Java tutorial
/* * (c) 2005 David B. Bracewell * * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, * software distributed under the License is distributed on an * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY * KIND, either express or implied. See the License for the * specific language governing permissions and limitations * under the License. */ package com.davidbracewell.ml.regression; import com.davidbracewell.ml.Instance; import com.google.common.collect.Lists; import com.google.common.primitives.Doubles; import org.apache.commons.math3.stat.correlation.PearsonsCorrelation; import java.util.List; /** * Utilities for Regression * * @author David B. Bracewell */ public final class Regression { /** * Pearson's correlation coefficient. * * @param model the model * @param data the data * @return the double */ public static double correlationCoefficient(RegressionModel model, List<Instance> data) { PearsonsCorrelation correlation = new PearsonsCorrelation(); List<Double> gold = Lists.newArrayList(); List<Double> pred = Lists.newArrayList(); for (Instance instance : data) { if (instance.hasTargetValue()) { gold.add(instance.getTargetValue()); pred.add(model.estimate(instance)); } } return correlation.correlation(Doubles.toArray(gold), Doubles.toArray(pred)); } }//END OF Regression