/*
* 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.examples;
import org.ujmp.core.DenseMatrix;
import org.ujmp.core.Matrix;
import org.ujmp.core.SparseMatrix;
public class QuickStart {
public static void main(String[] args) throws Exception {
// create a dense empty matrix with 4 rows and 4 columns
Matrix dense = DenseMatrix.Factory.zeros(4, 4);
// set entry at row 2 and column 3 to the value 5.0
dense.setAsDouble(5.0, 2, 3);
// set some other values
dense.setAsDouble(1.0, 0, 0);
dense.setAsDouble(3.0, 1, 1);
dense.setAsDouble(4.0, 2, 2);
dense.setAsDouble(-2.0, 3, 3);
dense.setAsDouble(-2.0, 1, 3);
// print the final matrix on the console
System.out.println(dense);
// create a sparse empty matrix with 4 rows and 4 columns
Matrix sparse = SparseMatrix.Factory.zeros(4, 4);
sparse.setAsDouble(2.0, 0, 0);
// basic calculations
Matrix transpose = dense.transpose();
Matrix sum = dense.plus(sparse);
Matrix difference = dense.minus(sparse);
Matrix matrixProduct = dense.mtimes(sparse);
Matrix scaled = dense.times(2.0);
Matrix inverse = dense.inv();
Matrix pseudoInverse = dense.pinv();
double determinant = dense.det();
Matrix[] singularValueDecomposition = dense.svd();
Matrix[] eigenValueDecomposition = dense.eig();
Matrix[] luDecomposition = dense.lu();
Matrix[] qrDecomposition = dense.qr();
Matrix choleskyDecomposition = dense.chol();
}
}