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/*
* Copyright (C) 2016 University of Pittsburgh.
*
* 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., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301 USA
*/
package edu.cmu.tetrad.stat;
import java.util.concurrent.ForkJoinPool;
import java.util.concurrent.ForkJoinTask;
import java.util.concurrent.RecursiveAction;
/**
* Feb 9, 2016 3:15:29 PM
*
* @author Kevin V. Bui ([email protected])
*/
public class RealVarianceVectorForkJoin implements RealVariance {
private final double[][] data;
private final int numOfRows;
private final int numOfCols;
private final int numOfThreads;
public RealVarianceVectorForkJoin(double[][] data, int numOfThreads) {
this.data = data;
this.numOfRows = data.length;
this.numOfCols = data[0].length;
this.numOfThreads = numOfThreads;
}
@Override
public double[] compute(boolean biasCorrected) {
double[] means = new double[this.numOfCols];
ForkJoinPool pool = ForkJoinPool.commonPool();
pool.invoke(new MeanAction(this.data, means, 0, this.numOfCols - 1));
pool.invoke(new VarianceAction(this.data, means, biasCorrected, 0, this.numOfCols - 1));
pool.shutdown();
return means;
}
class VarianceAction extends RecursiveAction {
private static final long serialVersionUID = 8630127061304877790L;
private final double[][] data;
private final double[] means;
private final boolean biasCorrected;
private final int start;
private final int end;
public VarianceAction(double[][] data, double[] means, boolean biasCorrected, int start, int end) {
this.data = data;
this.means = means;
this.biasCorrected = biasCorrected;
this.start = start;
this.end = end;
}
private void computeVariance() {
for (int col = this.start; col <= this.end; col++) {
double mean = this.means[col];
double value = 0;
double squareValue = 0;
for (int row = 0; row < RealVarianceVectorForkJoin.this.numOfRows; row++) {
double val = this.data[row][col] - mean;
squareValue += val * val;
value += val;
}
this.means[col] = (this.biasCorrected)
? (squareValue - (value * value / RealVarianceVectorForkJoin.this.numOfRows)) / (RealVarianceVectorForkJoin.this.numOfRows - 1.0f)
: (squareValue - (value * value / RealVarianceVectorForkJoin.this.numOfRows)) / RealVarianceVectorForkJoin.this.numOfRows;
}
}
@Override
protected void compute() {
int length = this.end - this.start;
int limit = RealVarianceVectorForkJoin.this.numOfCols / RealVarianceVectorForkJoin.this.numOfThreads;
int delta = RealVarianceVectorForkJoin.this.numOfCols % RealVarianceVectorForkJoin.this.numOfThreads;
int size = limit + delta;
if (length <= size) {
computeVariance();
} else {
int middle = (this.end + this.start) / 2;
ForkJoinTask.invokeAll(new VarianceAction(this.data, this.means, this.biasCorrected, this.start, middle), new VarianceAction(this.data, this.means, this.biasCorrected, middle + 1, this.end));
}
}
}
class MeanAction extends RecursiveAction {
private static final long serialVersionUID = 3741759201009022262L;
private final double[][] data;
private final double[] means;
private final int start;
private final int end;
public MeanAction(double[][] data, double[] means, int start, int end) {
this.data = data;
this.means = means;
this.start = start;
this.end = end;
}
private void computeMean() {
for (int col = this.start; col <= this.end; col++) {
double sum = 0;
for (int row = 0; row < RealVarianceVectorForkJoin.this.numOfRows; row++) {
sum += this.data[row][col];
}
this.means[col] = sum / RealVarianceVectorForkJoin.this.numOfRows;
}
}
@Override
protected void compute() {
int length = this.end - this.start;
int limit = RealVarianceVectorForkJoin.this.numOfCols / RealVarianceVectorForkJoin.this.numOfThreads;
int delta = RealVarianceVectorForkJoin.this.numOfCols % RealVarianceVectorForkJoin.this.numOfThreads;
int size = limit + delta;
if (length <= size) {
computeMean();
} else {
int middle = (this.end + this.start) / 2;
ForkJoinTask.invokeAll(new MeanAction(this.data, this.means, this.start, middle), new MeanAction(this.data, this.means, middle + 1, this.end));
}
}
}
}
