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

org.apache.mahout.math.Vector.norm()
kipedia.org/wiki/Lp_space
Technically, when 0 > power < 1, we don't have a norm, just a metric, but we'll overload this here. Also supports power == 0 (number of non-zero elements) and power = {@link Double#POSITIVE_INFINITY} (max element). Again, see the Wikipedia page formore info. @param power The power to use. @see #normalize(double)

    Vector v;
    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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    int n = mtx.columnSize();
    int rank = 0;
    for (int i = 0; i < n; i++) {
      Vector ei = mtx.getColumn(i);


      double norm = ei.norm(2);


      if (Math.abs(1 - norm) < epsilon) {
        rank++;
      } else {
        Assert.assertTrue(Math.abs(norm) < epsilon);

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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 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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