horn-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From edwardy...@apache.org
Subject [3/5] incubator-horn git commit: HORN-3: Import initial source code from Hama ML package
Date Thu, 29 Oct 2015 04:27:08 GMT
http://git-wip-us.apache.org/repos/asf/incubator-horn/blob/3779483e/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
----------------------------------------------------------------------
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));
+  }
+
+}

http://git-wip-us.apache.org/repos/asf/incubator-horn/blob/3779483e/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
----------------------------------------------------------------------
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();
+    }
+  }
+
+}

http://git-wip-us.apache.org/repos/asf/incubator-horn/blob/3779483e/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
----------------------------------------------------------------------
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();
+    }
+  }
+
+}

http://git-wip-us.apache.org/repos/asf/incubator-horn/blob/3779483e/src/test/resources/dimensional_reduction.txt
----------------------------------------------------------------------
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
+5.229370	8.210316	4.671893
+2.998831	7.019181	6.807834
+5.822185	7.044617	3.579246
+3.502988	8.529447	6.470388
+3.556294	8.944615	6.431043
+0.275645	7.626098	7.813860
+6.350581	8.483290	0.624596
+-1.029876	9.017951	7.916638
+-1.236492	8.501062	7.912964
+6.214590	8.318062	2.353006
+2.357908	5.884950	7.157349
+4.657961	8.877248	5.416277
+-3.966537	6.800191	7.346643
+1.655427	7.619326	7.453822
+-3.261750	7.149449	7.588636
+5.816712	7.414349	-1.557348
+1.032510	5.810382	7.650472
+6.294199	6.563924	0.071914
+1.120549	7.415623	7.626208
+5.858183	8.279860	3.493880
+-2.267918	8.699031	7.813994
+6.141022	6.650649	2.658369
+2.042799	7.661015	7.300742
+1.572004	8.557056	7.483607
+4.002859	7.401975	6.069959
+-8.902572	8.193579	2.234928
+-6.757595	6.495666	5.539775
+5.814173	6.947760	3.597812
+3.928729	8.669335	-3.858716
+2.746337	5.551667	-4.487923
+6.222903	7.098401	2.313919
+3.296003	7.563959	-4.243062
+4.094151	5.513191	5.988561
+-2.383802	8.495619	7.794405
+-8.119292	7.726085	3.842728
+-1.721302	8.385161	7.883222
+5.840237	7.248265	3.536846
+6.045281	8.429626	-0.974619
+-6.645581	7.511995	5.646504
+3.676010	6.462470	6.339899
+-6.524017	6.610462	-7.983916
+3.022285	8.377986	6.793441
+5.535313	8.150169	4.167033
+5.417565	7.607157	4.373057
+-7.903988	7.482833	4.174878
+5.886684	7.312755	3.423871
+0.275682	6.865824	7.813853
+-9.015928	8.891424	-3.931973
+-1.964493	8.183805	7.857120
+0.868877	6.122895	7.692594
+-6.501610	5.924574	5.778242
+-4.445926	8.431146	7.139840
+3.630562	7.560270	6.374962
+-0.724959	7.132482	7.912209
+3.984812	8.029555	6.085724
+3.112338	5.620074	6.737046
+6.158734	7.283831	2.590392
+3.505755	8.124096	6.468364
+3.901459	5.420980	6.157185
+-3.650869	7.628908	7.463849
+3.777994	7.352483	6.259097
+2.904796	6.157412	6.864344
+-7.607583	6.222248	4.584928
+-4.892777	9.297294	6.913692
+6.361003	6.435683	0.991536
+-8.797081	8.179497	-4.544607
+-8.796629	8.225805	2.507647
+6.359129	9.044684	1.142701
+1.193283	6.636904	7.605304
+-0.747523	6.669483	7.912940
+1.997140	5.531188	7.320083
+-0.352661	6.222056	7.890768
+5.849521	6.999574	3.514722
+-1.104270	7.883201	7.915935
+2.862075	8.044723	6.889394
+-2.952922	6.923279	7.672752
+-4.735157	6.955731	6.997307
+-0.181899	6.131445	7.874975
+1.418946	8.296576	7.535425
+-8.385729	8.203527	3.380724
+0.424236	7.739502	7.788016
+1.542411	8.581142	7.493910
+3.148428	7.671558	6.713933
+-8.159330	9.583673	3.777221
+5.351837	7.865185	4.481318
+0.300457	9.028326	7.809751
+-7.052613	8.727547	5.239419
+5.823358	10.398561	-1.542658
+6.346598	4.926079	1.416869
+-1.320844	6.475045	7.909918
+-2.261126	9.082600	7.815089
+1.895567	9.200700	7.361846
+-1.584108	7.255155	7.894646
+5.644996	4.143754	-1.904898
+4.966373	7.129021	5.040286
+6.346146	8.891149	1.423544
+5.942655	7.292993	3.279345
+5.224576	9.170574	-2.575991
+0.711951	8.680131	7.729412
+4.371493	6.613097	5.723145
+-2.321962	5.922016	7.805074
+5.039264	8.445435	-2.817415
+6.266070	8.601982	2.088217
+-3.598651	7.143981	7.481840
+-0.668148	5.607563	7.910081
+6.245001	7.990574	-0.215363
+-3.184933	8.201757	7.610774
+-7.744719	7.206444	-6.521306
+-3.850260	6.913175	7.391520
+-4.029040	6.273264	7.321684
+4.252708	10.519454	5.840322
+-9.466489	9.109256	-0.542468
+6.117972	7.595596	2.742709
+-9.173549	7.484529	1.374899
+-0.022494	6.172525	7.856812
+-5.760021	6.991215	6.371314
+6.244617	6.520502	-0.217323
+1.607393	6.167728	7.471106
+6.044820	8.261442	2.985655
+2.244340	8.182271	7.211061
+4.557560	7.204057	5.528005
+-8.086230	6.975473	3.895884
+-2.121861	7.079526	7.836223
+-7.691109	6.849068	-6.598656
+-6.820449	7.711009	5.478187
+0.184109	8.427748	7.828303
+1.907641	7.339092	7.356973
+3.424941	7.709557	-4.174253
+-6.153544	7.248713	6.073392
+3.524789	7.606886	6.454387
+3.539394	7.409512	6.443599
+0.548160	9.020287	7.764171
+2.533926	7.235065	7.069406
+-2.419615	7.439628	7.788000
+0.102776	7.542637	-4.732985
+6.291273	6.933579	1.931287
+4.855127	6.289525	5.182087
+-6.765639	5.535820	5.531962
+-1.100403	6.350964	7.915989
+-9.473760	7.699958	-1.253486
+5.680254	7.902587	-1.838043
+-0.335856	6.952193	7.889381
+4.575161	7.371058	5.508760
+2.505241	10.157312	-4.572280
+0.369977	8.331159	7.797799
+-8.453016	5.951523	3.253151
+5.805336	9.182754	3.618115
+1.730073	9.662341	7.426234
+4.997530	6.370577	4.999191
+-3.249081	7.502106	7.592343
+-9.477245	7.115397	-1.025054
+5.750761	5.547668	-1.697717
+-2.605591	6.960813	7.752059
+5.786660	7.811105	3.660435
+-7.248153	6.970221	5.023369
+-9.477241	7.067643	-0.962881
+4.286932	7.717918	5.807119
+2.131911	7.675562	-4.677103
+5.866604	6.504499	3.473428
+-9.321725	8.247840	0.710182
+4.522854	8.831682	5.565542
+1.824403	8.171250	7.390083
+3.895328	7.311882	6.162355
+0.500799	7.841007	7.773532
+6.360553	6.134380	1.063968
+-1.243760	9.391655	7.912737
+-3.372722	6.932812	7.555215
+6.357561	7.453840	0.779004
+2.587772	6.191203	7.041330
+4.060982	8.791239	6.018459
+5.602012	8.081433	4.042479
+-9.077068	6.740655	-3.733853
+3.981327	9.929923	6.088756
+5.920037	6.508152	3.338944
+-3.276955	9.328079	7.584158
+6.003296	7.780698	3.110387
+6.359511	8.711738	0.850391
+-4.866301	8.356692	6.928038
+-6.916398	7.079579	5.381715
+-0.102307	7.976592	7.866321
+3.979945	7.817220	6.089957
+4.211940	6.575031	5.879302
+5.887451	6.015154	3.421953
+-0.733913	7.878341	7.912507
+-3.125329	6.697193	7.627394
+-6.922420	8.085911	5.375560
+3.286479	7.029660	-4.247965
+3.687861	7.261034	-4.019285
+5.681640	7.883537	3.885107
+1.263660	8.481316	7.584332
+6.347892	5.847123	0.580506
+5.262682	6.908178	4.621396
+-3.782596	7.820711	7.416715
+0.061697	9.240942	7.845876
+5.729288	8.707359	3.785810
+5.166742	7.690181	4.764320
+-0.091667	7.694332	7.865102
+-8.476749	7.434249	-5.269119
+-5.718910	8.261278	6.400428
+-4.201475	7.439952	7.249750
+0.250055	6.496613	7.818010
+6.303937	6.958160	1.841469
+6.358481	7.403499	0.809088
+5.617285	4.820202	-1.956023
+6.224497	6.137625	-0.315755
+-4.868422	6.989113	6.926893
+2.872551	7.663263	6.883287
+0.427736	6.187043	7.787372
+5.170819	6.040786	4.758399
+6.247120	9.079665	2.192629
+3.647424	6.889324	6.362020
+4.301086	5.540704	5.793258
+2.141243	9.051478	7.257823
+3.458850	7.566013	6.502405
+3.955735	7.960201	6.110903
+-4.908849	7.589488	6.904924
+2.083486	7.460633	7.283207
+5.391952	7.996370	4.415779
+6.346483	6.635142	0.559219
+-4.257017	8.559807	7.225604
+-2.037754	6.963493	7.847785
+1.238738	6.348253	-4.807544
+2.192859	7.660561	7.234647
+4.835362	7.332150	5.206514
+0.648422	8.661534	7.743335
+-1.521003	6.165460	7.899103
+6.083312	6.844283	2.862010
+4.735022	8.063465	5.327186
+4.636124	8.031571	-3.263851
+1.410927	7.979688	7.538041
+5.738476	6.918446	3.766177
+-6.375569	6.960824	5.888803
+-1.170237	8.480965	7.914728
+6.320491	8.625886	0.273281
+6.251438	9.217744	-0.181965
+-2.234117	8.170640	7.819382
+4.944896	6.349166	-2.930763
+2.297860	7.184230	7.186040
+6.070834	7.694718	2.903031
+-9.274463	8.414727	-2.952000
+3.217512	6.933662	6.668853
+-6.102736	7.751450	6.113895
+5.772427	6.752888	3.692175
+5.791323	7.481863	3.649942
+5.568795	7.088601	4.105280
+0.577830	7.832262	7.758150
+4.144203	5.926029	-3.698474
+6.129768	6.829884	-0.699540
+-3.297193	8.024220	7.578148
+6.358020	7.620347	0.793416
+3.902236	6.684126	6.156529
+4.224781	7.729601	5.867091
+-1.270074	7.713791	7.911859
+6.359562	7.721199	1.123708
+-2.708990	7.785976	7.730119
+6.349131	8.407377	0.600185
+6.021141	7.252759	-1.045575
+3.147632	7.237756	6.714446
+5.400282	6.420153	4.401961
+6.354156	8.134127	1.283659
+0.803408	6.196823	7.708377
+5.402218	6.184263	4.398739
+-5.233859	9.140946	6.717635
+4.011303	7.469947	6.062546
+-1.962944	7.524212	7.857310
+-9.346823	6.747060	0.568108
+4.266689	7.563927	-3.599749
+6.284477	7.934805	0.008150
+-7.089520	8.879071	5.199737
+6.316611	8.114970	0.240059
+3.048044	7.632666	6.777494
+0.910120	7.493826	7.682340
+-3.917933	8.683322	7.365646
+5.434955	7.994632	-2.265536
+4.233330	8.035306	5.858927
+6.118830	7.008004	2.739644
+0.353716	6.738118	7.800653
+3.585398	7.109843	6.409244
+-0.551278	7.456853	7.904411
+-3.326026	4.393035	7.569487
+2.085628	8.474764	7.282276
+6.105830	9.409802	2.785460
+-6.229617	7.220079	6.011559
+5.247765	8.136057	-2.543826
+-0.574499	7.308740	7.905677
+-0.729957	8.409236	7.912376
+-0.345802	8.516231	7.890206
+2.569365	7.975445	7.050990
+4.631701	7.908545	5.445962
+2.927578	8.951455	6.850827
+6.143835	6.242815	2.647770
+2.083295	7.110173	7.283290
+-3.881560	7.792531	7.379637
+6.024319	9.191448	3.048260
+-6.397327	7.901329	5.870025
+4.647852	8.578470	5.427744
+4.964773	9.060097	-2.907381
+4.124430	7.313521	5.960941
+-8.072332	6.383679	3.917983
+-9.265460	7.468098	-2.994653
+4.167869	9.560358	5.920757
+-6.457934	8.405787	5.817045
+6.245927	8.113575	2.198894
+2.200433	14.164363	7.231206
+6.277532	14.095384	2.019850
+-4.220554	13.524413	7.241510
+-1.318065	12.418892	7.910033
+-8.149477	13.914129	-5.880997
+-3.887267	12.666497	7.377455
+0.148262	12.069997	7.833655
+5.094620	8.701143	4.866950
+3.949762	11.800855	6.116041
+1.511088	12.142680	7.504666
+6.276827	9.878912	2.024190
+6.355193	12.665769	1.260367
+5.017600	11.788278	4.972383
+-7.360885	13.129024	4.891973
+-5.622475	14.109412	6.467306
+-4.158722	13.220162	7.268011
+2.284093	12.306755	7.192525
+-3.644775	10.761219	7.465969
+-7.657254	11.230894	4.519568
+-0.258630	11.401916	7.882539
+-0.350257	14.186523	7.890572
+-9.123218	11.672188	-3.573463
+-2.949955	12.359559	7.673498
+6.174855	12.262373	2.525785
+3.044262	13.392558	6.779844
+2.220515	12.822477	7.222035
+2.195097	11.007434	7.233631
+0.100823	13.814423	7.840475
+5.373599	10.806169	-2.360648
+-1.604513	13.561378	7.893097
+3.867103	12.184161	6.186007
+3.698579	12.144320	6.322273
+4.513979	12.038316	5.575054
+4.227247	14.107116	5.864738
+1.120093	13.679655	7.626336
+6.188756	11.989198	-0.473422
+3.438085	12.280810	6.517294
+6.112719	10.956084	2.761336
+-3.187831	11.986597	7.609954
+3.933784	13.348806	6.129733
+0.182424	12.800039	7.828558
+2.048057	11.698212	7.298492
+3.928296	13.008112	6.134416
+-2.308261	11.020246	7.807371
+-6.045383	12.809784	6.158867
+6.155309	12.219690	-0.605931
+0.240435	10.818987	7.819548
+6.108442	11.364076	2.776353
+4.545351	12.861107	5.541272
+0.235485	12.115360	7.820334
+-8.667115	12.855218	2.810004
+0.284741	13.335377	7.812363
+3.887776	13.376884	-3.887347
+-8.887502	13.046304	-4.306740
+2.136939	12.566704	7.259735
+4.528727	13.555790	5.559227
+-2.438147	11.251261	7.784621
+-8.458652	11.071519	3.242236
+2.806073	13.502271	6.921654
+-7.725708	12.797749	4.427379
+-0.003525	11.421310	7.854430
+6.358981	12.445085	1.148688
+4.585620	13.790860	5.497256
+3.143811	11.802578	6.716906
+-4.133530	12.826963	7.278638
+-2.439599	11.199172	7.784354
+4.217928	12.501718	5.873615
+2.049950	11.928073	7.297681
+2.949844	11.699537	6.837510
+2.456571	11.932754	7.108770
+4.029759	12.196578	6.046263
+-2.190809	11.503567	7.826071
+-6.459364	12.207927	5.815784
+4.536451	11.906068	5.550900
+-3.589441	11.646578	7.484972
+5.546791	10.098034	-2.080909
+-5.405388	13.270638	6.610830
+1.061260	10.977444	7.642673
+6.290275	11.654097	1.938002
+-4.230323	12.889407	7.237269
+6.203905	12.010467	-0.408922
+4.772615	12.709266	5.282614
+-1.431253	11.690071	7.904576
+2.580461	11.803336	7.045174
+-8.594275	10.839684	2.967809
+5.871124	13.221555	3.462370
+2.153124	13.809412	7.252530
+6.359263	11.244083	0.839400
+4.429757	12.805778	-3.458944
+-0.820555	12.311015	7.914864
+5.847632	13.556211	3.519243
+5.314352	13.779052	-2.448780
+1.945985	11.703117	7.341333
+-4.670287	13.355747	7.030485
+5.135028	11.670193	4.809933
+-9.311509	13.428087	-2.765352
+3.566544	11.807070	6.423392
+-3.699740	12.688370	7.446655
+1.232220	11.272387	7.593792
+-9.191700	10.931599	1.304463
+3.955532	10.072039	6.111078
+1.418355	11.334162	7.535618
+-3.643768	12.687898	7.466318
+-7.684714	12.596101	4.482886
+5.833969	13.917750	3.551659
+6.308725	12.920192	1.804926
+-1.940517	13.537663	7.860027
+1.530055	12.863642	7.498171
+5.886935	10.905312	-1.396406
+5.005207	12.428249	-2.859015
+3.903090	10.956713	6.155808
+-6.420539	11.382701	5.849852
+1.216096	13.316237	7.598587
+0.875209	13.006677	7.691036
+-1.997680	13.429089	7.852976
+-1.973448	12.520848	7.856016
+-3.425593	11.960627	7.538689
+6.268910	12.903694	-0.085811
+5.990917	12.857821	3.146061
+1.012205	10.830698	7.655909
+-1.846537	12.420164	7.870717
+6.183338	13.237705	2.490626
+5.448923	14.035248	4.319779
+5.995965	13.767690	3.131592
+6.274832	14.495142	-0.051112
+6.359164	12.723624	0.835211
+-7.593493	11.184679	4.603240
+3.083310	12.681520	6.755422
+0.260013	11.221469	7.816405
+0.343636	10.990475	7.802404
+6.277683	12.464771	2.018922
+-3.066784	12.482497	7.643248
+5.781331	12.400478	3.672372
+-3.621242	13.068836	7.474104
+-1.931696	13.049244	7.861078
+0.837351	12.632811	7.700270
+1.967123	11.270134	7.332606
+-9.394388	14.145462	0.253446
+-2.534120	13.438985	7.766405
+0.227254	11.350399	7.821635
+5.028503	15.122921	4.957706
+-0.948655	14.875576	7.916607
+3.986816	12.556429	6.083979
+2.514664	11.912112	-4.569233
+5.708457	11.516965	3.829728
+1.422349	11.121027	7.534313
+2.736121	12.413041	6.961043
+6.351253	11.739322	1.340829
+3.079889	11.625464	6.757575
+-2.089229	12.031501	7.840816
+-2.193304	12.123252	7.825692
+1.889709	10.868271	7.364203
+-3.206395	11.656659	7.604671
+6.148047	14.577196	2.631759
+-4.757755	14.360336	6.985581
+6.313249	12.424539	0.212440
+-5.792592	9.989841	6.347988
+2.163711	10.312711	7.247792
+6.258981	13.025250	2.128419
+-9.195631	11.647591	1.288903
+2.210246	12.189040	7.226733
+-5.003531	14.103574	6.852340
+5.550904	13.159012	-2.073816
+5.844144	13.173195	3.527564
+-5.941823	11.730335	6.238107
+-1.076183	13.556973	7.916283
+-9.417359	12.726085	-2.060862
+0.601662	13.071128	7.753225
+6.328447	11.973070	1.632760
+6.241337	13.075520	2.222702
+-6.916809	13.412766	5.381296
+5.773889	12.341480	-1.649569
+-1.917718	13.168649	7.862723
+6.261572	11.640070	2.113891
+5.714205	13.660235	3.817691
+4.934298	12.570212	5.081946
+3.293490	11.983398	6.617982
+2.273933	11.513960	7.197289
+3.272921	12.765062	-4.254902
+-3.338229	12.549393	7.565786
+2.291945	12.962360	7.188830
+5.323405	12.042522	4.526801
+6.163162	12.864248	2.572920
+3.642408	13.745150	6.365878
+-3.154212	13.596934	7.619401
+6.331762	11.176867	1.599055
+-7.429717	13.007145	4.809099
+6.133577	13.712728	2.686094
+6.360139	11.659783	1.093091
+3.321973	12.503798	6.598555
+4.630000	12.749431	5.447874
+-0.187077	11.757690	7.875510
+-0.026942	12.323031	-4.706588
+5.898208	13.115250	3.394906
+5.113889	12.352899	4.839911
+4.650267	12.218878	5.425009
+1.829654	10.485933	7.388028
+1.795357	12.294968	7.401369
+6.277540	12.579757	2.019801
+5.888066	12.701606	-1.393705
+-6.543243	12.863081	5.740756
+-2.852873	12.457608	7.697240
+1.664032	12.414204	7.450688
+1.366065	11.593582	7.552490
+4.471176	10.306188	5.620444
+-0.247407	10.363644	7.881480
+-7.280583	12.610463	4.986106
+3.403407	11.526202	6.541915
+-9.476786	12.750985	-0.896430
+3.846335	11.429080	6.203254
+-6.349315	13.675906	5.911294
+1.336542	12.372627	-4.801329
+-6.825960	11.448446	5.472727
+-4.207928	11.107317	-9.714631
+4.081699	12.052876	5.999829
+-5.513027	11.646216	6.540860
+4.861402	13.063875	5.174285
+5.812969	13.018677	3.600589
+4.918862	11.593634	5.101766
+6.360986	14.440337	0.973071
+6.354472	10.587327	0.700211
+5.860518	11.968779	-1.458480
+4.591805	10.905904	5.490430
+3.091216	15.205112	6.750436
+5.750453	10.551481	3.740338
+5.778717	11.294675	-1.639376
+6.193853	11.509911	-0.452065
+2.387238	12.996018	7.143096
+-3.947961	13.152992	7.353947
+1.393332	14.624528	-4.796817
+-5.124594	12.675945	6.782756
+-2.636198	13.273949	7.745711
+3.206701	11.884348	6.675981
+-0.746332	11.923439	7.912903
+0.605017	10.787804	7.752526
+6.131423	13.038070	-0.693648
+6.205244	13.275952	-0.403064
+-5.561020	11.049258	6.508909
+-6.083835	12.775993	6.128803
+-5.487547	12.654022	6.557633
+-9.161911	10.765233	1.418888
+2.692253	12.710407	6.985239
+6.344149	12.979093	0.526139
+3.651573	10.832002	6.358823
+6.172976	12.897683	2.533460
+0.172148	11.975954	7.830108
+-1.892359	11.576016	7.865645
+-5.500278	13.369908	6.549269
+5.658250	12.116016	3.932391
+-5.080908	11.447498	6.808173
+0.519807	11.466808	7.769812
+-6.060278	14.865419	6.147263
+-7.464249	11.110530	4.766732
+4.338877	15.118552	5.755867
+-8.627323	12.997509	2.897199
+4.015015	11.515665	6.059280
+-3.185039	13.168533	7.610744
+6.229678	13.322411	-0.291177
+6.353475	11.503833	1.298020
+3.771560	13.143656	6.264283
+6.169827	12.485710	2.546237
+-2.410118	12.906419	7.789715
+4.796591	12.690493	5.253791
+6.348361	11.880059	0.587843
+-0.195490	10.731857	7.876371
+-1.688917	12.856378	7.886133
+4.789836	12.629618	5.261943
+5.746570	11.622587	3.748745
+6.265486	12.448909	2.091582
+4.930546	12.092473	5.086777
+-6.084301	11.766311	6.128437
+5.140407	13.570652	4.802251
+5.713530	12.540809	3.819107
+3.292885	12.807401	6.618393
+-8.375042	12.816528	3.400541
+-1.849052	14.102131	7.870446
+6.360283	12.820072	0.892379
+-5.694074	13.916474	6.417840
+-1.709123	14.323741	7.884333
+-4.788526	12.469959	6.969475
+0.972192	14.028338	7.666447
+3.011308	13.105401	6.800192
+1.468204	12.717187	7.519144
+-0.128018	11.544309	7.869207
+1.769517	14.310779	7.411293
+3.416378	13.297534	6.532741
+6.091342	12.817288	2.835098
+4.903210	13.718114	5.121715
+1.861891	13.165251	7.375313
+5.894992	11.682738	-1.377079
+-3.290961	13.090343	7.580004
+-2.550072	12.844061	7.763261
+6.358691	12.985470	0.816711
+-6.282860	12.918771	5.967415
+0.582429	11.916590	7.757206
+5.509402	13.611536	4.213812
+4.462385	13.698571	-3.429406
+-2.074580	11.103913	7.842834
+-2.007179	12.690377	7.851764
+3.328435	13.246261	6.594120
+-4.361705	14.180183	7.178776
+5.427762	13.230125	4.355852
+-2.564246	10.412587	7.760439
+0.511257	13.505404	7.771491
+-7.118429	12.853766	5.168306
+-9.475765	13.800358	-0.824356
+-4.653438	12.465317	-9.455134
+-0.052124	12.139902	7.860440
+3.289446	11.502707	6.620725
+6.305397	11.844576	1.830494
+6.074502	11.202684	-0.884608
+-8.161062	11.140775	3.774358
+-5.573957	11.733566	6.500216
+2.046286	12.404400	7.299250
+4.699889	11.882113	-3.199379
+0.346451	13.200982	7.801916
+5.453170	13.565698	4.312478
+-8.778350	11.780831	2.552213
+1.446117	11.222921	7.526491
+5.733850	11.498124	-1.732233
+3.570606	12.396087	-4.090901
+2.283106	13.869582	7.192989
+1.005622	12.366440	7.657658
+-7.628607	11.979518	4.557416
+4.602979	13.059481	5.478051
+-7.374732	11.122810	4.875466
+5.695161	14.157429	-1.809139
+-8.930701	12.672494	2.157866
+4.604108	13.273024	5.476797
+-5.565179	12.244452	6.506119
+0.289461	16.133265	7.811582
+0.548967	11.458822	7.764009
+4.012242	12.936111	6.061721
+5.740129	9.532853	-1.719483
+1.335582	14.451628	7.562134
+-3.387440	10.952416	7.550654
+5.525784	12.191532	4.184337
+6.102361	11.889337	2.797478
+4.274842	12.854650	5.818899
+1.795004	11.690718	7.401505
+3.559171	12.862841	6.428899
+4.015833	12.018083	6.058560
+0.748977	11.787962	7.721038
+6.317957	10.756836	1.729344
+4.428278	12.278813	-3.460272
+4.980967	14.513476	5.021115
+-4.615852	13.423309	7.057780
+-8.992003	12.593540	1.981944
+6.315022	12.590719	1.754185
+1.492497	12.890922	7.510977
+4.255104	12.878121	5.838012
+-3.824468	10.568661	7.401203
+-9.378446	11.659904	-2.363303
+5.784078	15.986412	3.666226
+3.532043	11.874227	6.449036
+4.746011	13.176407	5.314234
+-9.427570	11.192457	-1.965880
+-2.733807	12.664026	7.724643
+1.495107	13.675998	7.510095
+0.897727	11.975868	7.685447
+-9.083527	11.597328	-3.712014
+-0.652447	12.807256	7.909421
+1.479503	11.562107	7.515357
+6.355416	11.297017	1.255073
+-7.650590	12.121900	4.528409
+-5.959013	11.854527	6.225127
+-0.086290	13.258193	7.864480
+-7.606603	11.621420	4.586204
+-7.326432	13.920220	4.932690
+0.262480	10.668410	-4.760242
+-5.978467	12.509034	6.210354
+2.050260	13.495431	7.297548
+-7.207742	12.208868	5.069218
+4.969893	11.880338	5.035675
+5.687324	10.996126	3.873475
+2.761243	11.914754	6.947012
+3.239989	14.571603	6.653946
+6.170931	11.848683	-0.545584
+5.779203	12.955048	-1.638347
+-2.195182	11.747440	7.825406
+-0.472425	13.861354	7.899600
+-4.767957	12.105010	6.980259
+-2.996437	12.406511	7.661683
+-9.261020	13.100410	1.011002
+-9.462484	12.115588	-1.524710
+-3.174954	11.106646	7.613591
+2.506613	13.339085	7.083434
+-2.677340	11.795304	7.736984
+6.219530	13.050186	2.329921
+3.336722	14.179254	6.588418
+4.221149	12.546914	5.870551
+1.913723	12.890158	7.354508
+-1.653337	12.937776	7.889179
+6.347230	12.100841	1.407362
+6.360152	11.963744	0.884033
+1.093849	11.142433	-4.813126
+3.964864	12.416487	6.103027
+0.194810	12.225559	7.826672
+-1.059100	12.965112	7.916445
+6.329662	11.932717	0.358924
+12.117801	7.615474	-2.463974
+12.476514	5.315066	2.802028
+12.283168	7.221501	3.839794
+12.603519	7.435058	0.750364
+12.592326	6.902404	1.582770
+9.170913	7.858727	-7.548165
+7.499041	6.116741	-8.970199
+6.869344	6.419820	-9.381533
+12.605719	6.432333	0.923674
+11.224176	6.819834	6.772856
+12.263786	8.649724	-1.911026
+12.360116	7.944104	3.480738
+12.016205	8.681077	-2.797770
+6.715064	5.769742	11.851487
+3.304638	7.618947	13.498445
+2.652706	9.302665	-10.916271
+12.198309	7.961886	-2.171957
+10.996977	8.512681	7.203566
+4.538645	5.885145	13.034513
+6.432457	7.909771	12.035184
+10.003697	8.758199	8.740944
+4.720601	8.631316	-10.400484
+-3.333133	7.570941	-10.144582
+10.727716	7.270708	-5.569854
+12.597712	8.025809	1.452255
+3.711269	7.638358	-10.704200
+10.560779	9.236596	-5.827907
+10.379283	7.613797	-6.093002
+-6.247664	6.468697	13.173585
+5.640737	7.111267	-10.030591
+4.954559	7.276987	-10.315167
+-2.928065	8.231343	14.040700
+-4.534519	7.544332	13.726638
+9.976473	7.938843	8.777152
+3.717911	10.089548	-10.702532
+10.817113	8.620865	-5.425469
+-3.103877	8.278997	-10.241276
+5.400397	8.898638	-10.136236
+6.120927	9.093006	-9.799335
+7.008441	8.161593	11.649963
+7.700290	6.716768	-8.826038
+12.432928	6.803510	3.082792
+1.224284	7.922068	13.996476
+12.058273	8.341711	4.686799
+9.872558	7.029309	-6.761280
+9.960082	7.203683	-6.652607
+8.156103	7.744110	-8.474580
+9.304862	7.261491	-7.408487
+12.215779	6.742451	4.119245
+12.572268	7.495197	0.077119
+3.889592	5.510177	-10.657932
+2.695033	9.675566	13.679702
+11.825405	7.049338	-3.348740
+12.344662	8.548160	3.557042
+-5.092813	6.342415	13.572089
+7.687894	7.226831	11.137239
+0.545483	8.299589	14.087910
+2.561366	6.515293	-10.929653
+-6.099383	7.351244	-8.382270
+12.200227	7.495353	4.180085
+8.266143	7.785569	-8.384221
+12.533724	7.268476	-0.348547
+12.598801	7.130035	0.573150
+12.297999	7.001475	3.774335
+4.048360	7.360770	-10.614112
+8.872720	6.945690	10.060920
+12.542827	5.578369	2.258338
+12.602721	8.515038	0.712509
+7.286051	7.962592	-9.115883
+6.954644	8.784232	11.687769
+12.448153	7.276414	-0.987164
+12.335934	7.950223	-1.590987
+12.536696	6.363012	2.318113
+11.917202	6.653421	-3.094186
+12.590491	7.132051	0.373562
+12.316961	8.367514	-1.679085
+12.393544	6.882112	-1.301746
+5.559799	7.292366	12.542932
+12.436356	8.214343	3.062120
+-2.073902	8.565516	-10.601441
+3.106069	7.628667	13.560833
+12.334180	7.305677	-1.599262
+3.345721	7.423119	13.485127
+8.986596	6.983190	-7.733120
+-8.287909	6.497748	-5.634545
+12.186905	8.300797	4.231241
+10.485464	4.578501	-5.939766
+8.015121	7.559538	-8.587133
+8.164512	8.288012	-8.467754
+5.044384	7.179834	-10.280860
+11.017901	6.517774	7.165477
+10.077070	7.193753	8.642057
+4.275679	7.833303	-10.547008
+6.048655	8.797256	-9.835909
+11.731815	7.346617	-3.591642
+7.222918	7.879450	-9.157749
+-1.858404	9.084682	-10.662265
+10.485500	7.391798	8.053432
+10.832532	6.598463	7.492996
+12.524820	6.870312	-0.428713
+9.339466	8.506801	9.557868
+10.347612	6.370546	-6.137741
+12.591658	8.151038	1.596983
+11.836186	5.860574	5.355112
+12.510739	8.290920	2.545682
+6.796725	7.501466	-9.425317
+8.180317	7.192581	-8.454888
+10.883362	6.485569	-5.315454
+8.504249	7.530093	-8.180837
+-0.724270	6.081097	-10.905232
+10.230831	7.691686	8.428378
+11.659548	6.699526	5.814559
+9.368641	5.855153	-7.340348
+2.159320	8.411914	13.814369
+12.346853	8.821002	-1.538822
+4.613264	6.143349	13.002049
+7.299669	7.867710	11.438387
+11.171941	7.129693	-4.802376
+9.925467	5.882082	-6.695897
+7.426975	6.194402	11.342114
+11.379003	8.193646	6.454989
+8.207717	9.758660	-8.432475
+7.724381	6.750428	-8.808343
+7.024567	6.584960	11.638554
+10.140969	7.909540	-6.419449
+8.871038	10.295108	-7.845010
+12.400649	7.468600	-1.263387
+10.173426	6.192666	8.509206
+11.593900	8.011367	-3.924181
+9.233049	4.820097	9.677198
+11.352597	8.280581	-4.448172
+10.382578	5.523170	-6.088322
+10.536784	7.003312	-5.863840
+-4.830463	6.965111	-9.343669
+8.075752	7.434647	10.812739
+12.239098	8.815696	-2.012262
+12.167772	8.975361	-2.286049
+6.465675	6.121817	-9.615920
+12.416100	6.175255	-1.177539
+12.328762	7.164798	3.633171
+-14.142083	5.971424	5.875845
+4.564249	7.545279	13.023433
+11.749210	5.381868	5.587850
+11.204405	7.065555	-4.740796
+-6.365646	7.854759	-8.137804
+-2.950412	5.811823	14.037624
+3.898303	7.728798	-10.655592
+11.837867	7.647293	5.350478
+10.154785	6.543691	8.535179
+2.637704	6.611702	-10.918522
+-5.071965	7.750295	-9.183445
+7.608727	6.840312	11.200500
+12.086434	7.716573	-2.570719
+7.833564	6.971144	11.018254
+7.227764	6.702410	-9.154557
+12.353757	6.196330	3.512425
+11.519611	9.276255	-4.092596
+12.561339	7.555327	-0.061327
+9.993982	8.255218	8.753894
+12.602398	6.957977	0.698539
+1.206998	8.206717	-11.038692
+9.068106	7.843693	-7.652334
+-2.944341	8.058730	14.038463
+12.068024	6.732886	-2.631763
+11.919195	8.530890	-3.088458
+12.605272	7.390704	1.125837
+-3.088801	6.941105	14.017777
+-7.149141	6.443641	-7.304602
+8.799339	8.027759	-7.912921
+-2.858662	7.329884	-10.337826
+12.557043	6.477845	-0.110975
+10.352265	7.967540	-6.131195
+8.907776	8.201834	10.024896
+10.261865	6.202061	-6.256736
+12.474163	8.564901	2.818297
+11.393506	6.564949	-4.363787
+9.039207	7.447365	-7.681156
+12.133834	8.010927	4.427035
+-8.714766	7.360652	11.922957
+12.079281	6.114276	4.616584
+-8.204597	8.298765	-5.784814
+1.651712	8.205140	-11.021253
+-1.083541	8.179799	-10.841956
+-0.120898	6.655204	14.145197
+12.601406	7.663968	1.334745
+10.070922	6.900444	8.650417
+6.105740	7.164763	-9.807074
+12.302969	7.309710	-1.742139
+10.524453	9.410295	-5.882197
+4.216486	7.773072	-10.564980
+-1.096785	8.498021	-10.839385
+9.622041	8.455131	-7.058593
+12.513221	8.262786	2.525405
+9.934394	7.663393	8.832613
+9.324376	6.022754	-7.387754
+-2.986244	7.293205	-10.288466
+9.789091	8.265431	-6.862540
+6.036937	7.172428	12.276046
+8.629953	7.134370	10.303165
+3.874619	5.555624	-10.661937
+12.253143	8.583001	-1.955124
+7.038052	8.301483	-9.276981
+11.180540	7.371983	6.858659
+11.662423	6.351946	-3.762420
+12.601848	8.279064	0.676144
+11.619500	6.735740	-3.864502
+9.613063	8.880852	9.237253
+10.533295	8.348035	-5.869041
+12.328526	5.744434	-1.625756
+5.381018	5.523841	-10.144469
+11.774699	11.182868	-3.482248
+11.742660	7.163888	-3.564266
+2.239191	7.885492	-10.970730
+-1.338280	7.890723	14.168280
+-4.848380	6.971962	-9.332111
+3.801503	7.325862	-10.681176
+9.861624	9.074855	8.927122
+6.149170	8.121749	-9.784869
+8.635898	8.199501	10.297380
+11.684111	8.427819	-3.709831
+2.738467	8.709106	-10.903004
+-0.873876	7.096826	-10.880393
+7.062165	6.830113	11.611816
+11.168130	7.174346	6.882779
+12.014050	7.026136	4.830075
+12.599734	7.267940	1.392283
+1.636717	8.173922	-11.022132
+5.878658	6.877892	12.367368
+11.114161	6.372688	6.986270
+4.156975	7.793698	-10.582694
+8.890907	8.117092	-7.825987
+11.038549	9.213902	-5.046824
+9.588704	10.278598	-7.096707
+-1.596210	8.130804	-10.729814
+-7.818001	6.802782	-6.413011
+10.883703	7.123479	-5.314882
+-2.879242	9.409180	-10.329991
+11.520928	8.247060	-4.089671
+-6.559795	7.054917	13.046762
+9.925937	8.487898	-6.695312
+7.325962	8.729254	11.418696
+11.627454	8.656857	5.892801
+9.505715	7.281099	-7.190184
+8.559530	6.920913	10.371162
+12.312234	6.971117	3.709964
+12.457532	8.181591	-0.927779
+4.875251	7.450266	12.883870
+11.337071	6.822165	6.543266
+-15.107192	9.753317	3.405405
+11.051246	7.690311	-5.024123
+11.647473	7.677173	-3.798270
+9.975793	9.289726	8.778052
+5.456009	10.889844	-10.112374
+11.615572	8.522256	5.921407
+8.908507	6.880747	10.024143
+10.890256	6.658959	-5.303852
+5.758559	8.533858	12.434796
+-4.763545	6.643364	13.666152
+2.011565	7.365296	-10.994029
+11.804479	8.417304	5.441542
+11.560636	6.134197	6.051229
+12.587921	6.384390	0.323744
+5.848055	8.313186	-9.934111
+-0.892825	5.809355	14.171987
+6.452808	7.933476	12.022287
+9.352387	7.723934	9.543182
+5.059926	8.119093	12.796556
+0.095258	6.720846	14.130104
+0.076211	7.247495	-11.002237
+8.820244	7.630691	-7.893237
+8.672126	9.678521	-8.030658
+7.863370	7.756448	-8.704382
+4.866293	8.145004	-10.348036
+11.133804	7.431459	-4.873642
+8.916412	8.737880	-7.801439
+4.386719	7.657166	13.098998
+12.598787	6.559614	0.572708
+10.634473	7.181063	7.820486
+-6.966182	7.771359	12.868818
+5.155142	7.331320	-10.237358
+11.914538	8.017058	5.132875
+9.741015	7.672185	-6.919849
+9.346666	8.207235	9.549690
+-0.517815	7.272222	-10.936008
+11.573480	7.901590	6.021224
+6.725729	8.468981	-9.467427
+11.543897	7.778207	-4.038294
+12.019814	6.125213	4.811731
+0.714647	7.412779	14.068272
+11.961952	7.641765	-2.963300
+12.043830	7.602178	4.734247
+11.001840	8.292581	7.194740
+-1.420704	6.634401	-10.771141
+-8.377614	8.070473	-5.465562
+6.837346	6.973116	-9.400915
+10.851725	7.145330	7.460083
+11.839520	8.157787	-3.310723
+-0.045765	6.816529	14.140329
+7.767068	6.716549	-8.776756
+11.143391	7.994854	-4.855834
+4.686888	6.970783	12.969503
+9.536979	5.656365	9.328494
+9.921247	6.053596	-6.701148
+10.043345	8.633219	-6.546758
+12.380600	8.376366	3.375698
+12.424267	8.227657	-1.130708
+8.834452	8.358028	-7.879805
+3.767730	7.730299	-10.689886
+6.663138	5.369636	-9.503986
+1.190215	6.223768	-11.039000
+12.165972	6.949678	4.309940
+1.518233	8.460718	-11.028366
+3.591034	9.063961	-10.733651
+1.079981	6.817149	14.018742
+-7.273814	7.706763	12.724155
+-9.052916	6.963534	11.702736
+8.996027	7.734946	-7.723853
+1.092803	7.368163	-11.040283
+4.363196	4.346755	13.108791
+0.690027	6.923059	-11.036465
+12.587260	8.779056	1.683240
+6.634957	7.657954	11.904573
+11.975506	8.589762	4.950373
+2.110904	7.448938	-10.984442
+11.540994	9.381644	-4.044822
+6.970112	7.780697	11.676939
+12.388652	6.189067	3.333082
+12.605075	6.378969	0.849993
+10.978084	8.772089	-5.153393
+12.585490	6.986055	1.715030
+2.751024	7.671819	13.664314
+12.537121	8.303021	-0.316649
+1.308415	6.876874	13.982782
+10.865184	9.460500	7.436870
+11.687053	7.223537	5.746323
+10.069951	7.900564	-6.512411
+7.175601	8.364393	-9.188740
+11.678670	7.591856	-3.723091
+12.461858	8.089101	-0.899739
+8.520515	7.664004	10.408408
+2.876256	8.835012	13.628983
+11.474377	8.312249	-4.191893
+0.647410	6.436073	-11.035200
+5.091069	9.039446	-10.262686
+-4.827053	7.633425	13.648665
+8.529233	6.425375	-8.158851
+-0.076436	7.897815	-10.988352
+11.858333	7.899197	-3.259440
+12.550631	6.787005	-0.181148
+12.275964	6.198066	-1.859672
+-7.551833	7.304432	-6.792690
+4.947653	6.204706	-10.317768
+11.973761	9.058357	-2.927937
+-5.622987	6.496857	-8.779646
+1.194783	6.917019	14.001153
+1.335675	6.229102	-11.035485
+9.038580	6.474315	-7.681779
+8.394513	8.804027	-8.275935
+8.215303	6.809095	10.689756
+11.509541	8.333502	6.168581
+12.210172	9.292370	-2.126418
+10.330403	8.084809	-6.161870
+12.566178	6.934155	-0.002417
+4.231479	7.393143	13.162688
+12.318176	6.529940	3.682619
+9.836524	9.706331	-6.805275
+6.608478	7.026045	11.921942
+10.630705	7.832623	-5.721568
+10.174660	5.118122	-6.374681
+12.473536	7.750357	-0.821830
+-12.668798	7.491675	8.270793
+-12.681141	7.181448	8.254342
+12.602678	7.825215	0.710613
+8.643159	7.682691	10.290303
+12.517936	6.741289	-0.487679
+12.503627	6.777442	-0.603181
+11.410904	7.516606	-4.327390
+1.230476	6.764797	-11.038219
+-2.530176	7.028375	-10.456400
+4.307273	7.006116	-10.537270
+11.290951	6.929425	6.638435
+0.579190	5.712850	14.084162
+11.450532	6.135391	6.300325
+-2.573520	6.860170	-10.441445
+11.388337	8.839326	-4.374542
+-0.954032	7.822137	-10.866200
+3.058484	7.543379	13.575299
+11.406602	8.985942	-4.336418
+3.806238	7.834165	-10.679946
+10.352962	8.041787	8.252019
+12.594873	6.920176	0.469262
+6.846841	8.138366	11.762367
+11.060773	6.515609	7.086486
+8.213095	8.047046	-8.428060
+9.757162	6.369705	-6.900682
+4.425773	8.537267	-10.499847
+8.500542	6.321640	10.427360
+3.301702	6.389957	13.499391
+5.278767	7.039769	-10.187218
+12.323542	7.817334	-1.648879
+6.816350	13.010634	-9.413556
+2.257864	11.652705	-10.968609
+10.024868	13.372918	-6.570460
+5.268847	12.710487	12.693676
+-7.648990	14.390018	12.535679
+7.442461	11.185614	11.330241
+10.861154	11.536425	-5.352630
+9.757610	11.431015	-6.900149
+12.539733	12.559690	-0.291576
+10.518617	13.486570	-5.890861
+6.102679	14.897688	12.237279
+6.818181	12.739468	-9.412456
+11.082247	12.576736	7.046426
+-4.953760	11.477301	-9.263072
+11.370186	12.719958	-4.412093
+0.291620	12.105859	14.113495
+10.359657	12.809526	-6.120777
+11.829601	12.905074	-3.337479
+10.795345	13.957346	-5.461048
+1.295528	12.272009	-11.036647
+10.503142	12.260357	8.026400
+12.041934	11.407420	-2.716374
+10.557027	11.828163	-5.833544
+-2.013150	12.391000	14.135999
+-2.751254	13.552213	-10.377934
+7.954050	12.971966	10.917240
+-0.547994	14.528935	14.165238
+12.026954	13.502657	-2.764003
+6.831490	11.947686	-9.404447
+-0.905839	13.302637	14.172077
+6.037660	13.315730	-9.841417
+12.539100	11.968838	2.295000
+5.802316	12.411208	-9.955829
+-2.794968	12.167921	14.058275
+12.595325	12.156981	1.514184
+11.348187	10.741401	6.520029
+11.414030	11.153702	-4.320817
+-0.991859	11.282232	14.172373
+12.042261	11.510797	4.739361
+12.541196	12.940338	-0.277318
+12.602103	13.545561	0.686324
+9.928511	11.325702	-6.692107
+12.509411	11.377441	2.556429
+11.241896	11.205101	-4.668596
+12.605887	10.807339	1.030159
+-15.708033	11.798480	-1.996230
+9.942177	12.886460	-6.675051
+8.102531	12.778732	-8.517768
+8.934461	13.118438	9.997292
+10.080695	11.672861	-6.498469
+11.055420	10.979794	-5.016635
+-8.458384	12.854959	-5.306337
+11.016262	12.748333	7.168472
+2.052397	12.626166	-10.990198
+12.070482	11.433381	4.646170
+11.302940	10.815754	6.613883
+10.239128	13.784510	8.416588
+11.707213	11.737680	-3.653036
+6.226668	11.325156	-9.744666
+11.921001	12.866313	5.113951
+6.548397	12.889001	11.961026
+3.072722	12.203996	-10.844769
+-3.092174	12.500619	-10.246044
+4.408490	11.850078	-10.505394
+8.133011	10.726120	-8.493260
+0.166699	12.214470	14.124382
+-9.422538	13.360862	-2.013800
+11.745099	11.950619	-3.558083
+11.270807	11.691360	-4.612097
+9.553701	13.403114	-7.136375
+8.033626	11.611145	-8.572562
+5.512306	11.541071	-10.087862
+4.805144	11.788611	12.916142
+12.032583	10.748820	4.770748
+7.400781	12.176786	-9.038270
+8.638421	14.775748	-8.061276
+8.608536	12.492453	-8.088225
+10.466820	13.145324	8.081896
+12.310793	12.713380	-1.707071
+12.553119	14.450536	2.150992
+9.044662	13.863294	-7.675731
+6.049073	12.224750	-9.835699
+6.308746	12.163116	12.112509
+11.801321	12.980314	-3.412737
+7.796368	12.046487	11.048959
+12.034987	12.044517	4.762978
+7.685738	13.342734	-8.836680
+4.438912	11.306218	13.077087
+12.602825	13.429962	1.276818
+10.299320	11.764462	8.330233
+6.810508	11.533278	-9.417063
+12.097073	11.420490	-2.534911
+12.397625	11.283920	3.284623
+12.594208	12.992940	1.540732
+12.265588	12.302767	-1.903488
+6.218907	13.352670	-9.748726
+9.823865	12.483501	8.975476
+8.821288	11.272393	-7.892252
+8.890174	12.198831	-7.826691
+11.012217	13.875882	-5.093544
+9.175404	12.335714	9.740652
+10.354194	11.896905	8.250208
+9.825917	13.183954	-6.818144
+12.605451	13.080329	1.106919
+10.222061	14.161065	-6.310945
+4.795493	12.229645	-10.373803
+11.312162	12.446728	-4.529985
+9.148875	11.966735	9.769580
+12.572241	10.591872	1.918801
+12.523526	13.636959	-0.439984
+-2.377144	14.246648	14.105221
+1.891963	13.138079	-11.004381
+9.572581	12.484909	-7.115023
+7.203831	11.524118	-9.170290
+1.215021	11.916613	-11.038536
+5.078032	12.699360	12.787813
+2.857864	12.100193	13.634252
+10.627966	11.916266	-5.725780
+-3.223613	12.470251	-10.191562
+2.656617	14.173930	13.690114
+7.722110	13.614281	-8.810016
+7.084823	14.231250	11.595610
+6.639163	12.587786	11.901806
+12.461683	12.443429	-0.900881
+-3.201935	14.196611	-10.200690
+12.571885	13.312754	0.071914
+11.694428	11.472622	5.727836
+-2.865375	14.017477	14.049136
+11.515710	12.461427	-4.101255
+12.349324	12.775042	-1.526859
+9.435628	13.653247	9.447511
+12.489510	9.970717	-0.709334
+10.752938	11.917662	7.627205
+1.509901	10.585991	13.947846
+12.573589	12.797583	0.095310
+6.431959	11.849570	12.035500
+12.364235	11.613482	3.459987
+11.995984	11.839635	-2.860398
+11.561106	11.746274	6.050134
+10.202932	11.574616	-6.336773
+8.668443	13.876106	10.265578
+12.067683	14.660040	-2.632884
+11.776051	12.477936	5.517463
+-5.792090	13.230049	-8.644095
+9.512349	12.348706	9.357678
+5.731143	14.105920	-9.989136
+6.372725	14.020783	-9.666853
+12.481504	11.511436	2.766999
+5.164611	13.948339	12.745556
+6.211802	12.918318	12.171827
+12.361837	13.506064	-1.465344
+5.710258	13.650101	12.461467
+-8.318645	12.802142	12.164631
+8.571070	12.980166	-8.121752
+11.884710	12.428090	-3.186317
+3.929851	13.009531	-10.647056
+10.174938	11.712964	-6.374309
+7.045846	11.371670	-9.272054
+5.486601	12.413141	-10.099098
+0.559375	11.929560	14.086375
+7.449142	12.459329	11.325107
+5.566381	12.872991	-10.063979
+12.286527	13.118911	-1.814320
+2.916571	11.257396	13.617340
+12.374604	12.621340	3.406936
+3.833434	12.283449	13.316062
+-1.231762	11.409338	-10.812203
+12.210339	12.747229	4.140668
+11.374602	13.289511	6.464335
+7.893535	13.758496	-8.681391
+0.792088	14.361978	14.058588
+11.825154	12.368874	5.385400
+-6.611497	13.245930	-7.896008
+1.038591	12.866272	-11.040626
+5.359629	13.396163	12.647587
+12.524565	12.821981	-0.430942
+5.654192	13.249748	12.492109
+12.224656	13.369546	-2.069830
+12.012856	13.307606	4.833863
+1.208399	13.554493	-11.038665
+9.779401	13.035889	-6.874150
+11.213722	12.968358	-4.722963
+6.578263	13.124704	11.941654
+10.379418	13.508596	-6.092810
+12.600783	12.577681	0.636792
+12.496548	12.115808	-0.657283
+8.943514	11.012765	-7.775191
+6.393159	11.448999	12.059948
+3.169164	12.720775	13.541364
+12.460552	14.052397	2.909725
+11.413797	13.785224	6.380418
+10.479274	13.094805	8.062938
+2.585194	11.950007	-10.926237
+5.179030	13.886467	12.738445
+-4.847286	10.088201	-9.332818
+5.492716	12.821335	-10.096432
+9.484899	12.988775	-7.213322
+12.206100	12.534476	4.157254
+12.386123	12.607700	3.346550
+9.470117	12.896677	-7.229680
+1.986997	13.149403	-10.996262
+11.484164	12.064795	6.225717
+9.003560	12.958334	-7.716435
+11.230558	14.423715	6.760175
+11.719316	13.339674	5.664829
+5.206505	12.015858	12.724837
+4.546260	13.496732	-10.460326
+7.824019	12.647525	-8.734144
+12.296771	13.504773	3.779817
+4.297477	11.727517	-10.540300
+8.769399	10.921765	-7.940944
+12.273029	11.992707	-1.872138
+11.440961	11.379585	-4.263766
+10.176717	12.068973	8.504606
+8.463815	12.436811	-8.216154
+11.757262	15.014985	5.566863
+9.375816	12.689328	-7.332615
+5.909809	11.834022	-9.904393
+-0.335466	12.868908	14.156885
+12.243396	12.320796	-1.994903
+12.018828	13.358858	-2.789560
+9.189282	14.075697	-7.529279
+10.763486	14.288849	-5.512625
+7.484766	12.003201	11.297624
+8.313569	13.434181	-8.344581
+6.614144	13.366956	-9.532238
+10.764833	13.001663	-5.510458
+-0.913614	13.054739	-10.873434
+12.426540	10.750134	3.120766
+8.796618	11.406992	-7.915475
+-8.338705	12.009549	12.152801
+9.746059	10.644060	9.073682
+5.245496	12.543541	-10.200878
+-5.216040	11.922802	13.534704
+12.507921	11.303995	-0.569430
+8.999698	11.851172	-7.720239
+7.999887	11.098867	-8.599084
+12.604435	11.896056	0.802713
+12.309317	12.407041	-1.713724
+9.091001	13.103821	9.832103
+12.578598	13.623783	0.167905
+12.605773	12.973943	0.933723
+3.006113	12.329530	-10.857207
+7.456406	13.072589	-8.999918
+10.497436	12.982610	-5.922165
+-0.006259	13.324285	14.137608
+-2.849774	12.717538	-10.341195
+5.122999	11.719644	-10.250120
+10.748490	12.534318	-5.536704
+11.613397	12.555988	-3.878809
+11.009435	10.958159	-5.098451
+1.519280	12.784584	-11.028317
+-11.360942	13.001116	9.789335
+7.702289	12.774521	11.125631
+-4.811628	11.626781	-9.355764
+12.182510	14.877548	-2.231530
+-2.373355	11.002604	14.105589
+11.731531	12.732347	5.633549
+12.315108	11.833380	3.696773
+11.606759	12.487041	5.942500
+12.165032	10.353026	-2.296078
+5.922907	13.682240	-9.898032
+10.132602	14.255898	8.565916
+-9.535315	12.455212	11.364959
+0.694441	13.177736	-11.036587
+12.445946	12.303132	3.003127
+11.377638	13.586940	6.457891
+7.984023	13.285935	10.891737
+12.597578	11.793295	0.538273
+10.676806	12.012207	7.752274
+-6.659653	14.462967	13.004399
+12.605892	13.361458	0.965502
+1.196684	13.353240	-11.038884
+-2.348789	12.394022	-10.516747
+7.623316	13.919811	-8.881942
+10.969680	14.792566	7.252812
+10.985844	9.951648	7.223712
+10.009515	13.349031	8.733173
+10.859816	12.378512	-5.354860
+-1.343427	12.780648	-10.788363
+7.629084	13.132946	11.184326
+12.573159	12.563987	1.906156
+10.228458	13.872663	-6.302276
+8.862402	11.989036	10.071472
+11.042450	11.174950	7.120405
+9.611716	12.667233	-7.070433
+11.435302	10.461704	6.333704
+12.507807	10.900643	2.569299
+12.457351	11.110768	-0.928945
+-6.162348	11.854508	-8.326001
+5.336361	13.401175	-10.163283
+9.354740	11.414215	-7.355292
+6.723762	11.572013	11.845674
+12.591959	13.412603	0.403892
+12.578839	13.500807	1.823640
+8.058742	11.480587	-8.552688
+2.518108	13.533819	-10.935723
+11.317532	13.155457	6.583824
+10.954981	12.570638	7.279126
+8.754584	11.017792	-7.954738
+7.594358	11.167188	-8.902726
+7.799488	11.127066	-8.752565
+11.618796	13.341903	-3.866156
+8.449134	11.947829	10.475784
+12.556698	12.974315	2.111241
+1.479085	11.899375	-11.030147
+12.567905	11.801877	0.019475
+7.194893	11.643279	11.515872
+12.464745	11.370067	2.882043
+11.490820	13.273288	6.210803
+6.042864	12.211185	12.272572
+10.882708	13.492933	-5.316554
+11.987782	11.876503	-2.885477
+11.947972	12.593412	-3.004712
+12.490590	12.422387	-0.701453
+12.157602	11.169810	-2.323104
+12.581520	12.124597	0.213393
+6.023082	11.662650	12.284153
+2.879205	13.263030	-10.879754
+7.738969	12.845647	-8.797583
+9.562994	11.629199	-7.125878
+10.923424	12.459630	-5.247616
+7.238147	12.918732	-9.147704
+0.701508	11.476862	-11.036778
+11.972887	12.732145	-2.930565
+10.041759	12.371146	-6.548798
+12.516962	12.507152	-0.495828
+10.494884	12.155889	-5.925923
+12.458969	12.129027	2.920068
+6.929190	10.921235	-9.344902
+3.280984	13.624737	-10.803188
+12.451864	11.441014	-0.963893
+5.860639	13.207941	12.377587
+5.831496	10.374859	-9.942002
+12.299420	11.651606	-1.757889
+9.875874	10.482600	8.908753
+11.285587	14.196296	-4.582928
+9.302950	13.222287	9.599141
+11.976094	10.925272	-2.920908
+3.225604	11.425352	13.523669
+-6.657874	11.786150	-7.848530
+1.812385	13.490156	-11.010549
+11.438219	12.407394	-4.269611
+11.851548	13.250619	5.312542
+8.254149	12.912385	10.654906
+7.655285	12.787032	11.163414
+4.965419	12.641398	12.841658
+3.861025	12.735867	13.305884
+9.688245	12.490498	9.145440
+12.399469	12.610112	3.274533
+7.573658	13.352368	-8.917501
+10.485309	13.939863	8.053724
+10.550176	12.944262	7.953620
+-1.619197	11.458821	-10.724171
+12.353292	12.051927	-1.507523
+12.352157	12.983628	3.520332
+12.071890	12.920490	4.641454
+4.737487	13.452414	-10.394520
+10.399206	12.404573	-6.064635
+11.392349	12.432645	6.426532
+12.480185	12.728288	2.776325
+11.565229	13.740171	-3.989998
+12.145384	13.470776	-2.367033
+12.477754	13.225845	-0.792832
+-3.348618	11.879689	-10.137822
+12.318879	11.227971	3.679364
+4.231117	11.635667	13.162834
+-5.480287	12.160751	-8.889617
+12.591062	12.795535	0.385167
+12.457524	12.755093	-0.927828
+10.223449	12.351174	8.438844
+9.593354	13.172997	-7.091410
+11.575734	12.112569	-3.966005
+12.152331	11.443093	4.360170
+12.348663	14.256436	-1.530064
+6.847284	11.194604	11.762064
+4.073817	14.546925	13.225130
+10.824423	12.162309	-5.413460
+11.865408	13.033303	5.273727
+10.140920	11.969731	-6.419514
+5.192349	11.128866	12.731858
+-3.407399	12.633173	-10.111893
+8.587361	12.673915	-8.107210
+-15.338441	12.281832	2.524663
+11.303333	13.162835	6.613076
+9.755416	12.628960	9.061971
+8.905650	11.418342	10.027089
+11.578258	12.843210	-3.960219
+10.973124	12.361134	-5.162023
+12.531230	12.972506	2.369204
+12.381398	12.228963	3.371510
+12.186071	12.875782	-2.218209
+-4.358588	12.063279	-9.630134
+8.443310	12.382025	10.481237
+9.553932	11.246692	-7.136115
+8.503806	13.110051	10.424269
+12.406159	13.831786	-1.233169
+12.440832	12.176057	-1.032280
+9.612878	11.898241	-7.069101
+-0.945664	11.501216	-10.867711
\ No newline at end of file


Mime
View raw message