algorithms-for-computing-li.../LinearRegressionTool/src/main/java/presenter/evaluation/ScaledErrorBasedMeasure.java

77 lines
2.0 KiB
Java

package presenter.evaluation;
import model.Line;
import presenter.algorithms.util.FastElementSelector;
import java.util.ArrayList;
import java.util.LinkedList;
/**
* Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
*
* @Author: Armin Wolf
* @Email: a_wolf28@uni-muenster.de
* @Date: 07.09.2017.
*/
public class ScaledErrorBasedMeasure {
private ArrayList<Double> sampsonError;
private ArrayList<Double> naivSampsonError;
private ArrayList<Double> scaledError;
public ScaledErrorBasedMeasure(final LinkedList<Line> lines, Double m, Double b, Double naivSlope, Double naivInterception) {
this.sampsonError = new ArrayList<>();
this.naivSampsonError = new ArrayList<>();
this.scaledError = new ArrayList<>();
for (Line line : lines) {
Double e = Math.pow(naivSlope * line.getM() - line.getB() + naivInterception, 2) / (Math.pow(naivSlope, 2) + 1);
naivSampsonError.add(e);
}
for (Line line : lines) {
Double e = Math.pow(m * line.getM() - line.getB() + b, 2) / (Math.pow(m, 2) + 1);
sampsonError.add(e);
}
for (int i=0;i<sampsonError.size();i++){
scaledError.add(sampsonError.get(i) / naivSampsonError.get(i));
}
}
/* Skalierungs Abhängige Approximationsgüten */
//unterschiedliche Alg.- auf einem Datensatz
public Double mse() {
double error = 0;
for (Double d : scaledError) {
error += Math.pow(d, 2);
}
error /= scaledError.size();
return error;
}
public Double rmse() {
return Math.sqrt(mse());
}
public Double mae() {
double error = 0;
for (Double d : scaledError) {
error += Math.abs(d);
}
error /= scaledError.size();
return error;
}
public Double mdae() {
return FastElementSelector
.randomizedSelect(scaledError, scaledError.size() * 0.5);
}
}