDI& e1, const PairDI& e2) const
{
return (e1.d < e2.d) || (e1.d == e2.d && e1.i < e2.i);
}
};
void createConcentricSpheresTestSet( int num_samples, int num_features, int num_classes,
OutputArray _samples, OutputArray _responses)
{
if( num_samples < 1 )
CV_Error( cv::Error::StsBadArg, "num_samples parameter must be positive" );
if( num_features < 1 )
CV_Error( cv::Error::StsBadArg, "num_features parameter must be positive" );
if( num_classes < 1 )
CV_Error( cv::Error::StsBadArg, "num_classes parameter must be positive" );
int i, cur_class;
_samples.create( num_samples, num_features, CV_32F );
_responses.create( 1, num_samples, CV_32S );
Mat responses = _responses.getMat();
Mat mean = Mat::zeros(1, num_features, CV_32F);
Mat cov = Mat::eye(num_features, num_features, CV_32F);
// fill the feature values matrix with random numbers drawn from standard normal distribution
randMVNormal( mean, cov, num_samples, _samples );
Mat samples = _samples.getMat();
// calculate distances from the origin to the samples and put them
// into the sequence along with indices
std::vector<PairDI> dis(samples.rows);
for( i = 0; i < samples.rows; i++ )
{
PairDI& elem = dis[i];
elem.i = i;
elem.d = norm(samples.row(i), NORM_L2);
}
std::sort(dis.begin(), dis.end(), CmpPairDI());
// assign class labels
num_classes = std::min( num_samples, num_classes );
for( i = 0, cur_class = 0; i < num_samples; ++cur_class )
{
int last_idx = num_samples * (cur_class + 1) / num_classes - 1;
double max_dst = dis[last_idx].d;
max_dst = std::max( max_dst, dis[i].d );
for( ; i < num_samples && dis[i].d <= max_dst; ++i )
responses.at<int>(dis[i].i) = cur_class;
}
}
}}
/* End of file. */
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