Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector.norm()

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    Vector v2 = new DenseVector(20);
    enc.setProbes(2);
    enc.addToVector("123", v2);
    assertEquals(123, v2.maxValue(), 0);
    assertEquals(2 * 123, v2.norm(1), 0);

    // v1 has one probe, v2 has two.  The first probe in v2 should be in the same
    // place as the only probe in v1
    v1 = v2.minus(v1);
    assertEquals(123, v1.maxValue(), 0);
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    FeatureVectorEncoder enc = new ConstantValueEncoder("foo");
    Vector v1 = new DenseVector(20);
    enc.addToVector((byte[]) null, -123, v1);
    assertEquals(-123, v1.minValue(), 0);
    assertEquals(0, v1.maxValue(), 0);
    assertEquals(123, v1.norm(1), 0);

    v1 = new DenseVector(20);
    enc.addToVector((byte[]) null, 123, v1);
    assertEquals(123, v1.maxValue(), 0);
    assertEquals(0, v1.minValue(), 0);
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    v1 = new DenseVector(20);
    enc.addToVector((byte[]) null, 123, v1);
    assertEquals(123, v1.maxValue(), 0);
    assertEquals(0, v1.minValue(), 0);
    assertEquals(123, v1.norm(1), 0);

    Vector v2 = new DenseVector(20);
    enc.setProbes(2);
    enc.addToVector((byte[]) null, 123, v2);
    assertEquals(123, v2.maxValue(), 0);
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    Vector v2 = new DenseVector(20);
    enc.setProbes(2);
    enc.addToVector((byte[]) null, 123, v2);
    assertEquals(123, v2.maxValue(), 0);
    assertEquals(2 * 123, v2.norm(1), 0);

    // v1 has one probe, v2 has two.  The first probe in v2 should be in the same
    // place as the only probe in v1
    v1 = v2.minus(v1);
    assertEquals(123, v1.maxValue(), 0);
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    int n = qt.length;
    int rank = 0;
    for (int i = 0; i < n; i++) {
      Vector ei = new DenseVector(qt[i], true);

      double norm = ei.norm(2);

      if (Math.abs(1.0 - norm) < epsilon) {
        rank++;
      } else if (Math.abs(norm) > epsilon) {
        return false; // not a rank deficiency, either
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      Vector v = new DenseVector(numCols);
      for(int col = 0; col < numCols; col++) {
        double val = r.nextGaussian();
        v.set(col, val);
      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }
    if(symmetric) {
      return matrix.times(matrix.transpose());
    }
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        assertEquals(lr.getModel().currentLearningRate(), model.currentLearningRate(), 1e-10);
        in.close();

        // with that many data points, model should point in the same direction as the original vector
        Vector v = model.getBeta().viewRow(0);
        double z = n.dot(v) / (n.norm(2) * v.norm(2));
        assertEquals(1.0, z, 1e-2);

        // just for grins, we should check whether the model actually computes the correct values
        List<String> categories = ImmutableList.of("0", "1");
        for (Tuple example : examples) {
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    int n = qt.length;
    int rank = 0;
    for (int i = 0; i < n; i++) {
      Vector ei = new DenseVector(qt[i], true);

      double norm = ei.norm(2);

      if (Math.abs(1.0 - norm) < epsilon) {
        rank++;
      } else if (Math.abs(norm) > epsilon) {
        return false; // not a rank deficiency, either
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    TextValueEncoder enc = new TextValueEncoder("text");
    Vector v1 = new DenseVector(200);
    enc.addToVector("test1 and more", v1);
    enc.flush(1, v1);
    // should set 6 distinct locations to 1
    assertEquals(6.0, v1.norm(1), 0);
    assertEquals(1.0, v1.maxValue(), 0);

    // now some fancy weighting
    StaticWordValueEncoder w = new StaticWordValueEncoder("text");
    w.setDictionary(ImmutableMap.<String, Double>of("word1", 3.0, "word2", 1.5));
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    enc.addToVector("test1 and more", v1);
    enc.flush(1, v1);

    //should be the same as text test above, since we are splitting on whitespace
    // should set 6 distinct locations to 1
    assertEquals(6.0, v1.norm(1), 0);
    assertEquals(1.0, v1.maxValue(), 0);

    v1 = new DenseVector(200);
    enc.addToVector("", v1);
    enc.flush(1, v1);
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