Merge pull request #15959 from mshabunin:refactor-ml-tests

ml: refactored tests

* use parametrized tests where appropriate
* use stable theRNG in most tests
* use modern style with EXPECT_/ASSERT_ checks

modules/ml/test/test_knearest.cpp

@@ -0,0 +1,77 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "test_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+using cv::ml::TrainData;
+using cv::ml::EM;
+using cv::ml::KNearest;
+
+TEST(ML_KNearest, accuracy)
+{
+    int sizesArr[] = { 500, 700, 800 };
+    int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
+
+    Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
+    vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
+    Mat means;
+    vector<Mat> covs;
+    defaultDistribs( means, covs );
+    generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
+
+    Mat testData( pointsCount, 2, CV_32FC1 );
+    Mat testLabels;
+    generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
+
+    {
+        SCOPED_TRACE("Default");
+        Mat bestLabels;
+        float err = 1000;
+        Ptr<KNearest> knn = KNearest::create();
+        knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
+        knn->findNearest(testData, 4, bestLabels);
+        EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
+        EXPECT_LE(err, 0.01f);
+    }
+    {
+        // TODO: broken
+#if 0
+        SCOPED_TRACE("KDTree");
+        Mat bestLabels;
+        float err = 1000;
+        Ptr<KNearest> knn = KNearest::create();
+        knn->setAlgorithmType(KNearest::KDTREE);
+        knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
+        knn->findNearest(testData, 4, bestLabels);
+        EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
+        EXPECT_LE(err, 0.01f);
+#endif
+    }
+}
+
+TEST(ML_KNearest, regression_12347)
+{
+    Mat xTrainData = (Mat_<float>(5,2) << 1, 1.1, 1.1, 1, 2, 2, 2.1, 2, 2.1, 2.1);
+    Mat yTrainLabels = (Mat_<float>(5,1) << 1, 1, 2, 2, 2);
+    Ptr<KNearest> knn = KNearest::create();
+    knn->train(xTrainData, ml::ROW_SAMPLE, yTrainLabels);
+
+    Mat xTestData = (Mat_<float>(2,2) << 1.1, 1.1, 2, 2.2);
+    Mat zBestLabels, neighbours, dist;
+    // check output shapes:
+    int K = 16, Kexp = std::min(K, xTrainData.rows);
+    knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
+    EXPECT_EQ(xTestData.rows, zBestLabels.rows);
+    EXPECT_EQ(neighbours.cols, Kexp);
+    EXPECT_EQ(dist.cols, Kexp);
+    // see if the result is still correct:
+    K = 2;
+    knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
+    EXPECT_EQ(1, zBestLabels.at<float>(0,0));
+    EXPECT_EQ(2, zBestLabels.at<float>(1,0));
+}
+
+}} // namespace