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Projekt an Gradle Struktur angepasst
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152
src/main/java/schule/ngb/zm/ml/MLMath.java
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152
src/main/java/schule/ngb/zm/ml/MLMath.java
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package schule.ngb.zm.ml;
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import java.util.Arrays;
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import java.util.function.DoubleUnaryOperator;
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import java.util.stream.IntStream;
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// See https://github.com/wheresvic/neuralnet
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public final class MLMath {
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public static double sigmoid( double x ) {
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return 1 / (1 + Math.exp(-x));
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}
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public static double sigmoidDerivative( double x ) {
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return x * (1 - x);
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}
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public static double tanh( double x ) {
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return Math.tanh(x);
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}
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public static double tanhDerivative( double x ) {
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return 1 - Math.tanh(x) * Math.tanh(x);
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}
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public static double[] normalize( double[] vector ) {
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final double sum = Arrays.stream(vector).sum();
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return Arrays.stream(vector).map(( d ) -> d / sum).toArray();
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}
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public static double[][] matrixMultiply( double[][] A, double[][] B ) {
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int a = A.length, b = A[0].length, c = B[0].length;
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if( B.length != b ) {
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throw new IllegalArgumentException(
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String.format("Matrix A needs equal columns to matrix B rows. (Currently <%d> vs <%d>)", a, B.length)
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);
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}
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return IntStream.range(0, a).parallel().mapToObj(
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( i ) -> IntStream.range(0, c).mapToDouble(
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( j ) -> IntStream.range(0, b).mapToDouble(
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( k ) -> A[i][k] * B[k][j]
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).sum()
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).toArray()
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).toArray(double[][]::new);
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}
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public static double[][] matrixScale( final double[][] A, final double[][] S ) {
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if( A.length != S.length || A[0].length != S[0].length ) {
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throw new IllegalArgumentException("Matrices need to be same size.");
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}
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return IntStream.range(0, A.length).parallel().mapToObj(
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( i ) -> IntStream.range(0, A[i].length).mapToDouble(
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( j ) -> A[i][j] * S[i][j]
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).toArray()
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).toArray(double[][]::new);
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}
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public static double[][] matrixSub( double[][] A, double[][] B ) {
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if( A.length != B.length || A[0].length != B[0].length ) {
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throw new IllegalArgumentException("Cannot subtract unequal matrices");
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}
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return IntStream.range(0, A.length).parallel().mapToObj(
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( i ) -> IntStream.range(0, A[i].length).mapToDouble(
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( j ) -> A[i][j] - B[i][j]
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).toArray()
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).toArray(double[][]::new);
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}
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public static double[][] matrixAdd( double[][] A, double[][] B ) {
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if( A.length != B.length || A[0].length != B[0].length ) {
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throw new IllegalArgumentException("Cannot add unequal matrices");
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}
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return IntStream.range(0, A.length).parallel().mapToObj(
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( i ) -> IntStream.range(0, A[i].length).mapToDouble(
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( j ) -> A[i][j] + B[i][j]
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).toArray()
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).toArray(double[][]::new);
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}
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public static double[][] matrixTranspose( double[][] matrix ) {
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int a = matrix.length, b = matrix[0].length;
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double[][] result = new double[matrix[0].length][matrix.length];
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for( int i = 0; i < a; i++ ) {
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for( int j = 0; j < b; ++j ) {
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result[j][i] = matrix[i][j];
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}
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}
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return result;
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}
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public static double[][] matrixApply( double[][] A, DoubleUnaryOperator op ) {
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return Arrays.stream(A).parallel().map(
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( arr ) -> Arrays.stream(arr).map(op).toArray()
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).toArray(double[][]::new);
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}
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public static double[][] copyMatrix( double[][] matrix ) {
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/*return Arrays.stream(matrix).map(
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(arr) -> Arrays.copyOf(arr, arr.length)
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).toArray(double[][]::new);*/
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double[][] result = new double[matrix.length][matrix[0].length];
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for( int i = 0; i < matrix.length; i++ ) {
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result[i] = Arrays.copyOf(matrix[i], matrix[i].length);
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}
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return result;
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}
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public static double[] toVector( double[][] matrix ) {
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return Arrays.stream(matrix).mapToDouble(
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( arr ) -> arr[0]
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).toArray();
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}
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public static double[][] toMatrix( double[] vector ) {
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return Arrays.stream(vector).mapToObj(
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( d ) -> new double[]{d}
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).toArray(double[][]::new);
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}
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public static double entropy(double[][] A, double[][] Y, int batch_size) {
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int m = A.length;
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int n = A[0].length;
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double[][] z = new double[m][n];
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for (int i = 0; i < m; i++) {
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for (int j = 0; j < n; j++) {
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z[i][j] = (Y[i][j] * Math.log(A[i][j])) + ((1 - Y[i][j]) * Math.log(1 - A[i][j]));
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}
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}
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double sum = 0;
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for (int i = 0; i < m; i++) {
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for (int j = 0; j < n; j++) {
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sum += z[i][j];
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}
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}
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return -sum / batch_size;
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}
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private MLMath() {
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}
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}
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78
src/main/java/schule/ngb/zm/ml/Matrix.java
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78
src/main/java/schule/ngb/zm/ml/Matrix.java
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package schule.ngb.zm.ml;
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import java.util.Arrays;
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public class Matrix {
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private int columns, rows;
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double[][] coefficients;
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public Matrix( int rows, int cols ) {
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this.rows = rows;
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this.columns = cols;
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coefficients = new double[rows][cols];
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}
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public Matrix( double[][] coefficients ) {
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this.coefficients = coefficients;
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this.rows = coefficients.length;
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this.columns = coefficients[0].length;
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}
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public int getColumns() {
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return columns;
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}
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public int getRows() {
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return rows;
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}
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public double[][] getCoefficients() {
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return coefficients;
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}
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public double get( int row, int col ) {
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return coefficients[row][col];
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}
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public void initializeRandom() {
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coefficients = MLMath.matrixApply(coefficients, (d) -> Math.random());
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}
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public void initializeRandom( double lower, double upper ) {
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coefficients = MLMath.matrixApply(coefficients, (d) -> ((upper-lower) * Math.random()) + lower);
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}
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public void initializeIdentity() {
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initializeZero();
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for( int i = 0; i < Math.min(rows, columns); i++ ) {
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this.coefficients[i][i] = 1.0;
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}
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}
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public void initializeOne() {
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coefficients = MLMath.matrixApply(coefficients, (d) -> 1.0);
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}
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public void initializeZero() {
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coefficients = MLMath.matrixApply(coefficients, (d) -> 0.0);
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}
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@Override
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public String toString() {
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//return Arrays.deepToString(coefficients);
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StringBuilder sb = new StringBuilder();
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sb.append('[');
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sb.append('\n');
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for( int i = 0; i < coefficients.length; i++ ) {
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sb.append('\t');
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sb.append(Arrays.toString(coefficients[i]));
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sb.append('\n');
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}
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sb.append(']');
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return sb.toString();
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}
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}
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84
src/main/java/schule/ngb/zm/ml/NeuralNetwork.java
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84
src/main/java/schule/ngb/zm/ml/NeuralNetwork.java
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package schule.ngb.zm.ml;
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import schule.ngb.zm.util.Log;
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public class NeuralNetwork {
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private NeuronLayer[] layers;
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private double[][] output;
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private double learningRate = 0.1;
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public NeuralNetwork( int inputs, int layer1, int outputs ) {
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this(new NeuronLayer(layer1, inputs), new NeuronLayer(outputs, layer1));
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}
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public NeuralNetwork( int inputs, int layer1, int layer2, int outputs ) {
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this(new NeuronLayer(layer1, inputs), new NeuronLayer(layer2, layer1), new NeuronLayer(outputs, layer2));
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}
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public NeuralNetwork( NeuronLayer layer1, NeuronLayer layer2 ) {
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this.layers = new NeuronLayer[2];
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this.layers[0] = layer1;
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this.layers[1] = layer2;
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layer1.connect(null, layer2);
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layer2.connect(layer1, null);
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}
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public NeuralNetwork( NeuronLayer layer1, NeuronLayer layer2, NeuronLayer layer3 ) {
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this.layers = new NeuronLayer[3];
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this.layers[0] = layer1;
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this.layers[1] = layer2;
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this.layers[2] = layer3;
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layer1.connect(null, layer2);
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layer2.connect(layer1, layer3);
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layer3.connect(layer2, null);
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}
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public int getLayerCount() {
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return layers.length;
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}
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public NeuronLayer getLayer( int i ) {
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return layers[i - 1];
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}
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public double getLearningRate() {
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return learningRate;
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}
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public void setLearningRate( double pLearningRate ) {
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this.learningRate = pLearningRate;
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}
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public double[][] getOutput() {
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return output;
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}
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public double[][] predict( double[][] inputs ) {
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//this.output = layers[1].apply(layers[0].apply(inputs));
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this.output = layers[0].apply(inputs);
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return this.output;
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}
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public void learn( double[][] expected ) {
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layers[layers.length-1].backprop(expected, learningRate);
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}
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public void train( double[][] inputs, double[][] expected, int iterations/*, double minChange, int timeout */ ) {
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for( int i = 0; i < iterations; i++ ) {
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// pass the training set through the network
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predict(inputs);
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// start backpropagation through all layers
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learn(expected);
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if( i % 10000 == 0 ) {
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LOG.trace("Training iteration %d of %d", i, iterations);
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}
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}
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}
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private static final Log LOG = Log.getLogger(NeuralNetwork.class);
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}
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127
src/main/java/schule/ngb/zm/ml/NeuronLayer.java
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127
src/main/java/schule/ngb/zm/ml/NeuronLayer.java
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package schule.ngb.zm.ml;
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import java.util.function.DoubleUnaryOperator;
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import java.util.function.Function;
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public class NeuronLayer implements Function<double[][], double[][]> {
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Matrix weights;
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NeuronLayer previous, next;
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DoubleUnaryOperator activationFunction, activationFunctionDerivative;
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double[][] lastOutput, lastInput;
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public NeuronLayer( int neurons, int inputs ) {
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weights = new Matrix(inputs, neurons);
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weights.initializeRandom(-1, 1);
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activationFunction = MLMath::sigmoid;
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activationFunctionDerivative = MLMath::sigmoidDerivative;
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}
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public void connect( NeuronLayer prev, NeuronLayer next ) {
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setPreviousLayer(prev);
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setNextLayer(next);
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}
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public NeuronLayer getPreviousLayer() {
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return previous;
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}
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public boolean hasPreviousLayer() {
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return previous != null;
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}
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public void setPreviousLayer( NeuronLayer pPreviousLayer ) {
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this.previous = pPreviousLayer;
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if( pPreviousLayer != null ) {
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pPreviousLayer.next = this;
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}
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}
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public NeuronLayer getNextLayer() {
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return next;
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}
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public boolean hasNextLayer() {
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return next != null;
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}
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public void setNextLayer( NeuronLayer pNextLayer ) {
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this.next = pNextLayer;
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if( pNextLayer != null ) {
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pNextLayer.previous = this;
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}
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}
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public Matrix getWeights() {
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return weights;
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}
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public int getNeuronCount() {
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return weights.coefficients[0].length;
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}
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public int getInputCount() {
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return weights.coefficients.length;
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}
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public double[][] getLastOutput() {
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return lastOutput;
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}
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public void setWeights( double[][] newWeights ) {
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weights.coefficients = MLMath.copyMatrix(newWeights);
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}
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public void adjustWeights( double[][] adjustment ) {
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weights.coefficients = MLMath.matrixAdd(weights.coefficients, adjustment);
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}
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@Override
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public String toString() {
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return weights.toString();
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}
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@Override
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public double[][] apply( double[][] inputs ) {
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lastInput = inputs;
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lastOutput = MLMath.matrixApply(MLMath.matrixMultiply(inputs, weights.coefficients), activationFunction);
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if( next != null ) {
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return next.apply(lastOutput);
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} else {
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return lastOutput;
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}
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}
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@Override
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public <V> Function<V, double[][]> compose( Function<? super V, ? extends double[][]> before ) {
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return ( in ) -> apply(before.apply(in));
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}
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@Override
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public <V> Function<double[][], V> andThen( Function<? super double[][], ? extends V> after ) {
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return ( in ) -> after.apply(apply(in));
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}
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public void backprop( double[][] expected, double learningRate ) {
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double[][] error, delta, adjustment;
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if( next == null ) {
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error = MLMath.matrixSub(expected, this.lastOutput);
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} else {
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error = MLMath.matrixMultiply(expected, MLMath.matrixTranspose(next.weights.coefficients));
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}
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delta = MLMath.matrixScale(error, MLMath.matrixApply(this.lastOutput,this.activationFunctionDerivative));
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if( previous != null ) {
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previous.backprop(delta, learningRate);
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}
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adjustment = MLMath.matrixMultiply(MLMath.matrixTranspose(lastInput), delta);
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adjustment = MLMath.matrixApply(adjustment, ( x ) -> learningRate * x);
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this.adjustWeights(adjustment);
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}
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}
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