Package org.apache.mahout.math

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

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    v.assign(0);
    t = csv.processLine("ignore,5.3,no,line, \"and more text and more\",ignore", v);
    assertEquals(1, t);

    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
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    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
    assertEquals(1.5849625007211563, v.maxValue(), 1.0e-6);

    v.assign(0);
    t = csv.processLine("ignore,5.3,invalid,line, \"and more text and more\",ignore", v);
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    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
    assertEquals(1.5849625007211563, v.maxValue(), 1.0e-6);

    v.assign(0);
    t = csv.processLine("ignore,5.3,invalid,line, \"and more text and more\",ignore", v);
    assertEquals(1, t);
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    v.assign(0);
    t = csv.processLine("ignore,5.3,invalid,line, \"and more text and more\",ignore", v);
    assertEquals(1, t);

    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
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    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
    assertEquals(1.5849625007211563, v.maxValue(), 1.0e-6);
  }

  @Test
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    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
    assertEquals(1.5849625007211563, v.maxValue(), 1.0e-6);
  }

  @Test
  public void testDictionaryOrder() {
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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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    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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