Matric interface umbenannt

build.gradle

@@ -28,7 +28,7 @@ dependencies {
 	runtimeOnly 'com.googlecode.soundlibs:tritonus-share:0.3.7.4'
 	runtimeOnly 'com.googlecode.soundlibs:mp3spi:1.9.5.4'
 
-	api 'colt:colt:1.2.0'
+	compileOnlyApi 'colt:colt:1.2.0'
 
     testImplementation 'org.junit.jupiter:junit-jupiter-api:5.8.1'
     testRuntimeOnly 'org.junit.jupiter:junit-jupiter-engine:5.8.1'

src/main/java/schule/ngb/zm/ml/DoubleMatrix.java

@@ -7,7 +7,7 @@ import java.util.function.DoubleUnaryOperator;
 
 // TODO: Move Math into Matrix class
 // TODO: Implement support for optional sci libs
-public class DoubleMatrix implements Matrix {
+public class DoubleMatrix implements MLMatrix {
 
 	private int columns, rows;
 
@@ -33,7 +33,7 @@ public class DoubleMatrix implements Matrix {
 		return rows;
 	}
 
-	public double[][] getCoefficients() {
+	public double[][] coefficients() {
 		return coefficients;
 	}
 
@@ -41,27 +41,27 @@ public class DoubleMatrix implements Matrix {
 		return coefficients[row][col];
 	}
 
-	public Matrix set( int row, int col, double value ) {
+	public MLMatrix set( int row, int col, double value ) {
 		coefficients[row][col] = value;
 		return this;
 	}
 
-	public Matrix initializeRandom() {
+	public MLMatrix initializeRandom() {
 		coefficients = MLMath.matrixApply(coefficients, (d) -> Constants.randomGaussian());
 		return this;
 	}
 
-	public Matrix initializeRandom( double lower, double upper ) {
+	public MLMatrix initializeRandom( double lower, double upper ) {
 		coefficients = MLMath.matrixApply(coefficients, (d) -> ((upper-lower) * (Constants.randomGaussian()+1) * .5) + lower);
 		return this;
 	}
 
-	public Matrix initializeOne() {
+	public MLMatrix initializeOne() {
 		coefficients = MLMath.matrixApply(coefficients, (d) -> 1.0);
 		return this;
 	}
 
-	public Matrix initializeZero() {
+	public MLMatrix initializeZero() {
 		coefficients = MLMath.matrixApply(coefficients, (d) -> 0.0);
 		return this;
 	}
@@ -83,64 +83,64 @@ public class DoubleMatrix implements Matrix {
 	}
 
 	@Override
-	public Matrix transpose() {
+	public MLMatrix transpose() {
 		coefficients = MLMath.matrixTranspose(coefficients);
 		return this;
 	}
 
 	@Override
-	public Matrix multiply( Matrix B ) {
-		coefficients = MLMath.matrixMultiply(coefficients, B.getCoefficients());
+	public MLMatrix multiply( MLMatrix B ) {
+		coefficients = MLMath.matrixMultiply(coefficients, B.coefficients());
 		return this;
 	}
 
 	@Override
-	public Matrix multiplyAddBias( Matrix B, Matrix C ) {
-		double[] biases = Arrays.stream(C.getCoefficients()).mapToDouble((arr) -> arr[0]).toArray();
+	public MLMatrix multiplyAddBias( MLMatrix B, MLMatrix C ) {
+		double[] biases = Arrays.stream(C.coefficients()).mapToDouble(( arr) -> arr[0]).toArray();
 		coefficients = MLMath.biasAdd(
-			MLMath.matrixMultiply(coefficients, B.getCoefficients()),
+			MLMath.matrixMultiply(coefficients, B.coefficients()),
 			biases
 		);
 		return this;
 	}
 
 	@Override
-	public Matrix multiplyLeft( Matrix B ) {
-		coefficients = MLMath.matrixMultiply(B.getCoefficients(), coefficients);
+	public MLMatrix multiplyLeft( MLMatrix B ) {
+		coefficients = MLMath.matrixMultiply(B.coefficients(), coefficients);
 		return this;
 	}
 
 	@Override
-	public Matrix add( Matrix B ) {
-		coefficients = MLMath.matrixAdd(coefficients, B.getCoefficients());
+	public MLMatrix add( MLMatrix B ) {
+		coefficients = MLMath.matrixAdd(coefficients, B.coefficients());
 		return this;
 	}
 
 	@Override
-	public Matrix sub( Matrix B ) {
-		coefficients = MLMath.matrixSub(coefficients, B.getCoefficients());
+	public MLMatrix sub( MLMatrix B ) {
+		coefficients = MLMath.matrixSub(coefficients, B.coefficients());
 		return this;
 	}
 
 	@Override
-	public Matrix scale( double scalar ) {
+	public MLMatrix scale( double scalar ) {
 		return this;
 	}
 
 	@Override
-	public Matrix scale( Matrix S ) {
-		coefficients = MLMath.matrixScale(coefficients, S.getCoefficients());
+	public MLMatrix scale( MLMatrix S ) {
+		coefficients = MLMath.matrixScale(coefficients, S.coefficients());
 		return this;
 	}
 
 	@Override
-	public Matrix apply( DoubleUnaryOperator op ) {
+	public MLMatrix apply( DoubleUnaryOperator op ) {
 		this.coefficients = MLMath.matrixApply(coefficients, op);
 		return this;
 	}
 
 	@Override
-	public Matrix duplicate() {
+	public MLMatrix duplicate() {
 		return new DoubleMatrix(MLMath.copyMatrix(coefficients));
 	}
 

src/main/java/schule/ngb/zm/ml/MLMatrix.java

@@ -0,0 +1,52 @@
+package schule.ngb.zm.ml;
+
+import java.util.function.DoubleUnaryOperator;
+
+public interface MLMatrix {
+
+	int columns();
+
+	int rows();
+
+	double[][] coefficients();
+
+	double get( int row, int col );
+
+	MLMatrix set( int row, int col, double value );
+
+	MLMatrix initializeRandom();
+
+	MLMatrix initializeRandom( double lower, double upper );
+
+	MLMatrix initializeOne();
+
+	MLMatrix initializeZero();
+
+	/**
+	 * Erzeugt die transponierte Matrix zu dieser.
+	 * @return
+	 */
+	MLMatrix transpose();
+
+	MLMatrix multiply( MLMatrix B );
+
+	MLMatrix multiplyAddBias( MLMatrix B, MLMatrix C );
+
+	MLMatrix multiplyLeft( MLMatrix B );
+
+	MLMatrix add( MLMatrix B );
+
+	MLMatrix sub( MLMatrix B );
+
+
+	MLMatrix scale( double scalar );
+
+	MLMatrix scale( MLMatrix S );
+
+	MLMatrix apply( DoubleUnaryOperator op );
+
+	MLMatrix duplicate();
+
+	String toString();
+
+}

src/main/java/schule/ngb/zm/ml/Matrix.java

@@ -1,48 +0,0 @@
-package schule.ngb.zm.ml;
-
-import java.util.function.DoubleUnaryOperator;
-
-public interface Matrix {
-
-	int columns();
-
-	int rows();
-
-	double[][] getCoefficients();
-
-	double get( int row, int col );
-
-	Matrix set( int row, int col, double value );
-
-	Matrix initializeRandom();
-
-	Matrix initializeRandom( double lower, double upper );
-
-	Matrix initializeOne();
-
-	Matrix initializeZero();
-
-	Matrix transpose();
-
-	Matrix multiply( Matrix B );
-
-	Matrix multiplyAddBias( Matrix B, Matrix C );
-
-	Matrix multiplyLeft( Matrix B );
-
-	Matrix add( Matrix B );
-
-	Matrix sub( Matrix B );
-
-
-	Matrix scale( double scalar );
-
-	Matrix scale( Matrix S );
-
-	Matrix apply( DoubleUnaryOperator op );
-
-	Matrix duplicate();
-
-	String toString();
-
-}

src/main/java/schule/ngb/zm/ml/MatrixFactory.java

@@ -5,8 +5,6 @@ import cern.colt.matrix.impl.DenseDoubleMatrix2D;
 import schule.ngb.zm.Constants;
 import schule.ngb.zm.util.Log;
 
-import java.lang.reflect.Constructor;
-import java.lang.reflect.InvocationTargetException;
 import java.util.function.DoubleUnaryOperator;
 
 public class MatrixFactory {
@@ -17,7 +15,7 @@ public class MatrixFactory {
 		);
 	}
 
-	public static final Matrix create( int rows, int cols ) {
+	public static final MLMatrix create( int rows, int cols ) {
 		try {
 			return getMatrixType().getDeclaredConstructor(int.class, int.class).newInstance(rows, cols);
 		} catch( Exception ex ) {
@@ -26,7 +24,7 @@ public class MatrixFactory {
 		return new DoubleMatrix(rows, cols);
 	}
 
-	public static final Matrix create( double[][] values ) {
+	public static final MLMatrix create( double[][] values ) {
 		try {
 			return getMatrixType().getDeclaredConstructor(double[][].class).newInstance((Object)values);
 		} catch( Exception ex ) {
@@ -35,9 +33,9 @@ public class MatrixFactory {
 		return new DoubleMatrix(values);
 	}
 
-	private static Class<? extends Matrix> matrixType = null;
+	private static Class<? extends MLMatrix> matrixType = null;
 
-	private static final Class<? extends Matrix> getMatrixType() {
+	private static final Class<? extends MLMatrix> getMatrixType() {
 		if( matrixType == null ) {
 			try {
 				Class<?> clazz = Class.forName("cern.colt.matrix.impl.DenseDoubleMatrix2D", false, MatrixFactory.class.getClassLoader());
@@ -53,7 +51,7 @@ public class MatrixFactory {
 
 	private static final Log LOG = Log.getLogger(MatrixFactory.class);
 
-	static class ColtMatrix implements Matrix {
+	static class ColtMatrix implements MLMatrix {
 
 		cern.colt.matrix.DoubleMatrix2D matrix;
 
@@ -81,68 +79,68 @@ public class MatrixFactory {
 		}
 
 		@Override
-		public Matrix set( int row, int col, double value ) {
+		public MLMatrix set( int row, int col, double value ) {
 			matrix.set(row, col, value);
 			return this;
 		}
 
 		@Override
-		public double[][] getCoefficients() {
+		public double[][] coefficients() {
 			return this.matrix.toArray();
 		}
 
 		@Override
-		public Matrix initializeRandom() {
+		public MLMatrix initializeRandom() {
 			matrix.assign((d) -> Constants.randomGaussian());
 			return this;
 		}
 
 		@Override
-		public Matrix initializeRandom( double lower, double upper ) {
+		public MLMatrix initializeRandom( double lower, double upper ) {
 			matrix.assign((d) -> ((upper-lower) * (Constants.randomGaussian()+1) * .5) + lower);
 			return this;
 		}
 
 		@Override
-		public Matrix initializeOne() {
+		public MLMatrix initializeOne() {
 			this.matrix.assign(1.0);
 			return this;
 		}
 
 		@Override
-		public Matrix initializeZero() {
+		public MLMatrix initializeZero() {
 			this.matrix.assign(0.0);
 			return this;
 		}
 
 		@Override
-		public Matrix apply( DoubleUnaryOperator op ) {
+		public MLMatrix apply( DoubleUnaryOperator op ) {
 			this.matrix.assign((d) -> op.applyAsDouble(d));
 			return this;
 		}
 
 		@Override
-		public Matrix transpose() {
+		public MLMatrix transpose() {
 			this.matrix = cern.colt.matrix.linalg.Algebra.DEFAULT.transpose(this.matrix);
 			return this;
 		}
 
 		@Override
-		public Matrix multiply( Matrix B ) {
+		public MLMatrix multiply( MLMatrix B ) {
 			ColtMatrix CB = (ColtMatrix)B;
 			this.matrix = cern.colt.matrix.linalg.Algebra.DEFAULT.mult(matrix, CB.matrix);
 			return this;
 		}
 
 		@Override
-		public Matrix multiplyLeft( Matrix B ) {
+		public MLMatrix multiplyLeft( MLMatrix B ) {
 			ColtMatrix CB = (ColtMatrix)B;
 			this.matrix = cern.colt.matrix.linalg.Algebra.DEFAULT.mult(CB.matrix, matrix);
 			return this;
 		}
 
 		@Override
-		public Matrix multiplyAddBias( Matrix B, Matrix C ) {
+		public MLMatrix multiplyAddBias( MLMatrix B, MLMatrix C ) {
 			ColtMatrix CB = (ColtMatrix)B;
 			this.matrix = cern.colt.matrix.linalg.Algebra.DEFAULT.mult(matrix, CB.matrix);
 			// TODO: add bias
@@ -150,33 +148,33 @@ public class MatrixFactory {
 		}
 
 		@Override
-		public Matrix add( Matrix B ) {
+		public MLMatrix add( MLMatrix B ) {
 			ColtMatrix CB = (ColtMatrix)B;
 			matrix.assign(CB.matrix, (d1,d2) -> d1+d2);
 			return this;
 		}
 
 		@Override
-		public Matrix sub( Matrix B ) {
+		public MLMatrix sub( MLMatrix B ) {
 			ColtMatrix CB = (ColtMatrix)B;
 			matrix.assign(CB.matrix, (d1,d2) -> d1-d2);
 			return this;
 		}
 
 		@Override
-		public Matrix scale( double scalar ) {
+		public MLMatrix scale( double scalar ) {
 			this.matrix.assign((d) -> d*scalar);
 			return this;
 		}
 
 		@Override
-		public Matrix scale( Matrix S ) {
+		public MLMatrix scale( MLMatrix S ) {
 			this.matrix.forEachNonZero((r, c, d) -> d * S.get(r, c));
 			return this;
 		}
 
 		@Override
-		public Matrix duplicate() {
+		public MLMatrix duplicate() {
 			ColtMatrix newMatrix = new ColtMatrix(matrix.rows(), matrix.columns());
 			newMatrix.matrix.assign(this.matrix);
 			return newMatrix;

src/main/java/schule/ngb/zm/ml/NeuralNetwork.java

@@ -107,7 +107,7 @@ public class NeuralNetwork {
 
 	private NeuronLayer[] layers;
 
-	private Matrix output;
+	private MLMatrix output;
 
 	private double learningRate = 0.1;
 
@@ -162,16 +162,16 @@ public class NeuralNetwork {
 		this.learningRate = pLearningRate;
 	}
 
-	public Matrix getOutput() {
+	public MLMatrix getOutput() {
 		return output;
 	}
 
-	public Matrix predict( double[][] inputs ) {
+	public MLMatrix predict( double[][] inputs ) {
 		//this.output = layers[1].apply(layers[0].apply(inputs));
 		return predict(MatrixFactory.create(inputs));
 	}
 
-	public Matrix predict( Matrix inputs ) {
+	public MLMatrix predict( MLMatrix inputs ) {
 		this.output = layers[0].apply(inputs);
 		return this.output;
 	}
@@ -180,7 +180,7 @@ public class NeuralNetwork {
 		learn(MatrixFactory.create(expected));
 	}
 
-	public void learn( Matrix expected ) {
+	public void learn( MLMatrix expected ) {
 		layers[layers.length - 1].backprop(expected, learningRate);
 	}
 

src/main/java/schule/ngb/zm/ml/NeuronLayer.java

@@ -1,10 +1,9 @@
 package schule.ngb.zm.ml;
 
-import java.util.Arrays;
 import java.util.function.DoubleUnaryOperator;
 import java.util.function.Function;
 
-public class NeuronLayer implements Function<Matrix, Matrix> {
+public class NeuronLayer implements Function<MLMatrix, MLMatrix> {
 
 	/*public static NeuronLayer fromArray( double[][] weights ) {
 		NeuronLayer layer = new NeuronLayer(weights[0].length, weights.length);
@@ -29,15 +28,15 @@ public class NeuronLayer implements Function<Matrix, Matrix> {
 		return layer;
 	}*/
 
-	Matrix weights;
+	MLMatrix weights;
 
-	Matrix biases;
+	MLMatrix biases;
 
 	NeuronLayer previous, next;
 
 	DoubleUnaryOperator activationFunction, activationFunctionDerivative;
 
-	Matrix lastOutput, lastInput;
+	MLMatrix lastOutput, lastInput;
 
 	public NeuronLayer( int neurons, int inputs ) {
 		weights = MatrixFactory
@@ -87,11 +86,11 @@ public class NeuronLayer implements Function<Matrix, Matrix> {
 		}
 	}
 
-	public Matrix getWeights() {
+	public MLMatrix getWeights() {
 		return weights;
 	}
 
-	public Matrix getBiases() {
+	public MLMatrix getBiases() {
 		return biases;
 	}
 
@@ -103,15 +102,15 @@ public class NeuronLayer implements Function<Matrix, Matrix> {
 		return weights.rows();
 	}
 
-	public Matrix getLastOutput() {
+	public MLMatrix getLastOutput() {
 		return lastOutput;
 	}
 
-	public void setWeights( Matrix newWeights ) {
+	public void setWeights( MLMatrix newWeights ) {
 		weights = newWeights.duplicate();
 	}
 
-	public void adjustWeights( Matrix adjustment ) {
+	public void adjustWeights( MLMatrix adjustment ) {
 		weights.add(adjustment);
 	}
 
@@ -121,7 +120,7 @@ public class NeuronLayer implements Function<Matrix, Matrix> {
 	}
 
 	@Override
-	public Matrix apply( Matrix inputs ) {
+	public MLMatrix apply( MLMatrix inputs ) {
 		lastInput = inputs;
 		lastOutput = inputs
 			.multiplyAddBias(weights, biases)
@@ -135,17 +134,17 @@ public class NeuronLayer implements Function<Matrix, Matrix> {
 	}
 
 	@Override
-	public <V> Function<V, Matrix> compose( Function<? super V, ? extends Matrix> before ) {
+	public <V> Function<V, MLMatrix> compose( Function<? super V, ? extends MLMatrix> before ) {
 		return ( in ) -> apply(before.apply(in));
 	}
 
 	@Override
-	public <V> Function<Matrix, V> andThen( Function<? super Matrix, ? extends V> after ) {
+	public <V> Function<MLMatrix, V> andThen( Function<? super MLMatrix, ? extends V> after ) {
 		return ( in ) -> after.apply(apply(in));
 	}
 
-	public void backprop( Matrix expected, double learningRate ) {
-		Matrix error, delta, adjustment;
+	public void backprop( MLMatrix expected, double learningRate ) {
+		MLMatrix error, delta, adjustment;
 		if( next == null ) {
 			error = expected.duplicate().sub(lastOutput);
 		} else {

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

@@ -5,13 +5,10 @@ import org.junit.jupiter.api.Test;
 import schule.ngb.zm.util.Log;
 import schule.ngb.zm.util.Timer;
 
-import java.io.File;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Random;
 
-import static org.junit.jupiter.api.Assertions.*;
-
 class NeuralNetworkTest {
 
 	@BeforeAll
@@ -79,7 +76,7 @@ class NeuralNetworkTest {
 		}
 
 		// calculate predictions
-		Matrix predictions = net.predict(inputs);
+		MLMatrix predictions = net.predict(inputs);
 		for( int i = 0; i < 4; i++ ) {
 			int parsed_pred = predictions.get(i, 0) < 0.5 ? 0 : 1;