Functions
template<typename GeneratorType = std::mt19937>
NdArray< bool >	bernoulli (GeneratorType &generator, const Shape &inShape, double inP=0.5)

template<typename GeneratorType = std::mt19937>
bool	bernoulli (GeneratorType &generator, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	beta (GeneratorType &generator, const Shape &inShape, dtype inAlpha, dtype inBeta)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	beta (GeneratorType &generator, dtype inAlpha, dtype inBeta)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	binomial (GeneratorType &generator, const Shape &inShape, dtype inN, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	binomial (GeneratorType &generator, dtype inN, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	cauchy (GeneratorType &generator, const Shape &inShape, dtype inMean=0, dtype inSigma=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	cauchy (GeneratorType &generator, dtype inMean=0, dtype inSigma=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	chiSquare (GeneratorType &generator, const Shape &inShape, dtype inDof)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	chiSquare (GeneratorType &generator, dtype inDof)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	choice (GeneratorType &generator, const NdArray< dtype > &inArray)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	choice (GeneratorType &generator, const NdArray< dtype > &inArray, uint32 inNum, Replace replace=Replace::YES)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	discrete (GeneratorType &generator, const NdArray< double > &inWeights)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	discrete (GeneratorType &generator, const Shape &inShape, const NdArray< double > &inWeights)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	exponential (GeneratorType &generator, const Shape &inShape, dtype inScaleValue=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	exponential (GeneratorType &generator, dtype inScaleValue=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	extremeValue (GeneratorType &generator, const Shape &inShape, dtype inA=1, dtype inB=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	extremeValue (GeneratorType &generator, dtype inA=1, dtype inB=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	f (GeneratorType &generator, const Shape &inShape, dtype inDofN, dtype inDofD)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	f (GeneratorType &generator, dtype inDofN, dtype inDofD)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	gamma (GeneratorType &generator, const Shape &inShape, dtype inGammaShape, dtype inScaleValue=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	gamma (GeneratorType &generator, dtype inGammaShape, dtype inScaleValue=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	geometric (GeneratorType &generator, const Shape &inShape, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	geometric (GeneratorType &generator, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	laplace (GeneratorType &generator, const Shape &inShape, dtype inLoc=0, dtype inScale=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	laplace (GeneratorType &generator, dtype inLoc=0, dtype inScale=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	lognormal (GeneratorType &generator, const Shape &inShape, dtype inMean=0, dtype inSigma=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	lognormal (GeneratorType &generator, dtype inMean=0, dtype inSigma=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	negativeBinomial (GeneratorType &generator, const Shape &inShape, dtype inN, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	negativeBinomial (GeneratorType &generator, dtype inN, double inP=0.5)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	nonCentralChiSquared (GeneratorType &generator, const Shape &inShape, dtype inK=1, dtype inLambda=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	nonCentralChiSquared (GeneratorType &generator, dtype inK=1, dtype inLambda=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	normal (GeneratorType &generator, const Shape &inShape, dtype inMean=0, dtype inSigma=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	normal (GeneratorType &generator, dtype inMean=0, dtype inSigma=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	permutation (GeneratorType &generator, const NdArray< dtype > &inArray)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	permutation (GeneratorType &generator, dtype inValue)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	poisson (GeneratorType &generator, const Shape &inShape, double inMean=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	poisson (GeneratorType &generator, double inMean=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	rand (GeneratorType &generator)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	rand (GeneratorType &generator, const Shape &inShape)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	randFloat (GeneratorType &generator, const Shape &inShape, dtype inLow, dtype inHigh=0.)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	randFloat (GeneratorType &generator, dtype inLow, dtype inHigh=0.)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	randInt (GeneratorType &generator, const Shape &inShape, dtype inLow, dtype inHigh=0)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	randInt (GeneratorType &generator, dtype inLow, dtype inHigh=0)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	randN (GeneratorType &generator)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	randN (GeneratorType &generator, const Shape &inShape)

template<typename dtype , typename GeneratorType = std::mt19937>
void	shuffle (GeneratorType &generator, NdArray< dtype > &inArray)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	standardNormal (GeneratorType &generator)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	standardNormal (GeneratorType &generator, const Shape &inShape)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	studentT (GeneratorType &generator, const Shape &inShape, dtype inDof)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	studentT (GeneratorType &generator, dtype inDof)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	triangle (GeneratorType &generator, const Shape &inShape, dtype inA=0, dtype inB=0.5, dtype inC=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	triangle (GeneratorType &generator, dtype inA=0, dtype inB=0.5, dtype inC=1)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	uniform (GeneratorType &generator, const Shape &inShape, dtype inLow, dtype inHigh)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	uniform (GeneratorType &generator, dtype inLow, dtype inHigh)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	uniformOnSphere (GeneratorType &generator, uint32 inNumPoints, uint32 inDims=2)

template<typename dtype , typename GeneratorType = std::mt19937>
NdArray< dtype >	weibull (GeneratorType &generator, const Shape &inShape, dtype inA=1, dtype inB=1)

template<typename dtype , typename GeneratorType = std::mt19937>
dtype	weibull (GeneratorType &generator, dtype inA=1, dtype inB=1)

Function Documentation

◆ bernoulli() [1/2]

template<typename GeneratorType = std::mt19937>

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

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

Parameters

generator	instance of a random number generator
inShape
inP	(probability of success [0, 1]). Default 0.5

Returns: NdArray

◆ bernoulli() [2/2]

template<typename GeneratorType = std::mt19937>

bool nc::random::detail::bernoulli	(	GeneratorType &	generator,
		double	inP = `0.5`
	)

Single random value sampled from the "bernoulli" distribution.

Parameters

generator	instance of a random number generator
inP	(probability of success [0, 1]). Default 0.5

Returns: NdArray

◆ beta() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inAlpha
inBeta

Returns: NdArray

◆ beta() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::beta	(	GeneratorType &	generator,
		dtype	inAlpha,
		dtype	inBeta
	)

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

generator	instance of a random number generator
inAlpha
inBeta

Returns: NdArray

◆ binomial() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inN	(number of trials)
inP	(probablity of success [0, 1])

Returns: NdArray

◆ binomial() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::binomial	(	GeneratorType &	generator,
		dtype	inN,
		double	inP = `0.5`
	)

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

generator	instance of a random number generator
inN	(number of trials)
inP	(probablity of success [0, 1])

Returns: NdArray

◆ cauchy() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

Parameters

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::cauchy	(	GeneratorType &	generator,
		dtype	inMean = `0`,
		dtype	inSigma = `1`
	)

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

Parameters

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inDof	(independent random variables)

Returns: NdArray

◆ chiSquare() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::chiSquare	(	GeneratorType &	generator,
		dtype	inDof
	)

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

generator	instance of a random number generator
inDof	(independent random variables)

Returns: NdArray

◆ choice() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::choice	(	GeneratorType &	generator,
		const NdArray< dtype > &	inArray
	)

Chooses a random sample from an input array.

Parameters

generator	instance of a random number generator
inArray

Returns: NdArray

◆ choice() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

Chooses inNum random samples from an input array.

Parameters

generator	instance of a random number generator
inArray
inNum
replace	Whether the sample is with or without replacement

Returns: NdArray

◆ discrete() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::discrete	(	GeneratorType &	generator,
		const NdArray< double > &	inWeights
	)

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

generator	instance of a random number generator
inWeights

Returns: NdArray

◆ discrete() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inWeights

Returns: NdArray

◆ exponential() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inScaleValue	(default 1)

Returns: NdArray

◆ exponential() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::exponential	(	GeneratorType &	generator,
		dtype	inScaleValue = `1`
	)

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

generator	instance of a random number generator
inScaleValue	(default 1)

Returns: NdArray

◆ extremeValue() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

Parameters

generator	instance of a random number generator
inShape
inA	(default 1)
inB	(default 1)

Returns: NdArray

◆ extremeValue() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::extremeValue	(	GeneratorType &	generator,
		dtype	inA = `1`,
		dtype	inB = `1`
	)

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

Parameters

generator	instance of a random number generator
inA	(default 1)
inB	(default 1)

Returns: NdArray

◆ f() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::f	(	GeneratorType &	generator,
		dtype	inDofN,
		dtype	inDofD
	)

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

generator	instance of a random number generator
inDofN	Degrees of freedom in numerator. Should be greater than zero.
inDofD	Degrees of freedom in denominator. Should be greater than zero.

Returns: NdArray

Examples: ReadMe.cpp.

◆ gamma() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inGammaShape
inScaleValue	(default 1)

Returns: NdArray

◆ gamma() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::gamma	(	GeneratorType &	generator,
		dtype	inGammaShape,
		dtype	inScaleValue = `1`
	)

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

generator	instance of a random number generator
inGammaShape
inScaleValue	(default 1)

Returns: NdArray

◆ geometric() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inP	(probablity of success [0, 1])

Returns: NdArray

◆ geometric() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::geometric	(	GeneratorType &	generator,
		double	inP = `0.5`
	)

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

generator	instance of a random number generator
inP	(probablity of success [0, 1])

Returns: NdArray

◆ laplace() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::laplace	(	GeneratorType &	generator,
		dtype	inLoc = `0`,
		dtype	inScale = `1`
	)

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::lognormal	(	GeneratorType &	generator,
		dtype	inMean = `0`,
		dtype	inSigma = `1`
	)

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inN	number of trials
inP	probablity of success [0, 1]

Returns: NdArray

◆ negativeBinomial() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::negativeBinomial	(	GeneratorType &	generator,
		dtype	inN,
		double	inP = `0.5`
	)

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

generator	instance of a random number generator
inN	number of trials
inP	probablity of success [0, 1]

Returns: NdArray

◆ nonCentralChiSquared() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inK	(default 1)
inLambda	(default 1)

Returns: NdArray

◆ nonCentralChiSquared() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::nonCentralChiSquared	(	GeneratorType &	generator,
		dtype	inK = `1`,
		dtype	inLambda = `1`
	)

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

generator	instance of a random number generator
inK	(default 1)
inLambda	(default 1)

Returns: NdArray

◆ normal() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::normal	(	GeneratorType &	generator,
		dtype	inMean = `0`,
		dtype	inSigma = `1`
	)

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

generator	instance of a random number generator
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 dtype , typename GeneratorType = std::mt19937>

NdArray< dtype > nc::random::detail::permutation	(	GeneratorType &	generator,
		const NdArray< dtype > &	inArray
	)

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

generator	instance of a random number generator
inArray

Returns: NdArray

◆ permutation() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

NdArray< dtype > nc::random::detail::permutation	(	GeneratorType &	generator,
		dtype	inValue
	)

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

generator	instance of a random number generator
inValue

Returns: NdArray

◆ poisson() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inMean	(default 1)

Returns: NdArray

◆ poisson() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::poisson	(	GeneratorType &	generator,
		double	inMean = `1`
	)

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

generator	instance of a random number generator
inMean	(default 1)

Returns: NdArray

◆ rand() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::rand ( GeneratorType & generator )

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

Parameters

generator instance of a random number generator

Returns: NdArray

◆ rand() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

NdArray< dtype > nc::random::detail::rand	(	GeneratorType &	generator,
		const Shape &	inShape
	)

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

generator	instance of a random number generator
inShape

Returns: NdArray

◆ randFloat() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inLow
inHigh	default 0.

Returns: NdArray

◆ randFloat() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::randFloat	(	GeneratorType &	generator,
		dtype	inLow,
		dtype	inHigh = `0.`
	)

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

generator	instance of a random number generator
inLow
inHigh	default 0.

Returns: NdArray

◆ randInt() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inLow
inHigh	default 0.

Returns: NdArray

◆ randInt() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::randInt	(	GeneratorType &	generator,
		dtype	inLow,
		dtype	inHigh = `0`
	)

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

generator	instance of a random number generator
inLow
inHigh	default 0.

Returns: NdArray

◆ randN() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::randN ( GeneratorType & generator )

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

Parameters

generator instance of a random number generator

Returns: dtype

◆ randN() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

NdArray< dtype > nc::random::detail::randN	(	GeneratorType &	generator,
		const Shape &	inShape
	)

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

generator	instance of a random number generator
inShape

Returns: NdArray

◆ shuffle()

template<typename dtype , typename GeneratorType = std::mt19937>

void nc::random::detail::shuffle	(	GeneratorType &	generator,
		NdArray< dtype > &	inArray
	)

Modify a sequence in-place by shuffling its contents.

Parameters

generator	instance of a random number generator
inArray

◆ standardNormal() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::standardNormal ( GeneratorType & generator )

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

Parameters

generator instance of a random number generator

Returns: NdArray

◆ standardNormal() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

NdArray< dtype > nc::random::detail::standardNormal	(	GeneratorType &	generator,
		const Shape &	inShape
	)

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

generator	instance of a random number generator
inShape

Returns: NdArray

◆ studentT() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inDof	independent random variables

Returns: NdArray

◆ studentT() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::studentT	(	GeneratorType &	generator,
		dtype	inDof
	)

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

generator	instance of a random number generator
inDof	independent random variables

Returns: NdArray

◆ triangle() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inA
inB
inC

Returns: NdArray

◆ triangle() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::triangle	(	GeneratorType &	generator,
		dtype	inA = `0`,
		dtype	inB = `0.5`,
		dtype	inC = `1`
	)

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

generator	instance of a random number generator
inA
inB
inC

Returns: NdArray

◆ uniform() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inLow
inHigh

Returns: NdArray

◆ uniform() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::uniform	(	GeneratorType &	generator,
		dtype	inLow,
		dtype	inHigh
	)

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

generator	instance of a random number generator
inLow
inHigh

Returns: NdArray

◆ uniformOnSphere()

template<typename dtype , typename GeneratorType = std::mt19937>

NdArray< dtype > nc::random::detail::uniformOnSphere	(	GeneratorType &	generator,
		uint32	inNumPoints,
		uint32	inDims = `2`
	)

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

generator	instance of a random number generator
inNumPoints
inDims	dimension of the sphere (default 2)

Returns: NdArray

◆ weibull() [1/2]

template<typename dtype , typename GeneratorType = std::mt19937>

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

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

generator	instance of a random number generator
inShape
inA	(default 1)
inB	(default 1)

Returns: NdArray

◆ weibull() [2/2]

template<typename dtype , typename GeneratorType = std::mt19937>

dtype nc::random::detail::weibull	(	GeneratorType &	generator,
		dtype	inA = `1`,
		dtype	inB = `1`
	)

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

generator	instance of a random number generator
inA	(default 1)
inB	(default 1)

Returns: NdArray

Functions

Function Documentation

◆ bernoulli() [1/2]

◆ bernoulli() [2/2]

◆ beta() [1/2]

◆ beta() [2/2]

◆ binomial() [1/2]

◆ binomial() [2/2]

◆ cauchy() [1/2]

◆ cauchy() [2/2]

◆ chiSquare() [1/2]

◆ chiSquare() [2/2]

◆ choice() [1/2]

◆ choice() [2/2]

◆ discrete() [1/2]

◆ discrete() [2/2]

◆ exponential() [1/2]

◆ exponential() [2/2]

◆ extremeValue() [1/2]

◆ extremeValue() [2/2]

◆ f() [1/2]

◆ f() [2/2]

◆ gamma() [1/2]

◆ gamma() [2/2]

◆ geometric() [1/2]

◆ geometric() [2/2]

◆ laplace() [1/2]

◆ laplace() [2/2]

◆ lognormal() [1/2]

◆ lognormal() [2/2]

◆ negativeBinomial() [1/2]

◆ negativeBinomial() [2/2]

◆ nonCentralChiSquared() [1/2]

◆ nonCentralChiSquared() [2/2]

◆ normal() [1/2]

◆ normal() [2/2]

◆ permutation() [1/2]

◆ permutation() [2/2]

◆ poisson() [1/2]

◆ poisson() [2/2]

◆ rand() [1/2]

◆ rand() [2/2]

◆ randFloat() [1/2]

◆ randFloat() [2/2]

◆ randInt() [1/2]

◆ randInt() [2/2]

◆ randN() [1/2]

◆ randN() [2/2]

◆ shuffle()

◆ standardNormal() [1/2]

◆ standardNormal() [2/2]

◆ studentT() [1/2]

◆ studentT() [2/2]

◆ triangle() [1/2]

◆ triangle() [2/2]

◆ uniform() [1/2]

◆ uniform() [2/2]

◆ uniformOnSphere()

◆ weibull() [1/2]

◆ weibull() [2/2]