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From yxji...@apache.org
Subject svn commit: r1549842 - in /hama/trunk: ./ ml/src/main/java/org/apache/hama/ml/ann/ ml/src/test/java/org/apache/hama/ml/ann/
Date Tue, 10 Dec 2013 13:41:44 GMT
Author: yxjiang
Date: Tue Dec 10 13:41:43 2013
New Revision: 1549842

URL: http://svn.apache.org/r1549842
Log:
HAMA-828: Improve code, fix typo and modify unclear comment in org.apache.hama.ml.ann package

Modified:
    hama/trunk/CHANGES.txt
    hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AbstractLayeredNeuralNetwork.java
    hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/NeuralNetwork.java
    hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetwork.java
    hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetworkMessage.java
    hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetwork.java

Modified: hama/trunk/CHANGES.txt
URL: http://svn.apache.org/viewvc/hama/trunk/CHANGES.txt?rev=1549842&r1=1549841&r2=1549842&view=diff
==============================================================================
--- hama/trunk/CHANGES.txt (original)
+++ hama/trunk/CHANGES.txt Tue Dec 10 13:41:43 2013
@@ -16,6 +16,7 @@ Release 0.7.0 (unreleased changes)
 
   IMPROVEMENTS
 
+   HAMA-828: Improve code, fix typo and modify unclear comment in org.apache.hama.ml.ann
package (Yexi Jiang)
    HAMA-699: Add commons module (Martin Illecker)
    HAMA-818: Remove useless comments in GroomServer (edwardyoon)
 

Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AbstractLayeredNeuralNetwork.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AbstractLayeredNeuralNetwork.java?rev=1549842&r1=1549841&r2=1549842&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AbstractLayeredNeuralNetwork.java (original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AbstractLayeredNeuralNetwork.java Tue
Dec 10 13:41:43 2013
@@ -31,6 +31,7 @@ import org.apache.hama.commons.math.Doub
 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
@@ -66,7 +67,7 @@ abstract class AbstractLayeredNeuralNetw
   protected LearningStyle learningStyle;
 
   public static enum TrainingMethod {
-    GRADIATE_DESCENT
+    GRADIENT_DESCENT
   }
   
   public static enum LearningStyle {
@@ -77,7 +78,7 @@ abstract class AbstractLayeredNeuralNetw
   public AbstractLayeredNeuralNetwork() {
     this.regularizationWeight = DEFAULT_REGULARIZATION_WEIGHT;
     this.momentumWeight = DEFAULT_MOMENTUM_WEIGHT;
-    this.trainingMethod = TrainingMethod.GRADIATE_DESCENT;
+    this.trainingMethod = TrainingMethod.GRADIENT_DESCENT;
     this.learningStyle = LearningStyle.SUPERVISED;
   }
 
@@ -229,7 +230,7 @@ abstract class AbstractLayeredNeuralNetw
 
     // read layer size list
     int numLayers = input.readInt();
-    this.layerSizeList = new ArrayList<Integer>();
+    this.layerSizeList = Lists.newArrayList();
     for (int i = 0; i < numLayers; ++i) {
       this.layerSizeList.add(input.readInt());
     }

Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/NeuralNetwork.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/NeuralNetwork.java?rev=1549842&r1=1549841&r2=1549842&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/NeuralNetwork.java (original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/NeuralNetwork.java Tue Dec 10 13:41:43
2013
@@ -39,6 +39,7 @@ import org.apache.hama.ml.util.DefaultFe
 import org.apache.hama.ml.util.FeatureTransformer;
 
 import com.google.common.base.Preconditions;
+import com.google.common.io.Closeables;
 
 /**
  * NeuralNetwork defines the general operations for all the derivative models.
@@ -85,7 +86,7 @@ abstract class NeuralNetwork implements 
    */
   public void setLearningRate(double learningRate) {
     Preconditions.checkArgument(learningRate > 0,
-        "Learning rate must larger than 0.");
+        "Learning rate must be larger than 0.");
     this.learningRate = learningRate;
   }
 
@@ -144,13 +145,16 @@ abstract class NeuralNetwork implements 
     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);
-      FSDataInputStream is = new FSDataInputStream(fs.open(new Path(modelPath)));
+      is = new FSDataInputStream(fs.open(new Path(modelPath)));
       this.readFields(is);
     } catch (URISyntaxException e) {
       e.printStackTrace();
+    } finally {
+      Closeables.close(is, false);
     }
   }
 
@@ -164,10 +168,17 @@ abstract class NeuralNetwork implements 
     Preconditions.checkArgument(this.modelPath != null,
         "Model path has not been set.");
     Configuration conf = new Configuration();
-    FileSystem fs = FileSystem.get(conf);
-    FSDataOutputStream stream = fs.create(new Path(this.modelPath), true);
-    this.write(stream);
-    stream.close();
+    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);
   }
 
   /**
@@ -215,7 +226,7 @@ abstract class NeuralNetwork implements 
     Constructor[] constructors = featureTransformerCls
         .getDeclaredConstructors();
     Constructor constructor = constructors[0];
-    
+
     try {
       this.featureTransformer = (FeatureTransformer) constructor
           .newInstance(new Object[] {});

Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetwork.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetwork.java?rev=1549842&r1=1549841&r2=1549842&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetwork.java (original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetwork.java Tue
Dec 10 13:41:43 2013
@@ -23,8 +23,8 @@ import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Map;
-import java.util.Random;
 
+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;
@@ -43,6 +43,7 @@ import org.apache.hama.commons.math.Func
 import org.mortbay.log.Log;
 
 import com.google.common.base.Preconditions;
+import com.google.common.collect.Lists;
 
 /**
  * SmallLayeredNeuralNetwork defines the general operations for derivative
@@ -70,10 +71,10 @@ public class SmallLayeredNeuralNetwork e
   protected int finalLayerIdx;
 
   public SmallLayeredNeuralNetwork() {
-    this.layerSizeList = new ArrayList<Integer>();
-    this.weightMatrixList = new ArrayList<DoubleMatrix>();
-    this.prevWeightUpdatesList = new ArrayList<DoubleMatrix>();
-    this.squashingFunctionList = new ArrayList<DoubleFunction>();
+    this.layerSizeList = Lists.newArrayList();
+    this.weightMatrixList = Lists.newArrayList();
+    this.prevWeightUpdatesList = Lists.newArrayList();
+    this.squashingFunctionList = Lists.newArrayList();
   }
 
   public SmallLayeredNeuralNetwork(String modelPath) {
@@ -86,7 +87,8 @@ public class SmallLayeredNeuralNetwork e
    */
   public int addLayer(int size, boolean isFinalLayer,
       DoubleFunction squashingFunction) {
-    Preconditions.checkArgument(size > 0, "Size of layer must larger than 0.");
+    Preconditions.checkArgument(size > 0,
+        "Size of layer must be larger than 0.");
     if (!isFinalLayer) {
       size += 1;
     }
@@ -107,11 +109,10 @@ public class SmallLayeredNeuralNetwork e
       int col = sizePrevLayer;
       DoubleMatrix weightMatrix = new DenseDoubleMatrix(row, col);
       // initialize weights
-      final Random rnd = new Random();
       weightMatrix.applyToElements(new DoubleFunction() {
         @Override
         public double apply(double value) {
-          return rnd.nextDouble() - 0.5;
+          return RandomUtils.nextDouble() - 0.5;
         }
 
         @Override
@@ -138,6 +139,10 @@ public class SmallLayeredNeuralNetwork e
     }
   }
 
+  /**
+   * Set the previous weight matrices.
+   * @param prevUpdates
+   */
   void setPrevWeightMatrices(DoubleMatrix[] prevUpdates) {
     this.prevWeightUpdatesList.clear();
     for (DoubleMatrix prevUpdate : prevUpdates) {
@@ -176,8 +181,8 @@ public class SmallLayeredNeuralNetwork e
    */
   public void setWeightMatrices(DoubleMatrix[] matrices) {
     this.weightMatrixList = new ArrayList<DoubleMatrix>();
-    for (int i = 0; i < matrices.length; ++i) {
-      this.weightMatrixList.add(matrices[i]);
+    for (DoubleMatrix matrix : matrices) {
+      this.weightMatrixList.add(matrix);
     }
   }
 
@@ -197,8 +202,9 @@ public class SmallLayeredNeuralNetwork e
 
   public void setWeightMatrix(int index, DoubleMatrix matrix) {
     Preconditions.checkArgument(
-        0 <= index && index < this.weightMatrixList.size(),
-        String.format("index [%d] out of range.", index));
+        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);
   }
 
@@ -208,7 +214,7 @@ public class SmallLayeredNeuralNetwork e
 
     // read squash functions
     int squashingFunctionSize = input.readInt();
-    this.squashingFunctionList = new ArrayList<DoubleFunction>();
+    this.squashingFunctionList = Lists.newArrayList();
     for (int i = 0; i < squashingFunctionSize; ++i) {
       this.squashingFunctionList.add(FunctionFactory
           .createDoubleFunction(WritableUtils.readString(input)));
@@ -216,8 +222,8 @@ public class SmallLayeredNeuralNetwork e
 
     // read weights and construct matrices of previous updates
     int numOfMatrices = input.readInt();
-    this.weightMatrixList = new ArrayList<DoubleMatrix>();
-    this.prevWeightUpdatesList = new ArrayList<DoubleMatrix>();
+    this.weightMatrixList = Lists.newArrayList();
+    this.prevWeightUpdatesList = Lists.newArrayList();
     for (int i = 0; i < numOfMatrices; ++i) {
       DoubleMatrix matrix = MatrixWritable.read(input);
       this.weightMatrixList.add(matrix);
@@ -257,8 +263,8 @@ public class SmallLayeredNeuralNetwork e
    */
   @Override
   public DoubleVector getOutput(DoubleVector instance) {
-    Preconditions.checkArgument(this.layerSizeList.get(0) == instance
-        .getDimension() + 1, String.format(
+    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
@@ -336,8 +342,6 @@ public class SmallLayeredNeuralNetwork e
   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;
@@ -389,11 +393,12 @@ public class SmallLayeredNeuralNetwork e
     calculateTrainingError(labels,
         output.deepCopy().sliceUnsafe(1, output.getDimension() - 1));
 
-    if (this.trainingMethod.equals(TrainingMethod.GRADIATE_DESCENT)) {
+    if (this.trainingMethod.equals(TrainingMethod.GRADIENT_DESCENT)) {
       return this.trainByInstanceGradientDescent(labels, internalResults);
+    } else {
+      throw new IllegalArgumentException(
+          String.format("Training method is not supported."));
     }
-    throw new IllegalArgumentException(
-        String.format("Training method is not supported."));
   }
 
   /**
@@ -483,9 +488,6 @@ public class SmallLayeredNeuralNetwork e
               * squashingFunction.applyDerivative(curLayerOutput.get(i)));
     }
 
-    // System.out.printf("Delta layer: %d, %s\n", curLayerIdx,
-    // delta.toString());
-
     // update weights
     for (int i = 0; i < weightUpdateMatrix.getRowCount(); ++i) {
       for (int j = 0; j < weightUpdateMatrix.getColumnCount(); ++j) {
@@ -495,9 +497,6 @@ public class SmallLayeredNeuralNetwork e
       }
     }
 
-    // System.out.printf("Weight Layer %d, %s\n", curLayerIdx,
-    // weightUpdateMatrix.toString());
-
     return delta;
   }
 
@@ -556,9 +555,7 @@ public class SmallLayeredNeuralNetwork e
   protected void calculateTrainingError(DoubleVector labels, DoubleVector output) {
     DoubleVector errors = labels.deepCopy().applyToElements(output,
         this.costFunction);
-    // System.out.printf("Labels: %s\tOutput: %s\n", labels, output);
     this.trainingError = errors.sum();
-    // System.out.printf("Training error: %s\n", errors);
   }
 
   /**

Modified: hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetworkMessage.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetworkMessage.java?rev=1549842&r1=1549841&r2=1549842&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetworkMessage.java
(original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetworkMessage.java
Tue Dec 10 13:41:43 2013
@@ -78,12 +78,12 @@ public class SmallLayeredNeuralNetworkMe
     } else {
       output.writeBoolean(true);
     }
-    for (int i = 0; i < curMatrices.length; ++i) {
-      MatrixWritable.write(curMatrices[i], output);
+    for (DoubleMatrix matrix : curMatrices) {
+      MatrixWritable.write(matrix, output);
     }
     if (prevMatrices != null) {
-      for (int i = 0; i < prevMatrices.length; ++i) {
-        MatrixWritable.write(prevMatrices[i], output);
+      for (DoubleMatrix matrix : prevMatrices) {
+        MatrixWritable.write(matrix, output);
       }
     }
   }

Modified: hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetwork.java
URL: http://svn.apache.org/viewvc/hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetwork.java?rev=1549842&r1=1549841&r2=1549842&view=diff
==============================================================================
--- hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetwork.java
(original)
+++ hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetwork.java
Tue Dec 10 13:41:43 2013
@@ -103,7 +103,7 @@ public class TestSmallLayeredNeuralNetwo
     assertEquals(momentumWeight, annCopy.getMomemtumWeight(), 0.000001);
     assertEquals(regularizationWeight, annCopy.getRegularizationWeight(),
         0.000001);
-    assertEquals(TrainingMethod.GRADIATE_DESCENT, annCopy.getTrainingMethod());
+    assertEquals(TrainingMethod.GRADIENT_DESCENT, annCopy.getTrainingMethod());
     assertEquals(LearningStyle.UNSUPERVISED, annCopy.getLearningStyle());
 
     // compare weights



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