Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector$DenseElement

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    assertEquals("model", cluster.asFormatString(null), result.asFormatString(null));
  }

  public void testCanopyAsFormatString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new Canopy(m, 123);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C123: [1.100, 2.200, 3.300]", formatString);
  }
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    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

  public void testCanopyAsFormatStringWithBindings() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new Canopy(m, 123);
    String[] bindings = { "fee", null, null };
    String formatString = cluster.asFormatString(bindings);
    System.out.println(formatString);
    assertEquals("format", "C123: [fee:1.100, 1:2.200, 2:3.300]", formatString);
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    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

  public void testClusterAsFormatString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new Cluster(m, 123);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C123: [1.100, 2.200, 3.300]", formatString);
  }
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    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

  public void testClusterAsFormatStringWithBindings() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new Cluster(m, 123);
    String[] bindings = { "fee", null, "foo" };
    String formatString = cluster.asFormatString(bindings);
    System.out.println(formatString);
    assertEquals("format", "C123: [fee:1.100, 1:2.200, foo:3.300]", formatString);
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    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

  public void testMSCanopyAsFormatString() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new MeanShiftCanopy(m, 123);
    String formatString = cluster.asFormatString(null);
    System.out.println(formatString);
    assertEquals("format", "C123: [1.100, 2.200, 3.300]", formatString);
  }
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    assertEquals("format", "C123: [0:1.100, 2:3.300]", formatString);
  }

  public void testMSCanopyAsFormatStringWithBindings() {
    double[] d = { 1.1, 2.2, 3.3 };
    Vector m = new DenseVector(d);
    Printable cluster = new MeanShiftCanopy(m, 123);
    String[] bindings = { "fee", null, "foo" };
    String formatString = cluster.asFormatString(bindings);
    System.out.println(formatString);
    assertEquals("format", "C123: [fee:1.100, 1:2.200, foo:3.300]", formatString);
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    rmr("testdata");
    raw = new Vector[100];
    for (int i = 0; i < 10; i++) {
      for (int j = 0; j < 10; j++) {
        int ix = i * 10 + j;
        Vector v = new DenseVector(3);
        v.setQuick(0, i);
        v.setQuick(1, j);
        if (i == j) {
          v.setQuick(2, 9);
        } else if (i + j == 9) {
          v.setQuick(2, 4.5);
        }
        raw[ix] = v;
      }
    }
  }
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          inputVector = new SequentialAccessSparseVector(inputVector);
        }
        int outDim = conf.getInt(OUTPUT_VECTOR_DIMENSION, Integer.MAX_VALUE);
        outputVector = conf.getBoolean(IS_SPARSE_OUTPUT, false)
                     ? new RandomAccessSparseVector(outDim, 10)
                     : new DenseVector(outDim);
      } catch (IOException ioe) {
        throw new IllegalStateException(ioe);
      }
    }
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   * @param initBias
   *          initial classification bias.
   * */
  protected LinearTrainer(int dimension, double threshold,
                          double init, double initBias) throws CardinalityException {
    DenseVector initialWeights = new DenseVector(dimension);
    initialWeights.assign(init);
    this.model = new LinearModel(initialWeights, initBias, threshold);
  }
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  private void generateSamples(int num, double mx, double my, double sd,
      int card) {
    System.out.println("Generating " + num + " samples m=[" + mx + ", " + my
        + "] sd=" + sd);
    for (int i = 0; i < num; i++) {
      DenseVector v = new DenseVector(card);
      for (int j = 0; j < card; j++)
        v.set(j, UncommonDistributions.rNorm(mx, sd));
      sampleData.add(new VectorWritable(v));
    }
  }
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