Package org.apache.mahout.math

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

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

    v.assign(0);
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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);
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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);
  }
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            metrics.AvgLogLikelihood = 0;
          }
         
          Vector p = new DenseVector(this.num_categories);
          this.polr.classifyFull(p, v);
          int estimated = p.maxValueIndex();
          int correct = (estimated == actual ? 1 : 0);
          metrics.AvgCorrect = metrics.AvgCorrect
              + (correct - metrics.AvgCorrect) / mu;
          this.polr.train(actual, v);
         
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