Examples of org.apache.hama.ml.math.DenseDoubleVector$DefaultIterator

org.apache.hama.ml.math.DenseDoubleVector
Iterator for all elements.


    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.");

View Full Code Here


    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.");

View Full Code Here


    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;
      }
    }

View Full Code Here

          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;
      }
    }

View Full Code Here

    
    int maxIteration = 2000;
    Random rnd = new Random();
    for (int iteration = 0; iteration < maxIteration; ++iteration) {
      for (int i = 0; i < instances.length; ++i) {
        encoder.trainOnline(new DenseDoubleVector(instances[rnd.nextInt(instances.length)]));
      }
    }


    for (int i = 0; i < instances.length; ++i) {
      DoubleVector encodeVec = encoder.encode(new DenseDoubleVector(
          instances[i]));
      DoubleVector decodeVec = encoder.decode(encodeVec);
      for (int d = 0; d < instances[i].length; ++d) {
        assertEquals(instances[i][d], decodeVec.get(d), 0.1);
      }

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      e.printStackTrace();
    }
    
    List<DoubleVector> vecInstanceList = new ArrayList<DoubleVector>();
    for (double[] instance : instanceList) {
      vecInstanceList.add(new DenseDoubleVector(instance));
    }
    AutoEncoder encoder = new AutoEncoder(3, 2);
    encoder.setLearningRate(0.05);
    encoder.setMomemtumWeight(0.1);
    int maxIteration = 2000;

View Full Code Here

      // normalize instances
      zeroOneNormalization(instanceList, instanceList.get(0).length);
      
      SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, LongWritable.class, VectorWritable.class);
      for (int i = 0; i < instanceList.size(); ++i) {
        DoubleVector vector = new DenseDoubleVector(instanceList.get(i));
        writer.append(new LongWritable(i), new VectorWritable(vector));
      }
      
      writer.close();
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    } catch (IOException e) {
      e.printStackTrace();
    } catch (URISyntaxException e) {
      e.printStackTrace();
    }
    
    AutoEncoder encoder = new AutoEncoder(3, 2);
    String modelPath = "/tmp/autoencoder-modelpath";
    encoder.setModelPath(modelPath);
    Map<String, String> trainingParams = new HashMap<String, String>();
    encoder.setLearningRate(0.5);
    trainingParams.put("tasks", "5");
    trainingParams.put("training.max.iterations", "3000");
    trainingParams.put("training.batch.size", "200");
    encoder.train(path, trainingParams);
    
    double errorInstance = 0;
    for (double[] instance : instanceList) {
      DoubleVector vector = new DenseDoubleVector(instance);
      DoubleVector decoded = encoder.getOutput(vector);
      DoubleVector diff = vector.subtract(decoded);
      double error = diff.dot(diff);
      if (error > 0.1) {
        ++errorInstance;
      }
    }

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      e.printStackTrace();
    }


    // initial the mlp with existing model meta-data and get the output
    MultiLayerPerceptron mlp = new SmallMultiLayerPerceptron(modelPath);
    DoubleVector input = new DenseDoubleVector(new double[] { 1, 2, 3 });
    try {
      DoubleVector result = mlp.output(input);
      assertArrayEquals(new double[] { 0.6636557, 0.7009963, 0.7213835 },
          result.toArray(), 0.0001);
    } catch (Exception e1) {

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   */
  @Test
  public void testTrainWithSquaredError() {
    // generate training data
    DoubleVector[] trainingData = new DenseDoubleVector[] {
        new DenseDoubleVector(new double[] { 0, 0, 0 }),
        new DenseDoubleVector(new double[] { 0, 1, 1 }),
        new DenseDoubleVector(new double[] { 1, 0, 1 }),
        new DenseDoubleVector(new double[] { 1, 1, 0 }) };


    // set parameters
    double learningRate = 0.3;
    double regularization = 0.02; // no regularization
    double momentum = 0; // no momentum
    String squashingFunctionName = "Sigmoid";
    String costFunctionName = "SquaredError";
    int[] layerSizeArray = new int[] { 2, 5, 1 };
    SmallMultiLayerPerceptron mlp = new SmallMultiLayerPerceptron(learningRate,
        regularization, momentum, squashingFunctionName, costFunctionName,
        layerSizeArray);


    try {
      // train by multiple instances
      Random rnd = new Random();
      for (int i = 0; i < 100000; ++i) {
        DenseDoubleMatrix[] weightUpdates = mlp
            .trainByInstance(trainingData[rnd.nextInt(4)]);
        mlp.updateWeightMatrices(weightUpdates);
      }


      // System.out.printf("Weight matrices: %s\n",
      // mlp.weightsToString(mlp.getWeightMatrices()));
      for (int i = 0; i < trainingData.length; ++i) {
        DenseDoubleVector testVec = (DenseDoubleVector) trainingData[i]
            .slice(2);
        double expected = trainingData[i].toArray()[2];
        double actual = mlp.output(testVec).toArray()[0];
        if (expected < 0.5 && actual >= 0.5 || expected >= 0.5 && actual < 0.5) {
          Log.info("Neural network failes to lear the XOR.");

View Full Code Here

   */
  @Test
  public void testTrainWithCrossEntropy() {
    // generate training data
    DoubleVector[] trainingData = new DenseDoubleVector[] {
        new DenseDoubleVector(new double[] { 0, 0, 0 }),
        new DenseDoubleVector(new double[] { 0, 1, 1 }),
        new DenseDoubleVector(new double[] { 1, 0, 1 }),
        new DenseDoubleVector(new double[] { 1, 1, 0 }) };


    // set parameters
    double learningRate = 0.3;
    double regularization = 0.0; // no regularization
    double momentum = 0; // no momentum
    String squashingFunctionName = "Sigmoid";
    String costFunctionName = "CrossEntropy";
    int[] layerSizeArray = new int[] { 2, 7, 1 };
    SmallMultiLayerPerceptron mlp = new SmallMultiLayerPerceptron(learningRate,
        regularization, momentum, squashingFunctionName, costFunctionName,
        layerSizeArray);


    try {
      // train by multiple instances
      Random rnd = new Random();
      for (int i = 0; i < 50000; ++i) {
        DenseDoubleMatrix[] weightUpdates = mlp
            .trainByInstance(trainingData[rnd.nextInt(4)]);
        mlp.updateWeightMatrices(weightUpdates);
      }


      // System.out.printf("Weight matrices: %s\n",
      // mlp.weightsToString(mlp.getWeightMatrices()));
      for (int i = 0; i < trainingData.length; ++i) {
        DenseDoubleVector testVec = (DenseDoubleVector) trainingData[i]
            .slice(2);
        double expected = trainingData[i].toArray()[2];
        double actual = mlp.output(testVec).toArray()[0];
        if (expected < 0.5 && actual >= 0.5 || expected >= 0.5 && actual < 0.5) {
          Log.info("Neural network failes to lear the XOR.");

View Full Code Here

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