Random mumber generation and pdf sampling functionalities. More...

Files
file	Random.inl

file	RandomUtils.h

Functions
template<typename Engine , hydra::detail::Backend BACKEND, typename Iterable , typename FUNCTOR >
std::enable_if< detail::random::is_matching_iterable< Engine, FUNCTOR, Iterable >::value, void >::type	hydra::fill_random (hydra::detail::BackendPolicy< BACKEND > const &policy, Iterable &&iterable, FUNCTOR const &functor, size_t seed, size_t rng_jump)
	Fill a range with numbers distributed according a user defined distribution. More...

template<typename Engine , typename Iterable , typename FUNCTOR >
std::enable_if< detail::random::is_matching_iterable< Engine, FUNCTOR, Iterable >::value, void >::type	hydra::fill_random (Iterable &&iterable, FUNCTOR const &functor, size_t seed, size_t rng_jump)
	Fill a range with numbers distributed according a user defined distribution. More...

template<typename Engine , hydra::detail::Backend BACKEND, typename Iterator , typename FUNCTOR >
std::enable_if< !hydra::detail::has_rng_formula< FUNCTOR >::value, void >::type	hydra::fill_random (hydra::detail::BackendPolicy< BACKEND > const &policy, Iterator begin, Iterator end, FUNCTOR const &functor, size_t seed, size_t rng_jump)
	Fall back function if RngFormula is not implemented for the requested functor. More...

template<typename Engine , typename Iterator , typename FUNCTOR >
std::enable_if< !hydra::detail::has_rng_formula< FUNCTOR >::value, void >::type	hydra::fill_random (Iterator begin, Iterator end, FUNCTOR const &functor, size_t seed, size_t rng_jump)
	Fall back function if RngFormula is not implemented for the requested functor. More...

template<typename RNG , typename IterableData , typename IterableWeight , hydra::detail::Backend BACKEND>
std::enable_if< detail::random::is_iterable< IterableData >::value &&detail::random::is_iterable< IterableWeight >::value, Range< decltype(std::declval< IterableData >).begin())> >::type	hydra::unweight (hydra::detail::BackendPolicy< BACKEND > const &policy, IterableData &&data, IterableWeight &&weights, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce a unweighted sample according to a weights. More...

template<typename RNG = default_random_engine, typename DerivedPolicy , typename IteratorData , typename IteratorWeight >
std::enable_if< detail::random::is_iterator< IteratorData >::value &&detail::random::is_iterator< IteratorWeight >::value, Range< IteratorData > >::type	hydra::unweight (hydra::thrust::detail::execution_policy_base< DerivedPolicy > const &policy, IteratorData data_begin, IteratorData data_end, IteratorWeight weights_begin, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce a unweighted sample according to the weights [wbegin, wend]. More...

template<typename RNG , typename Functor , typename Iterator , typename DerivedPolicy >
std::enable_if< detail::random::is_callable< Functor >::value &&detail::random::is_iterator< Iterator >::value, Range< Iterator >>::type	hydra::unweight (hydra::thrust::detail::execution_policy_base< DerivedPolicy > const &policy, Iterator begin, Iterator end, Functor const &functor, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce an unweighted sample according to. More...

template<typename RNG = default_random_engine, typename IteratorData , typename IteratorWeight , hydra::detail::Backend BACKEND>
std::enable_if< detail::random::is_iterator< IteratorData >::value &&detail::random::is_iterator< IteratorWeight >::value, Range< IteratorData > >::type	hydra::unweight (detail::BackendPolicy< BACKEND > const &policy, IteratorData data_begin, IteratorData data_end, IteratorWeight weights_begin, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce a unweighted sample according to the weights [wbegin, wend]. More...

template<typename RNG , typename Functor , typename Iterator , hydra::detail::Backend BACKEND>
std::enable_if< detail::random::is_callable< Functor >::value &&detail::random::is_iterator< Iterator >::value, Range< Iterator >>::type	hydra::unweight (hydra::detail::BackendPolicy< BACKEND > const &policy, Iterator begin, Iterator end, Functor const &functor, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce an unweighted sample according to. More...

template<typename RNG = default_random_engine, typename IteratorData , typename IteratorWeight >
std::enable_if< detail::random::is_iterator< IteratorData >::value &&detail::random::is_iterator< IteratorWeight >::value, Range< IteratorData >>::type	hydra::unweight (IteratorData data_begin, IteratorData data_end, IteratorData weights_begin, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce a unweighted sample according to the weights [wbegin, wend]. More...

template<typename RNG , typename Functor , typename Iterator >
std::enable_if< detail::random::is_callable< Functor >::value &&detail::random::is_iterator< Iterator >::value, Range< Iterator >>::type	hydra::unweight (Iterator begin, Iterator end, Functor const &functor, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce an unweighted sample according to. More...

template<typename RNG , typename Functor , typename Iterable , hydra::detail::Backend BACKEND>
std::enable_if< detail::random::is_callable< Functor >::value &&detail::random::is_iterable< Iterable >::value, Range< decltype(std::declval< Iterable >).begin())>>::type	hydra::unweight (hydra::detail::BackendPolicy< BACKEND > const &policy, Iterable &&iterable, Functor const &functor, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce an unweighted sample according to. More...

template<typename RNG = default_random_engine, typename IterableData , typename IterableWeight >
std::enable_if< detail::random::is_iterable< IterableData >::value &&detail::random::is_iterable< IterableWeight >::value, Range< decltype(std::declval< IterableData >).begin())>>::type	hydra::unweight (IterableData data, IterableWeight weights, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce an unweighted sample according to a weights. More...

template<typename RNG = default_random_engine, typename Functor , typename Iterable >
std::enable_if< detail::random::is_callable< Functor >::value &&detail::random::is_iterable< Iterable >::value,Range< decltype(std::declval< Iterable >).begin())> >::type	hydra::unweight (Iterable &&iterable, Functor const &functor, double max_pdf=-1.0, size_t rng_seed=0x8ec74d321e6b5a27, size_t rng_jump=0)
	This functions reorder a dataset to produce an unweighted sample according to. More...

Variables
template<typename Engine = hydra::default_random_engine, hydra::detail::Backend BACKEND, typename Iterator , typename FUNCTOR >
std::enable_if< hydra::detail::has_rng_formula< FUNCTOR >::value &&std::is_convertible< decltype(std::declval< RngFormula< FUNCTOR > >).Generate(std::declval< Engine & >), std::declval< FUNCTOR const & >))), typename hydra::thrust::iterator_traits< Iterator >::value_type >::value, void >::type	hydra::fill_random (hydra::detail::BackendPolicy< BACKEND > const &policy, Iterator begin, Iterator end, FUNCTOR const &functor, size_t seed=0x254a0afcf7da74a2, size_t rng_jump=0)
	Fill a range with numbers distributed according a user defined distribution using a RNG analytical formula. More...

Detailed Description

Random mumber generation and pdf sampling functionalities.

Function Documentation

◆ fill_random() [1/4]

template<typename Engine , hydra::detail::Backend BACKEND, typename Iterable , typename FUNCTOR >

std::enable_if< detail::random::is_matching_iterable< Engine, FUNCTOR, Iterable >::value, void >::type hydra::fill_random	(	hydra::detail::BackendPolicy< BACKEND > const &	policy,
		Iterable &&	iterable,
		FUNCTOR const &	functor,
		size_t	seed,
		size_t	rng_jump
	)

#include <RandomFill.inl>

Fill a range with numbers distributed according a user defined distribution.

Parameters

policy	backend to perform the calculation.
iterable	range storing the generated values
functor	distribution to be sampled
policy	backend to perform the calculation.
iterable	range storing the generated values
functor	distribution to be sampled
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

◆ fill_random() [2/4]

template<typename Engine , typename Iterable , typename FUNCTOR >

std::enable_if< detail::random::is_matching_iterable< Engine, FUNCTOR, Iterable >::value, void >::type hydra::fill_random	(	Iterable &&	iterable,
		FUNCTOR const &	functor,
		size_t	seed,
		size_t	rng_jump
	)

#include <RandomFill.inl>

Fill a range with numbers distributed according a user defined distribution.

Fall back function if the argument is not an Iterable or if itis not convertible to the Functor return value.

Parameters

iterable	range storing the generated values
functor	distribution to be sampled
iterable	range storing the generated values
functor	distribution to be sampled
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

◆ fill_random() [3/4]

template<typename Engine , hydra::detail::Backend BACKEND, typename Iterator , typename FUNCTOR >

std::enable_if< !hydra::detail::has_rng_formula< FUNCTOR >::value, void >::type hydra::fill_random	(	hydra::detail::BackendPolicy< BACKEND > const &	policy,
		Iterator	begin,
		Iterator	end,
		FUNCTOR const &	functor,
		size_t	seed = `0x254a0afcf7da74a2`,
		size_t	rng_jump = `0`
	)

#include <RandomFill.inl>

Fall back function if RngFormula is not implemented for the requested functor.

Parameters

policy	backend to perform the calculation.
begin	beginning of the range storing the generated values
end	ending of the range storing the generated values
functor	distribution to be sampled
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

◆ fill_random() [4/4]

template<typename Engine , typename Iterator , typename FUNCTOR >

std::enable_if< !hydra::detail::has_rng_formula< FUNCTOR >::value, void >::type hydra::fill_random	(	Iterator	begin,
		Iterator	end,
		FUNCTOR const &	functor,
		size_t	seed = `0x254a0afcf7da74a2`,
		size_t	rng_jump = `0`
	)

#include <RandomFill.inl>

Fall back function if RngFormula is not implemented for the requested functor.

Parameters

begin	beginning of the range storing the generated values
end	ending of the range storing the generated values
functor	distribution to be sampled
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

◆ unweight() [1/10]

template<typename RNG , typename IterableData , typename IterableWeight , hydra::detail::Backend BACKEND>

std::enable_if< detail::random::is_iterable< IterableData >::value &&detail::random::is_iterable< IterableWeight >::value, Range< decltype(std::declval< IterableData >).begin())> >::type hydra::unweight	(	hydra::detail::BackendPolicy< BACKEND > const &	policy,
		IterableData &&	data,
		IterableWeight &&	weights,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.inl>

This functions reorder a dataset to produce a unweighted sample according to a weights.

The length of the range

Parameters

weights	should be equal or greater than the
data	size.
policy	parallel backend to perform the unweighting
weights	the range of weights
data	the range corresponding dataset
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [2/10]

template<typename RNG = default_random_engine, typename DerivedPolicy , typename IteratorData , typename IteratorWeight >

std::enable_if<detail::random::is_iterator<IteratorData>::value && detail::random::is_iterator<IteratorWeight>::value,Range<IteratorData> >::type hydra::unweight	(	hydra::thrust::detail::execution_policy_base< DerivedPolicy > const &	policy,
		IteratorData	data_begin,
		IteratorData	data_end,
		IteratorWeight	weights_begin,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.h>

This functions reorder a dataset to produce a unweighted sample according to the weights [wbegin, wend].

The length of the range [wbegin, wend] should be equal or greater than the dataset size.

Parameters

policy	parallel backend to perform the unweighting
data_begin	iterator pointing to the begin of the range of weights
data_end	iterator pointing to the begin of the range of weights
weights_begin	iterator pointing to the begin of the range of data
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [3/10]

template<typename RNG , typename Functor , typename Iterator , typename DerivedPolicy >

std::enable_if< detail::random::is_callable<Functor>::value && detail::random::is_iterator<Iterator>::value, Range<Iterator>>::type hydra::unweight	(	hydra::thrust::detail::execution_policy_base< DerivedPolicy > const &	policy,
		Iterator	begin,
		Iterator	end,
		Functor const &	functor,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.inl>

This functions reorder a dataset to produce an unweighted sample according to.

Parameters

functor	.
policy
begin
end
functor
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [4/10]

template<typename RNG = default_random_engine, typename IteratorData , typename IteratorWeight , hydra::detail::Backend BACKEND>

std::enable_if<detail::random::is_iterator<IteratorData>::value && detail::random::is_iterator<IteratorWeight>::value,Range<IteratorData> >::type hydra::unweight	(	detail::BackendPolicy< BACKEND > const &	policy,
		IteratorData	data_begin,
		IteratorData	data_end,
		IteratorWeight	weights_begin,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.h>

This functions reorder a dataset to produce a unweighted sample according to the weights [wbegin, wend].

The length of the range [wbegin, wend] should be equal or greater than the dataset size.

Parameters

policy	parallel backend to perform the unweighting
data_begin	iterator pointing to the begin of the range of weights
data_end	iterator pointing to the begin of the range of weights
weights_begin	iterator pointing to the begin of the range of data
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [5/10]

template<typename RNG , typename Functor , typename Iterator , hydra::detail::Backend BACKEND>

std::enable_if< detail::random::is_callable<Functor>::value && detail::random::is_iterator<Iterator>::value, Range<Iterator>>::type hydra::unweight	(	hydra::detail::BackendPolicy< BACKEND > const &	policy,
		Iterator	begin,
		Iterator	end,
		Functor const &	functor,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.inl>

This functions reorder a dataset to produce an unweighted sample according to.

Parameters

functor	.
policy
begin
end
functor
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [6/10]

template<typename RNG = default_random_engine, typename IteratorData , typename IteratorWeight >

std::enable_if< detail::random::is_iterator<IteratorData>::value && detail::random::is_iterator<IteratorWeight>::value, Range<IteratorData>>::type hydra::unweight	(	IteratorData	data_begin,
		IteratorData	data_end,
		IteratorData	weights_begin,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.h>

This functions reorder a dataset to produce a unweighted sample according to the weights [wbegin, wend].

The length of the range [wbegin, wend] should be equal or greater than the dataset size.

Parameters

data_begin	iterator pointing to the begin of the range of weights
data_end	iterator pointing to the begin of the range of weights
weights_begin	iterator pointing to the begin of the range of data
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [7/10]

template<typename RNG , typename Functor , typename Iterator >

std::enable_if< detail::random::is_callable<Functor>::value && detail::random::is_iterator<Iterator>::value, Range<Iterator>>::type hydra::unweight	(	Iterator	begin,
		Iterator	end,
		Functor const &	functor,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.inl>

This functions reorder a dataset to produce an unweighted sample according to.

Parameters

functor	.
begin
end
functor
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [8/10]

template<typename RNG , typename Functor , typename Iterable , hydra::detail::Backend BACKEND>

std::enable_if< detail::random::is_callable<Functor>::value && detail::random::is_iterable<Iterable>::value , Range< decltype(std::declval<Iterable>).begin())>>::type hydra::unweight	(	hydra::detail::BackendPolicy< BACKEND > const &	policy,
		Iterable &&	iterable,
		Functor const &	functor,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.inl>

This functions reorder a dataset to produce an unweighted sample according to.

Parameters

functor	.
iterable
functor
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [9/10]

template<typename RNG = default_random_engine, typename IterableData , typename IterableWeight >

std::enable_if< detail::random::is_iterable<IterableData>::value && detail::random::is_iterable<IterableWeight>::value, Range< decltype(std::declval<IterableData>).begin())>>::type hydra::unweight	(	IterableData	data,
		IterableWeight	weights,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.h>

This functions reorder a dataset to produce an unweighted sample according to a weights.

The length of the range

Parameters

weights	should be equal or greater than the
data	size.
weights	the range of weights
data	the range corresponding dataset
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

◆ unweight() [10/10]

template<typename RNG = default_random_engine, typename Functor , typename Iterable >

std::enable_if<detail::random::is_callable<Functor>::value && detail::random::is_iterable<Iterable>::value ,Range< decltype(std::declval<Iterable>).begin())> >::type hydra::unweight	(	Iterable &&	iterable,
		Functor const &	functor,
		double	max_pdf = `-1.0`,
		size_t	rng_seed = `0x8ec74d321e6b5a27`,
		size_t	rng_jump = `0`
	)

#include <Random.h>

This functions reorder a dataset to produce an unweighted sample according to.

Parameters

functor	.
iterable
functor
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Returns: hydra::Range object pointing unweighted sample.

Variable Documentation

◆ fill_random

template<typename Engine = hydra::default_random_engine, hydra::detail::Backend BACKEND, typename Iterator , typename FUNCTOR >

hydra::fill_random	(	hydra::detail::BackendPolicy< BACKEND > const &	policy,
		Iterator	begin,
		Iterator	end,
		FUNCTOR const &	functor,
		size_t	seed = `0x254a0afcf7da74a2`,
		size_t	rng_jump = `0`
	)

#include <Random.h>

Initial value:

{
        
        hydra::device::vector<double>  data(nentries)

Fill a range with numbers distributed according a user defined distribution using a RNG analytical formula.

Fall back function if RngFormula::Generate() return value is not convertible to functor return value.

Parameters

policy	backend to perform the calculation.
begin	beginning of the range storing the generated values
end	ending of the range storing the generated values
functor	distribution to be sampled
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator
begin	beginning of the range storing the generated values
end	ending of the range storing the generated values
functor	distribution to be sampled
max_pdf	maximum pdf value for accept-reject method. If no value is set, the maximum value in the sample is used.
rng_seed	seed for the underlying pseudo-random number generator
rng_jump	sequence offset for the underlying pseudo-random number generator

Examples:: basic_fit_range_semantics.inl, binned_extended_logLL_fit.inl, cufft.inl, extended_logLL_fit.inl, fft.inl, fill_basic_distributions.inl, fractional_logLL_fit.inl, multidimensional_fit.inl, and splot.inl.

Files

Functions

Variables

Detailed Description

Function Documentation

◆ fill_random() [1/4]

◆ fill_random() [2/4]

◆ fill_random() [3/4]

◆ fill_random() [4/4]

◆ unweight() [1/10]

◆ unweight() [2/10]

◆ unweight() [3/10]

◆ unweight() [4/10]

◆ unweight() [5/10]

◆ unweight() [6/10]

◆ unweight() [7/10]

◆ unweight() [8/10]

◆ unweight() [9/10]

◆ unweight() [10/10]

Variable Documentation

◆ fill_random