Package org.apache.mahout.math

Examples of org.apache.mahout.math.Vector.all()

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      // together which have the same *combination* of indices; for example,
      // (1, 3) will have the same key as (3, 1) but a different key from (1, 1)
      // and (3, 3) (which, incidentally, will also not be grouped together)
      String type = context.getWorkingDirectory().getName();
      Vector vector = row.get();
      for (Vector.Element e : vector.all()) {
        String newkey = Math.max(key.get(), e.index()) + "_" + Math.min(key.get(), e.index());
        context.write(new Text(newkey), new VertexWritable(key.get(), e.index(), e.get(), type));
      }
    }
  }
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    if (samples.minValue() >= 1) {
      // compare to previous scores for other category
      Vector row = scores.viewRow(1 - category);
      double m = 0.0;
      double count = 0.0;
      for (Vector.Element element : row.all()) {
        double v = element.get();
        if (Double.isNaN(v)) {
          continue;
        }
        count++;
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  public void testTrain() {

    Random gen = RandomUtils.getRandom();
    Exponential exp = new Exponential(0.5, gen);
    Vector beta = new DenseVector(200);
    for (Vector.Element element : beta.all()) {
      int sign = 1;
      if (gen.nextDouble() < 0.5) {
        sign = -1;
      }
      element.set(sign * exp.nextDouble());
 
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  }

  private static AdaptiveLogisticRegression.TrainingExample getExample(int i, Random gen, Vector beta) {
    Vector data = new DenseVector(200);

    for (Vector.Element element : data.all()) {
      element.set(gen.nextDouble() < 0.3 ? 1 : 0);
    }

    double p = 1 / (1 + Math.exp(1.5 - data.dot(beta)));
    int target = 0;
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  @Test
  public void copyLearnsAsExpected() {
    Random gen = RandomUtils.getRandom();
    Exponential exp = new Exponential(0.5, gen);
    Vector beta = new DenseVector(200);
    for (Vector.Element element : beta.all()) {
        int sign = 1;
        if (gen.nextDouble() < 0.5) {
          sign = -1;
        }
      element.set(sign * exp.nextDouble());
 
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    for (IntWritable row : redWriter.getKeys()) {
      List<VectorWritable> results = redWriter.getValue(row);
      // there should only be 1 vector
      assertEquals("Only one vector with a given row number", 1, results.size());
      Vector therow = results.get(0).get();
      for (Vector.Element e : therow.all()) {
        // check the diagonal
        if (row.get() == e.index()) {
          assertEquals("Correct diagonal sum of cuts", sumOfRowCuts(row.get(),
              this.sensitivity), e.get(),EPSILON);
        } else {
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    for (IntWritable row : redWriter.getKeys()) {
      List<VectorWritable> list = redWriter.getValue(row);
      assertEquals("Should only be one vector", 1, list.size());
      // check that the elements in the array are correctly ordered
      Vector v = list.get(0).get();
      for (Vector.Element e : v.all()) {
        // find this value in the original map
        MatrixEntryWritable toCompare = new MatrixEntryWritable();
        toCompare.setRow(-1);
        toCompare.setCol(e.index());
        toCompare.setVal(e.get());
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  public void testTrain() {

    Random gen = RandomUtils.getRandom();
    Exponential exp = new Exponential(0.5, gen);
    Vector beta = new DenseVector(200);
    for (Vector.Element element : beta.all()) {
      int sign = 1;
      if (gen.nextDouble() < 0.5) {
        sign = -1;
      }
      element.set(sign * exp.nextDouble());
 
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  }

  private static AdaptiveLogisticRegression.TrainingExample getExample(int i, Random gen, Vector beta) {
    Vector data = new DenseVector(200);

    for (Vector.Element element : data.all()) {
      element.set(gen.nextDouble() < 0.3 ? 1 : 0);
    }

    double p = 1 / (1 + Math.exp(1.5 - data.dot(beta)));
    int target = 0;
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  @Test
  public void copyLearnsAsExpected() {
    Random gen = RandomUtils.getRandom();
    Exponential exp = new Exponential(0.5, gen);
    Vector beta = new DenseVector(200);
    for (Vector.Element element : beta.all()) {
        int sign = 1;
        if (gen.nextDouble() < 0.5) {
          sign = -1;
        }
      element.set(sign * exp.nextDouble());
 
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