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Base classes for the Cognitive Foundry including generally utilities and linear algebra.
/*
* File: UnivariateSummaryStatistics.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Apr 22, 2010, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.math;
import gov.sandia.cognition.collection.CollectionUtil;
import gov.sandia.cognition.collection.NumberComparator;
import gov.sandia.cognition.util.AbstractCloneableSerializable;
import gov.sandia.cognition.util.Pair;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
/**
* A Bayesian-style synopsis of a Collection of scalar data.
* @author Kevin R. Dixon
* @since 3.0
*/
public class UnivariateSummaryStatistics
extends AbstractCloneableSerializable
{
/**
* Region of the confidence interval, {@value}.
*/
public static final double CONFIDENCE_REGION = 0.95;
/**
* Min
*/
private double min;
/**
* Max
*/
private double max;
/**
* Quintile boundaries
*/
private double[] quintiles;
/**
* Lower 95% confidence region (alpha=0.025)
*/
private double confidenceLower;
/**
* Upper 95% confidence region (alpha=0.975)
*/
private double confidenceUpper;
/**
* Median
*/
private double median;
/**
* Number of samples
*/
private int numSamples;
/**
* Arithmetic mean
*/
private double mean;
/**
* Variance
*/
private double variance;
/**
* Skew
*/
private double skewness;
/**
* Excess kurtosis
*/
private double kurtosis;
/**
* Creates a new set of scalar summary statistics.
*
* @param min
* The minimum.
* @param max
* The maximum.
* @param quintiles
* The quintiles.
* @param confidenceLower
* The lower bound of the confidence.
* @param confidenceUpper
* The upper bound of the confidence.
* @param median
* The median.
* @param numSamples
* The number of samples.
* @param mean
* The mean.
* @param variance
* The variance.
* @param skewness
* The skewness.
* @param kurtosis
* The kurtosis.
*/
protected UnivariateSummaryStatistics(
double min,
double max,
double[] quintiles,
double confidenceLower,
double confidenceUpper,
double median,
int numSamples,
double mean,
double variance,
double skewness,
double kurtosis)
{
this.min = min;
this.max = max;
this.quintiles = quintiles;
this.confidenceLower = confidenceLower;
this.confidenceUpper = confidenceUpper;
this.median = median;
this.numSamples = numSamples;
this.mean = mean;
this.variance = variance;
this.skewness = skewness;
this.kurtosis = kurtosis;
}
@Override
public UnivariateSummaryStatistics clone()
{
UnivariateSummaryStatistics clone =
(UnivariateSummaryStatistics) super.clone();
return clone;
}
/**
* Creates a new instance of UnivariateSummaryStatistics from a Collection
* of scalar values.
* @param data
* Data from which to cull the results
* @return
* UnivariateSummaryStatistics describing the data
*/
public static UnivariateSummaryStatistics create(
Collection data )
{
ArrayList sortedData =
CollectionUtil.asArrayList(data);
Collections.sort(sortedData,NumberComparator.INSTANCE);
Pair result = UnivariateStatisticsUtil.computeMinAndMax(sortedData);
double min = result.getFirst();
double max = result.getSecond();
double median = UnivariateStatisticsUtil.computeMedian(sortedData);
double[] quintiles = new double[ 4 ];
quintiles[0] = UnivariateStatisticsUtil.computePercentile(sortedData, 0.2);
quintiles[1] = UnivariateStatisticsUtil.computePercentile(sortedData, 0.4);
quintiles[2] = UnivariateStatisticsUtil.computePercentile(sortedData, 0.6);
quintiles[3] = UnivariateStatisticsUtil.computePercentile(sortedData, 0.8);
double a2 = (1.0-CONFIDENCE_REGION)/2.0;
double confidenceLower = UnivariateStatisticsUtil.computePercentile(sortedData,a2);
double confidenceUpper = UnivariateStatisticsUtil.computePercentile(sortedData,1.0-a2);
int numSamples = data.size();
result = UnivariateStatisticsUtil.computeMeanAndVariance(sortedData);
double mean = result.getFirst();
double variance = result.getSecond();
double skewness = UnivariateStatisticsUtil.computeSkewness(sortedData);
double kurtosis = UnivariateStatisticsUtil.computeKurtosis(sortedData);
return new UnivariateSummaryStatistics(min, max, quintiles, confidenceLower,
confidenceUpper, median, numSamples, mean, variance, skewness,
kurtosis);
}
@Override
public String toString()
{
StringBuilder retval = new StringBuilder( 1000 );
retval.append( "Sample Count = " ).append( this.getNumSamples() );
retval.append( "\n" );
retval.append( "20/80 Region = [" ).append( this.getQuintiles()[0] ).append( ", " ).append( this.getQuintiles()[3] ).append( "]" );
retval.append( "\n" );
retval.append( "40/60 Region = [" ).append( this.getQuintiles()[1] ).append( ", " ).append( this.getQuintiles()[2] ).append( "]" );
retval.append( "\n" );
retval.append( "95% Region = [" ).append( this.getConfidenceLower() ).append( ", " ).append( this.getConfidenceUpper() ).append( "]" );
retval.append( "\n" );
retval.append( "Min and Max = [" ).append( this.getMin() ).append( ", " ).append( this.getMax() ).append( "]" );
retval.append( "\n" );
retval.append( "Median = " ).append( this.getMedian() );
retval.append( "\n" );
retval.append( "Mean = " ).append( this.getMean() ).append( ", StdDev = " ).append( Math.sqrt(this.getVariance()) );
retval.append( "\n" );
retval.append( "Skewness = " ).append( this.getSkewness() ).append( ", Kurtosis = " ).append( this.getKurtosis() );
retval.append( "\n" );
return retval.toString();
}
/**
* Getter for mean
* @return
* Mean
*/
public double getMean()
{
return this.mean;
}
/**
* Getter for variance
* @return
* Variance
*/
public double getVariance()
{
return this.variance;
}
/**
* Getter for skewness
* @return
* Skewness
*/
public double getSkewness()
{
return this.skewness;
}
/**
* Getter for Kurtosis
* @return
* Excess kurtosis
*/
public double getKurtosis()
{
return this.kurtosis;
}
/**
* Getter for numSamples
* @return
* Number of samples
*/
public int getNumSamples()
{
return this.numSamples;
}
/**
* Getter for median
* @return
* Median
*/
public double getMedian()
{
return this.median;
}
/**
* Getter for min
* @return Min
*/
public double getMin()
{
return this.min;
}
/**
* Getter for max
* @return Max
*/
public double getMax()
{
return this.max;
}
/**
* Getter for quintiles
* @return
* Quintile boundaries
*/
public double[] getQuintiles()
{
return this.quintiles;
}
/**
* Getter for confidenceLower
* @return
* Lower 95% confidence region (alpha=0.025)
*/
public double getConfidenceLower()
{
return this.confidenceLower;
}
/**
* Getter for confidenceUpper
* @return
* Upper 95% confidence region (alpha=0.975)
*/
public double getConfidenceUpper()
{
return this.confidenceUpper;
}
}