Example usage for org.apache.commons.math3.stat.descriptive.moment StandardDeviation incrementAll

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive.moment StandardDeviation incrementAll.

Prototype

public void incrementAll(double[] values) throws MathIllegalArgumentException 

Source Link

Document

This default implementation just calls #increment in a loop over the input array.

Usage

From source file:com.cloudera.oryx.rdf.common.information.NumericInformationTest.java

@Test
public void testInformationNumericFeature() {
    ExampleSet exampleSet = examplesForFeaturesValues(new float[] { 0.0f, 1.0f, 2.0f, 4.0f },
            new float[] { 1.0f, 1.5f, 2.0f, 5.0f });
    List<Decision> decisions = Decision.decisionsFromExamples(exampleSet, 0, 100);
    assertEquals(3, decisions.size());//from w  w  w .j a v a2  s . c  om
    assertEquals(3.0f, ((NumericDecision) decisions.get(0)).getThreshold());
    assertEquals(1.5f, ((NumericDecision) decisions.get(1)).getThreshold());
    assertEquals(0.5f, ((NumericDecision) decisions.get(2)).getThreshold());
    Pair<Decision, Double> best = NumericalInformation.bestGain(decisions, exampleSet);
    assertEquals(1.5f, ((NumericDecision) best.getFirst()).getThreshold());

    StandardDeviation all = new StandardDeviation();
    all.incrementAll(new double[] { 1.0, 1.5, 2.0, 5.0 });
    StandardDeviation positive = new StandardDeviation();
    positive.incrementAll(new double[] { 1.0, 1.5 });
    StandardDeviation negative = new StandardDeviation();
    negative.incrementAll(new double[] { 2.0, 5.0 });

    assertEquals(differentialEntropy(all) - (2.0 / 4.0) * differentialEntropy(positive)
            - (2.0 / 4.0) * differentialEntropy(negative), best.getValue().doubleValue());
}

---

From source file:com.cloudera.oryx.rdf.common.information.NumericInformationTest.java

@Test
public void testInformationCategoricalFeature() {
    ExampleSet exampleSet = examplesForValuesForCategories(new float[][] { new float[] { 1.0f, 1.5f },
            new float[] { 5.5f, 7.0f }, new float[] { 2.0f, 5.0f }, });
    List<Decision> decisions = Decision.decisionsFromExamples(exampleSet, 0, 100);
    assertEquals(2, decisions.size());//  ww w. ja  va 2  s .c  o  m
    BitSet categories0 = ((CategoricalDecision) decisions.get(0)).getCategoryIDs();
    BitSet categories1 = ((CategoricalDecision) decisions.get(1)).getCategoryIDs();
    assertEquals(1, categories0.cardinality());
    assertTrue(categories0.get(0));
    assertEquals(2, categories1.cardinality());
    assertTrue(categories1.get(0));
    assertTrue(categories1.get(2));

    Pair<Decision, Double> best = NumericalInformation.bestGain(decisions, exampleSet);
    assertEquals(categories0, ((CategoricalDecision) best.getFirst()).getCategoryIDs());

    StandardDeviation all = new StandardDeviation();
    all.incrementAll(new double[] { 1.0, 1.5, 5.5, 7.0, 2.0, 5.0 });
    StandardDeviation positive = new StandardDeviation();
    positive.incrementAll(new double[] { 1.0, 1.5 });
    StandardDeviation negative = new StandardDeviation();
    negative.incrementAll(new double[] { 5.5, 7.0, 2.0, 5.0 });

    assertEquals(differentialEntropy(all) - (2.0 / 6.0) * differentialEntropy(positive)
            - (4.0 / 6.0) * differentialEntropy(negative), best.getValue().doubleValue());
}

---