Author: Leesper
6.Pseudo-random number sampling
均匀分布 Uniform Distribution
伯努利分布 Bernoulli Distribution
卡方分布 Chi-Squared Distribution
Gamma分布 Gamma Distribution
Beta分布 Beta Distribution
费舍尔F分布 Fisher's F Distribution
柯西分布 Cauchy Distribution
韦伯分布 Weibull Distribution
Pareto分布 Pareto Distribution
对数高斯分布 Log Normal Distribution
指数分布 Exponential Distribution
学生T分布 Student's t-Distribution
二项分布 Binomial Distribution
泊松分布 Poisson Distribution
几何分布 Geometric Distribution
高斯分布 Gaussian Distribution
逻辑分布 Logistic Distribution
package rng
import "rng"
1) struct UniformGenerator
UniformGenerator is a random number generator for uniform
distribution. The zero value is invalid, use NewUniformGenerator to
create a generator
2) func NewUniformGenerator(seed int64) *UniformGenerator
NewUniformGenerator returns a uniform-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: urng
:= rng.NewUniformGenerator(time.Now().UnixNano())
3) func (ung UniformGenerator) Float32() float32
Float32 returns a random float32 in [0.0, 1.0)
4) func (ung UniformGenerator) Float32Range(a, b float32) float32
Float32Range returns a random float32 in [a, b)
5) func (ung UniformGenerator) Float32n(n float32) float32
Float32n returns a random float32 in [0.0, n)
6) func (ung UniformGenerator) Float64() float64
Float64 returns a random float64 in [0.0, 1.0)
7) func (ung UniformGenerator) Float64Range(a, b float64) float64
Float32Range returns a random float32 in [a, b)
8) func (ung UniformGenerator) Float64n(n float64) float64
Float64n returns a random float64 in [0.0, n)
9) func (ung UniformGenerator) Int32() int32
Int32 returns a random uint32
10) func (ung UniformGenerator) Int32Range(a, b int32) int32
Int32Range returns a random uint32 in [a, b)
11) func (ung UniformGenerator) Int32n(n int32) int32
Int32n returns a random uint32 in [0, n)
12) func (ung UniformGenerator) Int64() int64
Int64 returns a random uint64
13) func (ung UniformGenerator) Int64Range(a, b int64) int64
Int64Range returns a random uint64 in [a, b)
14) func (ung UniformGenerator) Int64n(n int64) int64
Int64n returns a random uint64 in [0, n)
15) func (ung UniformGenerator) Shuffle(arr []interface{})
Shuffle rearrange the elements of an array in random order
16) func (ung UniformGenerator) ShuffleRange(arr []interface{}, low, high int)
Shuffle rearrange the elements of the subarray[low..high] in random order
1) struct BernoulliGenerator
UniformGenerator is a random number generator for uniform distribution.
The zero value is invalid, use NewBernoulliGenerator to create a
generator
2) func NewBernoulliGenerator(seed int64) *BernoulliGenerator
NewBernoulliGenerator returns a bernoulli-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: urng
:= rng.NewBernoulliGenerator(time.Now().UnixNano())
3) func (beng BernoulliGenerator) Bernoulli() bool
bernoulli returns a bool, which is true with probablity 0.5
4) func (beng BernoulliGenerator) Bernoulli_P(p float32) bool
bernoulli_P returns a bool, which is true with probablity p
1) struct BinomialGenerator
BinomialGenerator is a random number generator for binomial
distribution. The zero value is invalid, use NewBinomialGenerator to
create a generator
2) func NewBinomialGenerator(seed int64) *BinomialGenerator
NewBinomialGenerator returns a binomial-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: urng
:= rng.NewBinomialGenerator(time.Now().UnixNano())
3) func (bing BinomialGenerator) Binomial(n int64, p float32) int64
Binomial returns a random number X ~ binomial(n, p)
1) struct GeometricGenerator
GeometricGenerator is a random number generator for geometric
distribution. The zero value is invalid, use NewGeometryGenerator to
create a generator
2) func NewGeometricGenerator(seed int64) *GeometricGenerator
NewGeometricGenerator returns a geometric-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: urng
:= rng.NewGeometricGenerator(time.Now().UnixNano())
3) func (grng GeometricGenerator) Geometric(p float64) int64
Geometric returns a random number X ~ binomial(n, p)
1) struct PoissonGenerator
PoissonGenerator is a random number generator for possion distribution.
The zero value is invalid, use NewPoissonGenerator to create a generator
2) func NewPoissonGenerator(seed int64) *PoissonGenerator
NewPoissonGenerator returns a possion-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: prng
:= rng.NewPoissonGenerator(time.Now().UnixNano())
3) func (prng PoissonGenerator) Possion(lambda float64) int64
Poisson returns a random number of possion distribution
1) struct ExpGenerator
ExpGenerator is a random number generator for exponential distribution.
The zero value is invalid, use NewExpGenerator to create a generator
2) func NewExpGenerator(seed int64) *ExpGenerator
NewExpGenerator returns a exponential-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: erng
:= rng.NewExpGenerator(time.Now().UnixNano())
3) func (erng ExpGenerator) Exp(lambda float64) float64
Exp returns a random number of exponential distribution
1) struct CauchyGenerator
CauchyGenerator is a random number generator for cauchy distribution.
The zero value is invalid, use NewCauchyGenerator to create a generator
2) func NewCauchyGenerator(seed int64) *CauchyGenerator
NewCauchyGenerator returns a cauchy-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: crng
:= rng.NewCauchyGenerator(time.Now().UnixNano())
3) func (crng CauchyGenerator) Cauchy(x0, gamma float64) float64
Cauchy returns a random number of cauchy distribution
4) func (crng CauchyGenerator) StandardCauchy() float64
StandardCauchy() returns a random number of standard cauchy distribution (x0 = 0.0, gamma = 1.0)
1) struct LogisticGenerator
LogisticGenerator is a random number generator for cauchy distribution.
The zero value is invalid, use NewLogisticGenerator to create a generator
2) func NewLogisticGenerator(seed int64) *LogisticGenerator
NewLogisticGenerator returns a logistic-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: lrng
:= rng.NewLogisticGenerator(time.Now().UnixNano())
3) func (lrng LogisticGenerator) Logistic(mu, s float64) float64
Logistic returns a random number of logistic distribution
1) struct GaussianGenerator
GaussianGenerator is a random number generator for gaussian
distribution. The zero value is invalid, use NewGaussianGenerator to
create a generator
2) func NewGaussianGenerator(seed int64) *GaussianGenerator
NewGaussianGenerator returns a gaussian-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: crng
:= rng.NewGaussianGenerator(time.Now().UnixNano())
3) func (grng GaussianGenerator) Gaussian(mean, stddev float64) float64
Gaussian returns a random number of gaussian distribution Gauss(mean, stddev^2)
4)func (grng GaussianGenerator) StdGaussian() float64
StdGaussian returns a random number of standard gaussian distribution
1) struct ParetoGenerator
ParetoGenerator is a random number generator for type I pareto
distribution. The zero value is invalid, use NewParetoGenerator to
create a generator
2) func NewParetoGenerator(seed int64) *ParetoGenerator
NewParetoGenerator returns a type I pareto-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: crng
:= rng.NewParetoGenerator(time.Now().UnixNano())
3) func (prng ParetoGenerator) Pareto(alpha float64) float64
Pareto returns a random number of type I pareto distribution (alpha > 0,0)
1) struct WeibullGenerator
WeibullGenerator is a random number generator for type weibull
distribution. The zero value is invalid, use NewWeibullGenerator to
create a generator
2) func NewWeibullGenerator(seed int64) *WeibullGenerator
NewWeibullGenerator returns a weibull-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: wrng
:= rng.NewWeibullGenerator(time.Now().UnixNano())
3) func (wrng WeibullGenerator) Weibull(lambda, k float64) float64
Weibull returns a random number of weibull distribution (lambda > 0.0 and k > 0.0)
1) struct GammaGenerator
GammaGenerator is a random number generator for gamma distribution. The
zero value is invalid, use NewGammaGenerator to create a generator
2) func NewGammaGenerator(seed int64) *GammaGenerator
NewGammaGenerator returns a type I pareto-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: grng
:= rng.NewGammaGenerator(time.Now().UnixNano())
3) func (grng GammaGenerator) Gamma(alpha, beta float64) float64
Gamma returns a random number of gamma distribution (alpha > 0.0 and beta > 0.0)
1) struct LognormalGenerator
LognormalGenerator is a random number generator for lognormal
distribution. The zero value is invalid, use NewLognormalGenerator to
create a generator
2) func NewLognormalGenerator(seed int64) *LognormalGenerator
NewLognormalGenerator returns a lognormal-distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: crng
:= rng.NewLognormalGenerator(time.Now().UnixNano())
3) func (lnng LognormalGenerator) Lognormal(mean, stddev float64) float64
Lognormal return a random number of lognormal distribution
1) struct BetaGenerator struct
BetaGenerator is a random number generator for beta distribution. The
zero value is invalid, use NewBetaGenerator to create a generator
2) func NewBetaGenerator(seed int64) *BetaGenerator
NewBetaGenerator returns a beta distribution generator it is recommended
using time.Now().UnixNano() as the seed, for example: brng :=
rng.NewBetaGenerator(time.Now().UnixNano())
3) func (brng BetaGenerator) Beta(alpha, beta float64) float64
Beta returns a random number of beta distribution (alpha > 0.0 and beta > 0.0)
1) struct ChiSquaredGenerator
ChiSquaredGenerator is a random number generator for chi-squared
distribution. The zero value is invalid, use NewChiSquaredGenerator to
create a generator
2) func NewChiSquaredGenerator(seed int64) *ChiSquaredGenerator
NewChiSquaredGenerator returns a chi-squared distribution generator it
is recommended using time.Now().UnixNano() as the seed, for example:
crng := rng.NewChiSquaredGenerator(time.Now().UnixNano())
3) func (crng ChiSquaredGenerator) ChiSquared(freedom int64) float64
ChiSquared returns a random number of chi-squared distribution
1) struct StudentTGenerator
StudentTGenerator is a random number generator for student-t
distribution. The zero value is invalid, use NewStudentTGenerator to
create a generator
2) func NewStudentTGenerator(seed int64) *StudentTGenerator
NewStudentTGenerator returns a student-t distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: stng
:= rng.NewStudentTGenerator(time.Now().UnixNano())
3) func (stng StudentTGenerator) Student(freedom int64) float64
Student returns a random number of student-t distribution (freedom > 0.0)
1) struct FisherFGenerator
FisherFGenerator is a random number generator for Fisher's F
distribution. The zero value is invalid, use NewFisherFGenerator to
create a generator
2) func NewFisherFGenerator(seed int64) *FisherFGenerator
NewFisherFGenerator returns a Fisher's F distribution generator it is
recommended using time.Now().UnixNano() as the seed, for example: frng
:= rng.NewFisherFGenerator(time.Now().UnixNano())
3) func (frng FisherFGenerator) Fisher(d1, d2 int64) float64
Fisher returns a random number of Fisher's F distribution (d1 > 0 and d2 > 0)