Package org.apache.mahout.math.random

Examples of org.apache.mahout.math.random.Normal

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public class LocalitySensitiveHashSearchTest {

  @Test
  public void testNormal() {
    Matrix testData = new DenseMatrix(100000, 10);
    final Normal gen = new Normal();
    testData.assign(gen);

    final EuclideanDistanceMeasure distance = new EuclideanDistanceMeasure();
    BruteSearch ref = new BruteSearch(distance);
    ref.addAllMatrixSlicesAsWeightedVectors(testData);
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    return t1;
  }

  @Test
  public void testDotCorrelation() {
    final Normal gen = new Normal();

    Matrix projection = new DenseMatrix(64, 10);
    projection.assign(gen);

    Vector query = new DenseVector(10);
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    int numDataVectors = 1000000;
    int numQueries = 100;
    int depth = 2000;

    Matrix data = new DenseMatrix(numDataVectors, dimension);
    data.assign(new Normal());

    Matrix queries = new DenseMatrix(numQueries, dimension);
    queries.assign(new Normal());

    Vector queryNorms = queries.aggregateRows(new VectorFunction() {
      @Override
      public double apply(Vector f) {
        return f.getLengthSquared();
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public class LocalitySensitiveHashSearchTest {

  @Test
  public void testNormal() {
    Matrix testData = new DenseMatrix(100000, 10);
    final Normal gen = new Normal();
    testData.assign(gen);

    final EuclideanDistanceMeasure distance = new EuclideanDistanceMeasure();
    BruteSearch ref = new BruteSearch(distance);
    ref.addAllMatrixSlicesAsWeightedVectors(testData);
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    return t1;
  }

  @Test
  public void testDotCorrelation() {
    final Normal gen = new Normal();

    Matrix projection = new DenseMatrix(64, 10);
    projection.assign(gen);

    Vector query = new DenseVector(10);
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Related Classes of org.apache.mahout.math.random.Normal

  • org.apache.mahout.knn.OverlapTest
  • org.apache.mahout.knn.search.LocalitySensitiveHashSearchTest
  • org.apache.mahout.math.neighborhood.LocalitySensitiveHashSearchTest
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