Example usage for org.apache.commons.math3.linear SingularValueDecomposition getCovariance

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear SingularValueDecomposition getCovariance.

Prototype

public RealMatrix getCovariance(final double minSingularValue) 

Source Link

Document

Returns the n × n covariance matrix.

Usage

From source file:cooccurrence.Omer_Levy.java

public static void main(String args[]) {
    String path = "";
    String writePath = "";
    BufferedReader br = null;//from  w ww .j av a  2 s. c om
    ArrayList<String> files = new ArrayList<>();
    //reading all the files in the directory
    //each file is PPMI matrix for an year
    listFilesForFolder(new File(path), files);
    for (String filePath : files) {
        System.out.println(filePath);
        String fileName = new File(filePath).getName();

        //data structure to store the PPMI matrix in the file
        HashMap<String, HashMap<String, Double>> cooccur = new HashMap<>();
        readFileContents(filePath, cooccur); //reading the file and storing the content in the hashmap
        //Because Matrices are identified by row and col id, the following 
        //lists maps id to corresponding string. Note that matrix is symmetric. 
        ArrayList<String> rowStrings = new ArrayList<>(cooccur.keySet());
        ArrayList<String> colStrings = new ArrayList<>(cooccur.keySet());

        //creating matrix with given dimensions and initializing it to 0
        RealMatrix matrixR = MatrixUtils.createRealMatrix(rowStrings.size(), colStrings.size());

        //creating the matrices for storing top rank-d matrices of SVD 
        RealMatrix matrixUd = MatrixUtils.createRealMatrix(D, D);
        RealMatrix matrixVd = MatrixUtils.createRealMatrix(D, D);
        RealMatrix coVarD = MatrixUtils.createRealMatrix(D, D);

        //populating the matrices based on the co-occur hashmap
        populateMatrixR(matrixR, cooccur, rowStrings, colStrings);

        //computing the svd
        SingularValueDecomposition svd = new SingularValueDecomposition(matrixR);

        //extracting the components of SVD factorization
        RealMatrix U = svd.getU();
        RealMatrix V = svd.getV();
        RealMatrix coVariance = svd.getCovariance(-1);

        //list to store indices of top-D singular values of coVar. 
        //Use this with rowsString (colStrings) to get the corresponding word and context
        ArrayList<Integer> indicesD = new ArrayList<>();
        //Extract topD singular value from covariance to store in coVarD and
        //extract corresponding columns from U and V to store in Ud and Vd
        getTopD(U, V, coVariance, matrixUd, matrixVd, coVarD, indicesD);
        //calulate the squareRoot of coVarD
        RealMatrix squareRootCoVarD = squareRoot(coVarD);
        RealMatrix W_svd = matrixUd.multiply(squareRootCoVarD);
        RealMatrix C_svd = matrixVd.multiply(squareRootCoVarD);
    }
}

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