generatorsrandom.py

class IGenRandom

Bases: ABC

Interface (abstract base class) for wrapping numpy random number generation to allow probability distribution parameters to be specified in the initialiser whereas the generation of random numbers has a common method that just takes the require shape to return. Allows for easy subsitution of different probability distributions.

abstractmethod generate(shape)

Abstract method. Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array of random numbers to be returned.

Returns:

np.ndarray – Array of random numbers with shape specified by the input shape.

class GenNormal(std=1.0, mean=0.0, seed=None)

Bases: IGenRandom

Class wrapping the numpy normal random number generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(std=1.0, mean=0.0, seed=None)

Parameters:

• std (float, optional) – Standard deviation of the normal distribution to sample, by default 1.0

• mean (float, optional) – Mean of the normal distribution to sample, by default 0.0

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenLogNormal(std=1.0, mean=0.0, seed=None)

Bases: IGenRandom

Class wrapping the numpy lognormal random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(std=1.0, mean=0.0, seed=None)

Parameters:

• std (float, optional) – Standard deviation of the normal distribution to sample, by default 1.0

• mean (float, optional) – Mean of the normal distribution to sample, by default 0.0

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenUniform(low=-1.0, high=1.0, seed=None)

Bases: IGenRandom

Class wrapping the numpy uniform random number generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(low=-1.0, high=1.0, seed=None)

Parameters:

• low (float, optional) – Lower bound of the uniform dsitribution., by default -1.0

• high (float, optional) – Upper bound of the uniform distribution, by default 1.0

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenExponential(scale=1.0, seed=None)

Bases: IGenRandom

Class wrapping the numpy exponential random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(scale=1.0, seed=None)

Parameters:

• scale (float, optional) – Scale parameter of the distribution which must be positive, by default 1.0

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenChiSquare(dofs, seed=None)

Bases: IGenRandom

Class wrapping the numpy chi square random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(dofs, seed=None)

Parameters:

• dofs (float) – Number of degrees of freedom of the distribution must be greater than zero.

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenDirichlet(alpha, seed=None)

Bases: IGenRandom

Class wrapping the numpy dirichlet random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(alpha, seed=None)

Parameters:

• alpha (float) – Alpha parameter of the distribution

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenF(dofs, seed=None)

Bases: IGenRandom

Class wrapping the numpy F distribution random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(dofs, seed=None)

Parameters:

• dofs (float) – Number of degrees of freedom of the distribution must be greater than zero

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenGamma(shape, scale=1.0, seed=None)

Bases: IGenRandom

Class wrapping the numpy gamma random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(shape, scale=1.0, seed=None)

Parameters:

• shape (float) – Shape parameter of the gamma distribution, must be greater than zero

• scale (float, optional) – Scale parameter of the gamma distribution which must be greater than zero, by default 1.0

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenStandardT(dofs, seed=None)

Bases: IGenRandom

Class wrapping the numpy t distribution random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(dofs, seed=None)

Parameters:

• dofs (float) – Number of degrees of freedom of the distribution must be greater than zero.

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenBeta(a, b, seed=None)

Bases: IGenRandom

Class wrapping the numpy beta distribution random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(a, b, seed=None)

Parameters:

• a (float) – Alpha parameter of the distribution which must be greater than zero

• b (float) – Beta parameter of the distribution which must be greater than zero

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.

class GenTriangular(left=-1.0, mode=0.0, right=1.0, seed=None)

Bases: IGenRandom

Class wrapping the numpy triangular random generator. Implements the IGeneratorRandom interface to allow for interchangeability with other random number generators.

__init__(left=-1.0, mode=0.0, right=1.0, seed=None)

Parameters:

• left (float, optional) – Left (min) corner of the triangular distribution, by default -1.0

• mode (float, optional) – Central peak of the triangular distribution, by default 0.0

• right (float, optional) – Right (max) corner of the triangular distribution , by default 1.0

• seed (int | None, optional) – Optional seed for the random generator to allow for reproducibility and testing, by default None

generate(shape)

Generates an array of random numbers with the shape specified by the input.

Parameters:

shape (tuple[int,...]) – Shape of the array to return.

Returns:

np.ndarray – Array of random numbers with the specified shape.