Namespaces
namespace	detail

Data Structures
class	RNG

Functions
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 inSeed)
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)

Variables
std::mt19937_64	generator_
	generator function

Function Documentation

◆ bernoulli() [1/2]

NdArray< bool > nc::random::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]

bool nc::random::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 dtype>

NdArray< dtype > nc::random::beta	(	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

inShape
inAlpha
inBeta

Returns: NdArray

◆ beta() [2/2]

template<typename dtype>

dtype nc::random::beta	(	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

inAlpha
inBeta

Returns: NdArray

◆ binomial() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::binomial	(	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

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

Returns: NdArray

◆ binomial() [2/2]

template<typename dtype>

dtype nc::random::binomial	(	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

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

Returns: NdArray

◆ cauchy() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::cauchy	(	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

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>

dtype nc::random::cauchy	(	dtype	inMean = 0,
		dtype	inSigma = 1 )

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 dtype>

NdArray< dtype > nc::random::chiSquare	(	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

inShape
inDof	(independent random variables)

Returns: NdArray

◆ chiSquare() [2/2]

template<typename dtype>

dtype nc::random::chiSquare ( 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

inDof (independent random variables)

Returns: NdArray

◆ choice() [1/2]

template<typename dtype>

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

Chooses a random sample from an input array.

Parameters

inArray

Returns: NdArray

Examples: ReadMe.cpp.

◆ choice() [2/2]

template<typename dtype>

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

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 dtype>

dtype nc::random::discrete ( 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

inWeights

Returns: NdArray

◆ discrete() [2/2]

template<typename dtype>

NdArray< dtype > nc::random::discrete	(	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

inShape
inWeights

Returns: NdArray

◆ exponential() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::exponential	(	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

inShape
inScaleValue	(default 1)

Returns: NdArray

◆ exponential() [2/2]

template<typename dtype>

dtype nc::random::exponential ( 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

inScaleValue (default 1)

Returns: NdArray

◆ extremeValue() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::extremeValue	(	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

inShape
inA	(default 1)
inB	(default 1)

Returns: NdArray

◆ extremeValue() [2/2]

template<typename dtype>

dtype nc::random::extremeValue	(	dtype	inA = 1,
		dtype	inB = 1 )

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

Parameters

inA	(default 1)
inB	(default 1)

Returns: NdArray

◆ f() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::f	(	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

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>

dtype nc::random::f	(	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

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 dtype>

NdArray< dtype > nc::random::gamma	(	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

inShape
inGammaShape
inScaleValue	(default 1)

Returns: NdArray

◆ gamma() [2/2]

template<typename dtype>

dtype nc::random::gamma	(	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

inGammaShape
inScaleValue	(default 1)

Returns: NdArray

◆ geometric() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::geometric	(	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

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

Returns: NdArray

◆ geometric() [2/2]

template<typename dtype>

dtype nc::random::geometric ( 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

inP	(probablity of success [0, 1])

Returns: NdArray

◆ laplace() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::laplace	(	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

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>

dtype nc::random::laplace	(	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

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>

NdArray< dtype > nc::random::lognormal	(	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

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>

dtype nc::random::lognormal	(	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

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>

NdArray< dtype > nc::random::negativeBinomial	(	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

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

Returns: NdArray

◆ negativeBinomial() [2/2]

template<typename dtype>

dtype nc::random::negativeBinomial	(	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

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

Returns: NdArray

◆ nonCentralChiSquared() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::nonCentralChiSquared	(	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

inShape
inK	(default 1)
inLambda	(default 1)

Returns: NdArray

◆ nonCentralChiSquared() [2/2]

template<typename dtype>

dtype nc::random::nonCentralChiSquared	(	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

inK	(default 1)
inLambda	(default 1)

Returns: NdArray

◆ normal() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::normal	(	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

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>

dtype nc::random::normal	(	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

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>

NdArray< dtype > nc::random::permutation ( 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

inArray

Returns: NdArray

◆ permutation() [2/2]

template<typename dtype>

NdArray< dtype > nc::random::permutation ( 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

inValue

Returns: NdArray

◆ poisson() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::poisson	(	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

inShape
inMean	(default 1)

Returns: NdArray

◆ poisson() [2/2]

template<typename dtype>

dtype nc::random::poisson ( 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

inMean (default 1)

Returns: NdArray

◆ rand() [1/2]

template<typename dtype>

dtype nc::random::rand ( )

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

Examples: ReadMe.cpp.

◆ rand() [2/2]

template<typename dtype>

NdArray< dtype > nc::random::rand ( 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

inShape

Returns: NdArray

◆ randFloat() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::randFloat	(	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

inShape
inLow
inHigh	default 0.

Returns: NdArray

◆ randFloat() [2/2]

template<typename dtype>

dtype nc::random::randFloat	(	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

inLow
inHigh	default 0.

Returns: NdArray

Examples: GaussNewtonNlls.cpp.

◆ randInt() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::randInt	(	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

inShape
inLow
inHigh	default 0.

Returns: NdArray

◆ randInt() [2/2]

template<typename dtype>

dtype nc::random::randInt	(	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

inLow
inHigh	default 0.

Returns: NdArray

Examples: InterfaceWithEigen.cpp, and ReadMe.cpp.

◆ randN() [1/2]

template<typename dtype>

dtype nc::random::randN ( )

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

Examples: GaussNewtonNlls.cpp, and ReadMe.cpp.

◆ randN() [2/2]

template<typename dtype>

NdArray< dtype > nc::random::randN ( 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

inShape

Returns: NdArray

◆ seed()

void nc::random::seed ( int inSeed )

inline

Seeds the random number generator

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

Parameters

inSeed

Examples: GaussNewtonNlls.cpp, and ReadMe.cpp.

◆ shuffle()

template<typename dtype>

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

Modify a sequence in-place by shuffling its contents.

Parameters

inArray

◆ standardNormal() [1/2]

template<typename dtype>

dtype nc::random::standardNormal ( )

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 dtype>

NdArray< dtype > nc::random::standardNormal ( 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

inShape

Returns: NdArray

◆ studentT() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::studentT	(	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

inShape
inDof	independent random variables

Returns: NdArray

◆ studentT() [2/2]

template<typename dtype>

dtype nc::random::studentT ( 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

inDof independent random variables

Returns: NdArray

◆ triangle() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::triangle	(	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

inShape
inA
inB
inC

Returns: NdArray

◆ triangle() [2/2]

template<typename dtype>

dtype nc::random::triangle	(	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

inA
inB
inC

Returns: NdArray

◆ uniform() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::uniform	(	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

inShape
inLow
inHigh

Returns: NdArray

◆ uniform() [2/2]

template<typename dtype>

dtype nc::random::uniform	(	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

inLow
inHigh

Returns: NdArray

◆ uniformOnSphere()

template<typename dtype>

NdArray< dtype > nc::random::uniformOnSphere	(	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

inNumPoints
inDims	dimension of the sphere (default 2)

Returns: NdArray

◆ weibull() [1/2]

template<typename dtype>

NdArray< dtype > nc::random::weibull	(	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

inShape
inA	(default 1)
inB	(default 1)

Returns: NdArray

◆ weibull() [2/2]

template<typename dtype>

dtype nc::random::weibull	(	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

inA	(default 1)
inB	(default 1)

Returns: NdArray

Variable Documentation

◆ generator_

std::mt19937_64 nc::random::generator_

inline

generator function

Namespaces

Data Structures

Functions

Variables

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]

◆ seed()

◆ 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]

Variable Documentation

◆ generator_