added PCA test

git-svn-id: https://code.ros.org/svn/opencv/trunk@2354 73c94f0f-984f-4a5f-82bc-2d8db8d8ee08

diff --git a/opencv/tests/cxcore/src/apca.cpp b/opencv/tests/cxcore/src/apca.cpp
new file mode 100644 (file)
index 0000000..0282d1b
--- /dev/null
+++ b/opencv/tests/cxcore/src/apca.cpp
@@ -0,0 +1,101 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////\r
+//\r
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.\r
+//\r
+//  By downloading, copying, installing or using the software you agree to this license.\r
+//  If you do not agree to this license, do not download, install,\r
+//  copy or use the software.\r
+//\r
+//\r
+//                        Intel License Agreement\r
+//                For Open Source Computer Vision Library\r
+//\r
+// Copyright (C) 2000, Intel Corporation, all rights reserved.\r
+// Third party copyrights are property of their respective owners.\r
+//\r
+// Redistribution and use in source and binary forms, with or without modification,\r
+// are permitted provided that the following conditions are met:\r
+//\r
+//   * Redistribution's of source code must retain the above copyright notice,\r
+//     this list of conditions and the following disclaimer.\r
+//\r
+//   * Redistribution's in binary form must reproduce the above copyright notice,\r
+//     this list of conditions and the following disclaimer in the documentation\r
+//     and/or other materials provided with the distribution.\r
+//\r
+//   * The name of Intel Corporation may not be used to endorse or promote products\r
+//     derived from this software without specific prior written permission.\r
+//\r
+// This software is provided by the copyright holders and contributors "as is" and\r
+// any express or implied warranties, including, but not limited to, the implied\r
+// warranties of merchantability and fitness for a particular purpose are disclaimed.\r
+// In no event shall the Intel Corporation or contributors be liable for any direct,\r
+// indirect, incidental, special, exemplary, or consequential damages\r
+// (including, but not limited to, procurement of substitute goods or services;\r
+// loss of use, data, or profits; or business interruption) however caused\r
+// and on any theory of liability, whether in contract, strict liability,\r
+// or tort (including negligence or otherwise) arising in any way out of\r
+// the use of this software, even if advised of the possibility of such damage.\r
+//\r
+//M*/\r
+\r
+#include "cxcoretest.h"\r
+\r
+using namespace cv;\r
+\r
+\r
+class CV_PCATest : public CvTest\r
+{\r
+public:\r
+    CV_PCATest() : CvTest( "pca", "PCA funcs" ) {}\r
+protected:\r
+    void run( int);\r
+};\r
+\r
+void CV_PCATest::run( int )\r
+{\r
+    int code = CvTS::OK, err;\r
+    int maxComponents = 1;\r
+    Mat points( 1000, 3, CV_32FC1);\r
+    randn( points, Scalar::all(0.0), Scalar::all(1.0) );\r
+    float mp[] = { 3.0f, 3.0f, 3.0f }, cp[] = { 0.5f, 0.0f, 0.0f,\r
+                                                0.0f, 1.0f, 0.0f,\r
+                                                0.0f, 0.0f, 0.3f };\r
+    Mat mean( 1, 3, CV_32FC1, mp ),\r
+        cov( 3, 3, CV_32FC1, cp );\r
+    for( int i = 0; i < points.rows; i++ )\r
+    {\r
+        Mat r(1, points.cols, CV_32FC1, points.ptr<float>(i));\r
+        r =  r * cov + mean; \r
+    }\r
+\r
+    PCA pca( points, Mat(), CV_PCA_DATA_AS_ROW, maxComponents );\r
+\r
+    // check project\r
+    Mat prjPoints = pca.project( points );\r
+    err = 0;\r
+    for( int i = 0; i < prjPoints.rows; i++ )\r
+    {\r
+        float val = prjPoints.at<float>(i,0);\r
+        if( val > 3.0f || val < -3.0f )\r
+            err++;\r
+    }\r
+    if( (float)err > prjPoints.rows*0.01f )\r
+    {
+        ts->printf( CvTS::LOG, "bad accuracy of project()" );
+        code = CvTS::FAIL_BAD_ACCURACY;
+    }
+
+    // check backProject
+    Mat points1 = pca.backProject( prjPoints );
+    err = 0;
+    for( int i = 0; i < points.rows; i++ )\r    {\r        if( fabs(points1.at<float>(i,0) - mean.at<float>(0,0)) > 0.05 ||\r            fabs(points1.at<float>(i,1) - points.at<float>(i,1)) > 0.02 ||\r            fabs(points1.at<float>(i,2) - mean.at<float>(0,2)) > 0.05 )\r            err++;\r    }\r    if( (float)err > prjPoints.rows*0.04f )\r
+    {
+        ts->printf( CvTS::LOG, "bad accuracy of backProject()" );
+        code = CvTS::FAIL_BAD_ACCURACY;
+    }
+
+    ts->set_failed_test_info( code );\r
+}\r
+\r
+CV_PCATest pca_test;
\ No newline at end of file

diff --git a/opencv/tests/ml/src/aemknearestkmeans.cpp b/opencv/tests/ml/src/aemknearestkmeans.cpp
index 1a3fee3df7f37e3febad8308f14423ae333a3165..3b90e5eaca9e182f89449e232ee221d787b0372a 100644 (file)
--- a/opencv/tests/ml/src/aemknearestkmeans.cpp
+++ b/opencv/tests/ml/src/aemknearestkmeans.cpp
@@ -46,16 +46,16 @@ using namespace cv;
 
 void defaultDistribs( vector<Mat>& means, vector<Mat>& covs )
 {
-    float mp0[] = {0.0f, 0.0f}, ep0[] = {0.67f, 0.0f, 0.0f, 0.67f};
-    float mp1[] = {5.0f, 0.0f}, ep1[] = {1.0f, 0.0f, 0.0f, 1.0f};
-    float mp2[] = {1.0f, 5.0f}, ep2[] = {1.0f, 0.0f, 0.0f, 1.0f};
-    Mat m0( 1, 2, CV_32FC1, mp0 ), e0( 2, 2, CV_32FC1, ep0 );
-    Mat m1( 1, 2, CV_32FC1, mp1 ), e1( 2, 2, CV_32FC1, ep1 );
-    Mat m2( 1, 2, CV_32FC1, mp2 ), e2( 2, 2, CV_32FC1, ep2 );
+    float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
+    float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
+    float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
+    Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
+    Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
+    Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
     means.resize(3), covs.resize(3);
-    m0.copyTo(means[0]), e0.copyTo(covs[0]);
-    m1.copyTo(means[1]), e1.copyTo(covs[1]);
-    m2.copyTo(means[2]), e2.copyTo(covs[2]);
+    m0.copyTo(means[0]), c0.copyTo(covs[0]);
+    m1.copyTo(means[1]), c1.copyTo(covs[1]);
+    m2.copyTo(means[2]), c2.copyTo(covs[2]);
 }
 
 // generate points sets by normal distributions