Weitere Tests eingefügt und verbessert

src/test/java/schule/ngb/zm/ml/MLMatrixTest.java

@@ -16,6 +16,23 @@ class MLMatrixTest {
 		this.info = info;
 	}
 
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void get( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 2, 3},
+			{4, 5, 6}
+		});
+
+		assertEquals(mType, M.getClass());
+
+		assertEquals(1.0, M.get(0,0));
+		assertEquals(4.0, M.get(1,0));
+		assertEquals(6.0, M.get(1,2));
+	}
+
 	@ParameterizedTest
 	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
 	void initializeOne( Class<? extends MLMatrix> mType ) {

src/test/java/schule/ngb/zm/ml/NeuralNetworkTest.java

@@ -20,8 +20,8 @@ class NeuralNetworkTest {
 	@BeforeAll
 	static void setupMatrixLibrary() {
 		Constants.setSeed(1001);
-		MatrixFactory.matrixType = MatrixFactory.ColtMatrix.class;
+		//MatrixFactory.matrixType = MatrixFactory.ColtMatrix.class;
-		//MatrixFactory.matrixType = DoubleMatrix.class;
+		MatrixFactory.matrixType = DoubleMatrix.class;
 	}
 
 	/*@Test
@@ -153,12 +153,11 @@ class NeuralNetworkTest {
 	}
 
 	private List<TestData> createTrainingSet( int trainingSetSize, CalcType operation ) {
-		Random random = new Random();
 		List<TestData> tuples = new ArrayList<>();
 
 		for( int i = 0; i < trainingSetSize; i++ ) {
-			double s1 = random.nextDouble() * 0.5;
+			double s1 = Constants.random() * 0.5;
-			double s2 = random.nextDouble() * 0.5;
+			double s2 = Constants.random() * 0.5;
 
 			switch( operation ) {
 				case ADD: