bham.leakiest

Class Stats

java.lang.Object

bham.leakiest.Stats

public class Stats
extends java.lang.Object

This is the class that contains some general statistic methods.

Version:

1.4.5

Author:

Tom Chothia, Yusuke Kawamoto

Field Summary

Fields

Modifier and Type	Field and Description
static double[]	chiSquareValues95 χ-squared distribution.

Constructor Summary

Constructors

Constructor and Description
Stats()

Method Summary

Modifier and Type	Method and Description
static double	chiSqu95Interval(int freedom) Returns the upper bound for the 95% confidence interval for the χ-squared distribution with n degrees of freedom.
static double	chiSquare(int[] observedCounts, double[] expectedCounts) Returns χ-square value given observed counts and expected counts.
static double	chiSquare(int[] observedCounts, int[] expectedCounts) Returns χ-square value given observed counts and expected counts.
static double	G(int[] observedCounts, double[] expectedCounts) Returns G value given observed counts and expected counts.
static double	lowerBoundNormal95(double mean, double variance) Calculates the lower bound for a 95% confidence interval for a normal distribution with given mean and variance i.e., the value above which 97.5% of sample take.
static double	lowerBoundNormal95Lower(double mean, double variance) Calculates the lower bound for a 95% confidence interval for a normal distribution with given mean and variance, upper only i.e., the value above which 95% of sample take.
static int	maxIndex(java.util.ArrayList<java.lang.Integer> array) Returns an index that takes the maximum element in the array list.
static int	maxIndex(double[] array) Returns an index that takes the maximum element in the array.
static int	maxIndex(int[] array) Returns an index that takes the maximum element in the array.
static int	minIndex(java.util.ArrayList<java.lang.Integer> array) Returns an index that takes the minimum element in the array list.
static int	minIndex(double[] array) Returns an index that takes the minimum element in the array.
static int	minIndex(int[] array) Returns an index that takes the minimum element in the array.
static double	round(double n, int dp) Rounds the double n to dp decimal places.
static double	sdtDevSampled(double[] data) Calculates the standard deviation for sampled data.
static double	sdtDevSampled(double[] data, double sum) Calculates the standard deviation for sampled data, using: s = sqrt( (Sigma_i (x_i - x)^2) / (n-1) )
static double	upperBoundForZero(int freedom, int sampleSize) Calculates the upper bound for zero information leakage.
static double	upperBoundNormal95(double mean, double variance) Calculates the upper bound for a 95% confidence interval for a normal distribution with given mean and variance i.e., the value below which 97.5% of sample take.
static double	upperBoundNormal95Upper(double mean, double variance) Calculates the upper bound for a 95% confidence interval for a normal distribution with given mean and variance, lower only i.e., the value below which 95% of sample take.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

chiSquareValues95

public static double[] chiSquareValues95

χ-squared distribution. chiSquareValues95[i] is the 95% value for the χ-square distribution with i+1 degrees of freedom.

Constructor Detail

Stats

public Stats()

Method Detail

chiSqu95Interval

public static double chiSqu95Interval(int freedom)

Returns the upper bound for the 95% confidence interval for the χ-squared distribution with n degrees of freedom.

Parameters:

freedom - freedom

Returns:

the upper bound for the 95% confidence interval

Throws:

java.lang.IllegalArgumentException - If freedom ≤ 0

chiSquare

public static double chiSquare(int[] observedCounts,
                               double[] expectedCounts)

Returns χ-square value given observed counts and expected counts.

Parameters:

observedCounts - array of observed counts

expectedCounts - array of expected counts

Returns:

χ-square value

chiSquare

public static double chiSquare(int[] observedCounts,
                               int[] expectedCounts)

Returns χ-square value given observed counts and expected counts.

Parameters:

observedCounts - array of observed counts

expectedCounts - array of expected counts

Returns:

χ-square value

G

public static double G(int[] observedCounts,
                       double[] expectedCounts)

Returns G value given observed counts and expected counts.

Parameters:

observedCounts - array of observed counts

expectedCounts - array of expected counts

Returns:

G value

lowerBoundNormal95

public static double lowerBoundNormal95(double mean,
                                        double variance)

Calculates the lower bound for a 95% confidence interval for a normal distribution with given mean and variance i.e., the value above which 97.5% of sample take.

Parameters:

mean - mean

variance - variance

Returns:

the lower bound for a 95% confidence interval

lowerBoundNormal95Lower

public static double lowerBoundNormal95Lower(double mean,
                                             double variance)

Calculates the lower bound for a 95% confidence interval for a normal distribution with given mean and variance, upper only i.e., the value above which 95% of sample take.

Parameters:

mean - mean

variance - variance

Returns:

the lower bound for a (upper) 95% confidence interval

maxIndex

public static int maxIndex(java.util.ArrayList<java.lang.Integer> array)

Returns an index that takes the maximum element in the array list.

Parameters:

array - integer array list

Returns:

an index that takes the maximum element in array

maxIndex

public static int maxIndex(double[] array)

Returns an index that takes the maximum element in the array.

Parameters:

array - double array

Returns:

an index that takes the maximum element in array

maxIndex

public static int maxIndex(int[] array)

Returns an index that takes the maximum element in the array.

Parameters:

array - integer array

Returns:

an index that takes the maximum element in array

minIndex

public static int minIndex(java.util.ArrayList<java.lang.Integer> array)

Returns an index that takes the minimum element in the array list.

Parameters:

array - integer array list

Returns:

an index that takes the minimum element in array

minIndex

public static int minIndex(double[] array)

Returns an index that takes the minimum element in the array.

Parameters:

array - integer array

Returns:

an index that takes the minimum element in array

minIndex

public static int minIndex(int[] array)

Returns an index that takes the minimum element in the array.

Parameters:

array - integer array

Returns:

an index that takes the minimum element in array

round

public static double round(double n,
                           int dp)

Rounds the double n to dp decimal places.

Parameters:

n - double value

dp - decimal places

Returns:

the result of rounding the double n to dp decimal places.

Throws:

java.lang.IllegalArgumentException - If dp < 0

sdtDevSampled

public static double sdtDevSampled(double[] data)

Calculates the standard deviation for sampled data.

Parameters:

data - sampled data

Returns:

the standard deviation for sampled data

sdtDevSampled

public static double sdtDevSampled(double[] data,
                                   double sum)

Calculates the standard deviation for sampled data, using:
s = sqrt( (Sigma_i (x_i - x)^2) / (n-1) )

Parameters:

data - sampled data

sum - summation of the data

Returns:

standard deviation for sampled data

upperBoundForZero

public static double upperBoundForZero(int freedom,
                                       int sampleSize)

Calculates the upper bound for zero information leakage.

Parameters:

freedom - freedom

sampleSize - the number of samples

Returns:

upper bound for zero leakage

upperBoundNormal95

public static double upperBoundNormal95(double mean,
                                        double variance)

Calculates the upper bound for a 95% confidence interval for a normal distribution with given mean and variance i.e., the value below which 97.5% of sample take.

Parameters:

mean - mean

variance - variance

Returns:

the upper bound for a 95% confidence interval

upperBoundNormal95Upper

public static double upperBoundNormal95Upper(double mean,
                                             double variance)

Calculates the upper bound for a 95% confidence interval for a normal distribution with given mean and variance, lower only i.e., the value below which 95% of sample take.

Parameters:

mean - mean

variance - variance

Returns:

the upper bound for a (lower) 95% confidence interval