Examples of org.apache.hama.commons.math.DoubleVector$DoubleVectorElement

org.apache.hama.commons.math.DoubleVector
Class for iteration of elements, consists of an index and a value at this index. Can be reused for performance purposes.

        int rowIdx = 0;
        while (reader.next(key, value)) {
          assertEquals("Expected rowIdx: '" + rowIdx + "' != '" + key.get()
              + "'", rowIdx, key.get());


          DoubleVector rowVector = value.getVector();


          for (int colIdx = 0; colIdx < rowVector.getLength(); colIdx++) {
            double colValue = rowVector.get(colIdx);
            assertEquals("Expected colValue: '" + matrix[rowIdx][colIdx]
                + "' != '" + colValue + "' in row: " + rowIdx + " values: "
                + rowVector.toString(), matrix[rowIdx][colIdx], colValue,
                Math.pow(10, (DOUBLE_PRECISION * -1)));
          }
          rowIdx++;
        }
        reader.close();

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    String str = Text.readString(in);
    // LOG.debug("readVector: '" + str + "'");


    String[] values = str.split(",");
    int len = values.length;
    DoubleVector vector = new DenseDoubleVector(len);
    for (int i = 0; i < len; i++) {
      vector.set(i, Double.parseDouble(values[i]));
    }
    return vector;
  }

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    }
  }


  public static DoubleVector readVector(DataInput in) throws IOException {
    int length = in.readInt();
    DoubleVector vector;
    vector = new DenseDoubleVector(length);
    for (int i = 0; i < length; i++) {
      vector.set(i, in.readDouble());
    }


    if (in.readBoolean()) {
      vector = new NamedDoubleVector(in.readUTF(), vector);
    }

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  public static int compareVector(VectorWritable a, VectorWritable o) {
    return compareVector(a.getVector(), o.getVector());
  }


  public static int compareVector(DoubleVector a, DoubleVector o) {
    DoubleVector subtract = a.subtractUnsafe(o);
    return (int) subtract.sum();
  }

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    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);
  }

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        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.");

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        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.");

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        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.");

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        (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;
      }
    }

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          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;
      }
    }

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