/*
* Copyright (C) 2008-2015 by Holger Arndt
*
* This file is part of the Universal Java Matrix Package (UJMP).
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership and licensing.
*
* UJMP is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* UJMP is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with UJMP; if not, write to the
* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
* Boston, MA 02110-1301 USA
*/
package org.ujmp.parallelcolt;
import org.ujmp.core.Matrix;
import org.ujmp.core.doublematrix.DenseDoubleMatrix2D;
import org.ujmp.core.doublematrix.stub.AbstractDenseDoubleMatrix2D;
import org.ujmp.core.interfaces.HasRowMajorDoubleArray2D;
import org.ujmp.core.interfaces.Wrapper;
import org.ujmp.core.mapmatrix.MapMatrix;
import org.ujmp.parallelcolt.calculation.Solve;
import cern.colt.matrix.tdouble.DoubleFactory2D;
import cern.colt.matrix.tdouble.DoubleMatrix2D;
import cern.colt.matrix.tdouble.algo.DenseDoubleAlgebra;
import cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleCholeskyDecomposition;
import cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleEigenvalueDecomposition;
import cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleLUDecomposition;
import cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleQRDecomposition;
import cern.colt.matrix.tdouble.algo.decomposition.DenseDoubleSingularValueDecomposition;
import cern.jet.math.tdouble.DoubleFunctions;
public class ParallelColtDenseDoubleMatrix2D extends AbstractDenseDoubleMatrix2D implements
Wrapper<cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D> {
private static final long serialVersionUID = -1941030601886654699L;
public static final ParallelColtDenseDoubleMatrix2DFactory Factory = new ParallelColtDenseDoubleMatrix2DFactory();
public static final DenseDoubleAlgebra ALG = new DenseDoubleAlgebra();
private final cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D matrix;
public ParallelColtDenseDoubleMatrix2D(int rows, int columns) {
super(rows, columns);
this.matrix = new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(rows, columns);
}
public ParallelColtDenseDoubleMatrix2D(DoubleMatrix2D m) {
super(m.rows(), m.columns());
if (m instanceof DenseDoubleMatrix2D) {
this.matrix = (cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D) m;
} else {
this.matrix = new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(m.toArray());
// this.matrix = new
// cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(
// m.rows(), m.columns());
// for (int r = 0; r < m.rows(); r++) {
// for (int c = 0; c < m.columns(); c++) {
// matrix.setQuick(r, c, m.getQuick(r, c));
// }
// }
}
}
public ParallelColtDenseDoubleMatrix2D(cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D m) {
super(m.rows(), m.columns());
this.matrix = m;
}
public ParallelColtDenseDoubleMatrix2D(Matrix source) {
super(source.getRowCount(), source.getColumnCount());
if (source instanceof HasRowMajorDoubleArray2D) {
final double[][] data = ((HasRowMajorDoubleArray2D) source).getRowMajorDoubleArray2D();
this.matrix = new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(data);
} else if (source instanceof DenseDoubleMatrix2D) {
this.matrix = new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D((int) source.getRowCount(),
(int) source.getColumnCount());
final DenseDoubleMatrix2D m2 = (DenseDoubleMatrix2D) source;
for (int r = (int) source.getRowCount(); --r >= 0;) {
for (int c = (int) source.getColumnCount(); --c >= 0;) {
matrix.setQuick(r, c, m2.getDouble(r, c));
}
}
} else {
this.matrix = new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D((int) source.getRowCount(),
(int) source.getColumnCount());
for (long[] c : source.availableCoordinates()) {
setDouble(source.getAsDouble(c), c);
}
}
if (source.getMetaData() != null) {
setMetaData(source.getMetaData().clone());
}
}
public double getDouble(long row, long column) {
return matrix.getQuick((int) row, (int) column);
}
public double getDouble(int row, int column) {
return matrix.getQuick(row, column);
}
public void setDouble(double value, long row, long column) {
matrix.setQuick((int) row, (int) column, value);
}
public void setDouble(double value, int row, int column) {
matrix.setQuick(row, column, value);
}
public cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D getWrappedObject() {
return matrix;
}
public Matrix plus(double value) {
Matrix result = new ParallelColtDenseDoubleMatrix2D((cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D) matrix
.copy().assign(DoubleFunctions.plus(value)));
MapMatrix<String, Object> a = getMetaData();
if (a != null) {
result.setMetaData(a.clone());
}
return result;
}
public Matrix inv() {
return new ParallelColtDenseDoubleMatrix2D(
(cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D) ALG.inverse(matrix));
}
public Matrix times(double value) {
Matrix result = new ParallelColtDenseDoubleMatrix2D((cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D) matrix
.copy().assign(DoubleFunctions.mult(value)));
MapMatrix<String, Object> a = getMetaData();
if (a != null) {
result.setMetaData(a.clone());
}
return result;
}
public Matrix transpose() {
return new ParallelColtDenseDoubleMatrix2D((cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D) matrix
.viewDice().copy());
}
public Matrix plus(Matrix m) {
if (m instanceof ParallelColtDenseDoubleMatrix2D) {
DoubleMatrix2D result = matrix.copy();
result.assign(((ParallelColtDenseDoubleMatrix2D) m).getWrappedObject(), DoubleFunctions.plus);
Matrix ret = new ParallelColtDenseDoubleMatrix2D(result);
MapMatrix<String, Object> a = getMetaData();
if (a != null) {
ret.setMetaData(a.clone());
}
return ret;
} else {
return super.plus(m);
}
}
public Matrix minus(Matrix m) {
if (m instanceof ParallelColtDenseDoubleMatrix2D) {
DoubleMatrix2D result = matrix.copy();
result.assign(((ParallelColtDenseDoubleMatrix2D) m).getWrappedObject(), DoubleFunctions.minus);
Matrix ret = new ParallelColtDenseDoubleMatrix2D(result);
MapMatrix<String, Object> a = getMetaData();
if (a != null) {
ret.setMetaData(a.clone());
}
return ret;
} else {
return super.minus(m);
}
}
public Matrix mtimes(Matrix m) {
if (m instanceof ParallelColtDenseDoubleMatrix2D) {
cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D ret = new cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D(
(int) getRowCount(), (int) m.getColumnCount());
matrix.zMult(((ParallelColtDenseDoubleMatrix2D) m).matrix, ret);
Matrix result = new ParallelColtDenseDoubleMatrix2D(ret);
MapMatrix<String, Object> a = getMetaData();
if (a != null) {
result.setMetaData(a.clone());
}
return result;
} else {
return super.mtimes(m);
}
}
public Matrix[] svd() {
DenseDoubleSingularValueDecomposition svd = new DenseDoubleSingularValueDecomposition(matrix, true, false);
Matrix u = new ParallelColtDenseDoubleMatrix2D(svd.getU());
Matrix s = new ParallelColtDenseDoubleMatrix2D(svd.getS());
Matrix v = new ParallelColtDenseDoubleMatrix2D(svd.getV());
return new Matrix[] { u, s, v };
}
public Matrix[] eig() {
DenseDoubleEigenvalueDecomposition eig = new DenseDoubleEigenvalueDecomposition(matrix);
Matrix v = new ParallelColtDenseDoubleMatrix2D(eig.getV());
Matrix d = new ParallelColtDenseDoubleMatrix2D(eig.getD());
return new Matrix[] { v, d };
}
public Matrix[] qr() {
DenseDoubleQRDecomposition qr = new DenseDoubleQRDecomposition(matrix);
Matrix q = new ParallelColtDenseDoubleMatrix2D(qr.getQ(false));
Matrix r = new ParallelColtDenseDoubleMatrix2D(qr.getR(false));
return new Matrix[] { q, r };
}
public Matrix[] lu() {
if (getRowCount() >= getColumnCount()) {
DenseDoubleLUDecomposition lu = new DenseDoubleLUDecomposition(matrix);
Matrix l = new ParallelColtDenseDoubleMatrix2D(lu.getL());
Matrix u = new ParallelColtDenseDoubleMatrix2D(lu.getU().viewPart(0, 0, (int) getColumnCount(),
(int) getColumnCount()));
int m = (int) getRowCount();
int[] piv = lu.getPivot();
Matrix p = new ParallelColtDenseDoubleMatrix2D(m, m);
for (int i = 0; i < m; i++) {
p.setAsDouble(1, i, piv[i]);
}
return new Matrix[] { l, u, p };
} else {
throw new RuntimeException("only supported for matrices m>=n");
}
}
public Matrix chol() {
DenseDoubleCholeskyDecomposition chol = new DenseDoubleCholeskyDecomposition(matrix);
Matrix r = new ParallelColtDenseDoubleMatrix2D(chol.getL());
return r;
}
public Matrix copy() {
Matrix m = new ParallelColtDenseDoubleMatrix2D(
(cern.colt.matrix.tdouble.impl.DenseDoubleMatrix2D) matrix.copy());
if (getMetaData() != null) {
m.setMetaData(getMetaData().clone());
}
return m;
}
public Matrix solve(Matrix b) {
return Solve.INSTANCE.calc(this, b);
}
public Matrix solveSPD(Matrix b) {
if (b instanceof ParallelColtDenseDoubleMatrix2D) {
ParallelColtDenseDoubleMatrix2D b2 = new ParallelColtDenseDoubleMatrix2D(b);
DenseDoubleCholeskyDecomposition chol = new DenseDoubleCholeskyDecomposition(matrix);
chol.solve(b2.matrix);
return b2;
} else {
return super.solve(b);
}
}
public Matrix invSPD() {
DenseDoubleCholeskyDecomposition chol = new DenseDoubleCholeskyDecomposition(matrix);
DoubleMatrix2D ret = DoubleFactory2D.dense.identity(matrix.rows());
chol.solve(ret);
return new ParallelColtDenseDoubleMatrix2D(ret);
}
public ParallelColtDenseDoubleMatrix2DFactory getFactory() {
return Factory;
}
}