1 /*M///////////////////////////////////////////////////////////////////////////////////////
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10 // Intel License Agreement
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39 //M*/
40
41 #include "_ml.h"
42
43 typedef struct CvDI
44 {
45 double d;
46 int i;
47 } CvDI;
48
49 int CV_CDECL
icvCmpDI(const void * a,const void * b,void *)50 icvCmpDI( const void* a, const void* b, void* )
51 {
52 const CvDI* e1 = (const CvDI*) a;
53 const CvDI* e2 = (const CvDI*) b;
54
55 return (e1->d < e2->d) ? -1 : (e1->d > e2->d);
56 }
57
58 CV_IMPL void
cvCreateTestSet(int type,CvMat ** samples,int num_samples,int num_features,CvMat ** responses,int num_classes,...)59 cvCreateTestSet( int type, CvMat** samples,
60 int num_samples,
61 int num_features,
62 CvMat** responses,
63 int num_classes, ... )
64 {
65 CvMat* mean = NULL;
66 CvMat* cov = NULL;
67 CvMemStorage* storage = NULL;
68
69 CV_FUNCNAME( "cvCreateTestSet" );
70
71 __BEGIN__;
72
73 if( samples )
74 *samples = NULL;
75 if( responses )
76 *responses = NULL;
77
78 if( type != CV_TS_CONCENTRIC_SPHERES )
79 CV_ERROR( CV_StsBadArg, "Invalid type parameter" );
80
81 if( !samples )
82 CV_ERROR( CV_StsNullPtr, "samples parameter must be not NULL" );
83
84 if( !responses )
85 CV_ERROR( CV_StsNullPtr, "responses parameter must be not NULL" );
86
87 if( num_samples < 1 )
88 CV_ERROR( CV_StsBadArg, "num_samples parameter must be positive" );
89
90 if( num_features < 1 )
91 CV_ERROR( CV_StsBadArg, "num_features parameter must be positive" );
92
93 if( num_classes < 1 )
94 CV_ERROR( CV_StsBadArg, "num_classes parameter must be positive" );
95
96 if( type == CV_TS_CONCENTRIC_SPHERES )
97 {
98 CvSeqWriter writer;
99 CvSeqReader reader;
100 CvMat sample;
101 CvDI elem;
102 CvSeq* seq = NULL;
103 int i, cur_class;
104
105 CV_CALL( *samples = cvCreateMat( num_samples, num_features, CV_32FC1 ) );
106 CV_CALL( *responses = cvCreateMat( 1, num_samples, CV_32SC1 ) );
107 CV_CALL( mean = cvCreateMat( 1, num_features, CV_32FC1 ) );
108 CV_CALL( cvSetZero( mean ) );
109 CV_CALL( cov = cvCreateMat( num_features, num_features, CV_32FC1 ) );
110 CV_CALL( cvSetIdentity( cov ) );
111
112 /* fill the feature values matrix with random numbers drawn from standard
113 normal distribution */
114 CV_CALL( cvRandMVNormal( mean, cov, *samples ) );
115
116 /* calculate distances from the origin to the samples and put them
117 into the sequence along with indices */
118 CV_CALL( storage = cvCreateMemStorage() );
119 CV_CALL( cvStartWriteSeq( 0, sizeof( CvSeq ), sizeof( CvDI ), storage, &writer ));
120 for( i = 0; i < (*samples)->rows; ++i )
121 {
122 CV_CALL( cvGetRow( *samples, &sample, i ));
123 elem.i = i;
124 CV_CALL( elem.d = cvNorm( &sample, NULL, CV_L2 ));
125 CV_WRITE_SEQ_ELEM( elem, writer );
126 }
127 CV_CALL( seq = cvEndWriteSeq( &writer ) );
128
129 /* sort the sequence in a distance ascending order */
130 CV_CALL( cvSeqSort( seq, icvCmpDI, NULL ) );
131
132 /* assign class labels */
133 num_classes = MIN( num_samples, num_classes );
134 CV_CALL( cvStartReadSeq( seq, &reader ) );
135 CV_READ_SEQ_ELEM( elem, reader );
136 for( i = 0, cur_class = 0; i < num_samples; ++cur_class )
137 {
138 int last_idx;
139 double max_dst;
140
141 last_idx = num_samples * (cur_class + 1) / num_classes - 1;
142 CV_CALL( max_dst = (*((CvDI*) cvGetSeqElem( seq, last_idx ))).d );
143 max_dst = MAX( max_dst, elem.d );
144
145 for( ; elem.d <= max_dst && i < num_samples; ++i )
146 {
147 CV_MAT_ELEM( **responses, int, 0, elem.i ) = cur_class;
148 if( i < num_samples - 1 )
149 {
150 CV_READ_SEQ_ELEM( elem, reader );
151 }
152 }
153 }
154 }
155
156 __END__;
157
158 if( cvGetErrStatus() < 0 )
159 {
160 if( samples )
161 cvReleaseMat( samples );
162 if( responses )
163 cvReleaseMat( responses );
164 }
165 cvReleaseMat( &mean );
166 cvReleaseMat( &cov );
167 cvReleaseMemStorage( &storage );
168 }
169
170 /* End of file. */
171