Package org.apache.mahout.math.matrix

Examples of org.apache.mahout.math.matrix.DoubleMatrix2D.columns()

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            {
                tdMatrix = vsmContext.termDocumentMatrix;
            }

            // Initial selection containing all columns, initial clustering
            final IntArrayList columns = new IntArrayList(tdMatrix.columns());
            for (int c = 0; c < tdMatrix.columns(); c++)
            {
                columns.add(c);
            }
            final List<IntArrayList> rawClusters = Lists.newArrayList();
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                tdMatrix = vsmContext.termDocumentMatrix;
            }

            // Initial selection containing all columns, initial clustering
            final IntArrayList columns = new IntArrayList(tdMatrix.columns());
            for (int c = 0; c < tdMatrix.columns(); c++)
            {
                columns.add(c);
            }
            final List<IntArrayList> rawClusters = Lists.newArrayList();
            rawClusters.addAll(split(partitionCount, tdMatrix, columns, maxIterations));
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        IntArrayList columns, int iterations)
    {
        // Prepare selected matrix
        final DoubleMatrix2D selected = input.viewSelection(null, columns.toArray())
            .copy();
        final IntIntMap selectedToInput = new IntIntOpenHashMap(selected.columns());
        for (int i = 0; i < columns.size(); i++)
        {
            selectedToInput.put(i, columns.get(i));
        }
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        // Prepare results holders
        List<IntArrayList> result = Lists.newArrayList();
        List<IntArrayList> previousResult = null;
        for (int i = 0; i < partitions; i++)
        {
            result.add(new IntArrayList(selected.columns()));
        }
        for (int i = 0; i < selected.columns(); i++)
        {
            result.get(i % partitions).add(i);
        }
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        List<IntArrayList> previousResult = null;
        for (int i = 0; i < partitions; i++)
        {
            result.add(new IntArrayList(selected.columns()));
        }
        for (int i = 0; i < selected.columns(); i++)
        {
            result.get(i % partitions).add(i);
        }

        // Matrices for centroids and document-centroid similarities
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            // Calculate similarity to centroids
            centroids.zMult(selected, similarities, 1, 0, true, false);

            // Assign documents to the nearest centroid
            for (int c = 0; c < similarities.columns(); c++)
            {
                int maxRow = 0;
                double max = similarities.get(0, c);
                for (int r = 1; r < similarities.rows(); r++)
                {
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        // Compute pairwise inner products
        DoubleMatrix2D cosines = A.zMult(A, null, 1, 0, true, false);

        for (int r = 0; r < cosines.rows(); r++)
        {
            for (int c = r + 1; c < cosines.columns(); c++)
            {
                orthogonality += cosines.getQuick(r, c);
            }
        }
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        // Initialize the centroids with some document vectors
        U = new DenseDoubleMatrix2D(A.rows(), k);
        U.assign(A.viewPart(0, 0, A.rows(), k));

        int [] minIndices = new int [D.columns()];
        double [] minValues = new double [D.columns()];

        for (iterationsCompleted = 0; iterationsCompleted < maxIterations; iterationsCompleted++)
        {
            // Calculate cosine distances
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        // Initialize the centroids with some document vectors
        U = new DenseDoubleMatrix2D(A.rows(), k);
        U.assign(A.viewPart(0, 0, A.rows(), k));

        int [] minIndices = new int [D.columns()];
        double [] minValues = new double [D.columns()];

        for (iterationsCompleted = 0; iterationsCompleted < maxIterations; iterationsCompleted++)
        {
            // Calculate cosine distances
            U.zMult(A, D, 1, 0, true, false);
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