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A comprehensive collection of matrix data structures, linear solvers, least squares methods,
eigenvalue, and singular value decompositions.
Forked from: https://github.com/fommil/matrix-toolkits-java
and added support for eigenvalue computation of general matrices
/*
* Copyright (C) 2003-2006 Bjørn-Ove Heimsund
*
* This file is part of MTJ.
*
* This library 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 2.1 of the License, or (at your
* option) any later version.
*
* This library 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 this library; if not, write to the Free Software Foundation,
* Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
package no.uib.cipr.matrix;
import java.text.DecimalFormat;
import java.util.Arrays;
/**
* Partial implementation of a dense matrix
*/
abstract class AbstractDenseMatrix extends AbstractMatrix {
/**
* Matrix contents
*/
double[] data;
/**
* Constructor for AbstractDenseMatrix. The matrix contents will be set to
* zero
*
* @param numRows
* Number of rows
* @param numColumns
* Number of columns
*/
public AbstractDenseMatrix(int numRows, int numColumns) {
super(numRows, numColumns);
// We know that numRows and numColumns is positive from the super
// constructor.
final long size = (long) numRows * numColumns;
if (size > Integer.MAX_VALUE) {
throw new IllegalArgumentException(
"Matrix of "
+ numRows
+ " x "
+ numColumns
+ " = "
+ size
+ " elements is too large to be allocated using a single Java array.");
}
data = new double[numRows * numColumns];
}
/**
* Constructor for AbstractDenseMatrix. Matrix is copied from the supplied
* matrix
*
* @param A
* Matrix to copy from
*/
public AbstractDenseMatrix(Matrix A) {
this(A, true);
}
/**
* Constructor for AbstractDenseMatrix. Matrix is copied from the supplied
* matrix
*
* @param A
* Matrix to copy from
* @param deep
* True for deep copy, false for reference
*/
public AbstractDenseMatrix(Matrix A, boolean deep) {
super(A);
if (deep) {
data = new double[numRows * numColumns];
copy(A);
} else
this.data = ((AbstractDenseMatrix) A).getData();
}
/**
* Set this matrix equal to the given matrix
*/
abstract void copy(Matrix A);
/**
* Returns the matrix contents. Ordering depends on the underlying storage
* assumptions
*/
public double[] getData() {
return data;
}
@Override
public void add(int row, int column, double value) {
data[getIndex(row, column)] += value;
}
@Override
public void set(int row, int column, double value) {
data[getIndex(row, column)] = value;
}
@Override
public double get(int row, int column) {
return data[getIndex(row, column)];
}
/**
* Checks the row and column indices, and returns the linear data index
*/
int getIndex(int row, int column) {
check(row, column);
return row + column * numRows;
}
@Override
public Matrix set(Matrix B) {
// using instanceof results in weird problems
// with implementations that mask some values
if (!(getClass().isAssignableFrom(B.getClass())))
return super.set(B);
checkSize(B);
double[] Bd = ((AbstractDenseMatrix) B).getData();
if (Bd == data)
return this;
System.arraycopy(Bd, 0, data, 0, data.length);
return this;
}
@Override
public Matrix zero() {
Arrays.fill(data, 0);
return this;
}
@Override
public String toString() {
StringBuilder out = new StringBuilder();
DecimalFormat df = new DecimalFormat("####0.00");
for (int i = 0; i < numRows(); i++) {
for (int j = 0; j < numColumns(); j++) {
double value = get(i, j);
if (value >= 0)
out.append(" ");
out.append(" " + df.format(value));
}
out.append("\n");
}
return out.toString();
}
}