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From yxji...@apache.org
Subject svn commit: r1564008 - in /hama/trunk: ./ commons/src/main/java/org/apache/hama/commons/math/ core/ examples/src/main/java/org/apache/hama/examples/ examples/src/main/java/org/apache/hama/examples/util/ examples/src/test/java/org/apache/hama/examples/ ...
Date Mon, 03 Feb 2014 19:15:16 GMT
Author: yxjiang
Date: Mon Feb  3 19:15:15 2014
New Revision: 1564008

URL: http://svn.apache.org/r1564008
Log:
remove cli2

Removed:
    hama/trunk/examples/src/main/java/org/apache/hama/examples/util/ParserUtil.java
Modified:
    hama/trunk/CHANGES.txt
    hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java
    hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java
    hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java
    hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
    hama/trunk/core/pom.xml
    hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
    hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
    hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java
    hama/trunk/pom.xml

Modified: hama/trunk/CHANGES.txt
URL: http://svn.apache.org/viewvc/hama/trunk/CHANGES.txt?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/CHANGES.txt (original)
+++ hama/trunk/CHANGES.txt Mon Feb  3 19:15:15 2014
@@ -3,7 +3,8 @@ Hama Change Log
 Release 0.7.0 (unreleased changes)
 
   NEW FEATURES
-  
+   
+   HAMA-864: Fix/improve DoubleVector and DenseDoubleVector (Yexi Jiang)
    HAMA-842: Add persistent queue option to JobConf (edwardyoon)
    HAMA-839: Support NullWritable in Hama Pipes (Martin Illecker)    
    HAMA-837: Add sort behaviour to runtime partitioner (edwardyoon)
@@ -26,7 +27,6 @@ Release 0.7.0 (unreleased changes)
 
   IMPROVEMENTS
 
-   HAMA-859: Leverage commons cli2 to parse the input argument for NeuralNetwork Example
(Yexi Jiang)
    HAMA-853: Refactor Outgoing message manager (edwardyoon)
    HAMA-852: Add MessageClass property in BSPJob (Martin Illecker)
    HAMA-843: Message communication overhead between master aggregation and vertex computation
supersteps (edwardyoon)

Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DenseDoubleVector.java Mon
Feb  3 19:15:15 2014
@@ -127,36 +127,6 @@ public final class DenseDoubleVector imp
 
   /*
    * (non-Javadoc)
-   * @see de.jungblut.math.DoubleVector#apply(de.jungblut.math.function.
-   * DoubleVectorFunction)
-   */
-  @Deprecated
-  @Override
-  public DoubleVector apply(DoubleVectorFunction func) {
-    DenseDoubleVector newV = new DenseDoubleVector(this.vector);
-    for (int i = 0; i < vector.length; i++) {
-      newV.vector[i] = func.calculate(i, vector[i]);
-    }
-    return newV;
-  }
-
-  /*
-   * (non-Javadoc)
-   * @see de.jungblut.math.DoubleVector#apply(de.jungblut.math.DoubleVector,
-   * de.jungblut.math.function.DoubleDoubleVectorFunction)
-   */
-  @Deprecated
-  @Override
-  public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func) {
-    DenseDoubleVector newV = (DenseDoubleVector) deepCopy();
-    for (int i = 0; i < vector.length; i++) {
-      newV.vector[i] = func.calculate(i, vector[i], other.get(i));
-    }
-    return newV;
-  }
-
-  /*
-   * (non-Javadoc)
    * @see de.jungblut.math.DoubleVector#add(de.jungblut.math.DoubleVector)
    */
   @Override

Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/DoubleVector.java Mon Feb
 3 19:15:15 2014
@@ -66,27 +66,6 @@ public interface DoubleVector {
    * @param func the function to apply.
    * @return a new vector with the applied function.
    */
-  @Deprecated
-  public DoubleVector apply(DoubleVectorFunction func);
-
-  /**
-   * Apply a given {@link DoubleDoubleVectorFunction} to this vector and the
-   * other given vector.
-   * 
-   * @param other the other vector.
-   * @param func the function to apply on this and the other vector.
-   * @return a new vector with the result of the function of the two vectors.
-   */
-  @Deprecated
-  public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func);
-
-  /**
-   * Apply a given {@link DoubleVectorFunction} to this vector and return a new
-   * one.
-   * 
-   * @param func the function to apply.
-   * @return a new vector with the applied function.
-   */
   public DoubleVector applyToElements(DoubleFunction func);
 
   /**
@@ -331,18 +310,22 @@ public interface DoubleVector {
   public Iterator<DoubleVectorElement> iterate();
 
   /**
+   * Return whether the vector is a sparse vector.
    * @return true if this instance is a sparse vector. Smarter and faster than
    *         instanceof.
    */
   public boolean isSparse();
 
   /**
+   * Return whether the vector is a named vector.
    * @return true if this instance is a named vector.Smarter and faster than
    *         instanceof.
    */
   public boolean isNamed();
 
   /**
+   * Get the name of the vector. 
+   * 
    * @return If this vector is a named instance, this will return its name. Or
    *         null if this is not a named instance.
    * 

Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java (original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/NamedDoubleVector.java Mon
Feb  3 19:15:15 2014
@@ -51,18 +51,6 @@ public final class NamedDoubleVector imp
   }
 
   @Override
-  @Deprecated
-  public DoubleVector apply(DoubleVectorFunction func) {
-    return vector.apply(func);
-  }
-
-  @Override
-  @Deprecated
-  public DoubleVector apply(DoubleVector other, DoubleDoubleVectorFunction func) {
-    return vector.apply(other, func);
-  }
-
-  @Override
   public DoubleVector applyToElements(DoubleFunction func) {
     return vector.applyToElements(func);
   }
@@ -238,8 +226,9 @@ public final class NamedDoubleVector imp
     return name;
   }
   
+  @Override
   public String toString() {
-    return name + ": " + vector.toString();
+    return String.format("%s: %s", name, vector.toArray());
   }
 
 }

Modified: hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
URL: http://svn.apache.org/viewvc/hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
(original)
+++ hama/trunk/commons/src/main/java/org/apache/hama/commons/math/SquareVectorFunction.java
Mon Feb  3 19:15:15 2014
@@ -17,12 +17,22 @@
  */
 package org.apache.hama.commons.math;
 
-@SuppressWarnings("deprecation")
-public class SquareVectorFunction implements DoubleVectorFunction {
+public class SquareVectorFunction extends DoubleFunction {
 
+  /* (non-Javadoc)
+   * @see org.apache.hama.commons.math.DoubleFunction#apply(double)
+   */
   @Override
-  public double calculate(int index, double value) {
-    return Math.pow(value, 2);
+  public double apply(double value) {
+    return value * value;
+  }
+
+  /* (non-Javadoc)
+   * @see org.apache.hama.commons.math.DoubleFunction#applyDerivative(double)
+   */
+  @Override
+  public double applyDerivative(double value) {
+    throw new UnsupportedOperationException();
   }
 
 }

Modified: hama/trunk/core/pom.xml
URL: http://svn.apache.org/viewvc/hama/trunk/core/pom.xml?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/core/pom.xml (original)
+++ hama/trunk/core/pom.xml Mon Feb  3 19:15:15 2014
@@ -51,6 +51,10 @@
       <artifactId>commons-logging</artifactId>
     </dependency>
     <dependency>
+      <groupId>commons-cli</groupId>
+      <artifactId>commons-cli</artifactId>
+    </dependency>
+    <dependency>
       <groupId>commons-configuration</groupId>
       <artifactId>commons-configuration</artifactId>
     </dependency>

Modified: hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
URL: http://svn.apache.org/viewvc/hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java (original)
+++ hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java Mon Feb
 3 19:15:15 2014
@@ -23,288 +23,194 @@ import java.io.InputStreamReader;
 import java.io.OutputStreamWriter;
 import java.net.URI;
 import java.util.HashMap;
-import java.util.List;
 import java.util.Map;
 
-import org.apache.commons.cli2.CommandLine;
-import org.apache.commons.cli2.Group;
-import org.apache.commons.cli2.Option;
-import org.apache.commons.cli2.builder.ArgumentBuilder;
-import org.apache.commons.cli2.builder.DefaultOptionBuilder;
-import org.apache.commons.cli2.builder.GroupBuilder;
-import org.apache.commons.cli2.commandline.Parser;
-import org.apache.commons.cli2.util.HelpFormatter;
 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.examples.util.ParserUtil;
 import org.apache.hama.ml.ann.SmallLayeredNeuralNetwork;
 
-import com.google.common.io.Closeables;
-
 /**
  * The example of using {@link SmallLayeredNeuralNetwork}, including the
  * training phase and labeling phase.
  */
 public class NeuralNetwork {
-  // either train or label
-  private static String mode;
-
-  // arguments for labeling
-  private static String featureDataPath;
-  private static String resultDataPath;
-  private static String modelPath;
-
-  // arguments for training
-  private static String trainingDataPath;
-  private static int featureDimension;
-  private static int labelDimension;
-  private static List<Integer> hiddenLayerDimension;
-  private static int iterations;
-  private static double learningRate;
-  private static double momemtumWeight;
-  private static double regularizationWeight;
-
-  public static boolean parseArgs(String[] args) {
-    DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
-    GroupBuilder groupBuilder = new GroupBuilder();
-    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
-
-    // the feature data (unlabeled data) path argument
-    Option featureDataPathOption = optionBuilder
-        .withLongName("feature-data-path")
-        .withShortName("fp")
-        .withDescription("the path of the feature data (unlabeled data).")
-        .withArgument(
-            argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
-                .create()).withRequired(true).create();
-
-    // the result data path argument
-    Option resultDataPathOption = optionBuilder
-        .withLongName("result-data-path")
-        .withShortName("rp")
-        .withDescription("the path to store the result.")
-        .withArgument(
-            argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
-                .create()).withRequired(true).create();
-
-    // the path to store the model
-    Option modelPathOption = optionBuilder
-        .withLongName("model-data-path")
-        .withShortName("mp")
-        .withDescription("the path to store the trained model.")
-        .withArgument(
-            argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
-                .create()).withRequired(true).create();
-
-    // the path of the training data
-    Option trainingDataPathOption = optionBuilder
-        .withLongName("training-data-path")
-        .withShortName("tp")
-        .withDescription("the path to store the trained model.")
-        .withArgument(
-            argumentBuilder.withName("path").withMinimum(1).withMaximum(1)
-                .create()).withRequired(true).create();
-
-    // the dimension of the features
-    Option featureDimensionOption = optionBuilder
-        .withLongName("feature dimension")
-        .withShortName("fd")
-        .withDescription("the dimension of the features.")
-        .withArgument(
-            argumentBuilder.withName("dimension").withMinimum(1).withMaximum(1)
-                .create()).withRequired(true).create();
-
-    // the dimension of the hidden layers, at most two hidden layers
-    Option hiddenLayerOption = optionBuilder
-        .withLongName("hidden layer dimension(s)")
-        .withShortName("hd")
-        .withDescription("the dimension of the hidden layer(s).")
-        .withArgument(
-            argumentBuilder.withName("dimension").withMinimum(0).withMaximum(2)
-                .create()).withRequired(true).create();
-
-    // the dimension of the labels
-    Option labelDimensionOption = optionBuilder
-        .withLongName("label dimension")
-        .withShortName("ld")
-        .withDescription("the dimension of the label(s).")
-        .withArgument(
-            argumentBuilder.withName("dimension").withMinimum(1).withMaximum(1)
-                .create()).withRequired(true).create();
-
-    // the number of iterations for training
-    Option iterationOption = optionBuilder
-        .withLongName("iterations")
-        .withShortName("itr")
-        .withDescription("the iterations for training.")
-        .withArgument(
-            argumentBuilder.withName("iterations").withMinimum(1)
-                .withMaximum(1).withDefault(1000).create()).create();
-
-    // the learning rate
-    Option learningRateOption = optionBuilder
-        .withLongName("learning-rate")
-        .withShortName("l")
-        .withDescription("the learning rate for training, default 0.1.")
-        .withArgument(
-            argumentBuilder.withName("learning-rate").withMinimum(1)
-                .withMaximum(1).withDefault(0.1).create()).create();
-
-    // the momemtum weight
-    Option momentumWeightOption = optionBuilder
-        .withLongName("momemtum-weight")
-        .withShortName("m")
-        .withDescription("the momemtum weight for training, default 0.1.")
-        .withArgument(
-            argumentBuilder.withName("momemtum weight").withMinimum(1)
-                .withMaximum(1).withDefault(0.1).create()).create();
-
-    // the regularization weight
-    Option regularizationWeightOption = optionBuilder
-        .withLongName("regularization-weight")
-        .withShortName("r")
-        .withDescription("the regularization weight for training, default 0.")
-        .withArgument(
-            argumentBuilder.withName("regularization weight").withMinimum(1)
-                .withMaximum(1).withDefault(0).create()).create();
-
-    // the parameters related to train mode
-    Group trainModeGroup = groupBuilder.withOption(trainingDataPathOption)
-        .withOption(modelPathOption).withOption(featureDimensionOption)
-        .withOption(labelDimensionOption).withOption(hiddenLayerOption)
-        .withOption(iterationOption).withOption(learningRateOption)
-        .withOption(momentumWeightOption)
-        .withOption(regularizationWeightOption).create();
-
-    // the parameters related to label mode
-    Group labelModeGroup = groupBuilder.withOption(modelPathOption)
-        .withOption(featureDataPathOption).withOption(resultDataPathOption)
-        .create();
-
-    Option trainModeOption = optionBuilder.withLongName("train")
-        .withShortName("train").withDescription("the train mode")
-        .withChildren(trainModeGroup).create();
-
-    Option labelModeOption = optionBuilder.withLongName("label")
-        .withShortName("label").withChildren(labelModeGroup)
-        .withDescription("the label mode").create();
-
-    Group normalGroup = groupBuilder.withOption(trainModeOption)
-        .withOption(labelModeOption).create();
-
-    Parser parser = new Parser();
-    parser.setGroup(normalGroup);
-    parser.setHelpFormatter(new HelpFormatter());
-    parser.setHelpTrigger("--help");
-    CommandLine cli = parser.parseAndHelp(args);
-    if (cli == null) {
-      return false;
-    }
 
-    // get the arguments
-    boolean hasTrainMode = cli.hasOption(trainModeOption);
-    boolean hasLabelMode = cli.hasOption(labelModeOption);
-    if (hasTrainMode && hasLabelMode) {
-      return false;
+  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();
 
-    mode = hasTrainMode ? "train" : "label";
-    if (mode.equals("train")) {
-      trainingDataPath = ParserUtil.getString(cli, trainingDataPathOption);
-      modelPath = ParserUtil.getString(cli, modelPathOption);
-      featureDimension = ParserUtil.getInteger(cli, featureDimensionOption);
-      labelDimension = ParserUtil.getInteger(cli, labelDimensionOption);
-      hiddenLayerDimension = ParserUtil.getInts(cli, hiddenLayerOption);
-      iterations = ParserUtil.getInteger(cli, iterationOption);
-      learningRate = ParserUtil.getDouble(cli, learningRateOption);
-      momemtumWeight = ParserUtil.getDouble(cli, momentumWeightOption);
-      regularizationWeight = ParserUtil.getDouble(cli,
-          regularizationWeightOption);
-    } else {
-      featureDataPath = ParserUtil.getString(cli, featureDataPathOption);
-      modelPath = ParserUtil.getString(cli, modelPathOption);
-      resultDataPath = ParserUtil.getString(cli, resultDataPathOption);
-    }
+      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)));
 
-    return true;
-  }
+      String line = null;
 
-  public static void main(String[] args) throws Exception {
-    if (parseArgs(args)) {
-      if (mode.equals("label")) {
-        HamaConfiguration conf = new HamaConfiguration();
-        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);
+      while ((line = br.readLine()) != null) {
+        if (line.trim().length() == 0) {
+          continue;
         }
-        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");
-            }
+        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());
         }
+        bw.write(sb.toString());
+      }
 
-        Closeables.close(br, true);
-        Closeables.close(bw, true);
-      } else { // train the model
-        SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
-        ann.setLearningRate(learningRate);
-        ann.setMomemtumWeight(momemtumWeight);
-        ann.setRegularizationWeight(regularizationWeight);
-        ann.addLayer(featureDimension, false,
-            FunctionFactory.createDoubleFunction("Sigmoid"));
-        if (hiddenLayerDimension != null) {
-          for (int dimension : hiddenLayerDimension) {
-            ann.addLayer(dimension, false,
-                FunctionFactory.createDoubleFunction("Sigmoid"));
-          }
+      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;
         }
-        ann.addLayer(labelDimension, true,
-            FunctionFactory.createDoubleFunction("Sigmoid"));
-        ann.setCostFunction(FunctionFactory
-            .createDoubleDoubleFunction("CrossEntropy"));
-        ann.setModelPath(modelPath);
-
-        Map<String, String> trainingParameters = new HashMap<String, String>();
-        trainingParameters.put("tasks", "5");
-        trainingParameters.put("training.max.iterations", "" + iterations);
-        trainingParameters.put("training.batch.size", "300");
-        trainingParameters.put("convergence.check.interval", "1000");
-        ann.train(new Path(trainingDataPath), trainingParameters);
       }
+      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");
   }
 
 }

Modified: hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
URL: http://svn.apache.org/viewvc/hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java (original)
+++ hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java Mon
Feb  3 19:15:15 2014
@@ -23,6 +23,8 @@ 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;
@@ -31,34 +33,32 @@ 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;
-import org.junit.Before;
-import org.junit.Test;
 
 /**
  * Test the functionality of NeuralNetwork Example.
  * 
  */
-public class NeuralNetworkTest {
+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";
-
-  @Before
-  public void setup() throws Exception {
+  
+  @Override
+  protected void setUp() throws Exception {
+    super.setUp();
     fs = FileSystem.get(conf);
   }
 
-  @Test
   public void testNeuralnetsLabeling() throws IOException {
     this.neuralNetworkTraining();
 
     String dataPath = "src/test/resources/neuralnets_classification_test.txt";
-    String mode = "-label";
+    String mode = "label";
     try {
       NeuralNetwork
-          .main(new String[] { mode, "-fp", dataPath, "-rp", RESULT_PATH, "-mp", MODEL_PATH
});
+          .main(new String[] { mode, dataPath, RESULT_PATH, MODEL_PATH });
 
       // compare results with ground-truth
       BufferedReader groundTruthReader = new BufferedReader(new FileReader(
@@ -98,7 +98,7 @@ public class NeuralNetworkTest {
   }
 
   private void neuralNetworkTraining() {
-    String mode = "-train";
+    String mode = "train";
     String strTrainingDataPath = "src/test/resources/neuralnets_classification_training.txt";
     int featureDimension = 8;
     int labelDimension = 1;
@@ -130,9 +130,8 @@ public class NeuralNetworkTest {
     }
 
     try {
-      NeuralNetwork.main(new String[] { mode, "-tp", SEQTRAIN_DATA, "-mp",
-          MODEL_PATH, "-fd", "" + featureDimension, "-hd",
-          "" + featureDimension, "-ld", "" + labelDimension, "-itr", "3000", "-m", "0.2",
"-l", "0.2" });
+      NeuralNetwork.main(new String[] { mode, SEQTRAIN_DATA,
+          MODEL_PATH, "" + featureDimension, "" + labelDimension });
     } catch (Exception e) {
       e.printStackTrace();
     }

Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java (original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/recommendation/cf/OnlineCF.java Mon Feb
 3 19:15:15 2014
@@ -473,7 +473,7 @@ public class OnlineCF implements Recomme
     // Euclidean distance
     return Math.pow( usr1Vector
                     .subtract(usr2Vector)
-                    .apply(new SquareVectorFunction())
+                    .applyToElements(new SquareVectorFunction())
                     .sum() , 0.5);
   }
 
@@ -514,7 +514,7 @@ public class OnlineCF implements Recomme
     // Euclidean distance
     return Math.pow( itm1Vector
                       .subtract(itm2Vector)
-                      .apply(new SquareVectorFunction())
+                      .applyToElements(new SquareVectorFunction())
                       .sum() , 0.5);
   }
 

Modified: hama/trunk/pom.xml
URL: http://svn.apache.org/viewvc/hama/trunk/pom.xml?rev=1564008&r1=1564007&r2=1564008&view=diff
==============================================================================
--- hama/trunk/pom.xml (original)
+++ hama/trunk/pom.xml Mon Feb  3 19:15:15 2014
@@ -87,7 +87,7 @@
   <properties>
     <!-- Dependencies -->
     <commons-logging.version>1.1.1</commons-logging.version>
-    <commons-cli.version>2.0-SNAPSHOT</commons-cli.version>
+    <commons-cli.version>1.2</commons-cli.version>
     <commons-configuration>1.7</commons-configuration>
     <commons-lang>2.6</commons-lang>
     <commons-httpclient>3.0.1</commons-httpclient>



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