/*
* Copyright (c) 2009-2012, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* EJML 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 3
* of the License, or (at your option) any later version.
*
* EJML 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 EJML. If not, see <http://www.gnu.org/licenses/>.
*/
package org.ejml.alg.dense.linsol;
import org.ejml.alg.dense.decomposition.qr.QRColPivDecompositionHouseholderColumn;
import org.ejml.alg.dense.linsol.qr.LinearSolverQrpHouseCol;
import org.ejml.alg.dense.linsol.qr.SolvePseudoInverseQrp;
import org.ejml.data.DenseMatrix64F;
import org.ejml.factory.LinearSolver;
import org.ejml.ops.RandomMatrices;
import java.util.Random;
/**
* @author Peter Abeles
*/
public class BenchmarkSolvePseudoInverse {
private static final long SEED = 6;
private static final Random rand = new Random();
private static DenseMatrix64F A;
private static DenseMatrix64F B;
private static boolean includeSet = true;
public static long solveBenchmark( LinearSolver<DenseMatrix64F> solver , int numTrials ) {
rand.setSeed(SEED);
DenseMatrix64F X = new DenseMatrix64F(B.numRows,B.numCols);
solver = new LinearSolverSafe<DenseMatrix64F>(solver);
if( !includeSet ) solver.setA(A);
long prev = System.currentTimeMillis();
for( long i = 0; i < numTrials; i++ ) {
if(includeSet) solver.setA(A);
solver.solve(B,X);
}
return System.currentTimeMillis() - prev;
}
private static void runAlgorithms( int numTrials )
{
// System.out.println("solve SVD = "+ solveBenchmark(
// new SolvePseudoInverseSvd(),numTrials));
System.out.println("solve Gen QRP Basic = "+ solveBenchmark(
new SolvePseudoInverseQrp(new QRColPivDecompositionHouseholderColumn(),false),numTrials));
System.out.println("solve Gen QRP = "+ solveBenchmark(
new SolvePseudoInverseQrp(new QRColPivDecompositionHouseholderColumn(),true),numTrials));
System.out.println("solve QRP Col Basic = "+ solveBenchmark(
new LinearSolverQrpHouseCol(new QRColPivDecompositionHouseholderColumn(),false),numTrials));
System.out.println("solve QRP Col = "+ solveBenchmark(
new LinearSolverQrpHouseCol(new QRColPivDecompositionHouseholderColumn(),true),numTrials));
}
public static void main( String args [] ) {
int size[] = new int[]{2,4,10,100,1000,2000};
int trials[] = new int[]{(int)1e6,(int)5e5,(int)1e5,500,2,1};
int trialsX[] = new int[]{(int)5e5,(int)4e5,(int)2e5,(int)7e4,4000,2000};
System.out.println("Increasing matrix A size");
for( int i = 0; i < size.length; i++ ) {
int w = size[i];
// create a singular matrix
double singularValues[] = new double[w];
for( int j = 0; j < w-1; j++ )
singularValues[j] = 10+w-j;
System.out.printf("Solving A size %3d for %12d trials\n",w,trials[i]);
A = RandomMatrices.createSingularValues(w, w, rand, singularValues);
B = new DenseMatrix64F(w,2);
runAlgorithms(trials[i]);
}
System.out.println("Increasing matrix B size");
for( int i = 0; i < size.length; i++ ) {
int w = size[i];
System.out.printf("Solving B size %3d for %12d trials\n",w,trialsX[i]);
A = RandomMatrices.createRandom(100,100,rand);
B = new DenseMatrix64F(100,w);
runAlgorithms(trialsX[i]/80);
}
}
}