data.DataSet Maven / Gradle / Ivy
/*
* Copyright (c) 2016 Jacob Rachiele
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of this software
* and associated documentation files (the "Software"), to deal in the Software without restriction
* including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense
* and/or sell copies of the Software, and to permit persons to whom the Software is furnished to
* do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all copies or
* substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED
* INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
* PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
* LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
* USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* Contributors:
*
* Jacob Rachiele
*/
package data;
import java.awt.Color;
import java.text.DecimalFormat;
import java.util.Arrays;
import org.knowm.xchart.SwingWrapper;
import org.knowm.xchart.XChartPanel;
import org.knowm.xchart.XYChart;
import org.knowm.xchart.XYChartBuilder;
import org.knowm.xchart.XYSeries.XYSeriesRenderStyle;
import org.knowm.xchart.style.Styler.ChartTheme;
import stats.Statistics;
import javax.swing.*;
/**
* A collection of numerical observations. This class is immutable and all subclasses must be immutable.
*
* @author Jacob Rachiele
*/
public class DataSet {
private final double[] data;
/**
* Construct a new data set from the given data.
*
* @param data the collection of observations.
*/
public DataSet(final double... data) {
if (data == null) {
throw new IllegalArgumentException("Null array passed to constructor.");
}
this.data = data.clone();
}
/**
* The sum of the observations.
*
* @return the sum of the observations.
*/
public final double sum() {
return Statistics.sumOf(this.data);
}
/**
* The sum of the squared observations.
*
* @return the sum of the squared observations.
*/
public final double sumOfSquares() {
return Statistics.sumOfSquared(this.data);
}
/**
* The mean of the observations.
*
* @return the mean of the observations.
*/
public final double mean() {
return Statistics.meanOf(this.data);
}
/**
* The median value of the observations.
*
* @return the median value of the observations.
*/
public final double median() {
return Statistics.medianOf(this.data);
}
/**
* The size of the data set.
*
* @return the size of the data set.
*/
public final int n() {
return this.data.length;
}
/**
* Multiply every element of this data set with the corresponding element of the given data set.
*
* @param otherData The data to multiply by.
* @return A new data set containing every element of this data set multiplied by
* the corresponding element of the given data set.
*/
public final DataSet times(final DataSet otherData) {
return new DataSet(Operators.productOf(this.data, otherData.data));
}
/**
* Add every element of this data set to the corresponding element of the given data set.
*
* @param otherData The data to add to.
* @return A new data set containing every element of this data set added to
* the corresponding element of the given data set.
*/
public final DataSet plus(final DataSet otherData) {
return new DataSet(Operators.sumOf(this.data, otherData.data));
}
/**
* The unbiased sample variance of the observations.
*
* @return the unbiased sample variance of the observations.
*/
public final double variance() {
return Statistics.varianceOf(this.data);
}
/**
* The unbiased sample standard deviation of the observations.
*
* @return the unbiased sample standard deviation of the observations.
*/
public final double stdDeviation() {
return Statistics.stdDeviationOf(this.data);
}
/**
* The unbiased sample covariance of these observations with the observations
* contained in the given data set.
*
* @param otherData the data to compute the covariance with.
* @return the unbiased sample covariance of these observations with the observations
* contained in the given data set.
*/
public final double covariance(final DataSet otherData) {
return Statistics.covarianceOf(this.data, otherData.data);
}
/**
* The unbiased sample correlation of these observations with the observations
* contained in the given data set.
*
* @param otherData the data to compute the correlation coefficient with.
* @return the unbiased sample correlation of these observations with the observations
* contained in the given data set.
*/
public final double correlation(DataSet otherData) {
return Statistics.correlationOf(this.data, otherData.data);
}
/**
* The observations.
*
* @return the observations.
*/
public final double[] data() {
return this.data.clone();
}
/**
* Plot this data set. This method will produce a scatter plot of the data values against the integers
* from 0 to n - 1, where n is the size of the data set.
*/
public void plot() {
new Thread(() -> {
final double[] indices = new double[this.data.length];
for (int i = 0; i < indices.length; i++) {
indices[i] = i;
}
XYChart chart = new XYChartBuilder().theme(ChartTheme.GGPlot2).
title("Scatter Plot").xAxisTitle("Index").yAxisTitle("Values").build();
chart.getStyler().setDefaultSeriesRenderStyle(XYSeriesRenderStyle.Scatter).
setChartFontColor(Color.BLACK).setSeriesColors(new Color[]{Color.BLUE});
chart.addSeries("data", indices, data);
JPanel panel = new XChartPanel<>(chart);
JFrame frame = new JFrame("Data Set");
frame.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
frame.add(panel);
frame.pack();
frame.setVisible(true);
}).run();
}
/**
* Plot this data set against the given data set. The given data set will be plotted on the x-axis, while
* this data set will be plotted on the y-axis.
*
* @param otherData the data set to plot this data set against.
*/
public void plotAgainst(final DataSet otherData) {
new Thread(() -> {
XYChart chart = new XYChartBuilder().theme(ChartTheme.GGPlot2).height(600).width(800)
.title("Scatter Plot").xAxisTitle("X").yAxisTitle("Y").build();
chart.getStyler().setDefaultSeriesRenderStyle(XYSeriesRenderStyle.Scatter).
setChartFontColor(Color.DARK_GRAY).setSeriesColors(new Color[]{Color.BLUE});
chart.addSeries("Y against X", otherData.data, this.data);
JPanel panel = new XChartPanel<>(chart);
JFrame frame = new JFrame("Scatter Plot");
frame.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
frame.add(panel);
frame.pack();
frame.setVisible(true);
}).run();
}
@Override
public String toString() {
DecimalFormat df = new DecimalFormat("0.##");
return "\nValues: " + Arrays.toString(data) + "\nLength: " + data.length + "\nMean: " + mean() +
"\nStandard deviation: " + df.format(stdDeviation());
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + Arrays.hashCode(data);
return result;
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
DataSet other = (DataSet) obj;
return Arrays.equals(data, other.data);
}
}
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