package io.prediction.examples.java.regression;
import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
import org.apache.commons.math3.linear.RealVector;
import java.util.Arrays;
import io.prediction.controller.java.EmptyParams;
import io.prediction.controller.java.LJavaAlgorithm;
public class OLSAlgorithm
extends LJavaAlgorithm<EmptyParams, TrainingData, Double[], Double[], Double> {
public Double[] train(TrainingData data) {
OLSMultipleLinearRegression r = new OLSMultipleLinearRegression();
// Convert Double[][] to double[][]
double[][] x = new double[data.r][data.c];
double[] y = new double[data.r];
for (int i=0; i<data.r; i++) {
for (int j=0; j<data.c; j++) {
x[i][j] = data.x[i][j].doubleValue();
}
y[i] = data.y[i].doubleValue();
}
r.newSampleData(y, x);
// Fixme. Add intercept
double[] p = r.estimateRegressionParameters();
Double[] pp = new Double[data.c];
for (int j=0; j<data.c; j++) {
pp[j] = p[j];
}
System.out.println("Regression Algo: " + Arrays.toString(pp));
return pp;
}
public Double predict(Double[] model, Double[] query) {
double dotProduct = 0.0;
for (int i = 0; i < model.length; i++) {
dotProduct += model[i] * query[i];
}
return dotProduct;
}
}