This file contains the Random class, a useful wrapper for generating cross platform random functions. This includes functions for uniform distributions (both integer and Float) and Gaussian distributions. More...

#include <Random.h>

Public Member Functions
	Random (unsigned long long seed=0)

	~Random ()

bool	setSeed (const unsigned long long seed=0)

int	getRandomNumberInt (int minRange, int maxRange)

int	getRandomNumberWeighted (const Vector< int > &values, const VectorFloat &weights)

int	getRandomNumberWeighted (Vector< IndexedDouble > weightedValues)

int	getRandomNumberWeighted (Vector< IndexedDouble > &weightedValues, VectorFloat &x)

Float	getRandomNumberUniform (Float minRange=0.0, Float maxRange=1.0)

Float	getUniform (const Float &minRange=0.0, const Float &maxRange=1.0)

Float	getRandomNumberGauss (Float mu=0.0, Float sigma=1.0)

Float	getGauss (const Float &mu=0.0, const Float &sigma=1.0)

VectorFloat	getRandomVectorUniform (UINT numDimensions, Float minRange=0.0, Float maxRange=1.0)

VectorFloat	getRandomVectorGauss (UINT numDimensions, Float mu=0.0, Float sigma=1.0)

Vector< unsigned int >	getRandomSubset (const unsigned int startRange, const unsigned int endRange, const unsigned int subsetSize)

Detailed Description

This file contains the Random class, a useful wrapper for generating cross platform random functions. This includes functions for uniform distributions (both integer and Float) and Gaussian distributions.

Examples:: ClusteringModulesExamples/GaussianMixtureModelsExample/GaussianMixtureModelsExample.cpp, PreprocessingModulesExamples/DeadZoneExample/DeadZoneExample.cpp, PreprocessingModulesExamples/DerivativeExample/DerivativeExample.cpp, PreprocessingModulesExamples/DoubleMovingAverageFilterExample/DoubleMovingAverageFilterExample.cpp, PreprocessingModulesExamples/MedianFilterExample/MedianFilterExample.cpp, and PreprocessingModulesExamples/MovingAverageFilterExample/MovingAverageFilterExample.cpp.

Definition at line 46 of file Random.h.

Constructor & Destructor Documentation

GRT_BEGIN_NAMESPACE Random::Random ( unsigned long long seed = 0 )

Default constructor. Sets the random seed. If no seed is supplied then the seed will be set using the current system time.

Parameters

seed	sets the current seed, If no seed is supplied then the seed will be set using the current system time

Definition at line 29 of file Random.cpp.

Random::~Random ( )

Default destructor.

Definition at line 38 of file Random.cpp.

Member Function Documentation

Float Random::getGauss	(	const Float &	mu = `0.0`,
		const Float &	sigma = `1.0`
	)

Gets a random Float, using a Gaussian distribution with mu 0 and sigma 1.0

Parameters

mu	the mu parameter for the Gaussian distribution
sigma	the sigma parameter for the Gaussian distribution

Returns: returns a Float from the Gaussian distribution controlled by mu and sigma

Definition at line 146 of file Random.cpp.

Float Random::getRandomNumberGauss	(	Float	mu = `0.0`,
		Float	sigma = `1.0`
	)

Gets a random Float, using a Gaussian distribution with mu 0 and sigma 1.0

Parameters

mu	the mu parameter for the Gaussian distribution
sigma	the sigma parameter for the Gaussian distribution

Returns: returns a Float from the Gaussian distribution controlled by mu and sigma

Examples:: ClusteringModulesExamples/GaussianMixtureModelsExample/GaussianMixtureModelsExample.cpp.

Definition at line 142 of file Random.cpp.

int Random::getRandomNumberInt	(	int	minRange,
		int	maxRange
	)

Gets a random integer in the range [minRange maxRange-1], using a uniform distribution

Parameters

minRange	the minimum value in the range (inclusive)
maxRange	the maximum value in the range (not inclusive)

Returns: returns an integer in the range [minRange maxRange-1]

Examples:: ClusteringModulesExamples/GaussianMixtureModelsExample/GaussianMixtureModelsExample.cpp.

Definition at line 59 of file Random.cpp.

Float Random::getRandomNumberUniform	(	Float	minRange = `0.0`,
		Float	maxRange = `1.0`
	)

Gets a random Float in the range [minRange maxRange], using a uniform distribution

Parameters

minRange	the minimum value in the range (inclusive)
maxRange	the maximum value in the range (inclusive)

Returns: returns a Float in the range [minRange maxRange]

Examples:: ClusteringModulesExamples/GaussianMixtureModelsExample/GaussianMixtureModelsExample.cpp, PreprocessingModulesExamples/DeadZoneExample/DeadZoneExample.cpp, PreprocessingModulesExamples/DoubleMovingAverageFilterExample/DoubleMovingAverageFilterExample.cpp, and PreprocessingModulesExamples/MovingAverageFilterExample/MovingAverageFilterExample.cpp.

Definition at line 129 of file Random.cpp.

int Random::getRandomNumberWeighted	(	const Vector< int > &	values,
		const VectorFloat &	weights
	)

Gets a random integer from the Vector values. The probability of choosing a specific integer from the values Vector is given by the corresponding weight in the weights Vector. The size of the values Vector must match the size of the weights Vector. The weights do not need to sum to 1.

For example, if the input values are: [1 2 3] and weights are: [0.7 0.2 0.1], then the 1 value would be randomly returned 70% of the time, the 2 value returned 20% of the time and the 3 value returned 10% of the time.

Parameters

values	a Vector containing the N possible values the function can return
weights	the corresponding weights for the values Vector (must be the same size as the values Vector)

Returns: returns a random integer from the values Vector, with a probability relative to the values weight

Definition at line 68 of file Random.cpp.

int Random::getRandomNumberWeighted ( Vector< IndexedDouble > weightedValues )

Gets a random integer from the input Vector. The probability of choosing a specific integer is given by the corresponding weight of that value. The weights do not need to sum to 1.

For example, if the input values are: [{1 0.7},{2 0.2}, {3 0.1}], then the 1 value would be randomly returned 70% of the time, the 2 value returned 20% of the time and the 3 value returned 10% of the time.

Parameters

weightedValues a Vector of IndexedDouble values, the (int) indexs represent the value that will be returned while the (Float) values represent the weight of choosing that specific index

Returns: returns a random integer from the values Vector, with a probability relative to the values weight

Definition at line 82 of file Random.cpp.

int Random::getRandomNumberWeighted	(	Vector< IndexedDouble > &	weightedValues,
		VectorFloat &	x
	)

This function is similar to the getRandomNumberWeighted(Vector< IndexedDouble > weightedValues), with the exception that the user needs to sort the weightedValues Vector and create the accumulated lookup table (x). This is useful if you need to call the same function multiple times on the same weightedValues, allowing you to only sort and build the loopup table once.

Gets a random integer from the input Vector. The probability of choosing a specific integer is given by the corresponding weight of that value. The weights do not need to sum to 1.

For example, if the input values are: [{1 0.7},{2 0.2}, {3 0.1}], then the 1 value would be randomly returned 70% of the time, the 2 value returned 20% of the time and the 3 value returned 10% of the time.

Parameters

weightedValues	a sorted Vector of IndexedDouble values, the (int) indexs represent the value that will be returned while the (Float) values represent the weight of choosing that specific index
x	a Vector containing the accumulated lookup table

Returns: returns a random integer from the values Vector, with a probability relative to the values weight

Definition at line 111 of file Random.cpp.

Vector< unsigned int > Random::getRandomSubset	(	const unsigned int	startRange,
		const unsigned int	endRange,
		const unsigned int	subsetSize
	)

Gets an N-dimensional Vector of random unsigned ints drawn from the range controlled by the start and end range parameters.

Parameters

startRange	indicates the start of the range the random subset will selected from (e.g. 0)
endRange	indicates the end of the range the random subset will selected from (e.g. 100)
subsetSize	controls the size of the Vector returned by the function (e.g. 50

Returns: returns a Vector of unsigned ints selected from the

Definition at line 185 of file Random.cpp.

VectorFloat Random::getRandomVectorGauss	(	UINT	numDimensions,
		Float	mu = `0.0`,
		Float	sigma = `1.0`
	)

Gets an N-dimensional Vector of random Floats drawn from the Gaussian distribution controlled by mu and sigma.

Parameters

numDimensions	the size of the Vector you require
mu	the mu parameter for the Gaussian distribution
sigma	the sigma parameter for the Gaussian distribution

Returns: returns a Vector of Floats drawn from the Gaussian distribution controlled by mu and sigma

Definition at line 177 of file Random.cpp.

VectorFloat Random::getRandomVectorUniform	(	UINT	numDimensions,
		Float	minRange = `0.0`,
		Float	maxRange = `1.0`
	)

Gets an N-dimensional Vector of random Floats drawn from the uniform distribution set by the minRange and maxRange.

Parameters

numDimensions	the size of the Vector you require
minRange	the minimum value in the range (inclusive)
maxRange	the maximum value in the range (inclusive)

Returns: returns a Vector of Floats drawn from the uniform distribution set by the minRange and maxRange

Definition at line 169 of file Random.cpp.

Float Random::getUniform	(	const Float &	minRange = `0.0`,
		const Float &	maxRange = `1.0`
	)

Gets a random Float in the range [minRange maxRange], using a uniform distribution

Parameters

minRange	the minimum value in the range (inclusive)
maxRange	the maximum value in the range (inclusive)

Returns: returns a Float in the range [minRange maxRange]

Definition at line 133 of file Random.cpp.

bool Random::setSeed ( const unsigned long long seed = 0 )

Sets the current seed used to compute the random distrubutions.

Parameters

seed	sets the current seed, If no seed is supplied then the seed will be set using the current system time

Returns: returns true if the seed was set successfully, false otherwise

Definition at line 40 of file Random.cpp.

The documentation for this class was generated from the following files:

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation