Package org.apache.mahout.math

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

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    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);
    assertEquals(2.0, v1.maxValue(), 0);

    Vector v2 = new DenseVector(20000);
    enc.addInteractionToVector("a","1.0",1.0,v2);
    wv.addToVector("a", v2);
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    enc.addInteractionToVector("a","1.0",1.0,v2);
    wv.addToVector("a", v2);
    cv.addToVector("1.0", v2);
    k = enc.getProbes();
    //this assumes no hash collision
    assertEquals((float) (k + wv.getProbes()+cv.getProbes()), v2.norm(1), 1.0e-3);
  }

  @Test
  public void testAddToVectorUsesProductOfWeights(){
    WordValueEncoder wv = new StaticWordValueEncoder("word");
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    InteractionValueEncoder enc = new InteractionValueEncoder("interactions", wv, cv);
    Vector v1 = new DenseVector(200);
    enc.addInteractionToVector("a","0.9",0.5, v1);
    int k = enc.getProbes();
    // should set k distinct locations to 0.9*0.5
    assertEquals((float) k*0.5*0.9, v1.norm(1), 0);
    assertEquals(0.5*0.9, v1.maxValue(), 0);
  }

  @Test
  public void testAddToVectorWithTextValueEncoder(){
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    InteractionValueEncoder enc = new InteractionValueEncoder("interactions", wv, tv);
    Vector v1 = new DenseVector(200);
    enc.addInteractionToVector("a","some text here",1.0, v1);
    int k = enc.getProbes();
    // should interact "a" with each of "some","text" and "here"
    assertEquals((float) k*3, v1.norm(1), 0);
  }

}
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      endTime(TimingSection.ITERATE);
      startTime(TimingSection.ORTHOGANLIZE);
      orthoganalizeAgainstAllButLast(nextVector, state);
      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;
      }
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    totalCorpusWeight = 0;
    int numNonZero = 0;
    for(int i=0; i<numDocuments; i++) {
      Vector v = corpusWeights.viewRow(i);
      double norm;
      if(v != null && (norm = v.norm(1)) != 0) {
        numNonZero += v.getNumNondefaultElements();
        totalCorpusWeight += norm;
      }
    }
    String s = "Initializing corpus with %d docs, %d terms, %d nonzero entries, total termWeight %f";
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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.viewColumn(i);

      double norm = ei.norm(2);

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