jhplot.math
Class StatisticSample

java.lang.Object
  jhplot.math.StatisticSample

public class StatisticSample
extends Object

A package to create random 1D and 2D arrays.

Author:

S.Chekanov and J.Richet

Constructor Summary

StatisticSample()

Method Summary

static double[][]	correlation(double[][] v) Correlation
static double[][]	correlation(double[][] v1, double[][] v2) Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)
static double	correlation(double[] v1, double[] v2) Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)
static double[][]	covariance(double[][] v) Covariance
static double[][]	covariance(double[][] v1, double[][] v2) Covariance
static double	covariance(double[] v1, double[] v2) Covariance
static double	mean(double[] v) Get mean value
static double[]	mean(double[][] v) Get mean
static double[]	randomBeta(int m, double a, double b) 1D Random Beta distribution
static double[][]	randomBeta(int m, int n, double a, double b) Random beata distribution
static double[]	randomCauchy(int m, double mu, double sigma) 1D Cauchy PDF
static double[][]	randomCauchy(int m, int n, double mu, double sigma) 2D Cauchy PDF
static double[]	randomChi2(int m, int d) 1D array with random numbers
static double[][]	randomChi2(int m, int n, int d) 2D array with Chi2
static double[]	randomDirac(int m, double[] values, double[] prob) 1D array with Dirac random values
static double[][]	randomDirac(int m, int n, double[] values, double[] prob) 2D array with Dirac random values
double[][]	randomDoubleArray(int rows, int columns, AbstractDistribution dist) Build 2D integer array list with integer numbers from input random number generator
DoubleArrayList	randomDoubleArrayList(int Ntot, AbstractDistribution dist) Build double array list with integer numbers from input random number generator
static double[]	randomExponential(int m, double lambda) 1D array with exponential numbers
static double[][]	randomExponential(int m, int n, double lambda) 2D array with exponential random distribution
static int[]	randomInt(int m, int i0, int i1) Random array with integers
static int[][]	randomInt(int m, int n, int i0, int i1) Random 2D array with integers
int[]	randomIntArray(int Ntot, Binomial dist) Build integer array list with integer numbers from input random number generator
int[][]	randomIntArray(int rows, int columns, AbstractDistribution dist) Build 2D integer array list with integer numbers from input random number generator
IntArrayList	randomIntArrayList(int Ntot, AbstractDistribution dist) Build integer array list with integer numbers from input random number generator
static double[]	randomLogNormal(int m, double mu, double sigma) 1D array with random Log-normal values
static double[][]	randomLogNormal(int m, int n, double mu, double sigma) 2D Log-normal distribution
static double[]	randomNormal(int m, double mu, double sigma) 1D array with Gaussian numbers
static double[][]	randomNormal(int m, int n, double mu, double sigma) 2D array with Gaussian numbers
static int[]	randomPoisson(int m, double mean) Build an array with Poisson distribution
static double[][]	randomRejection(int m, int n, graph.ParseFunction fun, double maxFun, double min, double max) Build 2D random array using analytic function.
static double[]	randomRejection(int m, graph.ParseFunction fun, double maxFun, double min, double max) Build 1D array using analytic function.
static double[]	randomTriangular(int m, double min, double max) 1D array with Triangular random PDF
static double[]	randomTriangular(int m, double min, double med, double max) 1D array for Triangular
static double[][]	randomTriangular(int m, int n, double min, double max) 2D array for Triangular random PDF
static double[][]	randomTriangular(int m, int n, double min, double med, double max) 2D array for Triangular
static double[]	randomWeibull(int m, double lambda, double c) 1D Weibull
static double[][]	randomWeibull(int m, int n, double lambda, double c) 2D Weibull
static double[]	randUniform(int m, double min, double max) 2D array with uniform values
static double[][]	randUniform(int m, int n, double min, double max) 2D array with random uniform values
static double	stddeviation(double[] v) Standard deviation
static double[]	stddeviation(double[][] v) Standard deviation
static double	variance(double[] v) Variance
static double[]	variance(double[][] v) Variance

Methods inherited from class java.lang.Object

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

Constructor Detail

StatisticSample

public StatisticSample()

Method Detail

randomInt

public static int[][] randomInt(int m,
                                int n,
                                int i0,
                                int i1)

Random 2D array with integers

Parameters:

m - Rows

n - Columns

i0 - Min value

i1 - max value

Returns:

2D array

randomInt

public static int[] randomInt(int m,
                              int i0,
                              int i1)

Random array with integers

Parameters:

m - array size

i0 - min value

i1 - max value

Returns:

array

randUniform

public static double[] randUniform(int m,
                                   double min,
                                   double max)

2D array with uniform values

Parameters:

m - Total number

min - Min value

max - Max value

Returns:

array

randUniform

public static double[][] randUniform(int m,
                                     int n,
                                     double min,
                                     double max)

2D array with random uniform values

Parameters:

m - Rows

n - Columns

min - Min value

max - Max value

Returns:

array

randomDirac

public static double[][] randomDirac(int m,
                                     int n,
                                     double[] values,
                                     double[] prob)

2D array with Dirac random values

Parameters:

m - Rows

n - Columns

values - Values for function

prob - Probabilities

Returns:

array

randomDirac

public static double[] randomDirac(int m,
                                   double[] values,
                                   double[] prob)

1D array with Dirac random values

Parameters:

m - Total number

values - array with values for the function

prob - probability

Returns:

array

randomPoisson

public static int[] randomPoisson(int m,
                                  double mean)

Build an array with Poisson distribution

Parameters:

mean - mean of Poisson distribution

randomNormal

public static double[][] randomNormal(int m,
                                      int n,
                                      double mu,
                                      double sigma)

2D array with Gaussian numbers

Parameters:

m - Rows

n - Columns

mu - mean

sigma - standard deviation

Returns:

array

randomNormal

public static double[] randomNormal(int m,
                                    double mu,
                                    double sigma)

1D array with Gaussian numbers

Parameters:

m - Total number

mu - mean

sigma - standard deviation

Returns:

array

randomChi2

public static double[][] randomChi2(int m,
                                    int n,
                                    int d)

2D array with Chi2

Parameters:

m - Rows

n - Columns

d - degrees of freedom

Returns:

array

randomChi2

public static double[] randomChi2(int m,
                                  int d)

1D array with random numbers

Parameters:

m - Total number

d - degree of freedoms

Returns:

array

randomLogNormal

public static double[][] randomLogNormal(int m,
                                         int n,
                                         double mu,
                                         double sigma)

2D Log-normal distribution

Parameters:

m - Rows

n - Columns

mu - mean

sigma - sigma

Returns:

array

randomLogNormal

public static double[] randomLogNormal(int m,
                                       double mu,
                                       double sigma)

1D array with random Log-normal values

Parameters:

m - total number

mu - mean

sigma - sigma

Returns:

array

randomExponential

public static double[][] randomExponential(int m,
                                           int n,
                                           double lambda)

2D array with exponential random distribution

Parameters:

m - Rows

n - Colums

lambda - lambda

Returns:

array

randomExponential

public static double[] randomExponential(int m,
                                         double lambda)

1D array with exponential numbers

Parameters:

m - total numbers

lambda - lambda

Returns:

array

randomTriangular

public static double[][] randomTriangular(int m,
                                          int n,
                                          double min,
                                          double max)

2D array for Triangular random PDF

Parameters:

m - Rows

n - Columns

min - Min

max - max

Returns:

array

randomTriangular

public static double[] randomTriangular(int m,
                                        double min,
                                        double max)

1D array with Triangular random PDF

Parameters:

m - total number

min - Min

max - max

Returns:

array

randomTriangular

public static double[][] randomTriangular(int m,
                                          int n,
                                          double min,
                                          double med,
                                          double max)

2D array for Triangular

Parameters:

m - Rows

n - Columns

min - Min

med - Median

max - Max

Returns:

array

randomTriangular

public static double[] randomTriangular(int m,
                                        double min,
                                        double med,
                                        double max)

1D array for Triangular

Parameters:

m - total number

min - Min

med - Median

max - Max

Returns:

array

randomBeta

public static double[][] randomBeta(int m,
                                    int n,
                                    double a,
                                    double b)

Random beata distribution

Parameters:

m - Rows

n - Columns

a - alpha

b - beta

Returns:

array

randomBeta

public static double[] randomBeta(int m,
                                  double a,
                                  double b)

1D Random Beta distribution

Parameters:

m - total number

a - alpha

b - beta

Returns:

array

randomCauchy

public static double[][] randomCauchy(int m,
                                      int n,
                                      double mu,
                                      double sigma)

2D Cauchy PDF

Parameters:

m - Rows

n - Colums

mu - Mean

sigma - Sigma

Returns:

array

randomCauchy

public static double[] randomCauchy(int m,
                                    double mu,
                                    double sigma)

1D Cauchy PDF

Parameters:

m - total number

mu - mean

sigma - sigma

Returns:

randomWeibull

public static double[][] randomWeibull(int m,
                                       int n,
                                       double lambda,
                                       double c)

2D Weibull

Parameters:

m - Rows

n - Columns

lambda - lambda

c - C

Returns:

array

randomWeibull

public static double[] randomWeibull(int m,
                                     double lambda,
                                     double c)

1D Weibull

Parameters:

m - Rows

lambda - lambda

c - C

Returns:

array

randomRejection

public static double[][] randomRejection(int m,
                                         int n,
                                         graph.ParseFunction fun,
                                         double maxFun,
                                         double min,
                                         double max)

Build 2D random array using analytic function. First build F1D, than get parse (getParse()) and use it as input for this method.

Parameters:

m - Number of points

fun - ParseFunction (get it as getParse() for F1D)

maxFun - max of the function

min - Min value in X

max - Max value in X

randomRejection

public static double[] randomRejection(int m,
                                       graph.ParseFunction fun,
                                       double maxFun,
                                       double min,
                                       double max)

Build 1D array using analytic function. First build F1D, than get parse (getParse()) and use it as input for this method.

Parameters:

m - Number of points

fun - ParseFunction (get it as getParse() for F1D)

maxFun - max of the function

min - Min value in X

max - Max value in X

mean

public static double mean(double[] v)

Get mean value

Parameters:

v - vector

mean

public static double[] mean(double[][] v)

Get mean

Parameters:

v - 2D array

Returns:

stddeviation

public static double stddeviation(double[] v)

Standard deviation

Parameters:

v - vector

Returns:

variance

public static double variance(double[] v)

Variance

Parameters:

v -

Returns:

vector

stddeviation

public static double[] stddeviation(double[][] v)

Standard deviation

Parameters:

v -

Returns:

variance

public static double[] variance(double[][] v)

Variance

Parameters:

v - vector

Returns:

covariance

public static double covariance(double[] v1,
                                double[] v2)

Covariance

Parameters:

v1 - first vector

v2 - second vector

Returns:

covariance

public static double[][] covariance(double[][] v1,
                                    double[][] v2)

Covariance

Parameters:

v1 - first 2D array

v2 - second 2D array

Returns:

covariance

public static double[][] covariance(double[][] v)

Covariance

Parameters:

v -

Returns:

correlation

public static double correlation(double[] v1,
                                 double[] v2)

Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)

Parameters:

v1 - first vector

v2 - second vector

Returns:

correlation

public static double[][] correlation(double[][] v1,
                                     double[][] v2)

Correlation coefficient, covariance(v1, v2) / Math.sqrt(variance(v1) * variance(v2)

Parameters:

v1 - first vector

v2 - second vector

Returns:

correlation

public static double[][] correlation(double[][] v)

Correlation

Parameters:

v -

Returns:

randomIntArrayList

public IntArrayList randomIntArrayList(int Ntot,
                                       AbstractDistribution dist)

Build integer array list with integer numbers from input random number generator

Parameters:

Ntot - total numbers

dist - input random number distribution

Returns:

array

randomIntArray

public int[] randomIntArray(int Ntot,
                            Binomial dist)

Build integer array list with integer numbers from input random number generator

Parameters:

Ntot - total numbers

dist - input random number distribution

Returns:

array

randomIntArray

public int[][] randomIntArray(int rows,
                              int columns,
                              AbstractDistribution dist)

Build 2D integer array list with integer numbers from input random number generator

Parameters:

rows - rows

colums - columns

dist - input random number distribution

Returns:

array

randomDoubleArray

public double[][] randomDoubleArray(int rows,
                                    int columns,
                                    AbstractDistribution dist)

Build 2D integer array list with integer numbers from input random number generator

Parameters:

rows - rows

colums - columns

dist - input random number distribution

Returns:

array

randomDoubleArrayList

public DoubleArrayList randomDoubleArrayList(int Ntot,
                                             AbstractDistribution dist)

Build double array list with integer numbers from input random number generator

Parameters:

Ntot -

dist -

Returns:

array