Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector

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    generateSamples(30, 1, 0, 0.1, 3);
    generateSamples(30, 0, 1, 0.1, 3);

    DirichletClusterer<VectorWritable> dc = new DirichletClusterer<VectorWritable>(
        sampleData, new SampledNormalDistribution(new VectorWritable(
            new DenseVector(3))), 1.0, 10, 1, 0);
    List<Model<VectorWritable>[]> result = dc.cluster(30);
    printResults(result, 2);
    assertNotNull(result);
  }
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    generateSamples(30, 1, 0, 0.1, 3);
    generateSamples(30, 0, 1, 0.1, 3);

    DirichletClusterer<VectorWritable> dc = new DirichletClusterer<VectorWritable>(
        sampleData, new AsymmetricSampledNormalDistribution(new VectorWritable(
            new DenseVector(3))), 1.0, 10, 1, 0);
    List<Model<VectorWritable>[]> result = dc.cluster(30);
    printResults(result, 2);
    assertNotNull(result);
  }
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      }
     
      assertEquals("Number of map results", k + 1, collector2.getData().size());
      // now verify that all points are accounted for
      int count = 0;
      Vector total = new DenseVector(2);
      for (String key : collector2.getKeys()) {
        List<KMeansInfo> values = collector2.getValue(key);
        assertEquals("too many values", 1, values.size());
        // String value = values.get(0).toString();
        KMeansInfo info = values.get(0);
       
        count += info.getPoints();
        total = total.plus(info.getPointTotal());
      }
      assertEquals("total points", 9, count);
      assertEquals("point total[0]", 27, (int) total.get(0));
      assertEquals("point total[1]", 27, (int) total.get(1));
    }
  }
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    @Override
    public void configure(JobConf conf) {
      int outputDimension = conf.getInt(OUTPUT_VECTOR_DIMENSION, Integer.MAX_VALUE);
      outputVector = conf.getBoolean(IS_SPARSE_OUTPUT, false)
                   ? new RandomAccessSparseVector(outputDimension, 10)
                   : new DenseVector(outputDimension);
    }
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    trainer = new WinnowTrainer(3);
  }

  public void testUpdate() throws Exception {
    double[] labels = { 0.0, 0.0, 0.0, 1.0 };
    Vector labelset = new DenseVector(labels);
    double[][] values = new double[3][4];
    for (int i = 0; i < 3; i++) {
      values[i][0] = 1.0;
      values[i][1] = 1.0;
      values[i][2] = 1.0;
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  @Override
  protected void setUp() throws Exception {
    super.setUp();
    double[] values = {0.0, 1.0, 0.0, 1.0, 0.0};
    Vector hyperplane = new DenseVector(values);
    this.model = new LinearModel(hyperplane, 0.1, 0.5);
  }
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    this.model = new LinearModel(hyperplane, 0.1, 0.5);
  }

  public void testClassify() {
    double[] valuesFalse = {1.0, 0.0, 1.0, 0.0, 1.0};
    Vector dataPointFalse = new DenseVector(valuesFalse);
    assertFalse(this.model.classify(dataPointFalse));

    double[] valuesTrue = {0.0, 1.0, 0.0, 1.0, 0.0};
    Vector dataPointTrue = new DenseVector(valuesTrue);
    assertTrue(this.model.classify(dataPointTrue));
  }
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    assertTrue(this.model.classify(dataPointTrue));
  }

  public void testAddDelta() {
    double[] values = {1.0, -1.0, 1.0, -1.0, 1.0};
    this.model.addDelta(new DenseVector(values));

    double[] valuesFalse = {1.0, 0.0, 1.0, 0.0, 1.0};
    Vector dataPointFalse = new DenseVector(valuesFalse);
    assertTrue(this.model.classify(dataPointFalse));

    double[] valuesTrue = {0.0, 1.0, 0.0, 1.0, 0.0};
    Vector dataPointTrue = new DenseVector(valuesTrue);
    assertFalse(this.model.classify(dataPointTrue));
  }
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    assertFalse(this.model.classify(dataPointTrue));
  }

  public void testTimesDelta() {
    double[] values = {-1.0, -1.0, -1.0, -1.0, -1.0};
    this.model.addDelta(new DenseVector(values));
    double[] dotval = {-1.0, -1.0, -1.0, -1.0, -1.0};
   
    for (int i = 0; i < dotval.length; i++) {
      this.model.timesDelta(i, dotval[i]);
    }

    double[] valuesFalse = {1.0, 0.0, 1.0, 0.0, 1.0};
    Vector dataPointFalse = new DenseVector(valuesFalse);
    assertTrue(this.model.classify(dataPointFalse));

    double[] valuesTrue = {0.0, 1.0, 0.0, 1.0, 0.0};
    Vector dataPointTrue = new DenseVector(valuesTrue);
    assertFalse(this.model.classify(dataPointTrue));
  }
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   * @param corpus
   * @return
   */
  @Override
  protected Vector getInitialVector(VectorIterable corpus) {
    Vector initialVector = new DenseVector(corpus.numCols());
    initialVector.assign(1/Math.sqrt(corpus.numCols()));
    return initialVector;
  }
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