nc::random::RNG< GeneratorType > Class Template Reference

#include <RNG.hpp>

Public Member Functions
	RNG ()=default
	RNG (int seed)
NdArray< bool >	bernoulli (const Shape &inShape, double inP=0.5)
bool	bernoulli (double inP=0.5)
template<typename dtype >
NdArray< dtype >	beta (const Shape &inShape, dtype inAlpha, dtype inBeta)
template<typename dtype >
dtype	beta (dtype inAlpha, dtype inBeta)
template<typename dtype >
NdArray< dtype >	binomial (const Shape &inShape, dtype inN, double inP=0.5)
template<typename dtype >
dtype	binomial (dtype inN, double inP=0.5)
template<typename dtype >
NdArray< dtype >	cauchy (const Shape &inShape, dtype inMean=0, dtype inSigma=1)
template<typename dtype >
dtype	cauchy (dtype inMean=0, dtype inSigma=1)
template<typename dtype >
NdArray< dtype >	chiSquare (const Shape &inShape, dtype inDof)
template<typename dtype >
dtype	chiSquare (dtype inDof)
template<typename dtype >
dtype	choice (const NdArray< dtype > &inArray)
template<typename dtype >
NdArray< dtype >	choice (const NdArray< dtype > &inArray, uint32 inNum, Replace replace=Replace::YES)
template<typename dtype >
dtype	discrete (const NdArray< double > &inWeights)
template<typename dtype >
NdArray< dtype >	discrete (const Shape &inShape, const NdArray< double > &inWeights)
template<typename dtype >
NdArray< dtype >	exponential (const Shape &inShape, dtype inScaleValue=1)
template<typename dtype >
dtype	exponential (dtype inScaleValue=1)
template<typename dtype >
NdArray< dtype >	extremeValue (const Shape &inShape, dtype inA=1, dtype inB=1)
template<typename dtype >
dtype	extremeValue (dtype inA=1, dtype inB=1)
template<typename dtype >
NdArray< dtype >	f (const Shape &inShape, dtype inDofN, dtype inDofD)
template<typename dtype >
dtype	f (dtype inDofN, dtype inDofD)
template<typename dtype >
NdArray< dtype >	gamma (const Shape &inShape, dtype inGammaShape, dtype inScaleValue=1)
template<typename dtype >
dtype	gamma (dtype inGammaShape, dtype inScaleValue=1)
template<typename dtype >
NdArray< dtype >	geometric (const Shape &inShape, double inP=0.5)
template<typename dtype >
dtype	geometric (double inP=0.5)
template<typename dtype >
NdArray< dtype >	laplace (const Shape &inShape, dtype inLoc=0, dtype inScale=1)
template<typename dtype >
dtype	laplace (dtype inLoc=0, dtype inScale=1)
template<typename dtype >
NdArray< dtype >	lognormal (const Shape &inShape, dtype inMean=0, dtype inSigma=1)
template<typename dtype >
dtype	lognormal (dtype inMean=0, dtype inSigma=1)
template<typename dtype >
NdArray< dtype >	negativeBinomial (const Shape &inShape, dtype inN, double inP=0.5)
template<typename dtype >
dtype	negativeBinomial (dtype inN, double inP=0.5)
template<typename dtype >
NdArray< dtype >	nonCentralChiSquared (const Shape &inShape, dtype inK=1, dtype inLambda=1)
template<typename dtype >
dtype	nonCentralChiSquared (dtype inK=1, dtype inLambda=1)
template<typename dtype >
NdArray< dtype >	normal (const Shape &inShape, dtype inMean=0, dtype inSigma=1)
template<typename dtype >
dtype	normal (dtype inMean=0, dtype inSigma=1)
template<typename dtype >
NdArray< dtype >	permutation (const NdArray< dtype > &inArray)
template<typename dtype >
NdArray< dtype >	permutation (dtype inValue)
template<typename dtype >
NdArray< dtype >	poisson (const Shape &inShape, double inMean=1)
template<typename dtype >
dtype	poisson (double inMean=1)
template<typename dtype >
dtype	rand ()
template<typename dtype >
NdArray< dtype >	rand (const Shape &inShape)
template<typename dtype >
NdArray< dtype >	randFloat (const Shape &inShape, dtype inLow, dtype inHigh=0.)
template<typename dtype >
dtype	randFloat (dtype inLow, dtype inHigh=0.)
template<typename dtype >
NdArray< dtype >	randInt (const Shape &inShape, dtype inLow, dtype inHigh=0)
template<typename dtype >
dtype	randInt (dtype inLow, dtype inHigh=0)
template<typename dtype >
dtype	randN ()
template<typename dtype >
NdArray< dtype >	randN (const Shape &inShape)
void	seed (int value) noexcept
template<typename dtype >
void	shuffle (NdArray< dtype > &inArray)
template<typename dtype >
dtype	standardNormal ()
template<typename dtype >
NdArray< dtype >	standardNormal (const Shape &inShape)
template<typename dtype >
NdArray< dtype >	studentT (const Shape &inShape, dtype inDof)
template<typename dtype >
dtype	studentT (dtype inDof)
template<typename dtype >
NdArray< dtype >	triangle (const Shape &inShape, dtype inA=0, dtype inB=0.5, dtype inC=1)
template<typename dtype >
dtype	triangle (dtype inA=0, dtype inB=0.5, dtype inC=1)
template<typename dtype >
NdArray< dtype >	uniform (const Shape &inShape, dtype inLow, dtype inHigh)
template<typename dtype >
dtype	uniform (dtype inLow, dtype inHigh)
template<typename dtype >
NdArray< dtype >	uniformOnSphere (uint32 inNumPoints, uint32 inDims=2)
template<typename dtype >
NdArray< dtype >	weibull (const Shape &inShape, dtype inA=1, dtype inB=1)
template<typename dtype >
dtype	weibull (dtype inA=1, dtype inB=1)

Detailed Description

template<typename GeneratorType = std::mt19937_64>
class nc::random::RNG< GeneratorType >

Random Number Generater Class with non-global state

Constructor & Destructor Documentation

RNG() [1/2]

template<typename GeneratorType = std::mt19937_64>

nc::random::RNG< GeneratorType >::RNG ( )

default

Defualt Constructor

RNG() [2/2]

template<typename GeneratorType = std::mt19937_64>

nc::random::RNG< GeneratorType >::RNG ( int  seed )

inlineexplicit

Seed Constructor

Parameters

seed	the seed value

Member Function Documentation

bernoulli() [1/2]

template<typename GeneratorType = std::mt19937_64>

NdArray< bool > nc::random::RNG< GeneratorType >::bernoulli ( const Shape &  inShape, double  inP = 0.5  )

inline

Create an array of the given shape and populate it with random samples from the "bernoulli" distribution.

Parameters

inShape
inP	(probability of success [0, 1]). Default 0.5

Returns

NdArray

bernoulli() [2/2]

template<typename GeneratorType = std::mt19937_64>

bool nc::random::RNG< GeneratorType >::bernoulli ( double  inP = 0.5 )

inline

Single random value sampled from the "bernoulli" distribution.

Parameters

inP	(probability of success [0, 1]). Default 0.5

Returns

NdArray

beta() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::beta ( const Shape &  inShape, dtype  inAlpha, dtype  inBeta  )

inline

Create an array of the given shape and populate it with random samples from the "beta" distribution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.beta.html#numpy.random.beta

Parameters

inShape
inAlpha
inBeta

Returns

NdArray

beta() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::beta ( dtype  inAlpha, dtype  inBeta  )

inline

Single random value sampled from the from the "beta" distribution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.beta.html#numpy.random.beta

Parameters

inAlpha
inBeta

Returns

NdArray

binomial() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::binomial ( const Shape &  inShape, dtype  inN, double  inP = 0.5  )

inline

Create an array of the given shape and populate it with random samples from the "binomial" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.binomial.html#numpy.random.binomial

Parameters

inShape
inN	(number of trials)
inP	(probablity of success [0, 1])

Returns

NdArray

binomial() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::binomial ( dtype  inN, double  inP = 0.5  )

inline

Single random value sampled from the from the "binomial" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.binomial.html#numpy.random.binomial

Parameters

inN	(number of trials)
inP	(probablity of success [0, 1])

Returns

NdArray

cauchy() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::cauchy ( const Shape &  inShape, dtype  inMean = 0, dtype  inSigma = 1  )

inline

Create an array of the given shape and populate it with random samples from a "cauchy" distrubution.

Parameters

inShape
inMean	Mean value of the underlying normal distribution. Default is 0.
inSigma	Standard deviation of the underlying normal distribution. Should be greater than zero. Default is 1.

Returns

NdArray

cauchy() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::cauchy ( dtype  inMean = 0, dtype  inSigma = 1  )

inline

Single random value sampled from the from the "cauchy" distrubution.

Parameters

inMean	Mean value of the underlying normal distribution. Default is 0.
inSigma	Standard deviation of the underlying normal distribution. Should be greater than zero. Default is 1.

Returns

NdArray

chiSquare() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::chiSquare ( const Shape &  inShape, dtype  inDof  )

inline

Create an array of the given shape and populate it with random samples from the "chi square" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.chisquare.html#numpy.random.chisquare

Parameters

inShape
inDof	(independent random variables)

Returns

NdArray

chiSquare() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::chiSquare ( dtype  inDof )

inline

Single random value sampled from the from the "chi square" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.chisquare.html#numpy.random.chisquare

Parameters

inDof

(independent random variables)

Returns

NdArray

choice() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::choice ( const NdArray< dtype > &  inArray )

inline

Chooses a random sample from an input array.

Parameters

inArray

Returns

NdArray

choice() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::choice ( const NdArray< dtype > &  inArray, uint32  inNum, Replace  replace = Replace::YES  )

inline

Chooses inNum random samples from an input array.

Parameters

inArray
inNum
replace	Whether the sample is with or without replacement

Returns

NdArray

discrete() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::discrete ( const NdArray< double > &  inWeights )

inline

Single random value sampled from the from the "discrete" distrubution. It produces integers in the range [0, n) with the probability of producing each value is specified by the parameters of the distribution.

Parameters

inWeights

Returns

NdArray

discrete() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::discrete ( const Shape &  inShape, const NdArray< double > &  inWeights  )

inline

Create an array of the given shape and populate it with random samples from a "discrete" distrubution. It produces integers in the range [0, n) with the probability of producing each value is specified by the parameters of the distribution.

Parameters

inShape
inWeights

Returns

NdArray

exponential() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::exponential ( const Shape &  inShape, dtype  inScaleValue = 1  )

inline

Create an array of the given shape and populate it with random samples from a "exponential" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.exponential.html#numpy.random.exponential

Parameters

inShape
inScaleValue	(default 1)

Returns

NdArray

exponential() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::exponential ( dtype  inScaleValue = 1 )

inline

Single random value sampled from the "exponential" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.exponential.html#numpy.random.exponential

Parameters

inScaleValue

(default 1)

Returns

NdArray

extremeValue() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::extremeValue ( const Shape &  inShape, dtype  inA = 1, dtype  inB = 1  )

inline

Create an array of the given shape and populate it with random samples from a "extreme value" distrubution.

Parameters

inShape
inA	(default 1)
inB	(default 1)

Returns

NdArray

extremeValue() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::extremeValue ( dtype  inA = 1, dtype  inB = 1  )

inline

Single random value sampled from the "extreme value" distrubution.

Parameters

inA	(default 1)
inB	(default 1)

Returns

NdArray

f() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::f ( const Shape &  inShape, dtype  inDofN, dtype  inDofD  )

inline

Create an array of the given shape and populate it with random samples from a "F" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.f.html#numpy.random.f

Parameters

inShape
inDofN	Degrees of freedom in numerator. Should be greater than zero.
inDofD	Degrees of freedom in denominator. Should be greater than zero.

Returns

NdArray

f() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::f ( dtype  inDofN, dtype  inDofD  )

inline

Single random value sampled from the "F" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.f.html#numpy.random.f

Parameters

inDofN	Degrees of freedom in numerator. Should be greater than zero.
inDofD	Degrees of freedom in denominator. Should be greater than zero.

Returns

NdArray

gamma() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::gamma ( const Shape &  inShape, dtype  inGammaShape, dtype  inScaleValue = 1  )

inline

Create an array of the given shape and populate it with random samples from a "gamma" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.gamma.html#numpy.random.gamma

Parameters

inShape
inGammaShape
inScaleValue	(default 1)

Returns

NdArray

gamma() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::gamma ( dtype  inGammaShape, dtype  inScaleValue = 1  )

inline

Single random value sampled from the "gamma" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.gamma.html#numpy.random.gamma

Parameters

inGammaShape
inScaleValue	(default 1)

Returns

NdArray

geometric() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::geometric ( const Shape &  inShape, double  inP = 0.5  )

inline

Create an array of the given shape and populate it with random samples from a "geometric" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.geometric.html#numpy.random.geometric

Parameters

inShape
inP	(probablity of success [0, 1])

Returns

NdArray

geometric() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::geometric ( double  inP = 0.5 )

inline

Single random value sampled from the "geometric" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.geometric.html#numpy.random.geometric

Parameters

inP	(probablity of success [0, 1])

Returns

NdArray

laplace() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::laplace ( const Shape &  inShape, dtype  inLoc = 0, dtype  inScale = 1  )

inline

Create an array of the given shape and populate it with random samples from a "laplace" distrubution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.laplace.html#numpy.random.laplace

Parameters

inShape
inLoc	(The position, mu, of the distribution peak. Default is 0)
inScale	(float optional the exponential decay. Default is 1)

Returns

NdArray

laplace() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::laplace ( dtype  inLoc = 0, dtype  inScale = 1  )

inline

Single random value sampled from the "laplace" distrubution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.laplace.html#numpy.random.laplace

Parameters

inLoc	(The position, mu, of the distribution peak. Default is 0)
inScale	(float optional the exponential decay. Default is 1)

Returns

NdArray

lognormal() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::lognormal ( const Shape &  inShape, dtype  inMean = 0, dtype  inSigma = 1  )

inline

Create an array of the given shape and populate it with random samples from a "lognormal" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.lognormal.html#numpy.random.lognormal

Parameters

inShape
inMean	Mean value of the underlying normal distribution. Default is 0.
inSigma	Standard deviation of the underlying normal distribution. Should be greater than zero. Default is 1.

Returns

NdArray

lognormal() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::lognormal ( dtype  inMean = 0, dtype  inSigma = 1  )

inline

Single random value sampled from the "lognormal" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.lognormal.html#numpy.random.lognormal

Parameters

inMean	Mean value of the underlying normal distribution. Default is 0.
inSigma	Standard deviation of the underlying normal distribution. Should be greater than zero. Default is 1.

Returns

NdArray

negativeBinomial() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::negativeBinomial ( const Shape &  inShape, dtype  inN, double  inP = 0.5  )

inline

Create an array of the given shape and populate it with random samples from the "negative Binomial" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.negative_binomial.html#numpy.random.negative_binomial

Parameters

inShape
inN	number of trials
inP	probablity of success [0, 1]

Returns

NdArray

negativeBinomial() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::negativeBinomial ( dtype  inN, double  inP = 0.5  )

inline

Single random value sampled from the "negative Binomial" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.negative_binomial.html#numpy.random.negative_binomial

Parameters

inN	number of trials
inP	probablity of success [0, 1]

Returns

NdArray

nonCentralChiSquared() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::nonCentralChiSquared ( const Shape &  inShape, dtype  inK = 1, dtype  inLambda = 1  )

inline

Create an array of the given shape and populate it with random samples from a "non central chi squared" distrubution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.noncentral_chisquare.html#numpy.random.noncentral_chisquare

Parameters

inShape
inK	(default 1)
inLambda	(default 1)

Returns

NdArray

nonCentralChiSquared() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::nonCentralChiSquared ( dtype  inK = 1, dtype  inLambda = 1  )

inline

Single random value sampled from the "non central chi squared" distrubution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.noncentral_chisquare.html#numpy.random.noncentral_chisquare

Parameters

inK	(default 1)
inLambda	(default 1)

Returns

NdArray

normal() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::normal ( const Shape &  inShape, dtype  inMean = 0, dtype  inSigma = 1  )

inline

Create an array of the given shape and populate it with random samples from a "normal" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html#numpy.random.normal

Parameters

inShape
inMean	Mean value of the underlying normal distribution. Default is 0.
inSigma	Standard deviation of the underlying normal distribution. Should be greater than zero. Default is 1.

Returns

NdArray

normal() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::normal ( dtype  inMean = 0, dtype  inSigma = 1  )

inline

Single random value sampled from the "normal" distrubution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html#numpy.random.normal

Parameters

inMean	Mean value of the underlying normal distribution. Default is 0.
inSigma	Standard deviation of the underlying normal distribution. Should be greater than zero. Default is 1.

Returns

NdArray

permutation() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::permutation ( const NdArray< dtype > &  inArray )

inline

Randomly permute a sequence, or return a permuted range. If x is an integer, randomly permute np.arange(x). If x is an array, make a copy and shuffle the elements randomly.

Parameters

inArray

Returns

NdArray

permutation() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::permutation ( dtype  inValue )

inline

Randomly permute a sequence, or return a permuted range. If x is an integer, randomly permute np.arange(x). If x is an array, make a copy and shuffle the elements randomly.

Parameters

inValue

Returns

NdArray

poisson() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::poisson ( const Shape &  inShape, double  inMean = 1  )

inline

Create an array of the given shape and populate it with random samples from the "poisson" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.poisson.html#numpy.random.poisson

Parameters

inShape
inMean	(default 1)

Returns

NdArray

poisson() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::poisson ( double  inMean = 1 )

inline

Single random value sampled from the "poisson" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.poisson.html#numpy.random.poisson

Parameters

inMean

(default 1)

Returns

NdArray

rand() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::rand ( )

inline

Single random value sampled from the uniform distribution over [0, 1).

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rand.html#numpy.random.rand

Returns

NdArray

rand() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::rand ( const Shape &  inShape )

inline

Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rand.html#numpy.random.rand

Parameters

inShape

Returns

NdArray

randFloat() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::randFloat ( const Shape &  inShape, dtype  inLow, dtype  inHigh = 0.  )

inline

Return random floats from low (inclusive) to high (exclusive), with the given shape. If no high value is input then the range will go from [0, low).

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.ranf.html#numpy.random.ranf

Parameters

inShape
inLow
inHigh	default 0.

Returns

NdArray

randFloat() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::randFloat ( dtype  inLow, dtype  inHigh = 0.  )

inline

Return a single random float from low (inclusive) to high (exclusive), with the given shape. If no high value is input then the range will go from [0, low).

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.ranf.html#numpy.random.ranf

Parameters

inLow
inHigh	default 0.

Returns

NdArray

randInt() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::randInt ( const Shape &  inShape, dtype  inLow, dtype  inHigh = 0  )

inline

Return random integers from low (inclusive) to high (exclusive), with the given shape. If no high value is input then the range will go from [0, low).

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randint.html#numpy.random.randint

Parameters

inShape
inLow
inHigh	default 0.

Returns

NdArray

randInt() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::randInt ( dtype  inLow, dtype  inHigh = 0  )

inline

Return random integer from low (inclusive) to high (exclusive), with the given shape. If no high value is input then the range will go from [0, low).

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randint.html#numpy.random.randint

Parameters

inLow
inHigh	default 0.

Returns

NdArray

randN() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::randN ( )

inline

Returns a single random value sampled from the "standard normal" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randn.html#numpy.random.randn

Returns

dtype

randN() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::randN ( const Shape &  inShape )

inline

Create an array of the given shape and populate it with random samples from the "standard normal" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randn.html#numpy.random.randn

Parameters

inShape

Returns

NdArray

seed()

template<typename GeneratorType = std::mt19937_64>

void nc::random::RNG< GeneratorType >::seed ( int  value )

inlinenoexcept

Seed Constructor

Parameters

value

the seed value

shuffle()

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

void nc::random::RNG< GeneratorType >::shuffle ( NdArray< dtype > &  inArray )

inline

Modify a sequence in-place by shuffling its contents.

Parameters

inArray

standardNormal() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::standardNormal ( )

inline

Single random value sampled from the "standard normal" distrubution with mean = 0 and std = 1

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_normal.html#numpy.random.standard_normal

Returns

NdArray

standardNormal() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::standardNormal ( const Shape &  inShape )

inline

Create an array of the given shape and populate it with random samples from a "standard normal" distrubution with mean = 0 and std = 1

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_normal.html#numpy.random.standard_normal

Parameters

inShape

Returns

NdArray

studentT() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::studentT ( const Shape &  inShape, dtype  inDof  )

inline

Create an array of the given shape and populate it with random samples from the "student-T" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_t.html#numpy.random.standard_t

Parameters

inShape
inDof	independent random variables

Returns

NdArray

studentT() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::studentT ( dtype  inDof )

inline

Single random value sampled from the "student-T" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_t.html#numpy.random.standard_t

Parameters

inDof

independent random variables

Returns

NdArray

triangle() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::triangle ( const Shape &  inShape, dtype  inA = 0, dtype  inB = 0.5, dtype  inC = 1  )

inline

Create an array of the given shape and populate it with random samples from the "triangle" distribution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.triangular.html#numpy.random.triangular

Parameters

inShape
inA
inB
inC

Returns

NdArray

triangle() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::triangle ( dtype  inA = 0, dtype  inB = 0.5, dtype  inC = 1  )

inline

Single random value sampled from the "triangle" distribution. NOTE: Use of this function requires using the Boost includes.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.triangular.html#numpy.random.triangular

Parameters

inA
inB
inC

Returns

NdArray

uniform() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::uniform ( const Shape &  inShape, dtype  inLow, dtype  inHigh  )

inline

Draw samples from a uniform distribution.

Samples are uniformly distributed over the half - open interval[low, high) (includes low, but excludes high)

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.uniform.html#numpy.random.uniform

Parameters

inShape
inLow
inHigh

Returns

NdArray

uniform() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::uniform ( dtype  inLow, dtype  inHigh  )

inline

Draw sample from a uniform distribution.

Samples are uniformly distributed over the half - open interval[low, high) (includes low, but excludes high)

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.uniform.html#numpy.random.uniform

Parameters

inLow
inHigh

Returns

NdArray

uniformOnSphere()

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::uniformOnSphere ( uint32  inNumPoints, uint32  inDims = 2  )

inline

Such a distribution produces random numbers uniformly distributed on the unit sphere of arbitrary dimension dim. NOTE: Use of this function requires using the Boost includes.

Parameters

inNumPoints
inDims	dimension of the sphere (default 2)

Returns

NdArray

weibull() [1/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

NdArray< dtype > nc::random::RNG< GeneratorType >::weibull ( const Shape &  inShape, dtype  inA = 1, dtype  inB = 1  )

inline

Create an array of the given shape and populate it with random samples from the "weibull" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.weibull.html#numpy.random.weibull

Parameters

inShape
inA	(default 1)
inB	(default 1)

Returns

NdArray

weibull() [2/2]

template<typename GeneratorType = std::mt19937_64>

template<typename dtype >

dtype nc::random::RNG< GeneratorType >::weibull ( dtype  inA = 1, dtype  inB = 1  )

inline

Single random value sampled from the "weibull" distribution.

NumPy Reference: https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.weibull.html#numpy.random.weibull

Parameters

inA	(default 1)
inB	(default 1)

Returns

NdArray

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The documentation for this class was generated from the following file:

• RNG.hpp