This is the namespace enclosing the classes handling the usual probabilities Laws. More...

Classes
class	Bernoulli
	Bernoulli probability law. More...

class	Beta
	Beta distribution law. More...

class	Binomial
	Binomial probability law. More...

class	Categorical
	Categorical probability law. More...

class	Cauchy
	Cauchy distribution law. More...

struct	CdfcOp
	, More...

struct	CdfOp
	, More...

class	ChiSquared
	ChiSquared distribution law. More...

class	Empirical
	Empirical distribution law. More...

class	Exponential
	Exponential distribution law. More...

class	FisherSnedecor
	FisherSnedecor distribution law. More...

class	Gamma
	Gamma distribution law. More...

class	Geometric
	Geometric probability law. More...

class	HyperGeometric
	HyperGeometric probability law. More...

struct	IcdfOp
	, More...

class	ILawBase
	Interface base class for all the (univariate/multivariate) probabilities laws. More...

class	IUnivLaw
	Interface base class for all the univariate distributions. More...

struct	LogCdfcOp
	, More...

struct	LogCdfOp
	, More...

class	Logistic
	Logistic distribution law. More...

class	LogNormal
	LogNormal distribution law. More...

struct	LogPdfOp
	, More...

class	NegativeBinomial
	NegativeBinomial probability law. More...

class	Normal
	Normal distribution law. More...

struct	PdfOp
	, More...

class	Poisson
	Poisson distribution law. More...

class	Student
	Student distribution law. More...

class	Uniform
	class for the Uniform law distribution. More...

class	UniformDiscrete
	class for the Uniform law distribution. More...

class	Weibull
	Weibull distribution law. More...

Typedefs
typedef Normal	Gaussian
	A synonymous for the normal law.

Enumerations
enum	UnivariateDistribution { bernoulli_ , beta_ , binomial_ , categorical_ , cauchy_ , chisquared_ , exponential_ , fisher_snedecor_ , gamma_ , geometric_ , hypergeometric_ , logistic_ , lognormal_ , negative_binomial_ , normal_ , poisson_ , student_ , uniform_ , uniform_discrete_ , weibull_ , unknown_univ_distribution_ }
	list of the univariate distribution laws More...

Functions
UnivariateDistribution	stringToUnivariateDistribution (std::string const &dist)
	Convert a String to a UnivariateDistribution.

std::string	univariateDistributionToString (UnivariateDistribution const &type)
	convert a UnivariateDistribution to a String.

Detailed Description

This is the namespace enclosing the classes handling the usual probabilities Laws.

Typedef Documentation

◆ Gaussian

typedef Normal STK::Law::Gaussian

A synonymous for the normal law.

Definition at line 47 of file STK_Law_Normal.h.

Enumeration Type Documentation

◆ UnivariateDistribution

enum STK::Law::UnivariateDistribution

list of the univariate distribution laws

Enumerator
bernoulli_
beta_
binomial_
categorical_
cauchy_
chisquared_
exponential_
fisher_snedecor_
gamma_
geometric_
hypergeometric_
logistic_
lognormal_
negative_binomial_
normal_
poisson_
student_
uniform_
uniform_discrete_
weibull_
unknown_univ_distribution_

Definition at line 54 of file STK_Law_Util.h.

{
  bernoulli_,
  beta_,
  binomial_,
  categorical_,
  cauchy_,
  chisquared_,
  exponential_,
  fisher_snedecor_,
  gamma_,
  geometric_,
  hypergeometric_,
  logistic_,
  lognormal_,
  negative_binomial_,
  normal_,
  poisson_,
  student_,
  uniform_,
  uniform_discrete_,
  weibull_,
  unknown_univ_distribution_
};

Function Documentation

◆ stringToUnivariateDistribution()

UnivariateDistribution STK::Law::stringToUnivariateDistribution ( std::string const & dist )

inline

Convert a String to a UnivariateDistribution.

The recognized strings are

Distribution
"beta"
"binomial"
"categorical"
"cauchy"
"chi squared"
"exponential"
"fisher snedecor"
"gamma"
"geometric"
"hypergeometric"
"logistic"
"lognormal"
"negative binomial"
"normal"
"poisson"
"student"
"uniform"
"uniform discrete"
"weibull"

Parameters

dist	the String we want to convert

Returns: the Mixture represented by the String type. if the string does not match any known name, the unknown_mixture_ type is returned.

Definition at line 108 of file STK_Law_Util.h.

{
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("beta"))) return beta_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("binomial"))) return binomial_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("categorical"))) return categorical_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("cauchy"))) return cauchy_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("chisquared"))) return chisquared_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("exponential"))) return exponential_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("fishersnedecor"))) return fisher_snedecor_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("gamma"))) return gamma_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("geometric"))) return geometric_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("hypergeometric"))) return hypergeometric_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("logistic"))) return logistic_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("lognormal"))) return lognormal_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("negativebinomial"))) return negative_binomial_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("normal"))) return normal_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("poisson"))) return poisson_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("student"))) return student_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("uniform"))) return uniform_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("uniformdiscrete"))) return uniform_discrete_;
  if (toUpperString(removeWhiteSpaces(dist)) == toUpperString(_T("weibull"))) return weibull_;
#ifdef STK_STATISTIK_DEBUG
  stk_cout << _T("In stringToUnivariateDistribution, dist ") << dist << _T(" not found.\n");
#endif
  return unknown_univ_distribution_;
}

References _T, beta_, binomial_, categorical_, cauchy_, chisquared_, STK::dist(), exponential_, fisher_snedecor_, gamma_, geometric_, hypergeometric_, logistic_, lognormal_, negative_binomial_, normal_, poisson_, STK::removeWhiteSpaces(), stk_cout, student_, STK::toUpperString(), uniform_, uniform_discrete_, unknown_univ_distribution_, and weibull_.

◆ univariateDistributionToString()

std::string STK::Law::univariateDistributionToString ( UnivariateDistribution const & type )

inline

convert a UnivariateDistribution to a String.

Parameters

type	the type of UnivariateDistribution we want to convert

Returns: the string associated to this type.

See also: stringToUnivariateDistribution

Definition at line 141 of file STK_Law_Util.h.

{
  if (type == beta_) return String(_T("beta"));
  if (type == binomial_) return String(_T("binomial"));
  if (type == categorical_) return String(_T("categorical"));
  if (type == chisquared_) return String(_T("chi squared"));
  if (type == cauchy_) return String(_T("cauchy"));
  if (type == exponential_) return String(_T("exponential"));
  if (type == fisher_snedecor_) return String(_T("fisher snedecor"));
  if (type == gamma_) return String(_T("gamma"));
  if (type == geometric_) return String(_T("geometric"));
  if (type == hypergeometric_) return String(_T("hypergeometric"));
  if (type == logistic_) return String(_T("logistic"));
  if (type == lognormal_) return String(_T("lognormal"));
  if (type == negative_binomial_) return String(_T("negative binomial"));
  if (type == normal_) return String(_T("normal"));
  if (type == poisson_) return String(_T("poisson"));
  if (type == student_) return String(_T("student"));
  if (type == uniform_) return String(_T("uniform"));
  if (type == uniform_discrete_) return String(_T("uniform discrete"));
  if (type == weibull_) return String(_T("weibull"));
  return String(_T("unknown"));
}

References _T, beta_, binomial_, categorical_, cauchy_, chisquared_, exponential_, fisher_snedecor_, gamma_, geometric_, hypergeometric_, logistic_, lognormal_, negative_binomial_, normal_, poisson_, student_, uniform_, uniform_discrete_, and weibull_.

Classes

Typedefs

Enumerations

Functions