Example usage for org.apache.commons.math.linear RealMatrix subtract

Introduction

In this page you can find the example usage for org.apache.commons.math.linear RealMatrix subtract.

Prototype

RealMatrix subtract(RealMatrix m) throws IllegalArgumentException;

Source Link

Document

Compute this minus m.

Usage

From source file:net.sf.jtmt.clustering.Cluster.java

/**
 * Gets the radius.//  w ww  .  j a  v a2s.  co m
 *
 * @return the radius
 */
public double getRadius() {
    double radius = 0.0D;
    if (centroid != null) {
        for (String docName : docNames) {
            RealMatrix doc = getDocument(docName);
            radius += doc.subtract(centroid).getFrobeniusNorm();
        }
    }
    return radius / docNames.size();
}

---

From source file:net.sf.jtmt.clustering.Cluster.java

/**
 * Gets the eucledian distance.// w  w  w .  j a  va2 s .  co  m
 *
 * @param doc the doc
 * @return the eucledian distance
 */
public double getEucledianDistance(RealMatrix doc) {
    if (centroid != null) {
        return doc.subtract(centroid).getFrobeniusNorm();
    }
    return 0.0D;
}

---

From source file:net.sf.jtmt.clustering.Cluster.java

/**
 * Gets the complete linkage distance./*  w w w.ja  v a 2 s .  co m*/
 *
 * @param doc the doc
 * @return the complete linkage distance
 */
public double getCompleteLinkageDistance(RealMatrix doc) {
    Max max = new Max();
    if (docs.size() == 0) {
        return 0.0D;
    }
    double[] distances = new double[docs.size()];
    for (int i = 0; i < distances.length; i++) {
        RealMatrix clusterDoc = docs.get(docNames.get(i));
        distances[i] = clusterDoc.subtract(doc).getFrobeniusNorm();
    }
    return max.evaluate(distances);
}

---

From source file:dr.math.Procrustes.java

public Procrustes(RealMatrix X, RealMatrix Xstar, boolean allowTranslation, boolean allowDilation) {
    rowDimension = X.getRowDimension();//from   w  w w.jav  a 2s .c o m
    columnDimension = X.getColumnDimension();

    if (Xstar.getRowDimension() != rowDimension) {
        throw new IllegalArgumentException("X and Xstar do not have the same number of rows");
    }
    if (Xstar.getColumnDimension() != columnDimension) {
        throw new IllegalArgumentException("X and Xstar do not have the same number of columns");
    }

    RealMatrix J = new Array2DRowRealMatrix(rowDimension, rowDimension);

    if (allowTranslation) {
        //           J <- diag(n) - 1/n * matrix(1, n, n)
        //           for n = 3, J = {{1, -2/3, -2/3}, {-2/3, 1, -2/3}, {-2/3, -2/3, 1}}

        for (int i = 0; i < rowDimension; i++) {
            J.setEntry(i, i, 1.0 - (1.0 / rowDimension));

            for (int j = i + 1; j < rowDimension; j++) {
                J.setEntry(i, j, -1.0 / rowDimension);
                J.setEntry(j, i, -1.0 / rowDimension);
            }
        }
    } else {
        //           J <- diag(n)

        for (int i = 0; i < rowDimension; i++) {
            J.setEntry(i, i, 1);
        }

    }

    //       C <- t(Xstar) %*% J %*% X

    RealMatrix C = Xstar.transpose().multiply(J.multiply(X));

    //       svd.out <- svd(C)
    //       R <- svd.out$v %*% t(svd.out$u)
    //      NB: Apache math does a different SVD from R.  TODO Should use Colt library
    SingularValueDecomposition SVD = new SingularValueDecompositionImpl(C);
    R = SVD.getV().multiply(SVD.getUT());

    //       s <- 1
    double s = 1.0; // scale = 1 unless dilation is being used

    if (allowDilation) {
        //           mat1 <- t(Xstar) %*% J %*% X %*% R
        RealMatrix mat1 = Xstar.transpose().multiply(J.multiply(X.multiply(R)));

        //           mat2 <- t(X) %*% J %*% X
        RealMatrix mat2 = X.transpose().multiply(J.multiply(X));

        //           s.numer <- 0
        //           s.denom <- 0
        double numer = 0.0;
        double denom = 0.0;

        //           for (i in 1:m) {
        //               s.numer <- s.numer + mat1[i, i]
        //               s.denom <- s.denom + mat2[i, i]
        //           }
        for (int i = 0; i < columnDimension; i++) {
            numer = numer + mat1.getEntry(i, i);
            denom = denom + mat2.getEntry(i, i);
        }
        //           s <- s.numer/s.denom
        s = numer / denom;
    }
    this.s = s;

    //       tt <- matrix(0, m, 1)
    RealMatrix tmpT = new Array2DRowRealMatrix(columnDimension, 1); // a translation vector of zero unless translation is being used

    if (allowTranslation) {
        //           tt <- 1/n * t(Xstar - s * X %*% R) %*% matrix(1, n, 1)
        RealMatrix tmp = new Array2DRowRealMatrix(rowDimension, 1);
        for (int i = 0; i < rowDimension; i++) {
            tmp.setEntry(i, 0, 1);
        }
        tmpT = Xstar.subtract(X.multiply(R).scalarMultiply(s)).transpose().scalarMultiply(1.0 / rowDimension)
                .multiply(tmp);
    }

    T = tmpT;
}

---

From source file:tools.help.java

public static RealMatrix makeRealKronReduction(RealMatrix source, int ground_wires) {
    if (ground_wires == 0) {
        //no Kron Reduction needed
        return source;
    } else {//from w  ww  .  j  a  v a2 s . c om
        int rows_total = source.getRowDimension();
        int cols_total = source.getColumnDimension();
        int nn_rows = rows_total - ground_wires;
        int nn_cols = cols_total - ground_wires;
        int nm_rows = rows_total - ground_wires;
        int nm_cols = ground_wires;
        int mn_rows = ground_wires;
        int mn_cols = cols_total - ground_wires;
        int mm_rows = ground_wires;
        int mm_cols = ground_wires;

        RealMatrix NN = new Array2DRowRealMatrix(nn_rows, nn_cols);
        RealMatrix MN = new Array2DRowRealMatrix(mn_rows, mn_cols);
        RealMatrix NM = new Array2DRowRealMatrix(nm_rows, nm_cols);
        RealMatrix MM = new Array2DRowRealMatrix(mm_rows, mm_cols);

        for (int i = 0; i < rows_total; i++) {
            for (int j = 0; j < cols_total; j++) {
                if (i < nn_rows && j < nn_cols) {
                    NN.setEntry(i, j, source.getEntry(i, j));
                } else if (i >= nn_rows && j < nn_cols) {
                    MN.setEntry(i - nn_rows, j, source.getEntry(i, j));
                } else if (i < nn_rows && j >= nn_cols) {
                    NM.setEntry(i, j - nn_cols, source.getEntry(i, j));
                } else if (i >= nn_rows && j >= nn_cols) {
                    MM.setEntry(i - nn_rows, j - nn_cols, source.getEntry(i, j));
                }
            }
        }
        return NN.subtract(NM.multiply(MM.inverse()).multiply(MN));
    }
}

---