Examples of edu.ucla.sspace.matrix.SparseMatrix.rows()

Class edu.ucla.sspace.matrix.SparseMatrix

Examples of edu.ucla.sspace.matrix.SparseMatrix.rows()

edu.ucla.sspace.matrix.SparseMatrix.rows()

        corpusReader.close();


        // If the term is to be processed using fewer than all of its contexts,
        // then randomly select the maximum allowable contexts from the matrix
        if (maxContextsPerWord < Integer.MAX_VALUE &&
                contextsForCurTerm.rows() > maxContextsPerWord) {
            BitSet randomContexts = Statistics.randomDistribution(
                maxContextsPerWord, contextsForCurTerm.rows());
            contextsForCurTerm = 
                new SparseRowMaskedMatrix(contextsForCurTerm, randomContexts);
        }

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        // If the term is to be processed using fewer than all of its contexts,
        // then randomly select the maximum allowable contexts from the matrix
        if (maxContextsPerWord < Integer.MAX_VALUE &&
                contextsForCurTerm.rows() > maxContextsPerWord) {
            BitSet randomContexts = Statistics.randomDistribution(
                maxContextsPerWord, contextsForCurTerm.rows());
            contextsForCurTerm = 
                new SparseRowMaskedMatrix(contextsForCurTerm, randomContexts);
        }
        
        return contextsForCurTerm;

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        SparseMatrix sm = (SparseMatrix)m;
        
        // Create a bit set with the number of bits equal to the number of rows.
        // This serves as input to phase 2 where we indicate that all rows
        // should be considered for clustering at first.
        BitSet allRows = new BitSet(sm.rows());
        allRows.set(0, sm.rows());
        LOGGER.info("CBC begining Phase 2");
        List<Committee> committees = phase2(
            sm, allRows, avgLinkMergeThresh, 
            maxCommitteeSimThresh, residueSimThresh);

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        // Create a bit set with the number of bits equal to the number of rows.
        // This serves as input to phase 2 where we indicate that all rows
        // should be considered for clustering at first.
        BitSet allRows = new BitSet(sm.rows());
        allRows.set(0, sm.rows());
        LOGGER.info("CBC begining Phase 2");
        List<Committee> committees = phase2(
            sm, allRows, avgLinkMergeThresh, 
            maxCommitteeSimThresh, residueSimThresh);

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        // However, we currently don't optimize for this special case and
        // instead use our HAC class.  Because of the complexity of the edge
        // similarity function, we build our own similarity matrix and then pass
        // it in, rather than passing in the edge matrix directly.


        final int rows = sm.rows();
        numRows = rows;
        LOGGER.fine("Generating link similarity matrix for " + rows + " nodes");


        //  Rather than create an O(row^3) matrix for representing the edges,
        // compress the edge matrix by getting a mapping for each edge to a row

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    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));
            }

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                                            DoubleVector newV,
                                            DoubleVector v) {
        // Special case for sparse matrices.
        if (matrix instanceof SparseMatrix) {
            SparseMatrix smatrix = (SparseMatrix) matrix;
            for (int r = 0; r < smatrix.rows(); ++r) {
                double vValue = 0;
                SparseDoubleVector row = smatrix.getRowVector(r);
                int[] nonZeros = row.getNonZeroIndices();
                for (int c : nonZeros)
                    vValue += row.get(c) * newV.get(c);

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