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

    
    private static IntPair pickFirstTwo(Matrix dataPoints, 
                                        SimilarityFunction simFunc, 
                                        int[] weights, double[] inverseSimilarities) {
  double OPT_1 = 0; // optimal 1-means cost.
  DoubleVector centerOfMass = new DenseVector(dataPoints.columns());
  double sum = 0d;
  int rows = dataPoints.rows();
        int cols = dataPoints.columns();
        double[] probs = new double[rows];
  int totalWeight = 0; 


  for (int i = 0; i < rows; i++) {
            DoubleVector v = dataPoints.getRowVector(i);
            int weight = weights[i];
            // Update the center of mass for the entire solution based
            VectorMath.add(centerOfMass, new ScaledDoubleVector(v, weight));
            totalWeight += weight;
        }
         
        // Then rescale the center of mass based on the total weight
        for (int j = 0; j < cols; j++) 
            centerOfMass.set(j, centerOfMass.get(j) / totalWeight);
        
        
        for (int i = 0; i < rows; i++) {
            double sim = simFunc.sim(centerOfMass, dataPoints.getRowVector(i));
            sim = invertSim(sim);

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Examples of edu.ucla.sspace.vector.DenseVector

        clusterSizes = new int[numClusters];
        costs = new double[numClusters];


        // Initialize the clusters.
        for (int c = 0; c < numClusters; ++c)
            centroids[c] = new DenseVector(m.columns());


        // Form the cluster composite vectors, i.e. unscaled centroids.
        for (int i = 0; i < m.rows(); ++i) {
            int assignment = initialAssignments[i];
            VectorMath.add(centroids[assignment], matrix.get(i));

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Examples of edu.ucla.sspace.vector.DenseVector


            // For any extra slots that do not have a data point, use an empty
            // vector, which will have zero similarity with any other data
            // point.
            for (int i = dataPoints.rows(); i < numCentroids; ++i)
                centers[i] = new DenseVector(dataPoints.columns());


            return centers;
        }


        // Select a subset of data points to be the new centroids.

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Examples of edu.ucla.sspace.vector.DenseVector

        DoubleVector[] centroids = new DoubleVector[numCentroids];


        // Compute the centroid assuming that there is only one cluster to be
        // found.  This is required for computing the optimal cost of a single
        // cluster solution.
        DoubleVector singleCentroid = new DenseVector(dataPoints.columns());
        for (int i = 0; i < numDataPoints; ++i)
            VectorMath.add(singleCentroid, dataPoints.getRowVector(i));
        singleCentroid = new ScaledDoubleVector(
                singleCentroid, 1/((double)numDataPoints));

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Examples of edu.ucla.sspace.vector.DenseVector

        /**
         * {@inheritDoc}
         */
        protected DoubleVector computeSecondEigenVector(Matrix matrix,
                                                        int vectorLength) {
            DoubleVector Rinv = new DenseVector(vectorLength);
            DoubleVector baseVector = new DenseVector(vectorLength);
            for (int i = 0; i < vectorLength; ++i) {
                Rinv.set(i, 1/Math.sqrt(rho.get(i)));
                baseVector.set(i, rho.get(i) * Rinv.get(i));
            }
     
            // Step 1, generate a random vector, v,  that is orthogonal to
            // pi*D-Inverse.
            DoubleVector v = new DenseVector(vectorLength);
            for (int i = 0; i < v.length(); ++i)
                v.set(i, Math.random());


            Matrix RinvData = (matrix instanceof SparseMatrix)
                ? new RowScaledSparseMatrix((SparseMatrix) matrix, Rinv)
                : new RowScaledMatrix(matrix, Rinv);

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Examples of edu.ucla.sspace.vector.DenseVector

                    SparseVector sv = (SparseVector)sumVector;
                    for (int nz : sv.getNonZeroIndices())
                        centroid.set(nz, sumVector.get(nz) * d);
                }
                else {
                    centroid = new DenseVector(length);
                    for (int i = 0; i < length; ++i) 
                        centroid.set(i, sumVector.get(i) * d);                    
                }
            }
        }

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Examples of edu.ucla.sspace.vector.DenseVector

        boolean added = indices.add(index);
        assert added : "Adding duplicate indices to candidate facility";
        if (sumVector == null) {
            sumVector = (v instanceof SparseVector)
                ? new SparseHashDoubleVector(v)
                : new DenseVector(v);
        }
        else {
            VectorMath.add(sumVector, v);
            centroid = null;
        }

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Examples of edu.ucla.sspace.vector.DenseVector


    /**
     * {@inheritDoc}
     */
    public DoubleVector getColumnVector(int column) {
        return new DenseVector(getColumn(column));
    }

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Examples of edu.ucla.sspace.vector.DenseVector

                // Incorporate the context into the semantic vector for the
                // focus word.  If the focus word has no semantic vector yet,
                // create a new one, as determined by the index builder.
                DoubleVector meaning = termHolographs.get(focusWord);
                if (meaning == null) {
                    meaning = new DenseVector(indexVectorSize);
                    documentVectors.put(focusWord, meaning);
                }
                updateMeaning(meaning, prevWords, nextWords);
            }

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Examples of edu.ucla.sspace.vector.DenseVector

                               Queue<String> nextWords) {
        // Generate the semantics of the context using summation of index
        // vectors.
        if (semanticType == SemanticType.COMPOSITE ||
            semanticType == SemanticType.CONTEXT) {
            DoubleVector context = new DenseVector(indexVectorSize);


            // Sum the words prior to the focus word, skipping filtered tokens.
            for (String term: prevWords) {
                if (term.equals(IteratorFactory.EMPTY_TOKEN))
                    continue;

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