Package org.apache.mahout.math

Examples of org.apache.mahout.math.SequentialAccessSparseVector

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      } else {
        vector = vector.normalize(normPower);
      }
    }
    if (sequentialAccess) {
      vector = new SequentialAccessSparseVector(vector);
    }
   
    if (namedVector) {
      vector = new NamedVector(vector, key.toString());
    }
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  private double alpha0;

  @Override
  public Vector select(Vector probabilities) {
    int rMultinom = UncommonDistributions.rMultinom(probabilities.times(mixture));
    Vector weights = new SequentialAccessSparseVector(probabilities.size());
    weights.set(rMultinom, 1.0);
    return weights;
  }
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  }
 
  public static List<Vector> getPoints(double[][] raw) {
    List<Vector> points = Lists.newArrayList();
    for (double[] fr : raw) {
      Vector vec = new SequentialAccessSparseVector(fr.length);
      vec.assign(fr);
      points.add(vec);
    }
    return points;
  }
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                                                          double entryMean) {
    Matrix m = new SparseRowMatrix(numRows, numCols);
    //double n = 0;
    Random r = RandomUtils.getRandom();
    for (int i = 0; i < nonNullRows; i++) {
      Vector v = new SequentialAccessSparseVector(numCols);
      for (int j = 0; j < entriesPerRow; j++) {
        int col = r.nextInt(numCols);
        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
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    }

    private void extendAColIfNeeded(int col, int rowCount) {
      if (aCols[col] == null) {
        aCols[col] =
          new SequentialAccessSparseVector(rowCount < blockHeight ? blockHeight
              : rowCount, 1);
      } else if (aCols[col].size() < rowCount) {
        Vector newVec =
          new SequentialAccessSparseVector(rowCount + blockHeight,
                                           aCols[col].getNumNondefaultElements() << 1);
        newVec.viewPart(0, aCols[col].size()).assign(aCols[col]);
        aCols[col] = newVec;
      }
    }
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    }

    private void extendAColIfNeeded(int col, int rowCount) {
      if (aCols[col] == null) {
        aCols[col] =
          new SequentialAccessSparseVector(rowCount < 10000 ? 10000 : rowCount,
                                           1);
      } else if (aCols[col].size() < rowCount) {
        Vector newVec =
          new SequentialAccessSparseVector(rowCount << 1,
                                           aCols[col].getNumNondefaultElements() << 1);
        newVec.viewPart(0, aCols[col].size()).assign(aCols[col]);
        aCols[col] = newVec;
      }
    }
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        // output empty rows if we never output partial products for them
        // this happens in sparse matrices when last rows are all zeros
        // and is subsequently causing shorter Q matrix row count which we
        // probably don't want to repair there but rather here.
        Vector yDummy = new SequentialAccessSparseVector(kp);
        // outValue.set(yDummy);
        for (lastRowIndex += 1; lastRowIndex < aRowCount; lastRowIndex++) {
          // outKey.setTaskItemOrdinal(lastRowIndex);
          // context.write(outKey, outValue);
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  }

  @Test
  public void testCanopyAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("C-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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  }

  @Test
  public void testCanopyAsFormatStringSparseWithBindings() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new Canopy(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("C-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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  }

  @Test
  public void testClusterAsFormatStringSparse() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Cluster cluster = new org.apache.mahout.clustering.kmeans.Kluster(m, 123, measure);
    String formatString = cluster.asFormatString(null);
    assertEquals("CL-123{n=0 c=[0:1.100, 2:3.300] r=[]}", formatString);
  }
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