Package de.jungblut.math.sparse

Examples of de.jungblut.math.sparse.SparseDoubleVector

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  }

  @Test
  public void testMedianSparse() throws Exception {
    assertEquals(1,
        KDTree.median(new SparseDoubleVector(new double[] { 2, 3 }), 0));
    assertEquals(0,
        KDTree.median(new SparseDoubleVector(new double[] { 9, 6 }), 0));
    assertEquals(2,
        KDTree.median(new SparseDoubleVector(new double[] { 9, 6, 8 }), 0));
    assertEquals(1,
        KDTree.median(new SparseDoubleVector(new double[] { 9, 8, 7 }), 0));
    assertEquals(0,
        KDTree.median(new SparseDoubleVector(new double[] { 8, 9, 6 }), 0));

    assertEquals(
        7,
        KDTree.median(new SparseDoubleVector(new double[] { 8, 9, 6, 19, 25, 2,
            3, 4 }), 0));
  }
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  }

  @Test
  public void testSparseFunnelingWithDenseData() {
    VectorFunnel funnel = new VectorFunnel();
    SparseDoubleVector vec = new SparseDoubleVector(new double[] { 1, 1 });
    long hash = Hashing.murmur3_128().newHasher().putObject(vec, funnel).hash()
        .asLong();
    // should yield the same hashcode like the testDenseFunneling test
    assertEquals(4270060439366700849L, hash);
  }
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  }

  @Test
  public void testSparseFunneling() {
    VectorFunnel funnel = new VectorFunnel();
    SparseDoubleVector vec = new SparseDoubleVector(new double[] { 0d, 15, 25,
        0d, 255, 2, 20, 0d, 0d, 0d, 2 });
    long hash = Hashing.murmur3_128().newHasher().putObject(vec, funnel).hash()
        .asLong();
    assertEquals(-3943116774135188236L, hash);
  }
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   * @return the feature for the given word.
   */
  public DoubleVector vectorize(K word, Integer lastLabel) {
    List<String> computedFeatures = extractor.computeFeatures(
        Arrays.asList(word), lastLabel == null ? 0 : lastLabel, 0);
    DoubleVector feature = new SparseDoubleVector(dicts.length);
    for (String feat : computedFeatures) {
      int index = Arrays.binarySearch(dicts, feat);
      if (index >= 0) {
        feature.set(index, 1d);
      }
    }
    return feature;
  }
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    DoubleVector[] features = new DoubleVector[stringFeatures.size()];
    final int dimension = dicts.length;
    // translate the feature vector
    for (int i = 0; i < features.length; i++) {
      features[i] = new SparseDoubleVector(dimension);
      for (String feat : stringFeatures.get(i)) {
        int index = Arrays.binarySearch(dicts, feat);
        if (index >= 0) {
          features[i].set(index, 1d);
        }
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      Arrays.sort(dicts);
    }
    final int dimension = dicts.length;
    // translate the feature vector
    for (int i = 0; i < features.length; i++) {
      features[i] = new SparseDoubleVector(dimension);
      for (String feat : stringFeatures.get(i)) {
        int index = Arrays.binarySearch(dicts, feat);
        if (index >= 0) {
          features[i].set(index, 1d);
        }
      }

      if (classes == 2) {
        outcome[i] = new SingleEntryDoubleVector(labels.get(i));
      } else {
        outcome[i] = new SparseDoubleVector(classes);
        outcome[i].set(labels.get(i), 1d);
      }
    }

    return new Tuple<>(features, outcome);
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Related Classes of de.jungblut.math.sparse.SparseDoubleVector

  • de.jungblut.classification.bayes.MultinomialNaiveBayesTest
  • de.jungblut.classification.eval.EvaluationSplitTest
  • de.jungblut.classification.knn.SparseKNearestNeighboursTest
  • de.jungblut.classification.regression.LogisticRegression
  • de.jungblut.classification.tree.DecisionTree
  • de.jungblut.datastructure.InvertedIndexTest
  • de.jungblut.datastructure.KDTreeTest
  • de.jungblut.math.activation.AbstractActivationFunction
  • de.jungblut.ner.SparseFeatureExtractorHelper
  • de.jungblut.nlp.MinHashTest
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