Java tutorial
/** * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.mahout.classifier.rbm.model; import java.io.IOException; import java.util.Random; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FSDataInputStream; import org.apache.hadoop.fs.FSDataOutputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.mahout.classifier.rbm.layer.Layer; import org.apache.mahout.common.ClassUtils; import org.apache.mahout.math.DenseMatrix; import org.apache.mahout.math.Matrix; import org.apache.mahout.math.MatrixWritable; import org.apache.mahout.math.Vector; import com.google.common.io.Closeables; /** * The Class SimpleRBM consisting of a visible and hidden layer and a weight matrix that connects them. */ public class SimpleRBM extends RBMModel { /** The weight matrix. */ protected Matrix weightMatrix; //rownumber is visible unit, column is hidden /** * Instantiates a new simple rbm. * * @param visibleLayer the visible layer * @param hiddenLayer the hidden layer */ public SimpleRBM(Layer visibleLayer, Layer hiddenLayer) { this.visibleLayer = visibleLayer; this.hiddenLayer = hiddenLayer; // initialize the random number generator Random rand = new Random(); this.weightMatrix = new DenseMatrix(visibleLayer.getNeuronCount(), hiddenLayer.getNeuronCount()); //small random values chosen from a zero-mean Gaussian with //a standard deviation of about 0.01 for (int i = 0; i < weightMatrix.columnSize(); i++) { for (int j = 0; j < weightMatrix.rowSize(); j++) { weightMatrix.set(j, i, rand.nextGaussian() / 100); } } } /** * Instantiates a new simple rbm. * * @param visibleLayer the visible layer * @param hiddenLayer the hidden layer * @param weightMatrix the weight matrix */ public SimpleRBM(Layer visibleLayer, Layer hiddenLayer, Matrix weightMatrix) { this.visibleLayer = visibleLayer; this.hiddenLayer = hiddenLayer; this.weightMatrix = weightMatrix; } /** * Gets the weight matrix. * * @return the weight matrix */ public Matrix getWeightMatrix() { return weightMatrix; } /* (non-Javadoc) * @see org.apache.mahout.classifier.rbm.model.RBMModel#serialize(org.apache.hadoop.fs.Path, org.apache.hadoop.conf.Configuration) */ @Override public void serialize(Path output, Configuration conf) throws IOException { FileSystem fs = output.getFileSystem(conf); String rbmnr = conf.get("rbmnr"); FSDataOutputStream out = null; if (rbmnr != null) out = fs.create(new Path(output, conf.get("rbmnr")), true); else out = fs.create(output, true); try { out.writeChars(this.getClass().getName() + " "); out.writeChars(visibleLayer.getClass().getName() + " "); out.writeChars(hiddenLayer.getClass().getName() + " "); MatrixWritable.writeMatrix(out, weightMatrix); } finally { Closeables.closeQuietly(out); } } /** * Materialize. * * @param output path to serialize to * @param conf the hadoop config * @return the rbm * @throws IOException Signals that an I/O exception has occurred. */ public static SimpleRBM materialize(Path output, Configuration conf) throws IOException { FileSystem fs = output.getFileSystem(conf); Matrix weightMatrix; String visLayerType = ""; String hidLayerType = ""; FSDataInputStream in = fs.open(output); try { char chr; while ((chr = in.readChar()) != ' ') ; while ((chr = in.readChar()) != ' ') visLayerType += chr; while ((chr = in.readChar()) != ' ') hidLayerType += chr; weightMatrix = MatrixWritable.readMatrix(in); } finally { Closeables.closeQuietly(in); } Layer vl = ClassUtils.instantiateAs(visLayerType, Layer.class, new Class[] { int.class }, new Object[] { weightMatrix.rowSize() }); Layer hl = ClassUtils.instantiateAs(hidLayerType, Layer.class, new Class[] { int.class }, new Object[] { weightMatrix.columnSize() }); SimpleRBM rbm = new SimpleRBM(vl, hl); rbm.setWeightMatrix(weightMatrix); return rbm; } /* (non-Javadoc) * @see org.apache.mahout.classifier.rbm.model.RBMModel#exciteHiddenLayer(double, boolean) */ public void exciteHiddenLayer(double inputFactor, boolean addInput) { Matrix activations = visibleLayer.getTransposedActivations(); Matrix input = activations.times(weightMatrix).times(inputFactor); if (addInput) hiddenLayer.addInputs(input.viewRow(0)); else hiddenLayer.setInputs(input.viewRow(0)); hiddenLayer.exciteNeurons(); } /* (non-Javadoc) * @see org.apache.mahout.classifier.rbm.model.RBMModel#exciteVisibleLayer(double, boolean) */ public void exciteVisibleLayer(double inputFactor, boolean addInput) { Vector input = weightMatrix.times(getHiddenLayer().getActivations()).times(inputFactor); if (addInput) visibleLayer.addInputs(input); else visibleLayer.setInputs(input); visibleLayer.exciteNeurons(); } /** * Sets the weight matrix. * * @param weightMatrix the new weight matrix */ public void setWeightMatrix(Matrix weightMatrix) { this.weightMatrix = weightMatrix; } /* (non-Javadoc) * @see org.apache.mahout.classifier.rbm.model.RBMModel#clone() */ @Override public RBMModel clone() { SimpleRBM rbm = new SimpleRBM(visibleLayer.clone(), hiddenLayer.clone()); rbm.weightMatrix = weightMatrix.clone(); return rbm; } }