Merge pull request #3636 from kaingwade:ml_to_contrib

Move ml to opencv_contrib #3636 Main PR: opencv/opencv#25017
2025-10-20 04:25:42 +08:00 · 2024-02-27 20:54:59 +08:00
parent c5d22ddf14
commit cf63a7f71f
101 changed files with 23882 additions and 0 deletions
--- a/modules/ml/src/testset.cpp
+++ b/modules/ml/src/testset.cpp
@@ -0,0 +1,113 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                        Intel License Agreement
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// 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.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of Intel Corporation may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//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_StsBadArg, "num_samples parameter must be positive" );
+
+    if( num_features < 1 )
+        CV_Error( CV_StsBadArg, "num_features parameter must be positive" );
+
+    if( num_classes < 1 )
+        CV_Error( CV_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. */