Utopia::Models::Opinionet::Utils Namespace Reference

Classes
struct	Infrastructure
struct	TestNetworkU
struct	TestNetworkD
struct	TestNetworkD_small

Functions
	BOOST_FIXTURE_TEST_CASE (test_get_rand, Infrastructure)
	BOOST_AUTO_TEST_CASE (test_is_directed)
	BOOST_FIXTURE_TEST_CASE (test_get_rand_neighbor, TestNetworkU)
	BOOST_FIXTURE_TEST_CASE (test_set_and_normalize_weights, TestNetworkD, *boost::unit_test::tolerance(1e-12))
	BOOST_FIXTURE_TEST_CASE (test_select_neighbour, TestNetworkD_small, *boost::unit_test::tolerance(1.0))
template<typename RT , typename T , typename RNGType >
RT	get_rand (std::pair< T, T > range, RNGType &rng)
	Generate a random number within the given range. More...
template<typename NWType >
constexpr bool	is_directed ()
	Check whether the network type allows for directed edges. More...
template<typename NWType , typename VertexDescType , typename RNGType >
auto	get_rand_neighbor (const VertexDescType v, NWType &nw, RNGType &rng)
template<typename NWType , typename RNGType , typename VertexDescType >
VertexDescType	select_neighbor (const VertexDescType v, NWType &nw, std::uniform_real_distribution< double > &prob_distr, RNGType &rng)
template<typename NWType , typename VertexDescType >
double	opinion_difference (VertexDescType v, VertexDescType w, NWType &nw)
	Calculate the absolute opinion difference of two vertices. More...
template<typename NWType , typename VertexDescType >
void	set_and_normalize_weights (const VertexDescType v, NWType &nw, const double weighting)
	Set and normalize weights according to opinion difference. More...

Variables
std::uniform_real_distribution< double >	uniform_prob_distr

Function Documentation

BOOST_AUTO_TEST_CASE()

Utopia::Models::Opinionet::Utils::BOOST_AUTO_TEST_CASE

(

test_is_directed

)

BOOST_FIXTURE_TEST_CASE() [1/4]

Utopia::Models::Opinionet::Utils::BOOST_FIXTURE_TEST_CASE	(	test_get_rand	,
		Infrastructure
	)

BOOST_FIXTURE_TEST_CASE() [2/4]

Utopia::Models::Opinionet::Utils::BOOST_FIXTURE_TEST_CASE	(	test_get_rand_neighbor	,
		TestNetworkU
	)

BOOST_FIXTURE_TEST_CASE() [3/4]

Utopia::Models::Opinionet::Utils::BOOST_FIXTURE_TEST_CASE	(	test_select_neighbour	,
		TestNetworkD_small	,
		*	boost::unit_test::tolerance1.0
	)

BOOST_FIXTURE_TEST_CASE() [4/4]

Utopia::Models::Opinionet::Utils::BOOST_FIXTURE_TEST_CASE	(	test_set_and_normalize_weights	,
		TestNetworkD	,
		*	boost::unit_test::tolerance1e-12
	)

get_rand()

template<typename RT , typename T , typename RNGType >

RT Utopia::Models::Opinionet::Utils::get_rand	(	std::pair< T, T >	range,
		RNGType &	rng
	)

Generate a random number within the given range.

get_rand_neighbor()

template<typename NWType , typename VertexDescType , typename RNGType >

auto Utopia::Models::Opinionet::Utils::get_rand_neighbor	(	const VertexDescType	v,
		NWType &	nw,
		RNGType &	rng
	)

Get random neighbour of vertex v (for directed and undirected graphs). Only appliable to vertices with degree > 0

is_directed()

template<typename NWType >

constexpr bool Utopia::Models::Opinionet::Utils::is_directed ( )

constexpr

Check whether the network type allows for directed edges.

opinion_difference()

template<typename NWType , typename VertexDescType >

double Utopia::Models::Opinionet::Utils::opinion_difference	(	VertexDescType	v,
		VertexDescType	w,
		NWType &	nw
	)

Calculate the absolute opinion difference of two vertices.

select_neighbor()

template<typename NWType , typename RNGType , typename VertexDescType >

VertexDescType Utopia::Models::Opinionet::Utils::select_neighbor	(	const VertexDescType	v,
		NWType &	nw,
		std::uniform_real_distribution< double > &	prob_distr,
		RNGType &	rng
	)

Select random neighbor with probability proportional to edge weight. Only applicable to vertices with degree > 0

set_and_normalize_weights()

template<typename NWType , typename VertexDescType >

void Utopia::Models::Opinionet::Utils::set_and_normalize_weights	(	const VertexDescType	v,
		NWType &	nw,
		const double	weighting
	)

Set and normalize weights according to opinion difference.

Iterates over a vertex' out-edges and sets the weights to exp(- weighting * abs(opinion difference)), then normalizes them (softmax).

Warning

This assumes that the vertex has at least one out-edge. Make sure to check this in the calling scope, otherwise this will lead to a zero division error during normalisation.

Variable Documentation

uniform_prob_distr

std::uniform_real_distribution<double> Utopia::Models::Opinionet::Utils::uniform_prob_distr