Examples of YaleSparseMatrix

edu.ucla.sspace.matrix.YaleSparseMatrix
A sparse {@code Matrix} based on the Yale Sparse Matrix Format, asimplemented in {@link CompactSparseVector}. Each row is allocated a pair of arrays which keeps the non-zero column values in column order. Lookups are O(log n) where n is the number of non-zero values for the largest row. The size of this matrix is fixed, and attempts to access rows or columns beyond the size will throw an {@link IndexOutOfBoundsException}. @author David Jurgens

Examples of edu.ucla.sspace.matrix.YaleSparseMatrix

        for (Integer index : indicesToKeep)
            indexMap.put(index, newIndex++);


        // Create a reduced matrix that will have only the selected columns in
        // the final space.
        SparseMatrix reduced = new YaleSparseMatrix(
                words, indicesToKeep.size());


        // Iterate over the sparse values in the matrix for added efficiency.
        for (int row = 0; row < words; ++ row) {
            SparseDoubleVector sv = cooccurrenceMatrix.getRowVector(row);
            for (int col : sv.getNonZeroIndices()) {
                double v = cooccurrenceMatrix.get(row, col);


                // If the original column was retained, get it's new index
                // value and add it to the reduced matrix.
                Integer newColIndex = indexMap.get(col);
                if (newColIndex != null)
                    reduced.set(row, newColIndex, v);


                // If the transposed row column was retained, get it's new index
                // value and add it to the reduced matrix.  This turns the col
                // value into the row and the new index as the column.
                newColIndex = indexMap.get(row + words);
                if (newColIndex != null)
                    reduced.set(col, newColIndex, v);
            }
        }


        return reduced;
    }

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Examples of edu.ucla.sspace.matrix.YaleSparseMatrix

            new BufferedInputStream(new FileInputStream(compressedDocuments)));


        int documents = documentCounter.get();
        // Use the number of times the term occurred in the corpus to determine
        // how many rows (contexts) in the matrix.
        SparseMatrix contextsForCurTerm = new YaleSparseMatrix(
            termCounts.get(termIndex).get(), termToIndex.size());
        int contextsSeen = 0;
        for (int d = 0; d < documents; ++d) {
            final int docId = d;


            int tokensInDoc = corpusReader.readInt();
            int unfilteredTokens = corpusReader.readInt();
            // Read in the document
            int[] doc = new int[tokensInDoc];
            for (int i = 0; i < tokensInDoc; ++i)
                doc[i] = corpusReader.readInt();


            int contextsInDoc = 
                processIntDocument(termIndex, doc, contextsForCurTerm,
                                   contextsSeen, termFeatures);
            contextsSeen += contextsInDoc;
        }
        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);
        }
        
        return contextsForCurTerm;

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Examples of edu.ucla.sspace.matrix.YaleSparseMatrix

    };


    @Test public void testReduction() {
        MatrixFactorization reducer =
            new NonNegativeMatrixFactorizationMultiplicative();
        SparseMatrix matrix = new YaleSparseMatrix(VALUES);


        reducer.factorize(matrix, 2);


        Matrix W = reducer.dataClasses();
        assertEquals(4, W.rows());

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