Package org.apache.hama.ml.math

Examples of org.apache.hama.ml.math.DoubleVector.toArray()

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    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) {
      e1.printStackTrace();
    }

    // delete meta-data
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    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"));
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    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) {
      e1.printStackTrace();
    }

    // delete meta-data
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        for (int i = 0; i < tokens.length; ++i) {
          vals[i] = Double.parseDouble(tokens[i]);
        }
        DoubleVector instance = new DenseDoubleVector(vals);
        DoubleVector result = ann.getOutput(instance);
        double[] arrResult = result.toArray();
        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < arrResult.length; ++i) {
          sb.append(arrResult[i]);
          if (i != arrResult.length - 1) {
            sb.append(",");
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