chore: import upstream snapshot with attribution

modules/ml/test/test_rtrees.cpp

@@ -0,0 +1,119 @@
+// 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 {
+
+TEST(ML_RTrees, getVotes)
+{
+    int n = 12;
+    int count, i;
+    int label_size = 3;
+    int predicted_class = 0;
+    int max_votes = -1;
+    int val;
+    // RTrees for classification
+    Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
+
+    //data
+    Mat data(n, 4, CV_32F);
+    randu(data, 0, 10);
+
+    //labels
+    Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
+
+    rt->train(data, ml::ROW_SAMPLE, labels);
+
+    //run function
+    Mat test(1, 4, CV_32F);
+    Mat result;
+    randu(test, 0, 10);
+    rt->getVotes(test, result, 0);
+
+    //count vote amount and find highest vote
+    count = 0;
+    const int* result_row = result.ptr<int>(1);
+    for( i = 0; i < label_size; i++ )
+    {
+        val = result_row[i];
+        //predicted_class = max_votes < val? i;
+        if( max_votes < val )
+        {
+            max_votes = val;
+            predicted_class = i;
+        }
+        count += val;
+    }
+
+    EXPECT_EQ(count, (int)rt->getRoots().size());
+    EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
+}
+
+TEST(ML_RTrees, 11142_sample_weights_regression)
+{
+    int n = 3;
+    // RTrees for regression
+    Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
+    //simple regression problem of x -> 2x
+    Mat data = (Mat_<float>(n,1) << 1, 2, 3);
+    Mat values = (Mat_<float>(n,1) << 2, 4, 6);
+    Mat weights = (Mat_<float>(n, 1) << 10, 10, 10);
+
+    Ptr<TrainData> trainData = TrainData::create(data, ml::ROW_SAMPLE, values);
+    rt->train(trainData);
+    double error_without_weights = round(rt->getOOBError());
+    rt->clear();
+    Ptr<TrainData> trainDataWithWeights = TrainData::create(data, ml::ROW_SAMPLE, values, Mat(), Mat(), weights );
+    rt->train(trainDataWithWeights);
+    double error_with_weights = round(rt->getOOBError());
+    // error with weights should be larger than error without weights
+    EXPECT_GE(error_with_weights, error_without_weights);
+}
+
+TEST(ML_RTrees, 11142_sample_weights_classification)
+{
+    int n = 12;
+    // RTrees for classification
+    Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
+
+    Mat data(n, 4, CV_32F);
+    randu(data, 0, 10);
+    Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
+    Mat weights = (Mat_<float>(n, 1) << 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10);
+
+    rt->train(data, ml::ROW_SAMPLE, labels);
+    rt->clear();
+    double error_without_weights = round(rt->getOOBError());
+    Ptr<TrainData> trainDataWithWeights = TrainData::create(data, ml::ROW_SAMPLE, labels, Mat(), Mat(), weights );
+    rt->train(data, ml::ROW_SAMPLE, labels);
+    double error_with_weights = round(rt->getOOBError());
+    std::cout << error_without_weights << std::endl;
+    std::cout << error_with_weights << std::endl;
+    // error with weights should be larger than error without weights
+    EXPECT_GE(error_with_weights, error_without_weights);
+}
+
+TEST(ML_RTrees, bug_12974_throw_exception_when_predict_different_feature_count)
+{
+    int numFeatures = 5;
+    // create a 5 feature dataset and train the model
+    cv::Ptr<RTrees> model = RTrees::create();
+    Mat samples(10, numFeatures, CV_32F);
+    randu(samples, 0, 10);
+    Mat labels = (Mat_<int>(10,1) << 0,0,0,0,0,1,1,1,1,1);
+    cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, labels);
+    model->train(trainData);
+    // try to predict on data which have fewer features - this should throw an exception
+    for(int i = 1; i < numFeatures - 1; ++i) {
+        Mat test(1, i, CV_32FC1);
+        ASSERT_THROW(model->predict(test), Exception);
+    }
+    // try to predict on data which have more features - this should also throw an exception
+    Mat test(1, numFeatures + 1, CV_32FC1);
+    ASSERT_THROW(model->predict(test), Exception);
+}
+
+
+}} // namespace