smile.validation.metric.R2 Maven / Gradle / Ivy
/*
* Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
*
* Smile 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.
*
* Smile 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 Smile. If not, see .
*/
package smile.validation.metric;
import smile.math.MathEx;
/**
* R2. R2 coefficient of determination measures how well
* the regression line approximates the real data points. An R2
* of 1.0 indicates that the regression line perfectly fits the data.
*
* @author Haifeng Li
*/
public class R2 implements RegressionMetric {
private static final long serialVersionUID = 2L;
/** Default instance. */
public final static R2 instance = new R2();
@Override
public double score(double[] truth, double[] prediction) {
return of(truth, prediction);
}
/**
* Calculates the R squared coefficient.
* @param truth the ground truth.
* @param prediction the prediction.
* @return the metric.
*/
public static double of(double[] truth, double[] prediction) {
if (truth.length != prediction.length) {
throw new IllegalArgumentException(String.format("The vector sizes don't match: %d != %d.", truth.length, prediction.length));
}
double RSS = 0.0;
double TSS = 0.0;
double ybar = MathEx.mean(truth);
int n = truth.length;
for (int i = 0; i < n; i++) {
double r = truth[i] - prediction[i];
RSS += r * r;
double t = truth[i] - ybar;
TSS += t * t;
}
return 1.0 - RSS / TSS;
}
@Override
public String toString() {
return "R2";
}
}