Package org.apache.mahout.math

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

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    FeatureVectorEncoder encoder = new StaticWordValueEncoder("text");
    for (String word : doc.elementSet()) {
      encoder.addToVector(word, w.weight(word, doc.count(word)), v);
    }
    if (normalize) {
      return v.assign(Functions.div(v.norm(2)));
    } else {
      return v;
    }
  }
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  public static void assertOrthonormal(Matrix currentEigens, double errorMargin) {
    for (int i = 0; i < currentEigens.numRows(); i++) {
      Vector ei = currentEigens.getRow(i);
      for (int j = 0; j <= i; j++) {
        Vector ej = currentEigens.getRow(j);
        if (ei.norm(2) == 0 || ej.norm(2) == 0) {
          continue;
        }
        double dot = ei.dot(ej);
        if (i == j) {
          assertTrue("not norm 1 : " + dot + " (eigen #" + i + ')', (Math.abs(1 - dot) < errorMargin));
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      endTime(TimingSection.ITERATE);
      startTime(TimingSection.ORTHOGANLIZE);
      orthoganalizeAgainstAllButLast(nextVector, basis);
      endTime(TimingSection.ORTHOGANLIZE);
      // and normalize
      beta = nextVector.norm(2);
      if (outOfRange(beta) || outOfRange(alpha)) {
        log.warn("Lanczos parameters out of range: alpha = {}, beta = {}.  Bailing out early!", alpha, beta);
        break;
      }
      final double b = beta;
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    do {
      double r = rng.nextDouble();
      index = (int) (r * corpus.numRows());
      v = corpus.getRow(index);
    }
    while (v == null || v.norm(2) == 0 || v.getNumNondefaultElements() < 5);
    return index;
  }

  /**
   * Uses the {@link SingularVectorVerifier } to check for convergence
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    }
    for(Map.Entry<Integer, Vector> entry : mMap.entrySet()) {
      Integer key = entry.getKey();
      Vector value = entry.getValue();
      if(value == null || mttMap.get(key) == null) {
        assertTrue(value == null || value.norm(2) == 0);
        assertTrue(mttMap.get(key) == null || mttMap.get(key).norm(2) == 0);
      } else {
        assertTrue(
            value.getDistanceSquared(mttMap.get(key)) < errorTolerance);
      }
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    FeatureVectorEncoder enc = new ContinuousValueEncoder("foo");
    Vector v1 = new DenseVector(20);
    enc.addToVector("-123", v1);
    assertEquals(-123, v1.minValue(), 0);
    assertEquals(0, v1.maxValue(), 0);
    assertEquals(123, v1.norm(1), 0);

    v1 = new DenseVector(20);
    enc.addToVector("123", v1);
    assertEquals(123, v1.maxValue(), 0);
    assertEquals(0, v1.minValue(), 0);
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    v1 = new DenseVector(20);
    enc.addToVector("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("123", v2);
    assertEquals(123, v2.maxValue(), 0);
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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 = v2.minus(v1);
    assertEquals(123, v1.maxValue(), 0);
    assertEquals(123, v1.norm(1), 0);

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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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    InteractionValueEncoder enc = new InteractionValueEncoder("interactions", wv, cv);
    Vector v1 = new DenseVector(200);
    enc.addInteractionToVector("a","1.0",1.0, v1);
    int k = enc.getProbes();
    // should set k distinct locations to 1
    assertEquals((float) k, v1.norm(1), 0);
    assertEquals(1.0, v1.maxValue(), 0);

    // adding same interaction again should increment weights
    enc.addInteractionToVector("a","1.0",1.0,v1);
    assertEquals((float) k*2, v1.norm(1), 0);
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