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/*
* Copyright (C) 2014-2016 Brian L. Browning
*
* This file is part of Beagle
*
* Beagle is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Beagle 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package math;
import java.util.concurrent.atomic.DoubleAdder;
import java.util.concurrent.atomic.LongAdder;
/**
* <p>Class {@code Regress} estimates a regression coefficient.
* </p>
* <p>Instances of class {@code Regress} are not thread-safe, but
* concurrent updates are permitted, and invocation of the {@code beta()}
* method in the absence of concurrent updates returns an accurate result.
* </p>
*
* @author Brian L. Browning {@code <browning@uw.edu>}
*/
public class Regress {
private final LongAdder cnt;
private final DoubleAdder sumX;
private final DoubleAdder sumY;
private final DoubleAdder sumXX;
private final DoubleAdder sumXY;
/**
* Constructs a new {@code Regress} instance.
*/
public Regress() {
this.cnt = new LongAdder();
this.sumX = new DoubleAdder();
this.sumY = new DoubleAdder();
this.sumXX = new DoubleAdder();
this.sumXY = new DoubleAdder();
}
/**
* Records the specified values of the dependent and independent variables.
* @param x the value of the independent variable
* @param y the value of the dependent variable
*/
public void add(double x, double y) {
cnt.increment();
sumX.add(x);
sumY.add(y);
sumXX.add(x*x);
sumXY.add(x*y);
}
/**
* Records the specified values of the dependent and independent variables.
* @param regress recorded values of the independent and dependent variables
* @throws NullPointerException if {@code regress == null}t
*/
public void add(Regress regress) {
this.sumX.add(regress.sumX());
this.sumY.add(regress.sumY());
this.sumXX.add(regress.sumXX());
this.sumXY.add(regress.sumXY());
this.cnt.add(regress.cnt());
}
/**
* Returns the number of recorded values of the independent variable.
* The returned value is NOT an atomic snapshot. An accurate result is
* guaranteed only if no concurrent updates occur during method invocation.
*
* @return the number of recorded values of the independent variable
*/
public long cnt() {
return cnt.sum();
}
/**
* Returns the sum of recorded values of the independent variable.
* The returned value is NOT an atomic snapshot. An accurate result is
* guaranteed only if no concurrent updates occur during method invocation.
* @return the sum of recorded values of the independent variable
*/
public double sumX() {
return sumX.sum();
}
/**
* Returns the sum of recorded values of the dependent variable.
* The returned value is NOT an atomic snapshot. An accurate result is
* guaranteed only if no concurrent updates occur during method invocation.
* @return the sum of recorded values of the dependent variable
*/
public double sumY() {
return sumY.sum();
}
/**
* Returns the sum of the squared recorded values of the independent
* variable.
* The returned value is NOT an atomic snapshot. An accurate result is
* guaranteed only if no concurrent updates occur during method invocation.
* @return the sum of the squared recorded values of the independent variable
*/
public double sumXX() {
return sumXX.sum();
}
/**
* Returns the sum of the products of the recorded values of the
* independent and dependent variables.
* The returned value is NOT an atomic snapshot. An accurate result is
* guaranteed only if no concurrent updates occur during method invocation.
* @return the sum of the products of the recorded values of the
* independent and dependent variables
*/
public double sumXY() {
return sumXY.sum();
}
/**
* Deletes all recorded values from {@code this}.
*/
public void reset() {
cnt.reset();
sumX.reset();
sumY.reset();
sumXX.reset();
sumXY.reset();
}
/**
* Returns the regression coefficient for the recorded values of the
* independent and dependent variables. The returned value is NOT an
* atomic snapshot. An accurate result is guaranteed only if no concurrent
* updates occur during method invocation.
* @return the regression coefficient for the recorded values of the
* independent and dependent variables
*/
public double beta() {
long n = cnt.sum();
double sx = sumX.sum();
double sy = sumY.sum();
double sxx = sumXX.sum();
double sxy = sumXY.sum();
return (n*sxy - (sx*sy))/(n*sxx - (sx*sx));
}
// public static void main(String[] args) {
// double[] x = new double[] {1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63, 1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83};
// double[] y = new double[] {52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93, 61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46};
//
// Regress regress = new Regress();
// for (int j=0; j<x.length; ++j) {
// regress.add(x[j], y[j]);
// }
// System.out.println("Beta from Wikipedia Simple Linear Regression article: 61.272");
// System.out.println("Calculated Beta: " + regress.beta());
// }
}
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