initialize temp_train_samples in SVM::autoTrain

add smoke test

modules/ml/src/svm.cpp

@@ -1795,10 +1795,10 @@ class SVMImpl : public SVM
                 if( !do_train( temp_train_samples, temp_train_responses ))
                     continue;
 
-                for( i = 0; i < train_sample_count; i++ )
+                for( i = 0; i < test_sample_count; i++ )
                 {
                     j = sidx[(i+start+train_sample_count) % sample_count];
-                    memcpy(temp_train_samples.ptr(i), samples.ptr(j), sample_size);
+                    memcpy(temp_test_samples.ptr(i), samples.ptr(j), sample_size);
                 }
 
                 predict(temp_test_samples, temp_test_responses, 0);

modules/ml/test/test_svmtrainauto.cpp

@@ -87,3 +87,34 @@ void CV_SVMTrainAutoTest::run( int /*start_from*/ )
 }
 
 TEST(ML_SVM, trainauto) { CV_SVMTrainAutoTest test; test.safe_run(); }
+
+
+TEST(ML_SVM, trainAuto_regression_5369)
+{
+    int datasize = 100;
+    cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
+    cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
+
+    RNG rng(0); // fixed!
+    for (int i = 0; i < datasize; ++i)
+    {
+        int response = rng.uniform(0, 2);  // Random from {0, 1}.
+        samples.at<float>( i, 0 ) = 0;
+        samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
+        responses.at<int>( i, 0 ) = response;
+    }
+
+    cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
+    cv::Ptr<SVM> svm = SVM::create();
+    svm->trainAuto( data, 10 );  // 2-fold cross validation.
+
+    float test_data0[2] = {0.25f, 0.25f};
+    cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
+    float result0 = svm->predict( test_point0 );
+    float test_data1[2] = {0.75f, 0.75f};
+    cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
+    float result1 = svm->predict( test_point1 );
+
+    EXPECT_EQ(0., result0);
+    EXPECT_EQ(1., result1);
+}