Package org.apache.mahout.math

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

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    assertEquals(1.0, v1.maxValue(), 0);

    v1 = new DenseVector(200);
    enc.addToVector("", v1);
    enc.flush(1, v1);
    assertEquals(0.0, v1.norm(1), 0);
    assertEquals(0.0, v1.maxValue(), 0);

    v1 = new DenseVector(200);
    StringBuilder builder = new StringBuilder(5000);
    for (int i = 0; i < 1000; i++) {//lucene's internal buffer length request is 4096, so let's make sure we can handle larger size
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      builder.append("token_").append(i).append(' ');
    }
    enc.addToVector(builder.toString(), v1);
    enc.flush(1, v1);
    //System.out.println(v1);
    assertEquals(2000.0, v1.norm(1), 0);
    assertEquals(19.0, v1.maxValue(), 0);
  }
}
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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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    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);
  }
 
  @Test
  public void testTraceDictionary() {
    StaticWordValueEncoder encoder1 = new StaticWordValueEncoder("first");
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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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          if (previousEigen == null) {
            previousEigen = currentEigen.clone();
          } else {
            double dot = currentEigen.dot(previousEigen);
            if (dot > 0.0) {
              dot /= currentEigen.norm(2) * previousEigen.norm(2);
            }
           // log.info("Current pass * previous pass = {}", dot);
          }
        }
      }
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      }
      // converged!
      double eigenValue = state.getStatusProgress().get(state.getStatusProgress().size() - 1).getEigenValue();
      // it's actually more efficient to do this to normalize than to call currentEigen = currentEigen.normalize(),
      // because the latter does a clone, which isn't necessary here.
      currentEigen.assign(new TimesFunction(), 1 / currentEigen.norm(2));
      eigens.assignRow(i, currentEigen);
      eigenValues.add(eigenValue);
      state.setCurrentEigenValues(eigenValues);
      log.info("Found eigenvector {}, eigenvalue: {}", i, eigenValue);
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