HORN-3: Import initial source code from Hama ML package
diff --git a/pom.xml b/pom.xml
new file mode 100644
index 0000000..bffd62e
--- /dev/null
+++ b/pom.xml
@@ -0,0 +1,118 @@
+<!--
+ 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.
+-->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
+
+ <parent>
+ <groupId>org.apache</groupId>
+ <artifactId>apache</artifactId>
+ <version>8</version>
+ </parent>
+
+ <modelVersion>4.0.0</modelVersion>
+ <groupId>org.apache.horn</groupId>
+ <artifactId>horn</artifactId>
+ <name>Horn</name>
+ <version>0.1.0-SNAPSHOT</version>
+ <packaging>jar</packaging>
+
+ <properties>
+ <hama.version>0.7.0</hama.version>
+ <hadoop.version>2.7.0</hadoop.version>
+ <protobuf.version>2.5.0</protobuf.version>
+ <junit.version>4.8.1</junit.version>
+ </properties>
+
+ <dependencies>
+ <dependency>
+ <groupId>org.apache.hama</groupId>
+ <artifactId>hama-commons</artifactId>
+ <version>${hama.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hama</groupId>
+ <artifactId>hama-core</artifactId>
+ <version>${hama.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hama</groupId>
+ <artifactId>hama-ml</artifactId>
+ <version>${hama.version}</version>
+ </dependency>
+
+ <dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-common</artifactId>
+ <version>${hadoop.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-hdfs</artifactId>
+ <version>${hadoop.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-hdfs</artifactId>
+ <classifier>tests</classifier>
+ <version>${hadoop.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-mapreduce-client-core</artifactId>
+ <version>${hadoop.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-auth</artifactId>
+ <version>${hadoop.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>com.google.protobuf</groupId>
+ <artifactId>protobuf-java</artifactId>
+ <version>${protobuf.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.hadoop</groupId>
+ <artifactId>hadoop-yarn-client</artifactId>
+ <version>${hadoop.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>junit</groupId>
+ <artifactId>junit</artifactId>
+ <version>${junit.version}</version>
+ </dependency>
+
+ </dependencies>
+
+ <build>
+ <finalName>horn-${project.version}</finalName>
+ <plugins>
+
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-compiler-plugin</artifactId>
+ <version>2.3.2</version>
+ <configuration>
+ <source>1.7</source>
+ <target>1.7</target>
+ </configuration>
+ </plugin>
+
+ </plugins>
+ </build>
+</project>
+
diff --git a/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java b/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java
new file mode 100644
index 0000000..c29559d
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java
@@ -0,0 +1,261 @@
+/**
+ * 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.horn.bsp;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+import java.util.List;
+
+import org.apache.hadoop.io.WritableUtils;
+import org.apache.hama.commons.math.DoubleDoubleFunction;
+import org.apache.hama.commons.math.DoubleFunction;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+
+import com.google.common.base.Preconditions;
+import com.google.common.collect.Lists;
+
+/**
+ * AbstractLayeredNeuralNetwork defines the general operations for derivative
+ * layered models, include Linear Regression, Logistic Regression, Multilayer
+ * Perceptron, Autoencoder, and Restricted Boltzmann Machine, etc.
+ *
+ * In general, these models consist of neurons which are aligned in layers.
+ * Between layers, for any two adjacent layers, the neurons are connected to
+ * form a bipartite weighted graph.
+ *
+ */
+abstract class AbstractLayeredNeuralNetwork extends NeuralNetwork {
+
+ private static final double DEFAULT_REGULARIZATION_WEIGHT = 0;
+ private static final double DEFAULT_MOMENTUM_WEIGHT = 0.1;
+
+ double trainingError;
+
+ /* The weight of regularization */
+ protected double regularizationWeight;
+
+ /* The momentumWeight */
+ protected double momentumWeight;
+
+ /* The cost function of the model */
+ protected DoubleDoubleFunction costFunction;
+
+ /* Record the size of each layer */
+ protected List<Integer> layerSizeList;
+
+ protected TrainingMethod trainingMethod;
+
+ protected LearningStyle learningStyle;
+
+ public static enum TrainingMethod {
+ GRADIENT_DESCENT
+ }
+
+ public static enum LearningStyle {
+ UNSUPERVISED,
+ SUPERVISED
+ }
+
+ public AbstractLayeredNeuralNetwork() {
+ this.regularizationWeight = DEFAULT_REGULARIZATION_WEIGHT;
+ this.momentumWeight = DEFAULT_MOMENTUM_WEIGHT;
+ this.trainingMethod = TrainingMethod.GRADIENT_DESCENT;
+ this.learningStyle = LearningStyle.SUPERVISED;
+ }
+
+ public AbstractLayeredNeuralNetwork(String modelPath) {
+ super(modelPath);
+ }
+
+ /**
+ * Set the regularization weight. Recommend in the range [0, 0.1), More
+ * complex the model is, less weight the regularization is.
+ *
+ * @param regularizationWeight
+ */
+ public void setRegularizationWeight(double regularizationWeight) {
+ Preconditions.checkArgument(regularizationWeight >= 0
+ && regularizationWeight < 1.0,
+ "Regularization weight must be in range [0, 1.0)");
+ this.regularizationWeight = regularizationWeight;
+ }
+
+ public double getRegularizationWeight() {
+ return this.regularizationWeight;
+ }
+
+ /**
+ * Set the momemtum weight for the model. Recommend in range [0, 0.5].
+ *
+ * @param momentumWeight
+ */
+ public void setMomemtumWeight(double momentumWeight) {
+ Preconditions.checkArgument(momentumWeight >= 0 && momentumWeight <= 1.0,
+ "Momentum weight must be in range [0, 1.0]");
+ this.momentumWeight = momentumWeight;
+ }
+
+ public double getMomemtumWeight() {
+ return this.momentumWeight;
+ }
+
+ public void setTrainingMethod(TrainingMethod method) {
+ this.trainingMethod = method;
+ }
+
+ public TrainingMethod getTrainingMethod() {
+ return this.trainingMethod;
+ }
+
+ public void setLearningStyle(LearningStyle style) {
+ this.learningStyle = style;
+ }
+
+ public LearningStyle getLearningStyle() {
+ return this.learningStyle;
+ }
+
+ /**
+ * Set the cost function for the model.
+ *
+ * @param costFunction
+ */
+ public void setCostFunction(DoubleDoubleFunction costFunction) {
+ this.costFunction = costFunction;
+ }
+
+ /**
+ * Add a layer of neurons with specified size. If the added layer is not the
+ * first layer, it will automatically connects the neurons between with the
+ * previous layer.
+ *
+ * @param size
+ * @param isFinalLayer If false, add a bias neuron.
+ * @param squashingFunction The squashing function for this layer, input layer
+ * is f(x) = x by default.
+ * @return The layer index, starts with 0.
+ */
+ public abstract int addLayer(int size, boolean isFinalLayer,
+ DoubleFunction squashingFunction);
+
+ /**
+ * Get the size of a particular layer.
+ *
+ * @param layer
+ * @return The layer size.
+ */
+ public int getLayerSize(int layer) {
+ Preconditions.checkArgument(
+ layer >= 0 && layer < this.layerSizeList.size(),
+ String.format("Input must be in range [0, %d]\n",
+ this.layerSizeList.size() - 1));
+ return this.layerSizeList.get(layer);
+ }
+
+ /**
+ * Get the layer size list.
+ *
+ * @return The layer size list.
+ */
+ protected List<Integer> getLayerSizeList() {
+ return this.layerSizeList;
+ }
+
+ /**
+ * Get the weights between layer layerIdx and layerIdx + 1
+ *
+ * @param layerIdx The index of the layer
+ * @return The weights in form of {@link DoubleMatrix}
+ */
+ public abstract DoubleMatrix getWeightsByLayer(int layerIdx);
+
+ /**
+ * Get the updated weights using one training instance.
+ *
+ * @param trainingInstance The trainingInstance is the concatenation of
+ * feature vector and class label vector.
+ * @return The update of each weight, in form of matrix list.
+ * @throws Exception
+ */
+ public abstract DoubleMatrix[] trainByInstance(DoubleVector trainingInstance);
+
+ /**
+ * Get the output calculated by the model.
+ *
+ * @param instance The feature instance.
+ * @return a new vector with the result of the operation.
+ */
+ public abstract DoubleVector getOutput(DoubleVector instance);
+
+ /**
+ * Calculate the training error based on the labels and outputs.
+ *
+ * @param labels
+ * @param output
+ */
+ protected abstract void calculateTrainingError(DoubleVector labels,
+ DoubleVector output);
+
+ @Override
+ public void readFields(DataInput input) throws IOException {
+ super.readFields(input);
+ // read regularization weight
+ this.regularizationWeight = input.readDouble();
+ // read momentum weight
+ this.momentumWeight = input.readDouble();
+
+ // read cost function
+ this.costFunction = FunctionFactory
+ .createDoubleDoubleFunction(WritableUtils.readString(input));
+
+ // read layer size list
+ int numLayers = input.readInt();
+ this.layerSizeList = Lists.newArrayList();
+ for (int i = 0; i < numLayers; ++i) {
+ this.layerSizeList.add(input.readInt());
+ }
+
+ this.trainingMethod = WritableUtils.readEnum(input, TrainingMethod.class);
+ this.learningStyle = WritableUtils.readEnum(input, LearningStyle.class);
+ }
+
+ @Override
+ public void write(DataOutput output) throws IOException {
+ super.write(output);
+ // write regularization weight
+ output.writeDouble(this.regularizationWeight);
+ // write momentum weight
+ output.writeDouble(this.momentumWeight);
+
+ // write cost function
+ WritableUtils.writeString(output, costFunction.getFunctionName());
+
+ // write layer size list
+ output.writeInt(this.layerSizeList.size());
+ for (Integer aLayerSizeList : this.layerSizeList) {
+ output.writeInt(aLayerSizeList);
+ }
+
+ WritableUtils.writeEnum(output, this.trainingMethod);
+ WritableUtils.writeEnum(output, this.learningStyle);
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/AutoEncoder.java b/src/main/java/org/apache/horn/bsp/AutoEncoder.java
new file mode 100644
index 0000000..6c84dc2
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/AutoEncoder.java
@@ -0,0 +1,195 @@
+/**
+ * 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.horn.bsp;
+
+import java.util.Map;
+
+import org.apache.hadoop.fs.Path;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleFunction;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.apache.hama.ml.util.FeatureTransformer;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * AutoEncoder is a model used for dimensional reduction and feature learning.
+ * It is a special kind of {@link NeuralNetwork} that consists of three layers
+ * of neurons, where the first layer and third layer contains the same number of
+ * neurons.
+ *
+ */
+public class AutoEncoder {
+
+ private final SmallLayeredNeuralNetwork model;
+
+ /**
+ * Initialize the autoencoder.
+ *
+ * @param inputDimensions The number of dimensions for the input feature.
+ * @param compressedDimensions The number of dimensions for the compressed
+ * information.
+ */
+ public AutoEncoder(int inputDimensions, int compressedDimensions) {
+ model = new SmallLayeredNeuralNetwork();
+ model.addLayer(inputDimensions, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ model.addLayer(compressedDimensions, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ model.addLayer(inputDimensions, true,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ model.setLearningStyle(AbstractLayeredNeuralNetwork.LearningStyle.UNSUPERVISED);
+ model.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ }
+
+ public AutoEncoder(String modelPath) {
+ model = new SmallLayeredNeuralNetwork(modelPath);
+ }
+
+ public AutoEncoder setLearningRate(double learningRate) {
+ model.setLearningRate(learningRate);
+ return this;
+ }
+
+ public AutoEncoder setMomemtumWeight(double momentumWeight) {
+ model.setMomemtumWeight(momentumWeight);
+ return this;
+ }
+
+ public AutoEncoder setRegularizationWeight(double regularizationWeight) {
+ model.setRegularizationWeight(regularizationWeight);
+ return this;
+ }
+
+ public AutoEncoder setModelPath(String modelPath) {
+ model.setModelPath(modelPath);
+ return this;
+ }
+
+ /**
+ * Train the autoencoder with given data. Note that the training data is
+ * pre-processed, where the features
+ *
+ * @param dataInputPath
+ * @param trainingParams
+ */
+ public void train(Path dataInputPath, Map<String, String> trainingParams) {
+ model.train(dataInputPath, trainingParams);
+ }
+
+ /**
+ * Train the model with one instance.
+ *
+ * @param trainingInstance
+ */
+ public void trainOnline(DoubleVector trainingInstance) {
+ model.trainOnline(trainingInstance);
+ }
+
+ /**
+ * Get the matrix M used to encode the input features.
+ *
+ * @return this matrix with encode the input.
+ */
+ public DoubleMatrix getEncodeWeightMatrix() {
+ return model.getWeightsByLayer(0);
+ }
+
+ /**
+ * Get the matrix M used to decode the compressed information.
+ *
+ * @return this matrix with decode the compressed information.
+ */
+ public DoubleMatrix getDecodeWeightMatrix() {
+ return model.getWeightsByLayer(1);
+ }
+
+ /**
+ * Transform the input features.
+ *
+ * @param inputInstance
+ * @return The compressed information.
+ */
+ private DoubleVector transform(DoubleVector inputInstance, int inputLayer) {
+ DoubleVector internalInstance = new DenseDoubleVector(inputInstance.getDimension() + 1);
+ internalInstance.set(0, 1);
+ for (int i = 0; i < inputInstance.getDimension(); ++i) {
+ internalInstance.set(i + 1, inputInstance.get(i));
+ }
+ DoubleFunction squashingFunction = model
+ .getSquashingFunction(inputLayer);
+ DoubleMatrix weightMatrix = null;
+ if (inputLayer == 0) {
+ weightMatrix = this.getEncodeWeightMatrix();
+ } else {
+ weightMatrix = this.getDecodeWeightMatrix();
+ }
+ DoubleVector vec = weightMatrix.multiplyVectorUnsafe(internalInstance);
+ vec = vec.applyToElements(squashingFunction);
+ return vec;
+ }
+
+ /**
+ * Encode the input instance.
+ * @param inputInstance
+ * @return a new vector with the encode input instance.
+ */
+ public DoubleVector encode(DoubleVector inputInstance) {
+ Preconditions
+ .checkArgument(
+ inputInstance.getDimension() == model.getLayerSize(0) - 1,
+ String.format("The dimension of input instance is %d, but the model requires dimension %d.",
+ inputInstance.getDimension(), model.getLayerSize(1) - 1));
+ return this.transform(inputInstance, 0);
+ }
+
+ /**
+ * Decode the input instance.
+ * @param inputInstance
+ * @return a new vector with the decode input instance.
+ */
+ public DoubleVector decode(DoubleVector inputInstance) {
+ Preconditions
+ .checkArgument(
+ inputInstance.getDimension() == model.getLayerSize(1) - 1,
+ String.format("The dimension of input instance is %d, but the model requires dimension %d.",
+ inputInstance.getDimension(), model.getLayerSize(1) - 1));
+ return this.transform(inputInstance, 1);
+ }
+
+ /**
+ * Get the label(s) according to the given features.
+ * @param inputInstance
+ * @return a new vector with output of the model according to given feature instance.
+ */
+ public DoubleVector getOutput(DoubleVector inputInstance) {
+ return model.getOutput(inputInstance);
+ }
+
+ /**
+ * Set the feature transformer.
+ * @param featureTransformer
+ */
+ public void setFeatureTransformer(FeatureTransformer featureTransformer) {
+ this.model.setFeatureTransformer(featureTransformer);
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/NeuralNetwork.java b/src/main/java/org/apache/horn/bsp/NeuralNetwork.java
new file mode 100644
index 0000000..c7f14de
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/NeuralNetwork.java
@@ -0,0 +1,271 @@
+/**
+ * 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.horn.bsp;
+
+import com.google.common.base.Preconditions;
+import com.google.common.io.Closeables;
+import org.apache.commons.lang.SerializationUtils;
+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.hadoop.io.Writable;
+import org.apache.hadoop.io.WritableUtils;
+import org.apache.hama.ml.util.DefaultFeatureTransformer;
+import org.apache.hama.ml.util.FeatureTransformer;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+import java.lang.reflect.Constructor;
+import java.lang.reflect.InvocationTargetException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.Map;
+
+/**
+ * NeuralNetwork defines the general operations for all the derivative models.
+ * Typically, all derivative models such as Linear Regression, Logistic
+ * Regression, and Multilayer Perceptron consist of neurons and the weights
+ * between neurons.
+ *
+ */
+abstract class NeuralNetwork implements Writable {
+
+ private static final double DEFAULT_LEARNING_RATE = 0.5;
+
+ protected double learningRate;
+ protected boolean learningRateDecay = false;
+
+ // the name of the model
+ protected String modelType;
+ // the path to store the model
+ protected String modelPath;
+
+ protected FeatureTransformer featureTransformer;
+
+ public NeuralNetwork() {
+ this.learningRate = DEFAULT_LEARNING_RATE;
+ this.modelType = this.getClass().getSimpleName();
+ this.featureTransformer = new DefaultFeatureTransformer();
+ }
+
+ public NeuralNetwork(String modelPath) {
+ try {
+ this.modelPath = modelPath;
+ this.readFromModel();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
+
+ /**
+ * Set the degree of aggression during model training, a large learning rate
+ * can increase the training speed, but it also decrease the chance of model
+ * converge. Recommend in range (0, 0.3).
+ *
+ * @param learningRate
+ */
+ public void setLearningRate(double learningRate) {
+ Preconditions.checkArgument(learningRate > 0,
+ "Learning rate must be larger than 0.");
+ this.learningRate = learningRate;
+ }
+
+ public double getLearningRate() {
+ return this.learningRate;
+ }
+
+ public void isLearningRateDecay(boolean decay) {
+ this.learningRateDecay = decay;
+ }
+
+ public String getModelType() {
+ return this.modelType;
+ }
+
+ /**
+ * Train the model with the path of given training data and parameters.
+ *
+ * @param dataInputPath The path of the training data.
+ * @param trainingParams The parameters for training.
+ * @throws IOException
+ */
+ public void train(Path dataInputPath, Map<String, String> trainingParams) {
+ Preconditions.checkArgument(this.modelPath != null,
+ "Please set the model path before training.");
+ // train with BSP job
+ try {
+ trainInternal(dataInputPath, trainingParams);
+ // write the trained model back to model path
+ this.readFromModel();
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (InterruptedException e) {
+ e.printStackTrace();
+ } catch (ClassNotFoundException e) {
+ e.printStackTrace();
+ }
+ }
+
+ /**
+ * Train the model with the path of given training data and parameters.
+ *
+ * @param dataInputPath
+ * @param trainingParams
+ */
+ protected abstract void trainInternal(Path dataInputPath,
+ Map<String, String> trainingParams) throws IOException,
+ InterruptedException, ClassNotFoundException;
+
+ /**
+ * Read the model meta-data from the specified location.
+ *
+ * @throws IOException
+ */
+ protected void readFromModel() throws IOException {
+ Preconditions.checkArgument(this.modelPath != null,
+ "Model path has not been set.");
+ Configuration conf = new Configuration();
+ FSDataInputStream is = null;
+ try {
+ URI uri = new URI(this.modelPath);
+ FileSystem fs = FileSystem.get(uri, conf);
+ is = new FSDataInputStream(fs.open(new Path(modelPath)));
+ this.readFields(is);
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ } finally {
+ Closeables.close(is, false);
+ }
+ }
+
+ /**
+ * Write the model data to specified location.
+ *
+ * @throws IOException
+ */
+ public void writeModelToFile() throws IOException {
+ Preconditions.checkArgument(this.modelPath != null,
+ "Model path has not been set.");
+ Configuration conf = new Configuration();
+ FSDataOutputStream is = null;
+ try {
+ URI uri = new URI(this.modelPath);
+ FileSystem fs = FileSystem.get(uri, conf);
+ is = fs.create(new Path(this.modelPath), true);
+ this.write(is);
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ }
+
+ Closeables.close(is, false);
+ }
+
+ /**
+ * Set the model path.
+ *
+ * @param modelPath
+ */
+ public void setModelPath(String modelPath) {
+ this.modelPath = modelPath;
+ }
+
+ /**
+ * Get the model path.
+ *
+ * @return the path to store the model.
+ */
+ public String getModelPath() {
+ return this.modelPath;
+ }
+
+ @SuppressWarnings({ "rawtypes", "unchecked" })
+ @Override
+ public void readFields(DataInput input) throws IOException {
+ // read model type
+ this.modelType = WritableUtils.readString(input);
+ // read learning rate
+ this.learningRate = input.readDouble();
+ // read model path
+ this.modelPath = WritableUtils.readString(input);
+
+ if (this.modelPath.equals("null")) {
+ this.modelPath = null;
+ }
+
+ // read feature transformer
+ int bytesLen = input.readInt();
+ byte[] featureTransformerBytes = new byte[bytesLen];
+ for (int i = 0; i < featureTransformerBytes.length; ++i) {
+ featureTransformerBytes[i] = input.readByte();
+ }
+
+ Class<? extends FeatureTransformer> featureTransformerCls = (Class<? extends FeatureTransformer>) SerializationUtils
+ .deserialize(featureTransformerBytes);
+
+ Constructor[] constructors = featureTransformerCls
+ .getDeclaredConstructors();
+ Constructor constructor = constructors[0];
+
+ try {
+ this.featureTransformer = (FeatureTransformer) constructor
+ .newInstance(new Object[] {});
+ } catch (InstantiationException e) {
+ e.printStackTrace();
+ } catch (IllegalAccessException e) {
+ e.printStackTrace();
+ } catch (IllegalArgumentException e) {
+ e.printStackTrace();
+ } catch (InvocationTargetException e) {
+ e.printStackTrace();
+ }
+ }
+
+ @Override
+ public void write(DataOutput output) throws IOException {
+ // write model type
+ WritableUtils.writeString(output, modelType);
+ // write learning rate
+ output.writeDouble(learningRate);
+ // write model path
+ if (this.modelPath != null) {
+ WritableUtils.writeString(output, modelPath);
+ } else {
+ WritableUtils.writeString(output, "null");
+ }
+
+ // serialize the class
+ Class<? extends FeatureTransformer> featureTransformerCls = this.featureTransformer
+ .getClass();
+ byte[] featureTransformerBytes = SerializationUtils
+ .serialize(featureTransformerCls);
+ output.writeInt(featureTransformerBytes.length);
+ output.write(featureTransformerBytes);
+ }
+
+ public void setFeatureTransformer(FeatureTransformer featureTransformer) {
+ this.featureTransformer = featureTransformer;
+ }
+
+ public FeatureTransformer getFeatureTransformer() {
+ return this.featureTransformer;
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java b/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java
new file mode 100644
index 0000000..1c8a198
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java
@@ -0,0 +1,107 @@
+/**
+ * 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.horn.bsp;
+
+import java.io.IOException;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hama.bsp.BSP;
+import org.apache.hama.bsp.BSPPeer;
+import org.apache.hama.bsp.sync.SyncException;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.ml.perception.MLPMessage;
+import org.apache.hama.ml.util.DefaultFeatureTransformer;
+import org.apache.hama.ml.util.FeatureTransformer;
+
+/**
+ * The trainer that is used to train the {@link SmallLayeredNeuralNetwork} with
+ * BSP. The trainer would read the training data and obtain the trained
+ * parameters of the model.
+ *
+ */
+public abstract class NeuralNetworkTrainer extends
+ BSP<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> {
+
+ protected static final Log LOG = LogFactory
+ .getLog(NeuralNetworkTrainer.class);
+
+ protected Configuration conf;
+ protected int maxIteration;
+ protected int batchSize;
+ protected String trainingMode;
+
+ protected FeatureTransformer featureTransformer;
+
+ @Override
+ final public void setup(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+ throws IOException, SyncException, InterruptedException {
+ conf = peer.getConfiguration();
+ featureTransformer = new DefaultFeatureTransformer();
+ this.extraSetup(peer);
+ }
+
+ /**
+ * Handle extra setup for sub-classes.
+ *
+ * @param peer
+ * @throws IOException
+ * @throws SyncException
+ * @throws InterruptedException
+ */
+ protected void extraSetup(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+ throws IOException, SyncException, InterruptedException {
+
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override
+ public abstract void bsp(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+ throws IOException, SyncException, InterruptedException;
+
+ @Override
+ public void cleanup(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+ throws IOException {
+ this.extraCleanup(peer);
+ // write model to modelPath
+ }
+
+ /**
+ * Handle cleanup for sub-classes. Write the trained model back.
+ *
+ * @param peer
+ * @throws IOException
+ * @throws SyncException
+ * @throws InterruptedException
+ */
+ protected void extraCleanup(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+ throws IOException {
+
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java
new file mode 100644
index 0000000..4aee4ce
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java
@@ -0,0 +1,567 @@
+/**
+ * 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.horn.bsp;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.lang.math.RandomUtils;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hadoop.io.WritableUtils;
+import org.apache.hama.HamaConfiguration;
+import org.apache.hama.bsp.BSPJob;
+import org.apache.hama.commons.io.MatrixWritable;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleFunction;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.mortbay.log.Log;
+
+import com.google.common.base.Preconditions;
+import com.google.common.collect.Lists;
+
+/**
+ * SmallLayeredNeuralNetwork defines the general operations for derivative
+ * layered models, include Linear Regression, Logistic Regression, Multilayer
+ * Perceptron, Autoencoder, and Restricted Boltzmann Machine, etc. For
+ * SmallLayeredNeuralNetwork, the training can be conducted in parallel, but the
+ * parameters of the models are assumes to be stored in a single machine.
+ *
+ * In general, these models consist of neurons which are aligned in layers.
+ * Between layers, for any two adjacent layers, the neurons are connected to
+ * form a bipartite weighted graph.
+ *
+ */
+public class SmallLayeredNeuralNetwork extends AbstractLayeredNeuralNetwork {
+
+ /* Weights between neurons at adjacent layers */
+ protected List<DoubleMatrix> weightMatrixList;
+
+ /* Previous weight updates between neurons at adjacent layers */
+ protected List<DoubleMatrix> prevWeightUpdatesList;
+
+ /* Different layers can have different squashing function */
+ protected List<DoubleFunction> squashingFunctionList;
+
+ protected int finalLayerIdx;
+
+ public SmallLayeredNeuralNetwork() {
+ this.layerSizeList = Lists.newArrayList();
+ this.weightMatrixList = Lists.newArrayList();
+ this.prevWeightUpdatesList = Lists.newArrayList();
+ this.squashingFunctionList = Lists.newArrayList();
+ }
+
+ public SmallLayeredNeuralNetwork(String modelPath) {
+ super(modelPath);
+ }
+
+ @Override
+ /**
+ * {@inheritDoc}
+ */
+ public int addLayer(int size, boolean isFinalLayer,
+ DoubleFunction squashingFunction) {
+ Preconditions.checkArgument(size > 0,
+ "Size of layer must be larger than 0.");
+ if (!isFinalLayer) {
+ size += 1;
+ }
+
+ this.layerSizeList.add(size);
+ int layerIdx = this.layerSizeList.size() - 1;
+ if (isFinalLayer) {
+ this.finalLayerIdx = layerIdx;
+ }
+
+ // add weights between current layer and previous layer, and input layer has
+ // no squashing function
+ if (layerIdx > 0) {
+ int sizePrevLayer = this.layerSizeList.get(layerIdx - 1);
+ // row count equals to size of current size and column count equals to
+ // size of previous layer
+ int row = isFinalLayer ? size : size - 1;
+ int col = sizePrevLayer;
+ DoubleMatrix weightMatrix = new DenseDoubleMatrix(row, col);
+ // initialize weights
+ weightMatrix.applyToElements(new DoubleFunction() {
+ @Override
+ public double apply(double value) {
+ return RandomUtils.nextDouble() - 0.5;
+ }
+
+ @Override
+ public double applyDerivative(double value) {
+ throw new UnsupportedOperationException("");
+ }
+ });
+ this.weightMatrixList.add(weightMatrix);
+ this.prevWeightUpdatesList.add(new DenseDoubleMatrix(row, col));
+ this.squashingFunctionList.add(squashingFunction);
+ }
+ return layerIdx;
+ }
+
+ /**
+ * Update the weight matrices with given matrices.
+ *
+ * @param matrices
+ */
+ public void updateWeightMatrices(DoubleMatrix[] matrices) {
+ for (int i = 0; i < matrices.length; ++i) {
+ DoubleMatrix matrix = this.weightMatrixList.get(i);
+ this.weightMatrixList.set(i, matrix.add(matrices[i]));
+ }
+ }
+
+ /**
+ * Set the previous weight matrices.
+ * @param prevUpdates
+ */
+ void setPrevWeightMatrices(DoubleMatrix[] prevUpdates) {
+ this.prevWeightUpdatesList.clear();
+ Collections.addAll(this.prevWeightUpdatesList, prevUpdates);
+ }
+
+ /**
+ * Add a batch of matrices onto the given destination matrices.
+ *
+ * @param destMatrices
+ * @param sourceMatrices
+ */
+ static void matricesAdd(DoubleMatrix[] destMatrices,
+ DoubleMatrix[] sourceMatrices) {
+ for (int i = 0; i < destMatrices.length; ++i) {
+ destMatrices[i] = destMatrices[i].add(sourceMatrices[i]);
+ }
+ }
+
+ /**
+ * Get all the weight matrices.
+ *
+ * @return The matrices in form of matrix array.
+ */
+ DoubleMatrix[] getWeightMatrices() {
+ DoubleMatrix[] matrices = new DoubleMatrix[this.weightMatrixList.size()];
+ this.weightMatrixList.toArray(matrices);
+ return matrices;
+ }
+
+ /**
+ * Set the weight matrices.
+ *
+ * @param matrices
+ */
+ public void setWeightMatrices(DoubleMatrix[] matrices) {
+ this.weightMatrixList = new ArrayList<DoubleMatrix>();
+ Collections.addAll(this.weightMatrixList, matrices);
+ }
+
+ /**
+ * Get the previous matrices updates in form of array.
+ *
+ * @return The matrices in form of matrix array.
+ */
+ public DoubleMatrix[] getPrevMatricesUpdates() {
+ DoubleMatrix[] prevMatricesUpdates = new DoubleMatrix[this.prevWeightUpdatesList
+ .size()];
+ for (int i = 0; i < this.prevWeightUpdatesList.size(); ++i) {
+ prevMatricesUpdates[i] = this.prevWeightUpdatesList.get(i);
+ }
+ return prevMatricesUpdates;
+ }
+
+ public void setWeightMatrix(int index, DoubleMatrix matrix) {
+ Preconditions.checkArgument(
+ 0 <= index && index < this.weightMatrixList.size(), String.format(
+ "index [%d] should be in range[%d, %d].", index, 0,
+ this.weightMatrixList.size()));
+ this.weightMatrixList.set(index, matrix);
+ }
+
+ @Override
+ public void readFields(DataInput input) throws IOException {
+ super.readFields(input);
+
+ // read squash functions
+ int squashingFunctionSize = input.readInt();
+ this.squashingFunctionList = Lists.newArrayList();
+ for (int i = 0; i < squashingFunctionSize; ++i) {
+ this.squashingFunctionList.add(FunctionFactory
+ .createDoubleFunction(WritableUtils.readString(input)));
+ }
+
+ // read weights and construct matrices of previous updates
+ int numOfMatrices = input.readInt();
+ this.weightMatrixList = Lists.newArrayList();
+ this.prevWeightUpdatesList = Lists.newArrayList();
+ for (int i = 0; i < numOfMatrices; ++i) {
+ DoubleMatrix matrix = MatrixWritable.read(input);
+ this.weightMatrixList.add(matrix);
+ this.prevWeightUpdatesList.add(new DenseDoubleMatrix(
+ matrix.getRowCount(), matrix.getColumnCount()));
+ }
+
+ }
+
+ @Override
+ public void write(DataOutput output) throws IOException {
+ super.write(output);
+
+ // write squashing functions
+ output.writeInt(this.squashingFunctionList.size());
+ for (DoubleFunction aSquashingFunctionList : this.squashingFunctionList) {
+ WritableUtils.writeString(output, aSquashingFunctionList
+ .getFunctionName());
+ }
+
+ // write weight matrices
+ output.writeInt(this.weightMatrixList.size());
+ for (DoubleMatrix aWeightMatrixList : this.weightMatrixList) {
+ MatrixWritable.write(aWeightMatrixList, output);
+ }
+
+ // DO NOT WRITE WEIGHT UPDATE
+ }
+
+ @Override
+ public DoubleMatrix getWeightsByLayer(int layerIdx) {
+ return this.weightMatrixList.get(layerIdx);
+ }
+
+ /**
+ * Get the output of the model according to given feature instance.
+ */
+ @Override
+ public DoubleVector getOutput(DoubleVector instance) {
+ Preconditions.checkArgument(this.layerSizeList.get(0) - 1 == instance
+ .getDimension(), String.format(
+ "The dimension of input instance should be %d.",
+ this.layerSizeList.get(0) - 1));
+ // transform the features to another space
+ DoubleVector transformedInstance = this.featureTransformer
+ .transform(instance);
+ // add bias feature
+ DoubleVector instanceWithBias = new DenseDoubleVector(
+ transformedInstance.getDimension() + 1);
+ instanceWithBias.set(0, 0.99999); // set bias to be a little bit less than
+ // 1.0
+ for (int i = 1; i < instanceWithBias.getDimension(); ++i) {
+ instanceWithBias.set(i, transformedInstance.get(i - 1));
+ }
+
+ List<DoubleVector> outputCache = getOutputInternal(instanceWithBias);
+ // return the output of the last layer
+ DoubleVector result = outputCache.get(outputCache.size() - 1);
+ // remove bias
+ return result.sliceUnsafe(1, result.getDimension() - 1);
+ }
+
+ /**
+ * Calculate output internally, the intermediate output of each layer will be
+ * stored.
+ *
+ * @param instanceWithBias The instance contains the features.
+ * @return Cached output of each layer.
+ */
+ public List<DoubleVector> getOutputInternal(DoubleVector instanceWithBias) {
+ List<DoubleVector> outputCache = new ArrayList<DoubleVector>();
+ // fill with instance
+ DoubleVector intermediateOutput = instanceWithBias;
+ outputCache.add(intermediateOutput);
+
+ for (int i = 0; i < this.layerSizeList.size() - 1; ++i) {
+ intermediateOutput = forward(i, intermediateOutput);
+ outputCache.add(intermediateOutput);
+ }
+ return outputCache;
+ }
+
+ /**
+ * Forward the calculation for one layer.
+ *
+ * @param fromLayer The index of the previous layer.
+ * @param intermediateOutput The intermediateOutput of previous layer.
+ * @return a new vector with the result of the operation.
+ */
+ protected DoubleVector forward(int fromLayer, DoubleVector intermediateOutput) {
+ DoubleMatrix weightMatrix = this.weightMatrixList.get(fromLayer);
+
+ DoubleVector vec = weightMatrix.multiplyVectorUnsafe(intermediateOutput);
+ vec = vec.applyToElements(this.squashingFunctionList.get(fromLayer));
+
+ // add bias
+ DoubleVector vecWithBias = new DenseDoubleVector(vec.getDimension() + 1);
+ vecWithBias.set(0, 1);
+ for (int i = 0; i < vec.getDimension(); ++i) {
+ vecWithBias.set(i + 1, vec.get(i));
+ }
+ return vecWithBias;
+ }
+
+ /**
+ * Train the model online.
+ *
+ * @param trainingInstance
+ */
+ public void trainOnline(DoubleVector trainingInstance) {
+ DoubleMatrix[] updateMatrices = this.trainByInstance(trainingInstance);
+ this.updateWeightMatrices(updateMatrices);
+ }
+
+ @Override
+ public DoubleMatrix[] trainByInstance(DoubleVector trainingInstance) {
+ DoubleVector transformedVector = this.featureTransformer
+ .transform(trainingInstance.sliceUnsafe(this.layerSizeList.get(0) - 1));
+
+ int inputDimension = this.layerSizeList.get(0) - 1;
+ int outputDimension;
+ DoubleVector inputInstance = null;
+ DoubleVector labels = null;
+ if (this.learningStyle == LearningStyle.SUPERVISED) {
+ outputDimension = this.layerSizeList.get(this.layerSizeList.size() - 1);
+ // validate training instance
+ Preconditions.checkArgument(
+ inputDimension + outputDimension == trainingInstance.getDimension(),
+ String
+ .format(
+ "The dimension of training instance is %d, but requires %d.",
+ trainingInstance.getDimension(), inputDimension
+ + outputDimension));
+
+ inputInstance = new DenseDoubleVector(this.layerSizeList.get(0));
+ inputInstance.set(0, 1); // add bias
+ // get the features from the transformed vector
+ for (int i = 0; i < inputDimension; ++i) {
+ inputInstance.set(i + 1, transformedVector.get(i));
+ }
+ // get the labels from the original training instance
+ labels = trainingInstance.sliceUnsafe(inputInstance.getDimension() - 1,
+ trainingInstance.getDimension() - 1);
+ } else if (this.learningStyle == LearningStyle.UNSUPERVISED) {
+ // labels are identical to input features
+ outputDimension = inputDimension;
+ // validate training instance
+ Preconditions.checkArgument(inputDimension == trainingInstance
+ .getDimension(), String.format(
+ "The dimension of training instance is %d, but requires %d.",
+ trainingInstance.getDimension(), inputDimension));
+
+ inputInstance = new DenseDoubleVector(this.layerSizeList.get(0));
+ inputInstance.set(0, 1); // add bias
+ // get the features from the transformed vector
+ for (int i = 0; i < inputDimension; ++i) {
+ inputInstance.set(i + 1, transformedVector.get(i));
+ }
+ // get the labels by copying the transformed vector
+ labels = transformedVector.deepCopy();
+ }
+
+ List<DoubleVector> internalResults = this.getOutputInternal(inputInstance);
+ DoubleVector output = internalResults.get(internalResults.size() - 1);
+
+ // get the training error
+ calculateTrainingError(labels,
+ output.deepCopy().sliceUnsafe(1, output.getDimension() - 1));
+
+ if (this.trainingMethod.equals(TrainingMethod.GRADIENT_DESCENT)) {
+ return this.trainByInstanceGradientDescent(labels, internalResults);
+ } else {
+ throw new IllegalArgumentException(
+ String.format("Training method is not supported."));
+ }
+ }
+
+ /**
+ * Train by gradient descent. Get the updated weights using one training
+ * instance.
+ *
+ * @param trainingInstance
+ * @return The weight update matrices.
+ */
+ private DoubleMatrix[] trainByInstanceGradientDescent(DoubleVector labels,
+ List<DoubleVector> internalResults) {
+
+ DoubleVector output = internalResults.get(internalResults.size() - 1);
+ // initialize weight update matrices
+ DenseDoubleMatrix[] weightUpdateMatrices = new DenseDoubleMatrix[this.weightMatrixList
+ .size()];
+ for (int m = 0; m < weightUpdateMatrices.length; ++m) {
+ weightUpdateMatrices[m] = new DenseDoubleMatrix(this.weightMatrixList
+ .get(m).getRowCount(), this.weightMatrixList.get(m).getColumnCount());
+ }
+ DoubleVector deltaVec = new DenseDoubleVector(
+ this.layerSizeList.get(this.layerSizeList.size() - 1));
+
+ DoubleFunction squashingFunction = this.squashingFunctionList
+ .get(this.squashingFunctionList.size() - 1);
+
+ DoubleMatrix lastWeightMatrix = this.weightMatrixList
+ .get(this.weightMatrixList.size() - 1);
+ for (int i = 0; i < deltaVec.getDimension(); ++i) {
+ double costFuncDerivative = this.costFunction.applyDerivative(
+ labels.get(i), output.get(i + 1));
+ // add regularization
+ costFuncDerivative += this.regularizationWeight
+ * lastWeightMatrix.getRowVector(i).sum();
+ deltaVec.set(
+ i,
+ costFuncDerivative
+ * squashingFunction.applyDerivative(output.get(i + 1)));
+ }
+
+ // start from previous layer of output layer
+ for (int layer = this.layerSizeList.size() - 2; layer >= 0; --layer) {
+ output = internalResults.get(layer);
+ deltaVec = backpropagate(layer, deltaVec, internalResults,
+ weightUpdateMatrices[layer]);
+ }
+
+ this.setPrevWeightMatrices(weightUpdateMatrices);
+
+ return weightUpdateMatrices;
+ }
+
+ /**
+ * Back-propagate the errors to from next layer to current layer. The weight
+ * updated information will be stored in the weightUpdateMatrices, and the
+ * delta of the prevLayer would be returned.
+ *
+ * @param layer Index of current layer.
+ * @param internalOutput Internal output of current layer.
+ * @param deltaVec Delta of next layer.
+ * @return the squashing function of the specified position.
+ */
+ private DoubleVector backpropagate(int curLayerIdx,
+ DoubleVector nextLayerDelta, List<DoubleVector> outputCache,
+ DenseDoubleMatrix weightUpdateMatrix) {
+
+ // get layer related information
+ DoubleFunction squashingFunction = this.squashingFunctionList
+ .get(curLayerIdx);
+ DoubleVector curLayerOutput = outputCache.get(curLayerIdx);
+ DoubleMatrix weightMatrix = this.weightMatrixList.get(curLayerIdx);
+ DoubleMatrix prevWeightMatrix = this.prevWeightUpdatesList.get(curLayerIdx);
+
+ // next layer is not output layer, remove the delta of bias neuron
+ if (curLayerIdx != this.layerSizeList.size() - 2) {
+ nextLayerDelta = nextLayerDelta.slice(1,
+ nextLayerDelta.getDimension() - 1);
+ }
+
+ DoubleVector delta = weightMatrix.transpose()
+ .multiplyVector(nextLayerDelta);
+ for (int i = 0; i < delta.getDimension(); ++i) {
+ delta.set(
+ i,
+ delta.get(i)
+ * squashingFunction.applyDerivative(curLayerOutput.get(i)));
+ }
+
+ // update weights
+ for (int i = 0; i < weightUpdateMatrix.getRowCount(); ++i) {
+ for (int j = 0; j < weightUpdateMatrix.getColumnCount(); ++j) {
+ weightUpdateMatrix.set(i, j,
+ -learningRate * nextLayerDelta.get(i) * curLayerOutput.get(j)
+ + this.momentumWeight * prevWeightMatrix.get(i, j));
+ }
+ }
+
+ return delta;
+ }
+
+ @Override
+ protected void trainInternal(Path dataInputPath,
+ Map<String, String> trainingParams) throws IOException,
+ InterruptedException, ClassNotFoundException {
+ // add all training parameters to configuration
+ Configuration conf = new Configuration();
+ for (Map.Entry<String, String> entry : trainingParams.entrySet()) {
+ conf.set(entry.getKey(), entry.getValue());
+ }
+
+ // if training parameters contains the model path, update the model path
+ String modelPath = trainingParams.get("modelPath");
+ if (modelPath != null) {
+ this.modelPath = modelPath;
+ }
+ // modelPath must be set before training
+ if (this.modelPath == null) {
+ throw new IllegalArgumentException(
+ "Please specify the modelPath for model, "
+ + "either through setModelPath() or add 'modelPath' to the training parameters.");
+ }
+
+ conf.set("modelPath", this.modelPath);
+ this.writeModelToFile();
+
+ HamaConfiguration hamaConf = new HamaConfiguration(conf);
+
+ // create job
+ BSPJob job = new BSPJob(hamaConf, SmallLayeredNeuralNetworkTrainer.class);
+ job.setJobName("Small scale Neural Network training");
+ job.setJarByClass(SmallLayeredNeuralNetworkTrainer.class);
+ job.setBspClass(SmallLayeredNeuralNetworkTrainer.class);
+ job.setInputPath(dataInputPath);
+ job.setInputFormat(org.apache.hama.bsp.SequenceFileInputFormat.class);
+ job.setInputKeyClass(LongWritable.class);
+ job.setInputValueClass(VectorWritable.class);
+ job.setOutputKeyClass(NullWritable.class);
+ job.setOutputValueClass(NullWritable.class);
+ job.setOutputFormat(org.apache.hama.bsp.NullOutputFormat.class);
+
+ int numTasks = conf.getInt("tasks", 1);
+ Log.info(String.format("Number of tasks: %d\n", numTasks));
+ job.setNumBspTask(numTasks);
+ job.waitForCompletion(true);
+
+ // reload learned model
+ Log.info(String.format("Reload model from %s.", this.modelPath));
+ this.readFromModel();
+
+ }
+
+ @Override
+ protected void calculateTrainingError(DoubleVector labels, DoubleVector output) {
+ DoubleVector errors = labels.deepCopy().applyToElements(output,
+ this.costFunction);
+ this.trainingError = errors.sum();
+ }
+
+ /**
+ * Get the squashing function of a specified layer.
+ *
+ * @param idx
+ * @return a new vector with the result of the operation.
+ */
+ public DoubleFunction getSquashingFunction(int idx) {
+ return this.squashingFunctionList.get(idx);
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java
new file mode 100644
index 0000000..2f8c287
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java
@@ -0,0 +1,126 @@
+/**
+ * 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.horn.bsp;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+
+import org.apache.hadoop.io.Writable;
+import org.apache.hama.commons.io.MatrixWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DoubleMatrix;
+
+/**
+ * NeuralNetworkMessage transmits the messages between peers during the training
+ * of neural networks.
+ *
+ */
+public class SmallLayeredNeuralNetworkMessage implements Writable {
+
+ protected double trainingError;
+ protected DoubleMatrix[] curMatrices;
+ protected DoubleMatrix[] prevMatrices;
+ protected boolean converge;
+
+ public SmallLayeredNeuralNetworkMessage() {
+ }
+
+ public SmallLayeredNeuralNetworkMessage(double trainingError,
+ boolean converge, DoubleMatrix[] weightMatrices,
+ DoubleMatrix[] prevMatrices) {
+ this.trainingError = trainingError;
+ this.converge = converge;
+ this.curMatrices = weightMatrices;
+ this.prevMatrices = prevMatrices;
+ }
+
+ @Override
+ public void readFields(DataInput input) throws IOException {
+ trainingError = input.readDouble();
+ converge = input.readBoolean();
+ int numMatrices = input.readInt();
+ boolean hasPrevMatrices = input.readBoolean();
+ curMatrices = new DenseDoubleMatrix[numMatrices];
+ // read matrice updates
+ for (int i = 0; i < curMatrices.length; ++i) {
+ curMatrices[i] = (DenseDoubleMatrix) MatrixWritable.read(input);
+ }
+
+ if (hasPrevMatrices) {
+ prevMatrices = new DenseDoubleMatrix[numMatrices];
+ // read previous matrices updates
+ for (int i = 0; i < prevMatrices.length; ++i) {
+ prevMatrices[i] = (DenseDoubleMatrix) MatrixWritable.read(input);
+ }
+ }
+ }
+
+ @Override
+ public void write(DataOutput output) throws IOException {
+ output.writeDouble(trainingError);
+ output.writeBoolean(converge);
+ output.writeInt(curMatrices.length);
+ if (prevMatrices == null) {
+ output.writeBoolean(false);
+ } else {
+ output.writeBoolean(true);
+ }
+ for (DoubleMatrix matrix : curMatrices) {
+ MatrixWritable.write(matrix, output);
+ }
+ if (prevMatrices != null) {
+ for (DoubleMatrix matrix : prevMatrices) {
+ MatrixWritable.write(matrix, output);
+ }
+ }
+ }
+
+ public double getTrainingError() {
+ return trainingError;
+ }
+
+ public void setTrainingError(double trainingError) {
+ this.trainingError = trainingError;
+ }
+
+ public boolean isConverge() {
+ return converge;
+ }
+
+ public void setConverge(boolean converge) {
+ this.converge = converge;
+ }
+
+ public DoubleMatrix[] getCurMatrices() {
+ return curMatrices;
+ }
+
+ public void setMatrices(DoubleMatrix[] curMatrices) {
+ this.curMatrices = curMatrices;
+ }
+
+ public DoubleMatrix[] getPrevMatrices() {
+ return prevMatrices;
+ }
+
+ public void setPrevMatrices(DoubleMatrix[] prevMatrices) {
+ this.prevMatrices = prevMatrices;
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java
new file mode 100644
index 0000000..132ec8c
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java
@@ -0,0 +1,244 @@
+/**
+ * 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.horn.bsp;
+
+import java.io.IOException;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hama.bsp.BSP;
+import org.apache.hama.bsp.BSPPeer;
+import org.apache.hama.bsp.sync.SyncException;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.mortbay.log.Log;
+
+/**
+ * The trainer that train the {@link SmallLayeredNeuralNetwork} based on BSP
+ * framework.
+ *
+ */
+public final class SmallLayeredNeuralNetworkTrainer
+ extends
+ BSP<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> {
+
+ private SmallLayeredNeuralNetwork inMemoryModel;
+ private Configuration conf;
+ /* Default batch size */
+ private int batchSize;
+
+ /* check the interval between intervals */
+ private double prevAvgTrainingError;
+ private double curAvgTrainingError;
+ private long convergenceCheckInterval;
+ private long iterations;
+ private long maxIterations;
+ private boolean isConverge;
+
+ private String modelPath;
+
+ @Override
+ /**
+ * If the model path is specified, load the existing from storage location.
+ */
+ public void setup(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+ if (peer.getPeerIndex() == 0) {
+ Log.info("Begin to train");
+ }
+ this.isConverge = false;
+ this.conf = peer.getConfiguration();
+ this.iterations = 0;
+ this.modelPath = conf.get("modelPath");
+ this.maxIterations = conf.getLong("training.max.iterations", 100000);
+ this.convergenceCheckInterval = conf.getLong("convergence.check.interval",
+ 2000);
+ this.modelPath = conf.get("modelPath");
+ this.inMemoryModel = new SmallLayeredNeuralNetwork(modelPath);
+ this.prevAvgTrainingError = Integer.MAX_VALUE;
+ this.batchSize = conf.getInt("training.batch.size", 50);
+ }
+
+ @Override
+ /**
+ * Write the trained model back to stored location.
+ */
+ public void cleanup(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+ // write model to modelPath
+ if (peer.getPeerIndex() == 0) {
+ try {
+ Log.info(String.format("End of training, number of iterations: %d.\n",
+ this.iterations));
+ Log.info(String.format("Write model back to %s\n",
+ inMemoryModel.getModelPath()));
+ this.inMemoryModel.writeModelToFile();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ }
+ }
+
+ @Override
+ public void bsp(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+ throws IOException, SyncException, InterruptedException {
+ while (this.iterations++ < maxIterations) {
+ // each groom calculate the matrices updates according to local data
+ calculateUpdates(peer);
+ peer.sync();
+
+ // master merge the updates model
+ if (peer.getPeerIndex() == 0) {
+ mergeUpdates(peer);
+ }
+ peer.sync();
+ if (this.isConverge) {
+ break;
+ }
+ }
+ }
+
+ /**
+ * Calculate the matrices updates according to local partition of data.
+ *
+ * @param peer
+ * @throws IOException
+ */
+ private void calculateUpdates(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+ throws IOException {
+ // receive update information from master
+ if (peer.getNumCurrentMessages() != 0) {
+ SmallLayeredNeuralNetworkMessage inMessage = peer.getCurrentMessage();
+ DoubleMatrix[] newWeights = inMessage.getCurMatrices();
+ DoubleMatrix[] preWeightUpdates = inMessage.getPrevMatrices();
+ this.inMemoryModel.setWeightMatrices(newWeights);
+ this.inMemoryModel.setPrevWeightMatrices(preWeightUpdates);
+ this.isConverge = inMessage.isConverge();
+ // check converge
+ if (isConverge) {
+ return;
+ }
+ }
+
+ DoubleMatrix[] weightUpdates = new DoubleMatrix[this.inMemoryModel.weightMatrixList
+ .size()];
+ for (int i = 0; i < weightUpdates.length; ++i) {
+ int row = this.inMemoryModel.weightMatrixList.get(i).getRowCount();
+ int col = this.inMemoryModel.weightMatrixList.get(i).getColumnCount();
+ weightUpdates[i] = new DenseDoubleMatrix(row, col);
+ }
+
+ // continue to train
+ double avgTrainingError = 0.0;
+ LongWritable key = new LongWritable();
+ VectorWritable value = new VectorWritable();
+ for (int recordsRead = 0; recordsRead < batchSize; ++recordsRead) {
+ if (!peer.readNext(key, value)) {
+ peer.reopenInput();
+ peer.readNext(key, value);
+ }
+ DoubleVector trainingInstance = value.getVector();
+ SmallLayeredNeuralNetwork.matricesAdd(weightUpdates,
+ this.inMemoryModel.trainByInstance(trainingInstance));
+ avgTrainingError += this.inMemoryModel.trainingError;
+ }
+ avgTrainingError /= batchSize;
+
+ // calculate the average of updates
+ for (int i = 0; i < weightUpdates.length; ++i) {
+ weightUpdates[i] = weightUpdates[i].divide(batchSize);
+ }
+
+ DoubleMatrix[] prevWeightUpdates = this.inMemoryModel
+ .getPrevMatricesUpdates();
+ SmallLayeredNeuralNetworkMessage outMessage = new SmallLayeredNeuralNetworkMessage(
+ avgTrainingError, false, weightUpdates, prevWeightUpdates);
+ peer.send(peer.getPeerName(0), outMessage);
+ }
+
+ /**
+ * Merge the updates according to the updates of the grooms.
+ *
+ * @param peer
+ * @throws IOException
+ */
+ private void mergeUpdates(
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+ throws IOException {
+ int numMessages = peer.getNumCurrentMessages();
+ boolean isConverge = false;
+ if (numMessages == 0) { // converges
+ isConverge = true;
+ return;
+ }
+
+ double avgTrainingError = 0;
+ DoubleMatrix[] matricesUpdates = null;
+ DoubleMatrix[] prevMatricesUpdates = null;
+
+ while (peer.getNumCurrentMessages() > 0) {
+ SmallLayeredNeuralNetworkMessage message = peer.getCurrentMessage();
+ if (matricesUpdates == null) {
+ matricesUpdates = message.getCurMatrices();
+ prevMatricesUpdates = message.getPrevMatrices();
+ } else {
+ SmallLayeredNeuralNetwork.matricesAdd(matricesUpdates,
+ message.getCurMatrices());
+ SmallLayeredNeuralNetwork.matricesAdd(prevMatricesUpdates,
+ message.getPrevMatrices());
+ }
+ avgTrainingError += message.getTrainingError();
+ }
+
+ if (numMessages != 1) {
+ avgTrainingError /= numMessages;
+ for (int i = 0; i < matricesUpdates.length; ++i) {
+ matricesUpdates[i] = matricesUpdates[i].divide(numMessages);
+ prevMatricesUpdates[i] = prevMatricesUpdates[i].divide(numMessages);
+ }
+ }
+ this.inMemoryModel.updateWeightMatrices(matricesUpdates);
+ this.inMemoryModel.setPrevWeightMatrices(prevMatricesUpdates);
+
+ // check convergence
+ if (iterations % convergenceCheckInterval == 0) {
+ if (prevAvgTrainingError < curAvgTrainingError) {
+ // error cannot decrease any more
+ isConverge = true;
+ }
+ // update
+ prevAvgTrainingError = curAvgTrainingError;
+ curAvgTrainingError = 0;
+ }
+ curAvgTrainingError += avgTrainingError / convergenceCheckInterval;
+
+ // broadcast updated weight matrices
+ for (String peerName : peer.getAllPeerNames()) {
+ SmallLayeredNeuralNetworkMessage msg = new SmallLayeredNeuralNetworkMessage(
+ 0, isConverge, this.inMemoryModel.getWeightMatrices(),
+ this.inMemoryModel.getPrevMatricesUpdates());
+ peer.send(peerName, msg);
+ }
+ }
+
+}
diff --git a/src/main/java/org/apache/horn/examples/NeuralNetwork.java b/src/main/java/org/apache/horn/examples/NeuralNetwork.java
new file mode 100644
index 0000000..249f0e8
--- /dev/null
+++ b/src/main/java/org/apache/horn/examples/NeuralNetwork.java
@@ -0,0 +1,216 @@
+/**
+ * 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.horn.examples;
+
+import java.io.BufferedReader;
+import java.io.BufferedWriter;
+import java.io.InputStreamReader;
+import java.io.OutputStreamWriter;
+import java.net.URI;
+import java.util.HashMap;
+import java.util.Map;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hama.HamaConfiguration;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.apache.hama.ml.ann.SmallLayeredNeuralNetwork;
+
+/**
+ * The example of using {@link SmallLayeredNeuralNetwork}, including the
+ * training phase and labeling phase.
+ */
+public class NeuralNetwork {
+
+ public static void main(String[] args) throws Exception {
+ if (args.length < 3) {
+ printUsage();
+ return;
+ }
+ String mode = args[0];
+ if (mode.equalsIgnoreCase("label")) {
+ if (args.length < 4) {
+ printUsage();
+ return;
+ }
+ HamaConfiguration conf = new HamaConfiguration();
+
+ String featureDataPath = args[1];
+ String resultDataPath = args[2];
+ String modelPath = args[3];
+
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
+
+ // process data in streaming approach
+ FileSystem fs = FileSystem.get(new URI(featureDataPath), conf);
+ BufferedReader br = new BufferedReader(new InputStreamReader(
+ fs.open(new Path(featureDataPath))));
+ Path outputPath = new Path(resultDataPath);
+ if (fs.exists(outputPath)) {
+ fs.delete(outputPath, true);
+ }
+ BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
+ fs.create(outputPath)));
+
+ String line = null;
+
+ while ((line = br.readLine()) != null) {
+ if (line.trim().length() == 0) {
+ continue;
+ }
+ String[] tokens = line.trim().split(",");
+ double[] vals = new double[tokens.length];
+ for (int i = 0; i < tokens.length; ++i) {
+ vals[i] = Double.parseDouble(tokens[i]);
+ }
+ DoubleVector instance = new DenseDoubleVector(vals);
+ DoubleVector result = ann.getOutput(instance);
+ double[] arrResult = result.toArray();
+ StringBuilder sb = new StringBuilder();
+ for (int i = 0; i < arrResult.length; ++i) {
+ sb.append(arrResult[i]);
+ if (i != arrResult.length - 1) {
+ sb.append(",");
+ } else {
+ sb.append("\n");
+ }
+ }
+ bw.write(sb.toString());
+ }
+
+ br.close();
+ bw.close();
+ } else if (mode.equals("train")) {
+ if (args.length < 5) {
+ printUsage();
+ return;
+ }
+
+ String trainingDataPath = args[1];
+ String trainedModelPath = args[2];
+
+ int featureDimension = Integer.parseInt(args[3]);
+ int labelDimension = Integer.parseInt(args[4]);
+
+ int iteration = 1000;
+ double learningRate = 0.4;
+ double momemtumWeight = 0.2;
+ double regularizationWeight = 0.01;
+
+ // parse parameters
+ if (args.length >= 6) {
+ try {
+ iteration = Integer.parseInt(args[5]);
+ System.out.printf("Iteration: %d\n", iteration);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("MAX_ITERATION format invalid. It should be a positive number.");
+ return;
+ }
+ }
+ if (args.length >= 7) {
+ try {
+ learningRate = Double.parseDouble(args[6]);
+ System.out.printf("Learning rate: %f\n", learningRate);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("LEARNING_RATE format invalid. It should be a positive double in range (0, 1.0)");
+ return;
+ }
+ }
+ if (args.length >= 8) {
+ try {
+ momemtumWeight = Double.parseDouble(args[7]);
+ System.out.printf("Momemtum weight: %f\n", momemtumWeight);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("MOMEMTUM_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
+ return;
+ }
+ }
+ if (args.length >= 9) {
+ try {
+ regularizationWeight = Double.parseDouble(args[8]);
+ System.out
+ .printf("Regularization weight: %f\n", regularizationWeight);
+ } catch (NumberFormatException e) {
+ System.err
+ .println("REGULARIZATION_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
+ return;
+ }
+ }
+
+ // train the model
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.setLearningRate(learningRate);
+ ann.setMomemtumWeight(momemtumWeight);
+ ann.setRegularizationWeight(regularizationWeight);
+ ann.addLayer(featureDimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(featureDimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(labelDimension, true,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("CrossEntropy"));
+ ann.setModelPath(trainedModelPath);
+
+ Map<String, String> trainingParameters = new HashMap<String, String>();
+ trainingParameters.put("tasks", "5");
+ trainingParameters.put("training.max.iterations", "" + iteration);
+ trainingParameters.put("training.batch.size", "300");
+ trainingParameters.put("convergence.check.interval", "1000");
+ ann.train(new Path(trainingDataPath), trainingParameters);
+ }
+
+ }
+
+ private static void printUsage() {
+ System.out
+ .println("USAGE: <MODE> <INPUT_PATH> <OUTPUT_PATH> <MODEL_PATH>|<FEATURE_DIMENSION> <LABEL_DIMENSION> [<MAX_ITERATION> <LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT>]");
+ System.out
+ .println("\tMODE\t- train: train the model with given training data.");
+ System.out
+ .println("\t\t- label: obtain the result by feeding the features to the neural network.");
+ System.out
+ .println("\tINPUT_PATH\tin 'train' mode, it is the path of the training data; in 'label' mode, it is the path of the to be evaluated data that lacks the label.");
+ System.out
+ .println("\tOUTPUT_PATH\tin 'train' mode, it is where the trained model is stored; in 'label' mode, it is where the labeled data is stored.");
+ System.out.println("\n\tConditional Parameters:");
+ System.out
+ .println("\tMODEL_PATH\tonly required in 'label' mode. It specifies where to load the trained neural network model.");
+ System.out
+ .println("\tMAX_ITERATION\tonly used in 'train' mode. It specifies how many iterations for the neural network to run. Default is 0.01.");
+ System.out
+ .println("\tLEARNING_RATE\tonly used to 'train' mode. It specifies the degree of aggregation for learning, usually in range (0, 1.0). Default is 0.1.");
+ System.out
+ .println("\tMOMEMTUM_WEIGHT\tonly used to 'train' mode. It specifies the weight of momemtum. Default is 0.");
+ System.out
+ .println("\tREGULARIZATION_WEIGHT\tonly required in 'train' model. It specifies the weight of reqularization.");
+ System.out.println("\nExample:");
+ System.out
+ .println("Train a neural network with with feature dimension 8, label dimension 1 and default setting:\n\tneuralnets train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1");
+ System.out
+ .println("Train a neural network with with feature dimension 8, label dimension 1 and specify learning rate as 0.1, momemtum rate as 0.2, and regularization weight as 0.01:\n\tneuralnets.train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1 0.1 0.2 0.01");
+ System.out
+ .println("Label the data with trained model:\n\tneuralnets evaluate hdfs://localhost:30002/unlabeled_data hdfs://localhost:30002/result hdfs://localhost:30002/model");
+ }
+
+}
diff --git a/src/test/java/org/apache/horn/bsp/MLTestBase.java b/src/test/java/org/apache/horn/bsp/MLTestBase.java
new file mode 100644
index 0000000..8001bcf
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/MLTestBase.java
@@ -0,0 +1,64 @@
+/**
+ * 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.horn.bsp;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * The common methods for testing machine learning algorithms
+ *
+ */
+public abstract class MLTestBase {
+
+ /**
+ * Conduct the 0-1 normalization.
+ *
+ * @param instances
+ */
+ protected static void zeroOneNormalization(List<double[]> instanceList,
+ int len) {
+ int dimension = len;
+
+ double[] mins = new double[dimension];
+ double[] maxs = new double[dimension];
+ Arrays.fill(mins, Double.MAX_VALUE);
+ Arrays.fill(maxs, Double.MIN_VALUE);
+
+ for (double[] instance : instanceList) {
+ for (int i = 0; i < len; ++i) {
+ if (mins[i] > instance[i]) {
+ mins[i] = instance[i];
+ }
+ if (maxs[i] < instance[i]) {
+ maxs[i] = instance[i];
+ }
+ }
+ }
+
+ for (double[] instance : instanceList) {
+ for (int i = 0; i < len; ++i) {
+ double range = maxs[i] - mins[i];
+ if (range != 0) {
+ instance[i] = (instance[i] - mins[i]) / range;
+ }
+ }
+ }
+
+ }
+}
diff --git a/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java b/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java
new file mode 100644
index 0000000..0aaa926
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java
@@ -0,0 +1,194 @@
+/**
+ * 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.horn.bsp;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.BufferedReader;
+import java.io.FileNotFoundException;
+import java.io.FileReader;
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.SequenceFile;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleVector;
+import org.junit.Test;
+import org.mortbay.log.Log;
+
+/**
+ * Test the functionality of {@link AutoEncoder}.
+ *
+ */
+public class TestAutoEncoder extends MLTestBase {
+
+ @Test
+ public void testAutoEncoderSimple() {
+ double[][] instances = { { 0, 0, 0, 1 }, { 0, 0, 1, 0 }, { 0, 1, 0, 0 },
+ { 0, 0, 0, 0 } };
+ AutoEncoder encoder = new AutoEncoder(4, 2);
+ encoder.setLearningRate(0.5);
+ encoder.setMomemtumWeight(0.2);
+
+ int maxIteration = 2000;
+ Random rnd = new Random();
+ for (int iteration = 0; iteration < maxIteration; ++iteration) {
+ for (int i = 0; i < instances.length; ++i) {
+ encoder.trainOnline(new DenseDoubleVector(instances[rnd.nextInt(instances.length)]));
+ }
+ }
+
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector encodeVec = encoder.encode(new DenseDoubleVector(
+ instances[i]));
+ DoubleVector decodeVec = encoder.decode(encodeVec);
+ for (int d = 0; d < instances[i].length; ++d) {
+ assertEquals(instances[i][d], decodeVec.get(d), 0.1);
+ }
+ }
+
+ }
+
+ @Test
+ public void testAutoEncoderSwissRollDataset() {
+ List<double[]> instanceList = new ArrayList<double[]>();
+ try {
+ BufferedReader br = new BufferedReader(new FileReader("src/test/resources/dimensional_reduction.txt"));
+ String line = null;
+ while ((line = br.readLine()) != null) {
+ String[] tokens = line.split("\t");
+ double[] instance = new double[tokens.length];
+ for (int i = 0; i < instance.length; ++i) {
+ instance[i] = Double.parseDouble(tokens[i]);
+ }
+ instanceList.add(instance);
+ }
+ br.close();
+ // normalize instances
+ zeroOneNormalization(instanceList, instanceList.get(0).length);
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (NumberFormatException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+
+ List<DoubleVector> vecInstanceList = new ArrayList<DoubleVector>();
+ for (double[] instance : instanceList) {
+ vecInstanceList.add(new DenseDoubleVector(instance));
+ }
+ AutoEncoder encoder = new AutoEncoder(3, 2);
+ encoder.setLearningRate(0.05);
+ encoder.setMomemtumWeight(0.1);
+ int maxIteration = 2000;
+ for (int iteration = 0; iteration < maxIteration; ++iteration) {
+ for (DoubleVector vector : vecInstanceList) {
+ encoder.trainOnline(vector);
+ }
+ }
+
+ double errorInstance = 0;
+ for (DoubleVector vector : vecInstanceList) {
+ DoubleVector decoded = encoder.getOutput(vector);
+ DoubleVector diff = vector.subtract(decoded);
+ double error = diff.dot(diff);
+ if (error > 0.1) {
+ ++errorInstance;
+ }
+ }
+ Log.info(String.format("Autoecoder error rate: %f%%\n", errorInstance * 100 / vecInstanceList.size()));
+
+ }
+
+ @Test
+ public void testAutoEncoderSwissRollDatasetDistributed() {
+ String strDataPath = "/tmp/dimensional_reduction.txt";
+ Path path = new Path(strDataPath);
+ List<double[]> instanceList = new ArrayList<double[]>();
+ try {
+ Configuration conf = new Configuration();
+ FileSystem fs = FileSystem.get(new URI(strDataPath), conf);
+ if (fs.exists(path)) {
+ fs.delete(path, true);
+ }
+
+ String line = null;
+ BufferedReader br = new BufferedReader(new FileReader("src/test/resources/dimensional_reduction.txt"));
+ while ((line = br.readLine()) != null) {
+ String[] tokens = line.split("\t");
+ double[] instance = new double[tokens.length];
+ for (int i = 0; i < instance.length; ++i) {
+ instance[i] = Double.parseDouble(tokens[i]);
+ }
+ instanceList.add(instance);
+ }
+ br.close();
+ // normalize instances
+ zeroOneNormalization(instanceList, instanceList.get(0).length);
+
+ SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, LongWritable.class, VectorWritable.class);
+ for (int i = 0; i < instanceList.size(); ++i) {
+ DoubleVector vector = new DenseDoubleVector(instanceList.get(i));
+ writer.append(new LongWritable(i), new VectorWritable(vector));
+ }
+
+ writer.close();
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ }
+
+ AutoEncoder encoder = new AutoEncoder(3, 2);
+ String modelPath = "/tmp/autoencoder-modelpath";
+ encoder.setModelPath(modelPath);
+ Map<String, String> trainingParams = new HashMap<String, String>();
+ encoder.setLearningRate(0.5);
+ trainingParams.put("tasks", "5");
+ trainingParams.put("training.max.iterations", "3000");
+ trainingParams.put("training.batch.size", "200");
+ encoder.train(path, trainingParams);
+
+ double errorInstance = 0;
+ for (double[] instance : instanceList) {
+ DoubleVector vector = new DenseDoubleVector(instance);
+ DoubleVector decoded = encoder.getOutput(vector);
+ DoubleVector diff = vector.subtract(decoded);
+ double error = diff.dot(diff);
+ if (error > 0.1) {
+ ++errorInstance;
+ }
+ }
+ Log.info(String.format("Autoecoder error rate: %f%%\n", errorInstance * 100 / instanceList.size()));
+ }
+
+}
diff --git a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
new file mode 100644
index 0000000..85c4b7a
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
@@ -0,0 +1,642 @@
+/**
+ * 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.horn.bsp;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+
+import java.io.BufferedReader;
+import java.io.FileNotFoundException;
+import java.io.FileReader;
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.SequenceFile;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.apache.horn.bsp.AbstractLayeredNeuralNetwork.LearningStyle;
+import org.apache.horn.bsp.AbstractLayeredNeuralNetwork.TrainingMethod;
+import org.apache.hama.ml.util.DefaultFeatureTransformer;
+import org.apache.hama.ml.util.FeatureTransformer;
+import org.junit.Test;
+import org.mortbay.log.Log;
+
+/**
+ * Test the functionality of SmallLayeredNeuralNetwork.
+ *
+ */
+public class TestSmallLayeredNeuralNetwork extends MLTestBase {
+
+ @Test
+ public void testReadWrite() {
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.addLayer(2, false,
+ FunctionFactory.createDoubleFunction("IdentityFunction"));
+ ann.addLayer(5, false,
+ FunctionFactory.createDoubleFunction("IdentityFunction"));
+ ann.addLayer(1, true,
+ FunctionFactory.createDoubleFunction("IdentityFunction"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ double learningRate = 0.2;
+ ann.setLearningRate(learningRate);
+ double momentumWeight = 0.5;
+ ann.setMomemtumWeight(momentumWeight);
+ double regularizationWeight = 0.05;
+ ann.setRegularizationWeight(regularizationWeight);
+ // intentionally initialize all weights to 0.5
+ DoubleMatrix[] matrices = new DenseDoubleMatrix[2];
+ matrices[0] = new DenseDoubleMatrix(5, 3, 0.2);
+ matrices[1] = new DenseDoubleMatrix(1, 6, 0.8);
+ ann.setWeightMatrices(matrices);
+ ann.setLearningStyle(LearningStyle.UNSUPERVISED);
+
+ FeatureTransformer defaultFeatureTransformer = new DefaultFeatureTransformer();
+ ann.setFeatureTransformer(defaultFeatureTransformer);
+
+
+ // write to file
+ String modelPath = "/tmp/testSmallLayeredNeuralNetworkReadWrite";
+ ann.setModelPath(modelPath);
+ try {
+ ann.writeModelToFile();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+
+ // read from file
+ SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+ assertEquals(annCopy.getClass().getSimpleName(), annCopy.getModelType());
+ assertEquals(modelPath, annCopy.getModelPath());
+ assertEquals(learningRate, annCopy.getLearningRate(), 0.000001);
+ assertEquals(momentumWeight, annCopy.getMomemtumWeight(), 0.000001);
+ assertEquals(regularizationWeight, annCopy.getRegularizationWeight(),
+ 0.000001);
+ assertEquals(TrainingMethod.GRADIENT_DESCENT, annCopy.getTrainingMethod());
+ assertEquals(LearningStyle.UNSUPERVISED, annCopy.getLearningStyle());
+
+ // compare weights
+ DoubleMatrix[] weightsMatrices = annCopy.getWeightMatrices();
+ for (int i = 0; i < weightsMatrices.length; ++i) {
+ DoubleMatrix expectMat = matrices[i];
+ DoubleMatrix actualMat = weightsMatrices[i];
+ for (int j = 0; j < expectMat.getRowCount(); ++j) {
+ for (int k = 0; k < expectMat.getColumnCount(); ++k) {
+ assertEquals(expectMat.get(j, k), actualMat.get(j, k), 0.000001);
+ }
+ }
+ }
+
+ FeatureTransformer copyTransformer = annCopy.getFeatureTransformer();
+ assertEquals(defaultFeatureTransformer.getClass().getName(), copyTransformer.getClass().getName());
+ }
+
+ @Test
+ /**
+ * Test the forward functionality.
+ */
+ public void testOutput() {
+ // first network
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.addLayer(2, false,
+ FunctionFactory.createDoubleFunction("IdentityFunction"));
+ ann.addLayer(5, false,
+ FunctionFactory.createDoubleFunction("IdentityFunction"));
+ ann.addLayer(1, true,
+ FunctionFactory.createDoubleFunction("IdentityFunction"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ ann.setLearningRate(0.1);
+ // intentionally initialize all weights to 0.5
+ DoubleMatrix[] matrices = new DenseDoubleMatrix[2];
+ matrices[0] = new DenseDoubleMatrix(5, 3, 0.5);
+ matrices[1] = new DenseDoubleMatrix(1, 6, 0.5);
+ ann.setWeightMatrices(matrices);
+
+ double[] arr = new double[] { 0, 1 };
+ DoubleVector training = new DenseDoubleVector(arr);
+ DoubleVector result = ann.getOutput(training);
+ assertEquals(1, result.getDimension());
+ // assertEquals(3, result.get(0), 0.000001);
+
+ // second network
+ SmallLayeredNeuralNetwork ann2 = new SmallLayeredNeuralNetwork();
+ ann2.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann2.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann2.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann2.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ ann2.setLearningRate(0.3);
+ // intentionally initialize all weights to 0.5
+ DoubleMatrix[] matrices2 = new DenseDoubleMatrix[2];
+ matrices2[0] = new DenseDoubleMatrix(3, 3, 0.5);
+ matrices2[1] = new DenseDoubleMatrix(1, 4, 0.5);
+ ann2.setWeightMatrices(matrices2);
+
+ double[] test = { 0, 0 };
+ double[] result2 = { 0.807476 };
+
+ DoubleVector vec = ann2.getOutput(new DenseDoubleVector(test));
+ assertArrayEquals(result2, vec.toArray(), 0.000001);
+
+ SmallLayeredNeuralNetwork ann3 = new SmallLayeredNeuralNetwork();
+ ann3.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann3.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann3.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann3.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ ann3.setLearningRate(0.3);
+ // intentionally initialize all weights to 0.5
+ DoubleMatrix[] initMatrices = new DenseDoubleMatrix[2];
+ initMatrices[0] = new DenseDoubleMatrix(3, 3, 0.5);
+ initMatrices[1] = new DenseDoubleMatrix(1, 4, 0.5);
+ ann3.setWeightMatrices(initMatrices);
+
+ double[] instance = { 0, 1 };
+ DoubleVector output = ann3.getOutput(new DenseDoubleVector(instance));
+ assertEquals(0.8315410, output.get(0), 0.000001);
+ }
+
+ @Test
+ public void testXORlocal() {
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ ann.setLearningRate(0.5);
+ ann.setMomemtumWeight(0.0);
+
+ int iterations = 50000; // iteration should be set to a very large number
+ double[][] instances = { { 0, 1, 1 }, { 0, 0, 0 }, { 1, 0, 1 }, { 1, 1, 0 } };
+ for (int i = 0; i < iterations; ++i) {
+ DoubleMatrix[] matrices = null;
+ for (int j = 0; j < instances.length; ++j) {
+ matrices = ann.trainByInstance(new DenseDoubleVector(instances[j
+ % instances.length]));
+ ann.updateWeightMatrices(matrices);
+ }
+ }
+
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+ // the expected output is the last element in array
+ double result = instances[i][2];
+ double actual = ann.getOutput(input).get(0);
+ if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+ Log.info("Neural network failes to lear the XOR.");
+ }
+ }
+
+ // write model into file and read out
+ String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocal";
+ ann.setModelPath(modelPath);
+ try {
+ ann.writeModelToFile();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+ // test on instances
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+ // the expected output is the last element in array
+ double result = instances[i][2];
+ double actual = annCopy.getOutput(input).get(0);
+ if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+ Log.info("Neural network failes to lear the XOR.");
+ }
+ }
+ }
+
+ @Test
+ public void testXORWithMomentum() {
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ ann.setLearningRate(0.6);
+ ann.setMomemtumWeight(0.3);
+
+ int iterations = 2000; // iteration should be set to a very large number
+ double[][] instances = { { 0, 1, 1 }, { 0, 0, 0 }, { 1, 0, 1 }, { 1, 1, 0 } };
+ for (int i = 0; i < iterations; ++i) {
+ for (int j = 0; j < instances.length; ++j) {
+ ann.trainOnline(new DenseDoubleVector(instances[j % instances.length]));
+ }
+ }
+
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+ // the expected output is the last element in array
+ double result = instances[i][2];
+ double actual = ann.getOutput(input).get(0);
+ if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+ Log.info("Neural network failes to lear the XOR.");
+ }
+ }
+
+ // write model into file and read out
+ String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocalWithMomentum";
+ ann.setModelPath(modelPath);
+ try {
+ ann.writeModelToFile();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+ // test on instances
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+ // the expected output is the last element in array
+ double result = instances[i][2];
+ double actual = annCopy.getOutput(input).get(0);
+ if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+ Log.info("Neural network failes to lear the XOR.");
+ }
+ }
+ }
+
+ @Test
+ public void testXORLocalWithRegularization() {
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("SquaredError"));
+ ann.setLearningRate(0.7);
+ ann.setMomemtumWeight(0.5);
+ ann.setRegularizationWeight(0.002);
+
+ int iterations = 5000; // iteration should be set to a very large number
+ double[][] instances = { { 0, 1, 1 }, { 0, 0, 0 }, { 1, 0, 1 }, { 1, 1, 0 } };
+ for (int i = 0; i < iterations; ++i) {
+ for (int j = 0; j < instances.length; ++j) {
+ ann.trainOnline(new DenseDoubleVector(instances[j % instances.length]));
+ }
+ }
+
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+ // the expected output is the last element in array
+ double result = instances[i][2];
+ double actual = ann.getOutput(input).get(0);
+ if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+ Log.info("Neural network failes to lear the XOR.");
+ }
+ }
+
+ // write model into file and read out
+ String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocalWithRegularization";
+ ann.setModelPath(modelPath);
+ try {
+ ann.writeModelToFile();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+ SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+ // test on instances
+ for (int i = 0; i < instances.length; ++i) {
+ DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+ // the expected output is the last element in array
+ double result = instances[i][2];
+ double actual = annCopy.getOutput(input).get(0);
+ if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+ Log.info("Neural network failes to lear the XOR.");
+ }
+ }
+ }
+
+ @Test
+ public void testTwoClassClassification() {
+ // use logistic regression data
+ String filepath = "src/test/resources/logistic_regression_data.txt";
+ List<double[]> instanceList = new ArrayList<double[]>();
+
+ try {
+ BufferedReader br = new BufferedReader(new FileReader(filepath));
+ String line = null;
+ while ((line = br.readLine()) != null) {
+ String[] tokens = line.trim().split(",");
+ double[] instance = new double[tokens.length];
+ for (int i = 0; i < tokens.length; ++i) {
+ instance[i] = Double.parseDouble(tokens[i]);
+ }
+ instanceList.add(instance);
+ }
+ br.close();
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ }
+
+ zeroOneNormalization(instanceList, instanceList.get(0).length - 1);
+
+ int dimension = instanceList.get(0).length - 1;
+
+ // divide dataset into training and testing
+ List<double[]> testInstances = new ArrayList<double[]>();
+ testInstances.addAll(instanceList.subList(instanceList.size() - 100,
+ instanceList.size()));
+ List<double[]> trainingInstances = instanceList.subList(0,
+ instanceList.size() - 100);
+
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.setLearningRate(0.001);
+ ann.setMomemtumWeight(0.1);
+ ann.setRegularizationWeight(0.01);
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("CrossEntropy"));
+
+ long start = new Date().getTime();
+ int iterations = 1000;
+ for (int i = 0; i < iterations; ++i) {
+ for (double[] trainingInstance : trainingInstances) {
+ ann.trainOnline(new DenseDoubleVector(trainingInstance));
+ }
+ }
+ long end = new Date().getTime();
+ Log.info(String.format("Training time: %fs\n",
+ (double) (end - start) / 1000));
+
+ double errorRate = 0;
+ // calculate the error on test instance
+ for (double[] testInstance : testInstances) {
+ DoubleVector instance = new DenseDoubleVector(testInstance);
+ double expected = instance.get(instance.getDimension() - 1);
+ instance = instance.slice(instance.getDimension() - 1);
+ double actual = ann.getOutput(instance).get(0);
+ if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
+ ++errorRate;
+ }
+ }
+ errorRate /= testInstances.size();
+
+ Log.info(String.format("Relative error: %f%%\n", errorRate * 100));
+ }
+
+ @Test
+ public void testLogisticRegression() {
+ this.testLogisticRegressionDistributedVersion();
+ this.testLogisticRegressionDistributedVersionWithFeatureTransformer();
+ }
+
+ public void testLogisticRegressionDistributedVersion() {
+ // write data into a sequence file
+ String tmpStrDatasetPath = "/tmp/logistic_regression_data";
+ Path tmpDatasetPath = new Path(tmpStrDatasetPath);
+ String strDataPath = "src/test/resources/logistic_regression_data.txt";
+ String modelPath = "/tmp/logistic-regression-distributed-model";
+
+ Configuration conf = new Configuration();
+ List<double[]> instanceList = new ArrayList<double[]>();
+ List<double[]> trainingInstances = null;
+ List<double[]> testInstances = null;
+
+ try {
+ FileSystem fs = FileSystem.get(new URI(tmpStrDatasetPath), conf);
+ fs.delete(tmpDatasetPath, true);
+ if (fs.exists(tmpDatasetPath)) {
+ fs.createNewFile(tmpDatasetPath);
+ }
+
+ BufferedReader br = new BufferedReader(new FileReader(strDataPath));
+ String line = null;
+ int count = 0;
+ while ((line = br.readLine()) != null) {
+ String[] tokens = line.trim().split(",");
+ double[] instance = new double[tokens.length];
+ for (int i = 0; i < tokens.length; ++i) {
+ instance[i] = Double.parseDouble(tokens[i]);
+ }
+ instanceList.add(instance);
+ }
+ br.close();
+
+ zeroOneNormalization(instanceList, instanceList.get(0).length - 1);
+
+ // write training data to temporal sequence file
+ SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
+ tmpDatasetPath, LongWritable.class, VectorWritable.class);
+ int testSize = 150;
+
+ Collections.shuffle(instanceList);
+ testInstances = new ArrayList<double[]>();
+ testInstances.addAll(instanceList.subList(instanceList.size() - testSize,
+ instanceList.size()));
+ trainingInstances = instanceList.subList(0, instanceList.size()
+ - testSize);
+
+ for (double[] instance : trainingInstances) {
+ DoubleVector vec = new DenseDoubleVector(instance);
+ writer.append(new LongWritable(count++), new VectorWritable(vec));
+ }
+ writer.close();
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ }
+
+ // create model
+ int dimension = 8;
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.setLearningRate(0.7);
+ ann.setMomemtumWeight(0.5);
+ ann.setRegularizationWeight(0.1);
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("CrossEntropy"));
+ ann.setModelPath(modelPath);
+
+ long start = new Date().getTime();
+ Map<String, String> trainingParameters = new HashMap<String, String>();
+ trainingParameters.put("tasks", "5");
+ trainingParameters.put("training.max.iterations", "2000");
+ trainingParameters.put("training.batch.size", "300");
+ trainingParameters.put("convergence.check.interval", "1000");
+ ann.train(tmpDatasetPath, trainingParameters);
+
+ long end = new Date().getTime();
+
+ // validate results
+ double errorRate = 0;
+ // calculate the error on test instance
+ for (double[] testInstance : testInstances) {
+ DoubleVector instance = new DenseDoubleVector(testInstance);
+ double expected = instance.get(instance.getDimension() - 1);
+ instance = instance.slice(instance.getDimension() - 1);
+ double actual = ann.getOutput(instance).get(0);
+ if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
+ ++errorRate;
+ }
+ }
+ errorRate /= testInstances.size();
+
+ Log.info(String.format("Training time: %fs\n",
+ (double) (end - start) / 1000));
+ Log.info(String.format("Relative error: %f%%\n", errorRate * 100));
+ }
+
+ public void testLogisticRegressionDistributedVersionWithFeatureTransformer() {
+ // write data into a sequence file
+ String tmpStrDatasetPath = "/tmp/logistic_regression_data_feature_transformer";
+ Path tmpDatasetPath = new Path(tmpStrDatasetPath);
+ String strDataPath = "src/test/resources/logistic_regression_data.txt";
+ String modelPath = "/tmp/logistic-regression-distributed-model-feature-transformer";
+
+ Configuration conf = new Configuration();
+ List<double[]> instanceList = new ArrayList<double[]>();
+ List<double[]> trainingInstances = null;
+ List<double[]> testInstances = null;
+
+ try {
+ FileSystem fs = FileSystem.get(new URI(tmpStrDatasetPath), conf);
+ fs.delete(tmpDatasetPath, true);
+ if (fs.exists(tmpDatasetPath)) {
+ fs.createNewFile(tmpDatasetPath);
+ }
+
+ BufferedReader br = new BufferedReader(new FileReader(strDataPath));
+ String line = null;
+ int count = 0;
+ while ((line = br.readLine()) != null) {
+ String[] tokens = line.trim().split(",");
+ double[] instance = new double[tokens.length];
+ for (int i = 0; i < tokens.length; ++i) {
+ instance[i] = Double.parseDouble(tokens[i]);
+ }
+ instanceList.add(instance);
+ }
+ br.close();
+
+ zeroOneNormalization(instanceList, instanceList.get(0).length - 1);
+
+ // write training data to temporal sequence file
+ SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
+ tmpDatasetPath, LongWritable.class, VectorWritable.class);
+ int testSize = 150;
+
+ Collections.shuffle(instanceList);
+ testInstances = new ArrayList<double[]>();
+ testInstances.addAll(instanceList.subList(instanceList.size() - testSize,
+ instanceList.size()));
+ trainingInstances = instanceList.subList(0, instanceList.size()
+ - testSize);
+
+ for (double[] instance : trainingInstances) {
+ DoubleVector vec = new DenseDoubleVector(instance);
+ writer.append(new LongWritable(count++), new VectorWritable(vec));
+ }
+ writer.close();
+ } catch (FileNotFoundException e) {
+ e.printStackTrace();
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ }
+
+ // create model
+ int dimension = 8;
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ ann.setLearningRate(0.7);
+ ann.setMomemtumWeight(0.5);
+ ann.setRegularizationWeight(0.1);
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(dimension, false,
+ FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+ ann.setCostFunction(FunctionFactory
+ .createDoubleDoubleFunction("CrossEntropy"));
+ ann.setModelPath(modelPath);
+
+ FeatureTransformer featureTransformer = new DefaultFeatureTransformer();
+
+ ann.setFeatureTransformer(featureTransformer);
+
+ long start = new Date().getTime();
+ Map<String, String> trainingParameters = new HashMap<String, String>();
+ trainingParameters.put("tasks", "5");
+ trainingParameters.put("training.max.iterations", "2000");
+ trainingParameters.put("training.batch.size", "300");
+ trainingParameters.put("convergence.check.interval", "1000");
+ ann.train(tmpDatasetPath, trainingParameters);
+
+
+ long end = new Date().getTime();
+
+ // validate results
+ double errorRate = 0;
+ // calculate the error on test instance
+ for (double[] testInstance : testInstances) {
+ DoubleVector instance = new DenseDoubleVector(testInstance);
+ double expected = instance.get(instance.getDimension() - 1);
+ instance = instance.slice(instance.getDimension() - 1);
+ instance = featureTransformer.transform(instance);
+ double actual = ann.getOutput(instance).get(0);
+ if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
+ ++errorRate;
+ }
+ }
+ errorRate /= testInstances.size();
+
+ Log.info(String.format("Training time: %fs\n",
+ (double) (end - start) / 1000));
+ Log.info(String.format("Relative error: %f%%\n", errorRate * 100));
+ }
+
+}
diff --git a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
new file mode 100644
index 0000000..e422d95
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
@@ -0,0 +1,172 @@
+/**
+ * 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.horn.bsp;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+
+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.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.junit.Test;
+
+/**
+ * Test the functionalities of SmallLayeredNeuralNetworkMessage.
+ *
+ */
+public class TestSmallLayeredNeuralNetworkMessage {
+
+ @Test
+ public void testReadWriteWithoutPrev() {
+ double error = 0.22;
+ double[][] matrix1 = new double[][] { { 0.1, 0.2, 0.8, 0.5 },
+ { 0.3, 0.4, 0.6, 0.2 }, { 0.5, 0.6, 0.1, 0.5 } };
+ double[][] matrix2 = new double[][] { { 0.8, 1.2, 0.5 } };
+ DoubleMatrix[] matrices = new DoubleMatrix[2];
+ matrices[0] = new DenseDoubleMatrix(matrix1);
+ matrices[1] = new DenseDoubleMatrix(matrix2);
+
+ boolean isConverge = false;
+
+ SmallLayeredNeuralNetworkMessage message = new SmallLayeredNeuralNetworkMessage(
+ error, isConverge, matrices, null);
+ Configuration conf = new Configuration();
+ String strPath = "/tmp/testReadWriteSmallLayeredNeuralNetworkMessage";
+ Path path = new Path(strPath);
+ try {
+ FileSystem fs = FileSystem.get(new URI(strPath), conf);
+ FSDataOutputStream out = fs.create(path);
+ message.write(out);
+ out.close();
+
+ FSDataInputStream in = fs.open(path);
+ SmallLayeredNeuralNetworkMessage readMessage = new SmallLayeredNeuralNetworkMessage(
+ 0, isConverge, null, null);
+ readMessage.readFields(in);
+ in.close();
+ assertEquals(error, readMessage.getTrainingError(), 0.000001);
+ assertFalse(readMessage.isConverge());
+ DoubleMatrix[] readMatrices = readMessage.getCurMatrices();
+ assertEquals(2, readMatrices.length);
+ for (int i = 0; i < readMatrices.length; ++i) {
+ double[][] doubleMatrices = ((DenseDoubleMatrix) readMatrices[i])
+ .getValues();
+ double[][] doubleExpected = ((DenseDoubleMatrix) matrices[i])
+ .getValues();
+ for (int r = 0; r < doubleMatrices.length; ++r) {
+ assertArrayEquals(doubleExpected[r], doubleMatrices[r], 0.000001);
+ }
+ }
+
+ DoubleMatrix[] readPrevMatrices = readMessage.getPrevMatrices();
+ assertNull(readPrevMatrices);
+
+ // delete
+ fs.delete(path, true);
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ }
+ }
+
+ @Test
+ public void testReadWriteWithPrev() {
+ double error = 0.22;
+ boolean isConverge = true;
+
+ double[][] matrix1 = new double[][] { { 0.1, 0.2, 0.8, 0.5 },
+ { 0.3, 0.4, 0.6, 0.2 }, { 0.5, 0.6, 0.1, 0.5 } };
+ double[][] matrix2 = new double[][] { { 0.8, 1.2, 0.5 } };
+ DoubleMatrix[] matrices = new DoubleMatrix[2];
+ matrices[0] = new DenseDoubleMatrix(matrix1);
+ matrices[1] = new DenseDoubleMatrix(matrix2);
+
+ double[][] prevMatrix1 = new double[][] { { 0.1, 0.1, 0.2, 0.3 },
+ { 0.2, 0.4, 0.1, 0.5 }, { 0.5, 0.1, 0.5, 0.2 } };
+ double[][] prevMatrix2 = new double[][] { { 0.1, 0.2, 0.5, 0.9 },
+ { 0.3, 0.5, 0.2, 0.6 }, { 0.6, 0.8, 0.7, 0.5 } };
+
+ DoubleMatrix[] prevMatrices = new DoubleMatrix[2];
+ prevMatrices[0] = new DenseDoubleMatrix(prevMatrix1);
+ prevMatrices[1] = new DenseDoubleMatrix(prevMatrix2);
+
+ SmallLayeredNeuralNetworkMessage message = new SmallLayeredNeuralNetworkMessage(
+ error, isConverge, matrices, prevMatrices);
+ Configuration conf = new Configuration();
+ String strPath = "/tmp/testReadWriteSmallLayeredNeuralNetworkMessageWithPrev";
+ Path path = new Path(strPath);
+ try {
+ FileSystem fs = FileSystem.get(new URI(strPath), conf);
+ FSDataOutputStream out = fs.create(path);
+ message.write(out);
+ out.close();
+
+ FSDataInputStream in = fs.open(path);
+ SmallLayeredNeuralNetworkMessage readMessage = new SmallLayeredNeuralNetworkMessage(
+ 0, isConverge, null, null);
+ readMessage.readFields(in);
+ in.close();
+
+ assertTrue(readMessage.isConverge());
+
+ DoubleMatrix[] readMatrices = readMessage.getCurMatrices();
+ assertEquals(2, readMatrices.length);
+ for (int i = 0; i < readMatrices.length; ++i) {
+ double[][] doubleMatrices = ((DenseDoubleMatrix) readMatrices[i])
+ .getValues();
+ double[][] doubleExpected = ((DenseDoubleMatrix) matrices[i])
+ .getValues();
+ for (int r = 0; r < doubleMatrices.length; ++r) {
+ assertArrayEquals(doubleExpected[r], doubleMatrices[r], 0.000001);
+ }
+ }
+
+ DoubleMatrix[] readPrevMatrices = readMessage.getPrevMatrices();
+ assertEquals(2, readPrevMatrices.length);
+ for (int i = 0; i < readPrevMatrices.length; ++i) {
+ double[][] doubleMatrices = ((DenseDoubleMatrix) readPrevMatrices[i])
+ .getValues();
+ double[][] doubleExpected = ((DenseDoubleMatrix) prevMatrices[i])
+ .getValues();
+ for (int r = 0; r < doubleMatrices.length; ++r) {
+ assertArrayEquals(doubleExpected[r], doubleMatrices[r], 0.000001);
+ }
+ }
+
+ // delete
+ fs.delete(path, true);
+ } catch (IOException e) {
+ e.printStackTrace();
+ } catch (URISyntaxException e) {
+ e.printStackTrace();
+ }
+ }
+
+}
diff --git a/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java b/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
new file mode 100644
index 0000000..462140c
--- /dev/null
+++ b/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
@@ -0,0 +1,140 @@
+/**
+ * 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.horn.examples;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+import junit.framework.TestCase;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.SequenceFile;
+import org.apache.hama.HamaConfiguration;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleVector;
+
+/**
+ * Test the functionality of NeuralNetwork Example.
+ *
+ */
+public class NeuralNetworkTest extends TestCase {
+ private Configuration conf = new HamaConfiguration();
+ private FileSystem fs;
+ private String MODEL_PATH = "/tmp/neuralnets.model";
+ private String RESULT_PATH = "/tmp/neuralnets.txt";
+ private String SEQTRAIN_DATA = "/tmp/test-neuralnets.data";
+
+ @Override
+ protected void setUp() throws Exception {
+ super.setUp();
+ fs = FileSystem.get(conf);
+ }
+
+ public void testNeuralnetsLabeling() throws IOException {
+ this.neuralNetworkTraining();
+
+ String dataPath = "src/test/resources/neuralnets_classification_test.txt";
+ String mode = "label";
+ try {
+ NeuralNetwork
+ .main(new String[] { mode, dataPath, RESULT_PATH, MODEL_PATH });
+
+ // compare results with ground-truth
+ BufferedReader groundTruthReader = new BufferedReader(new FileReader(
+ "src/test/resources/neuralnets_classification_label.txt"));
+ List<Double> groundTruthList = new ArrayList<Double>();
+ String line = null;
+ while ((line = groundTruthReader.readLine()) != null) {
+ groundTruthList.add(Double.parseDouble(line));
+ }
+ groundTruthReader.close();
+
+ BufferedReader resultReader = new BufferedReader(new FileReader(
+ RESULT_PATH));
+ List<Double> resultList = new ArrayList<Double>();
+ while ((line = resultReader.readLine()) != null) {
+ resultList.add(Double.parseDouble(line));
+ }
+ resultReader.close();
+ int total = resultList.size();
+ double correct = 0;
+ for (int i = 0; i < groundTruthList.size(); ++i) {
+ double actual = resultList.get(i);
+ double expected = groundTruthList.get(i);
+ if (actual < 0.5 && expected < 0.5 || actual >= 0.5 && expected >= 0.5) {
+ ++correct;
+ }
+ }
+ System.out.printf("Precision: %f\n", correct / total);
+
+ } catch (Exception e) {
+ e.printStackTrace();
+ } finally {
+ fs.delete(new Path(RESULT_PATH), true);
+ fs.delete(new Path(MODEL_PATH), true);
+ fs.delete(new Path(SEQTRAIN_DATA), true);
+ }
+ }
+
+ private void neuralNetworkTraining() {
+ String mode = "train";
+ String strTrainingDataPath = "src/test/resources/neuralnets_classification_training.txt";
+ int featureDimension = 8;
+ int labelDimension = 1;
+
+ Path sequenceTrainingDataPath = new Path(SEQTRAIN_DATA);
+ Configuration conf = new Configuration();
+ FileSystem fs;
+ try {
+ fs = FileSystem.get(conf);
+ SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
+ sequenceTrainingDataPath, LongWritable.class, VectorWritable.class);
+ BufferedReader br = new BufferedReader(
+ new FileReader(strTrainingDataPath));
+ String line = null;
+ // convert the data in sequence file format
+ while ((line = br.readLine()) != null) {
+ String[] tokens = line.split(",");
+ double[] vals = new double[tokens.length];
+ for (int i = 0; i < tokens.length; ++i) {
+ vals[i] = Double.parseDouble(tokens[i]);
+ }
+ writer.append(new LongWritable(), new VectorWritable(
+ new DenseDoubleVector(vals)));
+ }
+ writer.close();
+ br.close();
+ } catch (IOException e1) {
+ e1.printStackTrace();
+ }
+
+ try {
+ NeuralNetwork.main(new String[] { mode, SEQTRAIN_DATA,
+ MODEL_PATH, "" + featureDimension, "" + labelDimension });
+ } catch (Exception e) {
+ e.printStackTrace();
+ }
+ }
+
+}
diff --git a/src/test/resources/dimensional_reduction.txt b/src/test/resources/dimensional_reduction.txt
new file mode 100644
index 0000000..0528d42
--- /dev/null
+++ b/src/test/resources/dimensional_reduction.txt
@@ -0,0 +1,1600 @@
+-5.214647 7.087974 6.729247
+-0.421762 8.426638 7.896089
+-6.134601 5.686436 6.088567
+6.212569 8.307839 2.362335
+6.344603 7.664955 0.532386
+0.098673 6.992156 7.840777
+2.261138 8.088781 7.203263
+6.319323 6.770813 1.717501
+3.039391 6.589591 6.782867
+5.188841 7.283918 -2.624659
+-2.593684 7.969614 7.754495
+0.757685 7.698148 7.719040
+-2.493988 7.895625 7.774168
+-6.348292 9.235869 5.912167
+5.811158 8.661124 3.604759
+5.084188 9.415847 4.881474
+3.233400 10.133548 6.658328
+-2.671727 5.834501 7.738187
+-5.586962 6.801060 6.491443
+-0.300033 7.474045 7.886303
+2.959122 8.991690 6.831929
+-6.183061 8.313371 6.049571
+4.871536 9.088393 5.161636
+-7.851500 8.035905 4.251177
+6.314192 6.764987 0.220085
+4.037797 7.132615 6.039136
+4.866675 7.269669 5.167711
+2.389975 6.966941 -4.607930
+6.036700 7.196553 3.010703
+3.487451 7.463430 6.481716
+5.128315 6.459363 4.819489
+-3.312319 8.552966 7.573618
+5.381252 7.880799 4.433421
+-2.296213 7.214618 7.809371
+4.936740 7.142252 5.078796
+6.356512 7.265930 0.749298
+-5.989940 7.693886 6.201601
+-0.330259 8.197873 7.888911
+6.353076 7.452645 1.306126
+0.739180 7.147566 7.723272
+2.828880 6.862924 6.908594
+4.856910 6.987142 5.179873
+5.657584 8.035364 3.933724
+-1.239718 8.616024 7.912864
+-5.292073 8.727537 6.682023
+4.630170 6.844327 5.447683
+-7.776487 6.028616 4.357342
+0.254217 8.814937 7.817341
+-5.268034 7.535614 6.696807
+5.380876 8.718970 4.434040
+-1.523348 8.209247 7.898946
+4.738628 8.587521 -3.159172
+-9.342977 7.790923 -2.589265
+3.885419 6.894194 6.170687
+-2.898182 3.924822 7.686316
+-6.854335 6.854464 5.444460
+-3.369974 7.406866 7.556063
+6.350405 6.123195 0.621545
+4.233985 6.918638 5.858301
+4.322963 6.955515 5.771680
+-0.239685 7.946362 7.880742
+1.511782 7.418960 7.504429
+2.047813 9.715201 7.298597
+-9.452372 8.323733 -0.308520
+-9.453994 6.873090 -0.331165
+-9.253227 6.825797 -3.051144
+-6.102497 8.076519 6.114084
+6.258758 7.557662 2.129656
+4.954859 8.240420 -2.919076
+6.050266 8.209733 2.968663
+-2.841788 6.648304 7.699870
+3.024167 7.917924 6.792281
+3.114077 8.100438 6.735939
+3.632127 6.326400 6.373764
+-6.966740 7.420432 5.329874
+-5.306197 7.406685 6.673285
+3.354050 4.534522 6.576440
+-1.342129 6.924848 7.909008
+-8.440894 8.444134 3.276504
+3.953765 5.767462 6.112599
+-0.353391 7.464577 -4.622600
+-5.600283 7.198775 6.482420
+-1.138683 6.957302 -4.305837
+1.287620 5.885861 7.577024
+3.140525 7.719961 6.719019
+4.439921 8.120649 5.653091
+5.843908 8.567449 3.528124
+-1.999807 7.143920 7.852706
+-6.998283 8.320347 5.296949
+-1.641686 8.000756 7.890141
+-0.637122 7.255327 7.908746
+3.545277 8.370231 6.439237
+-5.057813 8.143913 6.821470
+-1.788540 7.325451 7.876755
+3.231971 5.134089 -4.275558
+0.014418 9.135857 7.852132
+-1.049889 8.435574 7.916518
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\ No newline at end of file
diff --git a/src/test/resources/logistic_regression_data.txt b/src/test/resources/logistic_regression_data.txt
new file mode 100644
index 0000000..b874bd8
--- /dev/null
+++ b/src/test/resources/logistic_regression_data.txt
@@ -0,0 +1,768 @@
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\ No newline at end of file
diff --git a/src/test/resources/neuralnets_classification_label.txt b/src/test/resources/neuralnets_classification_label.txt
new file mode 100644
index 0000000..e1b6789
--- /dev/null
+++ b/src/test/resources/neuralnets_classification_label.txt
Binary files differ
diff --git a/src/test/resources/neuralnets_classification_test.txt b/src/test/resources/neuralnets_classification_test.txt
new file mode 100644
index 0000000..b19107d
--- /dev/null
+++ b/src/test/resources/neuralnets_classification_test.txt
Binary files differ
diff --git a/src/test/resources/neuralnets_classification_training.txt b/src/test/resources/neuralnets_classification_training.txt
new file mode 100644
index 0000000..405fb69
--- /dev/null
+++ b/src/test/resources/neuralnets_classification_training.txt
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\ No newline at end of file
