Examples of DenseVector

edu.ucla.sspace.vector.DenseVector
A {@code Vector} where all values are held in memory. The underlyingimplementation is simply an array of doubles.
@author Keith Stevens
no.uib.cipr.matrix.DenseVector
Dense vector. Stored by a double[] array of the same length as the vector itself.
org.apache.mahout.math.DenseVector
Implements vector as an array of doubles
org.apache.mahout.matrix.DenseVector
Implements vector as an array of doubles

Examples of edu.ucla.sspace.vector.DenseVector

    protected static int computeCut(Matrix matrix, 
                                    DoubleVector rho, 
                                    double rhoSum,
                                    DoubleVector matrixRowSums) {
        // Compute the conductance of the newly sorted matrix.
        DoubleVector x = new DenseVector(matrix.columns());
        DoubleVector y = matrixRowSums;
        VectorMath.subtract(y, x);


        // First compute x and y, which are summations of different cuts of the
        // matrix, starting with x being the first row and y being the summation

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Examples of edu.ucla.sspace.vector.DenseVector


        Random random = new Random();


        final DoubleVector[] principles = new DoubleVector[numPrincipleVectors];
        for (int i = 0; i < principles.length; ++i)
            principles[i] = new DenseVector(sspace.getVectorLength());


        // Randomly assign the data set to different intitial clusters 
        final MultiMap<Integer,Integer> clusterAssignment = 
            new HashMultiMap<Integer,Integer>();
        for (int i = 0; i < numTerms; ++i) 
            clusterAssignment.put(random.nextInt(numPrincipleVectors), i);
        
        int numIters = 1;
        for (int iter = 0; iter < numIters; ++iter) {
            verbose(LOGGER, "Computing principle vectors (round %d/%d)", iter+1, numIters);
            // Compute the centroids
            for (Map.Entry<Integer,Set<Integer>> e
                     : clusterAssignment.asMap().entrySet()) {
                int cluster = e.getKey();
                DoubleVector principle = new DenseVector(sspace.getVectorLength());
                principles[cluster] = principle;
                for (Integer row : e.getValue())
                    VectorMath.add(principle, termVectors.get(row));
            }

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Examples of edu.ucla.sspace.vector.DenseVector

     * This method also assumes that {@code matrix} is row based and iterates
     * over each of the values in the row before iterating over another row.
     */
    protected static DoubleVector computeMatrixTransposeV(Matrix matrix,
                                                          DoubleVector v) {
        DoubleVector newV = new DenseVector(matrix.columns());
        if (matrix instanceof SparseMatrix) {
            SparseMatrix smatrix = (SparseMatrix) matrix;
            for (int r = 0; r < smatrix.rows(); ++r) {
                SparseDoubleVector row = smatrix.getRowVector(r);
                int[] nonZeros = row.getNonZeroIndices();
                for (int c : nonZeros)
                    newV.add(c, row.get(c) * v.get(r));
            }
        } else {
            for (int r = 0; r < matrix.rows(); ++r)
                for (int c = 0; c < matrix.columns(); ++c)
                    newV.add(c, matrix.get(r, c) * v.get(r));
        }
        return newV;
    }

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Examples of edu.ucla.sspace.vector.DenseVector

    public SortedMultiMap<Double,String> getMostSimilar(
             Set<String> terms, int numberOfSimilarWords) {
        if (terms.isEmpty())
            return null;
        // Compute the mean vector for all the terms
        DoubleVector mean = new DenseVector(sspace.getVectorLength());
        int found = 0;
        for (String term : terms) {
            Vector v = sspace.getVector(term);
            if (v == null)
                info(LOGGER, "No vector for term " + term);

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Examples of edu.ucla.sspace.vector.DenseVector

     * Compute the row sums of the values in {@code matrix} and returns the
     * values in a vector of length {@code matrix.columns()}.
     */
    protected static <T extends Matrix> DoubleVector computeMatrixRowSum(
            T matrix) {
        DoubleVector rowSums = new DenseVector(matrix.columns());
        for (int r = 0; r < matrix.rows(); ++r)
            VectorMath.add(rowSums, matrix.getRowVector(r));
        return rowSums;
    }

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Examples of edu.ucla.sspace.vector.DenseVector


    @Test
    public void testSetRowVector() {
        Matrix baseMatrix = new ArrayMatrix(baseValues);
        Matrix maskedMatrix = new CellMaskedMatrix(baseMatrix, rowMap, colMap);
        DoubleVector row = new DenseVector(new double[]{1, 2, 3, 4});
        maskedMatrix.setRow(0, row);
        for (int i = 0; i < 4; i++)
            assertEquals(getBaseValue(baseMatrix, 0, i), row.get(i), .000001);
    }

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Examples of edu.ucla.sspace.vector.DenseVector


    @Test
    public void testSetColumnVector() {
        Matrix baseMatrix = new ArrayMatrix(baseValues);
        Matrix maskedMatrix = new CellMaskedMatrix(baseMatrix, rowMap, colMap);
        DoubleVector col = new DenseVector(new double[] {1, 2, 3});
        maskedMatrix.setColumn(0, col);
        for (int i = 0; i < 3; i++)
            assertEquals(getBaseValue(baseMatrix, i, 0), col.get(i), .000001);
    }

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Examples of edu.ucla.sspace.vector.DenseVector


    @Test (expected=UnsupportedOperationException.class)
    public void testFailSetColumnFull() {
        SparseMatrix base = matrix();
        Matrix scaled = new RowScaledSparseMatrix(base, SCALE);
        scaled.setColumn(0, new DenseVector(new double[] {1, 1, 1}));
    }

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Examples of edu.ucla.sspace.vector.DenseVector


    @Test (expected=UnsupportedOperationException.class)
    public void testFailSetRowFull() {
        SparseMatrix base = matrix();
        Matrix scaled = new RowScaledSparseMatrix(base, SCALE);
        scaled.setRow(0, new DenseVector(VALUES[0]));
    }

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Examples of edu.ucla.sspace.vector.DenseVector


    @Test (expected=UnsupportedOperationException.class)
    public void testFailSetColumnFull() {
        Matrix base = new ArrayMatrix(VALUES);
        Matrix scaled = new RowScaledMatrix(base, SCALE);
        scaled.setColumn(0, new DenseVector(new double[] {1, 1, 1}));
    }

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