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1 /*M///////////////////////////////////////////////////////////////////////////////////////
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40 
41 #include "precomp.hpp"
42 
43 namespace cv { namespace ml {
44 
45 struct PairDI
46 {
47     double d;
48     int    i;
49 };
50 
51 struct CmpPairDI
52 {
operator ()cv::ml::CmpPairDI53     bool operator ()(const PairDI& e1, const PairDI& e2) const
54     {
55         return (e1.d < e2.d) || (e1.d == e2.d && e1.i < e2.i);
56     }
57 };
58 
createConcentricSpheresTestSet(int num_samples,int num_features,int num_classes,OutputArray _samples,OutputArray _responses)59 void createConcentricSpheresTestSet( int num_samples, int num_features, int num_classes,
60                                      OutputArray _samples, OutputArray _responses)
61 {
62     if( num_samples < 1 )
63         CV_Error( CV_StsBadArg, "num_samples parameter must be positive" );
64 
65     if( num_features < 1 )
66         CV_Error( CV_StsBadArg, "num_features parameter must be positive" );
67 
68     if( num_classes < 1 )
69         CV_Error( CV_StsBadArg, "num_classes parameter must be positive" );
70 
71     int i, cur_class;
72 
73     _samples.create( num_samples, num_features, CV_32F );
74     _responses.create( 1, num_samples, CV_32S );
75 
76     Mat responses = _responses.getMat();
77 
78     Mat mean = Mat::zeros(1, num_features, CV_32F);
79     Mat cov = Mat::eye(num_features, num_features, CV_32F);
80 
81     // fill the feature values matrix with random numbers drawn from standard normal distribution
82     randMVNormal( mean, cov, num_samples, _samples );
83     Mat samples = _samples.getMat();
84 
85     // calculate distances from the origin to the samples and put them
86     // into the sequence along with indices
87     std::vector<PairDI> dis(samples.rows);
88 
89     for( i = 0; i < samples.rows; i++ )
90     {
91         PairDI& elem = dis[i];
92         elem.i = i;
93         elem.d = norm(samples.row(i), NORM_L2);
94     }
95 
96     std::sort(dis.begin(), dis.end(), CmpPairDI());
97 
98     // assign class labels
99     num_classes = std::min( num_samples, num_classes );
100     for( i = 0, cur_class = 0; i < num_samples; ++cur_class )
101     {
102         int last_idx = num_samples * (cur_class + 1) / num_classes - 1;
103         double max_dst = dis[last_idx].d;
104         max_dst = std::max( max_dst, dis[i].d );
105 
106         for( ; i < num_samples && dis[i].d <= max_dst; ++i )
107             responses.at<int>(i) = cur_class;
108     }
109 }
110 
111 }}
112 
113 /* End of file. */
114