Colt als optionale Abhängigkeit

DAs Anlernen des NN geht um den Faktor 20 schneller, wenn Colt benutzt wird.

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

@@ -1,46 +0,0 @@
-package schule.ngb.zm.ml;
-
-import org.junit.jupiter.api.Test;
-
-import java.util.Arrays;
-
-import static org.junit.jupiter.api.Assertions.*;
-
-class DoubleMatrixTest {
-
-	@Test
-	void initializeOne() {
-		DoubleMatrix m = new DoubleMatrix(4, 4);
-		m.initializeOne();
-
-		double[] ones = new double[]{1.0, 1.0, 1.0, 1.0};
-		assertArrayEquals(ones, m.coefficients[0]);
-		assertArrayEquals(ones, m.coefficients[1]);
-		assertArrayEquals(ones, m.coefficients[2]);
-		assertArrayEquals(ones, m.coefficients[3]);
-	}
-
-	@Test
-	void initializeZero() {
-		DoubleMatrix m = new DoubleMatrix(4, 4);
-		m.initializeZero();
-
-		double[] zeros = new double[]{0.0, 0.0, 0.0, 0.0};
-		assertArrayEquals(zeros, m.coefficients[0]);
-		assertArrayEquals(zeros, m.coefficients[1]);
-		assertArrayEquals(zeros, m.coefficients[2]);
-		assertArrayEquals(zeros, m.coefficients[3]);
-	}
-
-	@Test
-	void initializeRandom() {
-		DoubleMatrix m = new DoubleMatrix(4, 4);
-		m.initializeRandom(-1, 1);
-
-		assertTrue(Arrays.stream(m.coefficients[0]).allMatch((d) -> -1.0 <= d && d < 1.0));
-		assertTrue(Arrays.stream(m.coefficients[1]).allMatch((d) -> -1.0 <= d && d < 1.0));
-		assertTrue(Arrays.stream(m.coefficients[2]).allMatch((d) -> -1.0 <= d && d < 1.0));
-		assertTrue(Arrays.stream(m.coefficients[3]).allMatch((d) -> -1.0 <= d && d < 1.0));
-	}
-
-}

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

@@ -0,0 +1,375 @@
+package schule.ngb.zm.ml;
+
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.TestInfo;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.ValueSource;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+class MLMatrixTest {
+
+	private TestInfo info;
+
+	@BeforeEach
+	void saveTestInfo( TestInfo info ) {
+		this.info = info;
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void initializeOne( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix m = MatrixFactory.create(4, 4);
+		m.initializeOne();
+
+		assertEquals(mType, m.getClass());
+
+		for( int i = 0; i < m.rows(); i++ ) {
+			for( int j = 0; j < m.columns(); j++ ) {
+				assertEquals(1.0, m.get(i, j));
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void initializeZero( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix m = MatrixFactory.create(4, 4);
+		m.initializeZero();
+
+		assertEquals(mType, m.getClass());
+
+		for( int i = 0; i < m.rows(); i++ ) {
+			for( int j = 0; j < m.columns(); j++ ) {
+				assertEquals(0.0, m.get(i, j));
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void initializeRandom( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix m = MatrixFactory.create(4, 4);
+		m.initializeRandom();
+
+		assertEquals(mType, m.getClass());
+
+		for( int i = 0; i < m.rows(); i++ ) {
+			for( int j = 0; j < m.columns(); j++ ) {
+				double d = m.get(i, j);
+				assertTrue(-1.0 <= d && d < 1.0);
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void multiplyTransposed( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix A = MatrixFactory.create(new double[][]{
+			{1.0, 2.0, 3.0, 4.0},
+			{1.0, 2.0, 3.0, 4.0},
+			{1.0, 2.0, 3.0, 4.0}
+		});
+		MLMatrix B = MatrixFactory.create(new double[][]{
+			{1, 3, 5, 7},
+			{2, 4, 6, 8}
+		});
+
+		MLMatrix C = A.multiplyTransposed(B);
+
+		assertEquals(mType, A.getClass());
+		assertEquals(mType, B.getClass());
+		assertEquals(mType, C.getClass());
+
+		assertEquals(3, C.rows());
+		assertEquals(2, C.columns());
+		for( int i = 0; i < C.rows(); i++ ) {
+			assertEquals(50.0, C.get(i, 0));
+			assertEquals(60.0, C.get(i, 1));
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void multiplyAddBias( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix A = MatrixFactory.create(new double[][]{
+			{1.0, 2.0, 3.0, 4.0},
+			{1.0, 2.0, 3.0, 4.0},
+			{1.0, 2.0, 3.0, 4.0}
+		});
+		MLMatrix B = MatrixFactory.create(new double[][]{
+			{1.0, 2.0},
+			{3.0, 4.0},
+			{5.0, 6.0},
+			{7.0, 8.0}
+		});
+		MLMatrix V = MatrixFactory.create(new double[][]{
+			{1000.0, 2000.0}
+		});
+
+		MLMatrix C = A.multiplyAddBias(B, V);
+
+		assertEquals(mType, A.getClass());
+		assertEquals(mType, B.getClass());
+		assertEquals(mType, C.getClass());
+		assertEquals(mType, V.getClass());
+
+		assertEquals(3, C.rows());
+		assertEquals(2, C.columns());
+		for( int i = 0; i < C.rows(); i++ ) {
+			assertEquals(1050.0, C.get(i, 0));
+			assertEquals(2060.0, C.get(i, 1));
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void transposedMultiplyAndScale( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix A = MatrixFactory.create(new double[][]{
+			{1, 1, 1},
+			{2, 2, 2},
+			{3, 3, 3},
+			{4, 4, 4}
+		});
+		MLMatrix B = MatrixFactory.create(new double[][]{
+			{1.0, 2.0},
+			{3.0, 4.0},
+			{5.0, 6.0},
+			{7.0, 8.0}
+		});
+
+		MLMatrix C = A.transposedMultiplyAndScale(B, 2.0);
+
+		assertEquals(mType, A.getClass());
+		assertEquals(mType, B.getClass());
+		assertEquals(mType, C.getClass());
+
+		assertEquals(3, C.rows());
+		assertEquals(2, C.columns());
+		for( int i = 0; i < C.rows(); i++ ) {
+			assertEquals(100.0, C.get(i, 0));
+			assertEquals(120.0, C.get(i, 1));
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void apply( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 1, 1},
+			{2, 2, 2},
+			{3, 3, 3},
+			{4, 4, 4}
+		});
+
+		MLMatrix R = M.apply(( d ) -> d * d);
+
+		assertEquals(mType, M.getClass());
+		assertEquals(mType, R.getClass());
+		assertNotSame(M, R);
+
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					(i + 1) * (i + 1), R.get(i, j),
+					msg("(%d,%d)", "apply", i, j)
+				);
+			}
+		}
+
+		MLMatrix M2 = M.applyInPlace(( d ) -> d * d * d);
+		assertSame(M, M2);
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					(i + 1) * (i + 1) * (i + 1), M.get(i, j),
+					msg("(%d,%d)", "applyInPlace", i, j)
+				);
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void add( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 1, 1},
+			{2, 2, 2},
+			{3, 3, 3},
+			{4, 4, 4}
+		});
+
+		MLMatrix R = M.add(M);
+
+		assertEquals(mType, M.getClass());
+		assertEquals(mType, R.getClass());
+		assertNotSame(M, R);
+
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					(i + 1) + (i + 1), R.get(i, j),
+					msg("(%d,%d)", "add", i, j)
+				);
+			}
+		}
+
+		MLMatrix M2 = M.addInPlace(R);
+		assertSame(M, M2);
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					(i + 1) + (i + 1) + (i + 1), M.get(i, j),
+					msg("(%d,%d)", "addInPlace", i, j)
+				);
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void sub( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 1, 1},
+			{2, 2, 2},
+			{3, 3, 3},
+			{4, 4, 4}
+		});
+
+		MLMatrix R = M.sub(M);
+
+		assertEquals(mType, M.getClass());
+		assertEquals(mType, R.getClass());
+		assertNotSame(M, R);
+
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					0.0, R.get(i, j),
+					msg("(%d,%d)", "sub", i, j)
+				);
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void colSums( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 2, 3},
+			{1, 2, 3},
+			{1, 2, 3},
+			{1, 2, 3}
+		});
+
+		MLMatrix R = M.colSums();
+
+		assertEquals(mType, M.getClass());
+		assertEquals(mType, R.getClass());
+		assertNotSame(M, R);
+
+		assertEquals(1, R.rows());
+		assertEquals(3, R.columns());
+		for( int j = 0; j < M.columns(); j++ ) {
+			assertEquals(
+				(j+1)*4, R.get(0, j),
+				msg("(%d,%d)", "colSums", 0, j)
+			);
+		}
+	}
+
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void duplicate( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 2, 3},
+			{1, 2, 3},
+			{1, 2, 3},
+			{1, 2, 3}
+		});
+
+		MLMatrix R = M.duplicate();
+
+		assertEquals(mType, M.getClass());
+		assertEquals(mType, R.getClass());
+		assertNotSame(M, R);
+
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					M.get(i, j), R.get(i, j),
+					msg("(%d,%d)", "duplicate", i, j)
+				);
+			}
+		}
+	}
+
+	@ParameterizedTest
+	@ValueSource( classes = {DoubleMatrix.class, MatrixFactory.ColtMatrix.class} )
+	void scale( Class<? extends MLMatrix> mType ) {
+		MatrixFactory.matrixType = mType;
+
+		MLMatrix M = MatrixFactory.create(new double[][]{
+			{1, 1, 1},
+			{2, 2, 2},
+			{3, 3, 3},
+			{4, 4, 4}
+		});
+
+		MLMatrix M2 = M.scaleInPlace(2.0);
+
+		assertEquals(mType, M.getClass());
+		assertEquals(mType, M2.getClass());
+		assertSame(M, M2);
+
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					(i+1)*2.0, M2.get(i, j),
+					msg("(%d,%d)", "scaleInPlace", i, j)
+				);
+			}
+		}
+
+		MLMatrix M3 = M.scaleInPlace(M);
+		assertSame(M, M3);
+		for( int i = 0; i < M.rows(); i++ ) {
+			for( int j = 0; j < M.columns(); j++ ) {
+				assertEquals(
+					((i+1)*2.0)*((i+1)*2.0), M.get(i, j),
+					msg("(%d,%d)", "addInPlace", i, j)
+				);
+			}
+		}
+	}
+
+	private String msg( String msg, String methodName, Object... args ) {
+		String testName = this.info.getTestMethod().get().getName();
+		String className = MatrixFactory.matrixType.getSimpleName();
+		return String.format("[" + testName + "(" + className + ") " + methodName + "()] " + msg, args);
+	}
+
+}

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

@@ -2,6 +2,7 @@ package schule.ngb.zm.ml;
 
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
+import schule.ngb.zm.Constants;
 import schule.ngb.zm.util.Log;
 import schule.ngb.zm.util.Timer;
 
@@ -16,6 +17,13 @@ class NeuralNetworkTest {
 		Log.enableGlobalDebugging();
 	}
 
+	@BeforeAll
+	static void setupMatrixLibrary() {
+		Constants.setSeed(1001);
+		MatrixFactory.matrixType = MatrixFactory.ColtMatrix.class;
+		//MatrixFactory.matrixType = DoubleMatrix.class;
+	}
+
 	/*@Test
 	void readWrite() {
 		// XOR Dataset
@@ -107,7 +115,7 @@ class NeuralNetworkTest {
 		for( int i = 0; i < trainingData.size(); i++ ) {
 			inputs[i][0] = trainingData.get(i).a;
 			inputs[i][1] = trainingData.get(i).b;
-			outputs[i][0] = trainingData.get(i).result;
+			outputs[i][0] = trainingData.get(i).getResult();
 		}
 
 		Timer timer = new Timer();
@@ -139,8 +147,8 @@ class NeuralNetworkTest {
 			"Prediction on data (%.2f, %.2f) was %.4f, expected %.2f (of by %.4f)\n",
 			data.a, data.b,
 			net.getOutput().get(0, 0),
-			data.result,
-			net.getOutput().get(0, 0) - data.result
+			data.getResult(),
+			net.getOutput().get(0, 0) - data.getResult()
 		);
 	}
 
@@ -183,7 +191,6 @@ class NeuralNetworkTest {
 
 		double a;
 		double b;
-		double result;
 		CalcType type;
 
 		TestData( double a, double b ) {
@@ -191,6 +198,8 @@ class NeuralNetworkTest {
 			this.b = b;
 		}
 
+		abstract double getResult();
+
 	}
 
 	private static final class AddData extends TestData {
@@ -199,7 +208,9 @@ class NeuralNetworkTest {
 
 		public AddData( double a, double b ) {
 			super(a, b);
-			result = a + b;
+		}
+		double getResult() {
+			return a+b;
 		}
 
 	}
@@ -210,7 +221,9 @@ class NeuralNetworkTest {
 
 		public SubData( double a, double b ) {
 			super(a, b);
-			result = a - b;
+		}
+		double getResult() {
+			return a-b;
 		}
 
 	}
@@ -221,7 +234,9 @@ class NeuralNetworkTest {
 
 		public MulData( double a, double b ) {
 			super(a, b);
-			result = a * b;
+		}
+		double getResult() {
+			return a*b;
 		}
 
 	}
@@ -235,7 +250,9 @@ class NeuralNetworkTest {
 			if( b == 0.0 ) {
 				b = .1;
 			}
-			result = a / b;
+		}
+		double getResult() {
+			return a/b;
 		}
 
 	}
@@ -246,7 +263,12 @@ class NeuralNetworkTest {
 
 		public ModData( double b, double a ) {
 			super(b, a);
-			result = a % b;
+			if( b == 0.0 ) {
+				b = .1;
+			}
+		}
+		double getResult() {
+			return a%b;
 		}
 
 	}