cc.recommenders.evaluation.data.BoxplotData.java Source code

Introduction

Here is the source code for cc.recommenders.evaluation.data.BoxplotData.java

Source

/**
 * Copyright (c) 2010, 2011 Darmstadt University of Technology.
 * All rights reserved. This program and the accompanying materials
 * are made available under the terms of the Eclipse Public License v1.0
 * which accompanies this distribution, and is available at
 * http://www.eclipse.org/legal/epl-v10.html
 * 
 * Contributors:
 *     Sebastian Proksch - initial API and implementation
 */
package cc.recommenders.evaluation.data;

import static cc.recommenders.assertions.Asserts.assertFalse;

import java.util.List;

import org.apache.commons.lang3.builder.EqualsBuilder;
import org.apache.commons.lang3.builder.HashCodeBuilder;
import org.apache.commons.math.stat.StatUtils;
import org.apache.commons.math.stat.descriptive.rank.Percentile;

import com.google.common.collect.Lists;

// TODO rename class to something more generic that also reflects mean/variance calculation
public class BoxplotData {

    private List<Double> data = Lists.newLinkedList();
    private double[] dataAsCachedArray;

    public void add(Double d) {
        data.add(d);
    }

    public boolean hasData() {
        return !data.isEmpty();
    }

    public Boxplot getBoxplot() {
        assertFalse(data.isEmpty());
        double mean = getMean();
        double p5 = getPercentil(5);
        double p25 = getPercentil(25);
        double p50 = getPercentil(50);
        double p75 = getPercentil(75);
        double p95 = getPercentil(95);
        return new Boxplot(data.size(), mean, p5, p25, p50, p75, p95);
    }

    public double getMean() {
        refreshCache();
        return StatUtils.mean(dataAsCachedArray);
    }

    public double getVarianceRaw() {
        refreshCache();
        return StatUtils.variance(dataAsCachedArray);
    }

    public Variance getVariance() {
        double mean = getMean();
        double variance = getVarianceRaw();
        return new Variance(data.size(), mean, variance);
    }

    public double getPercentil(int percent) {
        refreshCache();
        Percentile p = new Percentile(percent);
        // sorting is implicitly done in the evaluate method
        return p.evaluate(dataAsCachedArray);
    }

    private void refreshCache() {
        if (dataAsCachedArray == null || data.size() != dataAsCachedArray.length) {
            dataAsCachedArray = new double[data.size()];
            int i = 0;
            for (Double d : data) {
                dataAsCachedArray[i++] = d;
            }
        }
    }

    public void addAll(BoxplotData other) {
        for (double v : other.data) {
            add(v);
        }
    }

    public double[] getRawValues() {
        refreshCache();
        return dataAsCachedArray;
    }

    public void addAll(double[] values) {
        for (double v : values) {
            add(v);
        }
    }

    public static BoxplotData from(double[] vs) {
        BoxplotData bd = new BoxplotData();
        bd.addAll(vs);
        return bd;
    }

    @Override
    public boolean equals(Object obj) {
        return EqualsBuilder.reflectionEquals(this, obj);
    }

    @Override
    public int hashCode() {
        return HashCodeBuilder.reflectionHashCode(this);
    }
}