Package de.jungblut.math.sparse

Examples of de.jungblut.math.sparse.SparseDoubleRowMatrix

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  }

  @Test
  public void testMultiHammingLoss() {
    SparseDoubleRowMatrix groundTruth = new SparseDoubleRowMatrix(
        new double[][] { { 1d, 0d }, { 0d, 1d } });

    SparseDoubleRowMatrix hypo = new SparseDoubleRowMatrix(new double[][] {
        { 1d, 0d }, { 0d, 0d } });

    HammingLossFunction fnc = new HammingLossFunction(0.5d);

    double err = fnc.calculateError(groundTruth, hypo);
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  }

  @Test
  public void testSparseSerDe() throws Exception {
    SparseDoubleRowMatrix mat = new SparseDoubleRowMatrix(
        new DenseDoubleMatrix(new double[][] { { 1, 0, 3 }, { 0, 0, 0 },
            { 1, 0, 0 } }));

    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    DataOutputStream out = new DataOutputStream(baos);

    MatrixWritable.writeSparseMatrix(mat, out);

    ByteArrayInputStream bais = new ByteArrayInputStream(baos.toByteArray());
    DataInputStream in = new DataInputStream(bais);
    DoubleMatrix readMat = MatrixWritable.readSparseMatrix(in);

    assertEquals(0.0d, mat.subtract(readMat).sum(), 1e-4);

  }
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            "There wasn't at least a single featurevector, or the two array didn't match in size.");
    this.classes = outcome[0].getDimension() == 1 ? 2 : outcome[0]
        .getDimension();
    DoubleMatrix mat = null;
    if (features[0].isSparse()) {
      mat = new SparseDoubleRowMatrix(features);
    } else {
      mat = new DenseDoubleMatrix(features);
    }
    ConditionalLikelihoodCostFunction func = new ConditionalLikelihoodCostFunction(
        mat, new DenseDoubleMatrix(outcome));
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  }

  public DoubleVector predict(DoubleVector feature,
      DoubleVector[] featuresPerState) {
    return ViterbiUtils.decode(theta,
        new SparseDoubleRowMatrix(Collections.singletonList(feature)),
        new SparseDoubleRowMatrix(featuresPerState), classes).getRowVector(0);
  }
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Related Classes of de.jungblut.math.sparse.SparseDoubleRowMatrix

  • de.jungblut.classification.bayes.MultinomialNaiveBayes
  • de.jungblut.classification.regression.LogisticRegression
  • de.jungblut.classification.regression.SparseMultiLabelRegression
  • de.jungblut.math.activation.AbstractActivationFunction
  • de.jungblut.math.minimize.AbstractMiniBatchCostFunction
  • de.jungblut.math.squashing.HammingLossFunctionTest
  • de.jungblut.ner.MaxEntMarkovModel
  • de.jungblut.nlp.HMM
  • de.jungblut.nlp.MarkovChain
  • de.jungblut.writable.MatrixWritable
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