Examples of compute()

Examples of org.data2semantics.proppred.kernels.rdfgraphkernels.RDFWLSubTreeWithTextKernel.compute()

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        RDFGraphKernel k = new RDFIntersectionTreeEdgeVertexPathKernel(3,false, false, true);

        System.out.println("RDF EVP Kernel: " + frac);
        tic = System.currentTimeMillis();
        k.compute(dataset, instances, blackList);
        toc = System.currentTimeMillis();
        comp[i] = toc-tic;
      }
      res = new Result(comp, "comp time");
      resTable.addResult(res);
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Examples of org.eclipse.jgit.diff.RenameDetector.compute()

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    tw.reset(trees);

    List<DiffEntry> files = DiffEntry.scan(tw);
    RenameDetector rd = new RenameDetector(repository);
    rd.addAll(files);
    files = rd.compute();

    TreeFilter newFilter = oldFilter;
    for (DiffEntry ent : files) {
      if (isRename(ent) && ent.getNewPath().equals(oldFilter.getPath())) {
        newFilter = FollowFilter.create(ent.getOldPath());
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Examples of org.ejml.alg.dense.misc.DeterminantFromMinor.compute()

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        int width = 10;

        DenseMatrix64F A = RandomMatrices.createRandom(width,width,rand);

        DeterminantFromMinor minor = new DeterminantFromMinor(width);
        double minorVal = minor.compute(A);

        LUDecompositionAlt alg = new LUDecompositionAlt();
        alg.decompose(A);
        double luVal = alg.computeDeterminant();
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Examples of org.encog.mathutil.matrices.hessian.HessianCR.compute()

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    testFD.init(network, trainingData);
    testFD.compute();
       
    HessianCR testCR = new HessianCR();
    testCR.init(network, trainingData);
    testCR.compute();
   
    //dump(testFD, "FD");
    //dump(testCR, "CR");
    Assert.assertTrue(testCR.getHessianMatrix().equals(testFD.getHessianMatrix(), 4));
  }
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Examples of org.encog.mathutil.matrices.hessian.HessianFD.compute()

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    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);   
   
    HessianFD testFD = new HessianFD();
    testFD.init(network, trainingData);
    testFD.compute();
       
    HessianCR testCR = new HessianCR();
    testCR.init(network, trainingData);
    testCR.compute();
   
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Examples of org.encog.ml.fitting.gaussian.GaussianFitting.compute()

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    GaussianFitting fit = new GaussianFitting(INPUT[0].length);
    TrainGaussian train = new TrainGaussian(fit,data);
    train.iteration();
   
    for(MLDataPair pair: data){
      MLData output = fit.compute(pair.getInput());
      System.out.println( output.getData(0));
    }
   
    for( double[] d : fit.getSigma().getData()) {
      System.out.println( Arrays.toString(d));
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Examples of org.encog.ml.prg.EncogProgram.compute()

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  public double calculateScore(final MLMethod genome) {
    final EncogProgram prg = (EncogProgram) genome;
    final PrgPopulation pop = (PrgPopulation) prg.getPopulation();
    final MLData inputData = new BasicMLData(pop.getContext()
        .getDefinedVariables().size());
    prg.compute(inputData);
    return 0;
  }

  /**
   * {@inheritDoc}
 
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Examples of org.encog.neural.art.ART1.compute()

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    ART1 logic = new ART1(INPUT_NEURONS,OUTPUT_NEURONS);

    for (int i = 0; i < PATTERN.length; i++) {
      BiPolarNeuralData in = new BiPolarNeuralData(this.input[i]);
      BiPolarNeuralData out = new BiPolarNeuralData(OUTPUT_NEURONS);
      logic.compute(in, out);
      if (logic.hasWinner()) {
        System.out.println(PATTERN[i] + " - " + logic.getWinner());
      } else {
        System.out.println(PATTERN[i]
            + " - new Input and all Classes exhausted");
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Examples of org.encog.neural.flat.FlatNetwork.compute()

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    double[] output = new double[1];
    // test the neural network
    System.out.println("Neural Network Results:");
    for(MLDataPair pair: trainingSet ) {
      double[] input = pair.getInput().getData();
      network.compute(input, output);
      System.out.println(input[0] + "," + input[1] + ":" + output[0]);
    }
  }
}
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Examples of org.encog.neural.neat.NEATNetwork.compute()

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        input.setData(index++, d);
      }
      for (final double d : target.getLocation()) {
        input.setData(index++, d);
      }
      final MLData output = cppn.compute(input);

      double weight = output.getData(0);
      if (Math.abs(weight) > this.minWeight) {
        weight = (Math.abs(weight) - this.minWeight) * c
            * Math.signum(weight);
 
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