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From edwardy...@apache.org
Subject [5/5] hama git commit: HAMA-961: Remove ann package
Date Mon, 23 Nov 2015 02:26:51 GMT
HAMA-961: Remove ann package


Project: http://git-wip-us.apache.org/repos/asf/hama/repo
Commit: http://git-wip-us.apache.org/repos/asf/hama/commit/3a3ea7a3
Tree: http://git-wip-us.apache.org/repos/asf/hama/tree/3a3ea7a3
Diff: http://git-wip-us.apache.org/repos/asf/hama/diff/3a3ea7a3

Branch: refs/heads/master
Commit: 3a3ea7a37743b5f3759a86460e63ae414b2e9081
Parents: 0225205
Author: Edward J. Yoon <edwardyoon@apache.org>
Authored: Mon Nov 23 11:10:32 2015 +0900
Committer: Edward J. Yoon <edwardyoon@apache.org>
Committed: Mon Nov 23 11:24:49 2015 +0900

----------------------------------------------------------------------
 .../apache/hama/commons/math/CrossEntropy.java  |  58 --
 .../hama/commons/math/FunctionFactory.java      |  65 --
 .../hama/commons/math/IdentityFunction.java     |  36 -
 .../org/apache/hama/commons/math/Sigmoid.java   |  39 --
 .../apache/hama/commons/math/SquaredError.java  |  46 --
 .../java/org/apache/hama/commons/math/Tanh.java |  36 -
 .../hama/commons/math/TestFunctionFactory.java  |  82 ---
 .../org/apache/hama/bsp/TestPartitioning.java   |   2 +-
 .../org/apache/hama/examples/ExampleDriver.java |   2 -
 .../org/apache/hama/examples/NeuralNetwork.java | 216 ------
 .../apache/hama/examples/NeuralNetworkTest.java | 140 ----
 .../neuralnets_classification_label.txt         |   1 -
 .../neuralnets_classification_test.txt          |   1 -
 .../neuralnets_classification_training.txt      | 668 -------------------
 .../ml/ann/AbstractLayeredNeuralNetwork.java    | 261 --------
 .../org/apache/hama/ml/ann/AutoEncoder.java     | 197 ------
 .../org/apache/hama/ml/ann/NeuralNetwork.java   | 271 --------
 .../hama/ml/ann/NeuralNetworkTrainer.java       | 107 ---
 .../hama/ml/ann/SmallLayeredNeuralNetwork.java  | 567 ----------------
 .../ann/SmallLayeredNeuralNetworkMessage.java   | 126 ----
 .../ann/SmallLayeredNeuralNetworkTrainer.java   | 244 -------
 .../apache/hama/ml/perception/MLPMessage.java   |  45 --
 .../ml/perception/MultiLayerPerceptron.java     | 203 ------
 .../hama/ml/perception/PerceptronTrainer.java   |  96 ---
 .../hama/ml/perception/SmallMLPMessage.java     | 133 ----
 .../hama/ml/perception/SmallMLPTrainer.java     | 327 ---------
 .../perception/SmallMultiLayerPerceptron.java   | 574 ----------------
 .../hama/ml/regression/LinearRegression.java    | 188 ------
 .../hama/ml/regression/LogisticRegression.java  | 180 -----
 .../org/apache/hama/ml/ann/TestAutoEncoder.java | 195 ------
 .../ml/ann/TestSmallLayeredNeuralNetwork.java   | 643 ------------------
 .../TestSmallLayeredNeuralNetworkMessage.java   | 172 -----
 .../hama/ml/perception/TestSmallMLPMessage.java | 147 ----
 .../TestSmallMultiLayerPerceptron.java          | 524 ---------------
 .../ml/regression/TestLinearRegression.java     | 133 ----
 .../ml/regression/TestLogisticRegression.java   | 130 ----
 36 files changed, 1 insertion(+), 6854 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/main/java/org/apache/hama/commons/math/CrossEntropy.java
----------------------------------------------------------------------
diff --git a/commons/src/main/java/org/apache/hama/commons/math/CrossEntropy.java b/commons/src/main/java/org/apache/hama/commons/math/CrossEntropy.java
deleted file mode 100644
index 1378fc4..0000000
--- a/commons/src/main/java/org/apache/hama/commons/math/CrossEntropy.java
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-/**
- * The cross entropy cost function.
- * 
- * <pre>
- * cost(t, y) = - t * log(y) - (1 - t) * log(1 - y),
- * where t denotes the target value, y denotes the estimated value.
- * </pre>
- */
-public class CrossEntropy extends DoubleDoubleFunction {
-
-  @Override
-  public double apply(double target, double actual) {
-    double adjustedTarget = (target == 0 ? 0.000001 : target);
-    adjustedTarget = (target == 1.0 ? 0.999999 : target);
-    double adjustedActual = (actual == 0 ? 0.000001 : actual);
-    adjustedActual = (actual == 1 ? 0.999999 : actual);
-    return -adjustedTarget * Math.log(adjustedActual) - (1 - adjustedTarget)
-        * Math.log(1 - adjustedActual);
-  }
-
-  @Override
-  public double applyDerivative(double target, double actual) {
-    double adjustedTarget = target;
-    double adjustedActual = actual;
-    if (adjustedActual == 1) {
-      adjustedActual = 0.999;
-    } else if (actual == 0) {
-      adjustedActual = 0.001;
-    }
-    if (adjustedTarget == 1) {
-      adjustedTarget = 0.999;
-    } else if (adjustedTarget == 0) {
-      adjustedTarget = 0.001;
-    }
-    return -adjustedTarget / adjustedActual + (1 - adjustedTarget)
-        / (1 - adjustedActual);
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/main/java/org/apache/hama/commons/math/FunctionFactory.java
----------------------------------------------------------------------
diff --git a/commons/src/main/java/org/apache/hama/commons/math/FunctionFactory.java b/commons/src/main/java/org/apache/hama/commons/math/FunctionFactory.java
deleted file mode 100644
index 15c48be..0000000
--- a/commons/src/main/java/org/apache/hama/commons/math/FunctionFactory.java
+++ /dev/null
@@ -1,65 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-/**
- * Factory to create the functions.
- * 
- */
-public class FunctionFactory {
-
-  /**
-   * Create a double function with specified name.
-   * 
-   * @param functionName
-   * @return an appropriate double function.
-   */
-  public static DoubleFunction createDoubleFunction(String functionName) {
-    if (functionName.equalsIgnoreCase(Sigmoid.class.getSimpleName())) {
-      return new Sigmoid();
-    } else if (functionName.equalsIgnoreCase(Tanh.class.getSimpleName())) {
-      return new Tanh();
-    } else if (functionName.equalsIgnoreCase(IdentityFunction.class
-        .getSimpleName())) {
-      return new IdentityFunction();
-    }
-
-    throw new IllegalArgumentException(String.format(
-        "No double function with name '%s' exists.", functionName));
-  }
-
-  /**
-   * Create a double double function with specified name.
-   * 
-   * @param functionName
-   * @return an appropriate double double function.
-   */
-  public static DoubleDoubleFunction createDoubleDoubleFunction(
-      String functionName) {
-    if (functionName.equalsIgnoreCase(SquaredError.class.getSimpleName())) {
-      return new SquaredError();
-    } else if (functionName
-        .equalsIgnoreCase(CrossEntropy.class.getSimpleName())) {
-      return new CrossEntropy();
-    }
-
-    throw new IllegalArgumentException(String.format(
-        "No double double function with name '%s' exists.", functionName));
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/main/java/org/apache/hama/commons/math/IdentityFunction.java
----------------------------------------------------------------------
diff --git a/commons/src/main/java/org/apache/hama/commons/math/IdentityFunction.java b/commons/src/main/java/org/apache/hama/commons/math/IdentityFunction.java
deleted file mode 100644
index 6b60aad..0000000
--- a/commons/src/main/java/org/apache/hama/commons/math/IdentityFunction.java
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-/**
- * The identity function f(x) = x.
- * 
- */
-public class IdentityFunction extends DoubleFunction {
-
-  @Override
-  public double apply(double value) {
-    return value;
-  }
-
-  @Override
-  public double applyDerivative(double value) {
-    return 1;
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/main/java/org/apache/hama/commons/math/Sigmoid.java
----------------------------------------------------------------------
diff --git a/commons/src/main/java/org/apache/hama/commons/math/Sigmoid.java b/commons/src/main/java/org/apache/hama/commons/math/Sigmoid.java
deleted file mode 100644
index eb3e9c6..0000000
--- a/commons/src/main/java/org/apache/hama/commons/math/Sigmoid.java
+++ /dev/null
@@ -1,39 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-/**
- * The Sigmoid function
- * 
- * <pre>
- * f(x) = 1 / (1 + e^{-x})
- * </pre>
- */
-public class Sigmoid extends DoubleFunction {
-
-  @Override
-  public double apply(double value) {
-    return 1.0 / (1 + Math.exp(-value));
-  }
-
-  @Override
-  public double applyDerivative(double value) {
-    return value * (1 - value);
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/main/java/org/apache/hama/commons/math/SquaredError.java
----------------------------------------------------------------------
diff --git a/commons/src/main/java/org/apache/hama/commons/math/SquaredError.java b/commons/src/main/java/org/apache/hama/commons/math/SquaredError.java
deleted file mode 100644
index 42ff81b..0000000
--- a/commons/src/main/java/org/apache/hama/commons/math/SquaredError.java
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-/**
- * Square error cost function.
- * 
- * <pre>
- * cost(t, y) = 0.5 * (t - y) &circ; 2
- * </pre>
- */
-public class SquaredError extends DoubleDoubleFunction {
-
-  @Override
-  /**
-   * {@inheritDoc}
-   */
-  public double apply(double target, double actual) {
-    double diff = target - actual;
-    return 0.5 * diff * diff;
-  }
-
-  @Override
-  /**
-   * {@inheritDoc}
-   */
-  public double applyDerivative(double target, double actual) {
-    return actual - target;
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/main/java/org/apache/hama/commons/math/Tanh.java
----------------------------------------------------------------------
diff --git a/commons/src/main/java/org/apache/hama/commons/math/Tanh.java b/commons/src/main/java/org/apache/hama/commons/math/Tanh.java
deleted file mode 100644
index c1ef6cb..0000000
--- a/commons/src/main/java/org/apache/hama/commons/math/Tanh.java
+++ /dev/null
@@ -1,36 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-/**
- * Tanh function.
- * 
- */
-public class Tanh extends DoubleFunction {
-
-  @Override
-  public double apply(double value) {
-    return Math.tanh(value);
-  }
-
-  @Override
-  public double applyDerivative(double value) {
-    return 1 - value * value;
-  }
-  
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/commons/src/test/java/org/apache/hama/commons/math/TestFunctionFactory.java
----------------------------------------------------------------------
diff --git a/commons/src/test/java/org/apache/hama/commons/math/TestFunctionFactory.java b/commons/src/test/java/org/apache/hama/commons/math/TestFunctionFactory.java
deleted file mode 100644
index 43a4bcf..0000000
--- a/commons/src/test/java/org/apache/hama/commons/math/TestFunctionFactory.java
+++ /dev/null
@@ -1,82 +0,0 @@
-/**
- * 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.hama.commons.math;
-
-import static org.junit.Assert.assertEquals;
-
-import java.util.Random;
-
-import org.junit.Test;
-
-/**
- * Test case for {@link FunctionFactory}
- * 
- */
-public class TestFunctionFactory {
-
-  @Test
-  public void testCreateDoubleFunction() {
-    double input = 0.8;
-    
-    String sigmoidName = "Sigmoid";
-    DoubleFunction sigmoidFunction = FunctionFactory
-        .createDoubleFunction(sigmoidName);
-    assertEquals(sigmoidName, sigmoidFunction.getFunctionName());
-    
-    double sigmoidExcepted = 0.68997448;
-    assertEquals(sigmoidExcepted, sigmoidFunction.apply(input), 0.000001);
-    
-    
-    String tanhName = "Tanh";
-    DoubleFunction tanhFunction = FunctionFactory.createDoubleFunction(tanhName);
-    assertEquals(tanhName, tanhFunction.getFunctionName());
-    
-    double tanhExpected = 0.66403677;
-    assertEquals(tanhExpected, tanhFunction.apply(input), 0.00001);
-    
-    
-    String identityFunctionName = "IdentityFunction";
-    DoubleFunction identityFunction = FunctionFactory.createDoubleFunction(identityFunctionName);
-    
-    Random rnd = new Random();
-    double identityExpected = rnd.nextDouble();
-    assertEquals(identityExpected, identityFunction.apply(identityExpected), 0.000001);
-  }
-  
-  @Test
-  public void testCreateDoubleDoubleFunction() {
-    double target = 0.5;
-    double output = 0.8;
-    
-    String squaredErrorName = "SquaredError";
-    DoubleDoubleFunction squaredErrorFunction = FunctionFactory.createDoubleDoubleFunction(squaredErrorName);
-    assertEquals(squaredErrorName, squaredErrorFunction.getFunctionName());
-    
-    double squaredErrorExpected = 0.045;
-    
-    assertEquals(squaredErrorExpected, squaredErrorFunction.apply(target, output), 0.000001);
-    
-    String crossEntropyName = "CrossEntropy";
-    DoubleDoubleFunction crossEntropyFunction = FunctionFactory.createDoubleDoubleFunction(crossEntropyName);
-    assertEquals(crossEntropyName, crossEntropyFunction.getFunctionName());
-    
-    double crossEntropyExpected = 0.91629;
-    assertEquals(crossEntropyExpected, crossEntropyFunction.apply(target, output), 0.000001);
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/core/src/test/java/org/apache/hama/bsp/TestPartitioning.java
----------------------------------------------------------------------
diff --git a/core/src/test/java/org/apache/hama/bsp/TestPartitioning.java b/core/src/test/java/org/apache/hama/bsp/TestPartitioning.java
index 00c63fd..674c42a 100644
--- a/core/src/test/java/org/apache/hama/bsp/TestPartitioning.java
+++ b/core/src/test/java/org/apache/hama/bsp/TestPartitioning.java
@@ -55,7 +55,7 @@ public class TestPartitioning extends HamaCluster {
     configuration.set("bsp.local.dir", "/tmp/hama-test");
     configuration.set(Constants.ZOOKEEPER_QUORUM, "localhost");
     configuration.setInt(Constants.ZOOKEEPER_CLIENT_PORT, 21810);
-    configuration.set("hama.sync.client.class",
+    configuration.set("hama.sync.peer.class",
         org.apache.hama.bsp.sync.ZooKeeperSyncClientImpl.class
             .getCanonicalName());
   }

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java b/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
index 08559e6..89d289d 100644
--- a/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
+++ b/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
@@ -39,8 +39,6 @@ public class ExampleDriver {
       pgd.addClass("semi", SemiClusterJobDriver.class, "Semi Clustering");
       pgd.addClass("kmeans", Kmeans.class, "K-Means Clustering");
       pgd.addClass("gd", GradientDescentExample.class, "Gradient Descent");
-      pgd.addClass("neuralnets", NeuralNetwork.class,
-          "Neural Network classification");
       pgd.addClass("kcore", KCore.class, "kcore");
       pgd.addClass("gen", Generator.class, "Random Data Generator Util");
       pgd.driver(args);

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java b/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
deleted file mode 100644
index ef029a6..0000000
--- a/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
+++ /dev/null
@@ -1,216 +0,0 @@
-/**
- * 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.hama.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");
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
----------------------------------------------------------------------
diff --git a/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java b/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
deleted file mode 100644
index 6b4798d..0000000
--- a/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
+++ /dev/null
@@ -1,140 +0,0 @@
-/**
- * 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.hama.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();
-    }
-  }
-
-}

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/examples/src/test/resources/neuralnets_classification_label.txt
----------------------------------------------------------------------
diff --git a/examples/src/test/resources/neuralnets_classification_label.txt b/examples/src/test/resources/neuralnets_classification_label.txt
deleted file mode 100644
index e1b6789..0000000
--- a/examples/src/test/resources/neuralnets_classification_label.txt
+++ /dev/null
@@ -1 +0,0 @@
-1
0
0
0
0
0
0
0
1
1
0
1
0
0
1
0
1
0
0
0
0
0
1
0
1
0
1
0
1
1
0
0
0
0
1
1
0
0
0
1
0
1
1
0
0
1
0
0
1
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
0
0
1
0
0
1
0
1
1
1
0
0
1
1
1
0
1
0
1
0
1
0
0
0
0
1
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/hama/blob/3a3ea7a3/examples/src/test/resources/neuralnets_classification_test.txt
----------------------------------------------------------------------
diff --git a/examples/src/test/resources/neuralnets_classification_test.txt b/examples/src/test/resources/neuralnets_classification_test.txt
deleted file mode 100644
index b19107d..0000000
--- a/examples/src/test/resources/neuralnets_classification_test.txt
+++ /dev/null
@@ -1 +0,0 @@
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0,0.467336683,0.491803279,0,0,0.526080477,0.078992314,0.066666667
0.176470588,0.608040201,0.426229508,0,0,0.536512668,0.020922289,0.066666
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0.117647059,0.507537688,0.475409836,0.171717172,0.313238771,0.360655738,0.228864219,0.033333333
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0.235294118,0.628140704,0.655737705,0,0,0.481371088,0.195559351,0.1
0.294117647,0.683417085,0.672131148,0,0,0,0.239965841,0.8
0.117647059,0.648241206,0.606557377,0.262626263,0.242316785,0.494783905,0.219043553,0.066666667
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0.411764706,0.648241206,0.557377049,0.494949495,0.147754137,0.573770492,0.154141759,0.366666667
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0.058823529,0.844221106,0.721311475,0.292929293,0,
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0.117647059,0.648241206,0,0,0,0.573770492,0.096498719,0.333333333
0.235294118,0.552763819,0.62295082,0.202020202,0.11820331,0.423248882,0.017079419,0.1
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0.529411765,0.703517588,0.770491803,0,0,0.48733234,0.280102477,0.4
0.764705882,0.768844221,0.721311475,0.373737374,0.165484634,0.605067064,0.467976089,0.3
0.705882353,0.502512563,0.68852459,0.333333333,0.124113475,0.44709389,0.175064048,0.416666667
0.058823529,0.738693467,0.770491803,0.414141414,0,0.734724292,0.119555935,0.1
0.058823529,0.407035176,0.606557377,0.414141414,0.067375887,0.690014903,0.434671221,0.183333333
0.176470588,0.939698492,0.573770492,0.222222222,0.236406619,0.54247392,0.140905209,0.25
0.352941176,0.814070352,0.508196721,0,0,0.362146051,0.042698548,0.483333333
0.235294118,0.68341708
 5,0.573770492,0,0,0.464977645,0.471391973,0.016666667
0.058823529,0.608040201,0.639344262,0.393939394,0.087470449,0.581222057,0.078138343,0.116666667
0.176470588,0.542713568,0.508196721,0.242424242,0,0.387481371,0.061912895,0.066666667
0,0.909547739,0.721311475,0.444444444,0.602836879,0.645305514,0.061485909,0.083333333
0.470588235,0.773869347,0.639344262,0.323232323,0,0.482861401,0.155849701,0.4
0.058823529,0.64321608,0.721311475,0.393939394,0.130023641,0.543964232,0.418018787,0.266666667
0.411764706,0.688442211,0.737704918,0.414141414,0,0.476900149,0.133646456,0.3
0,0.618090452,0.590163934,0,0,0.540983607,0.076857387,0.516666667
0.058823529,0.532663317,0.62295082,0,0,0.558867362,0.050811272,0.083333333
0.352941176,0.954773869,0.754098361,0,0,0.529061103,0.085397096,0.75
0.117647059,0.442211055,0.475409836,0.262626263,0.01891253,0.423248882,0.293766012,0.016666667
0.529411765,0.854271357,0.606557377,0.313131313,0,0.655737705,0.138770282,0.366666667
0.529411765,0.447236181,0.5081967
 21,0,0,0.335320417,0.027327071,0.2
0.588235294,0.507537688,0.62295082,0.484848485,0.212765957,0.490312966,0.03970965,0.7
0.117647059,0.613065327,0.573770492,0.272727273,0,0.548435171,0.111870196,0.1
0.294117647,0.608040201,0.590163934,0.232323232,0.132387707,0.390461997,0.071306576,0.15
0.058823529,0.633165829,0.491803279,0,0,0.448584203,0.115713066,0.433333333
\ No newline at end of file


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