Random distribution of weights

Chisquare

pysensmcda.criteria.random_distribution.chisquare_distribution.chisquare_distribution(size: int, df: float = 1.0) → ndarray

Generate a set of normalized weights sampled from a normal distribution.

Parameters:

sizeint

Number of weights to generate.

dffloat, optional, default=1.0

Number of degrees of freedom. Must be > 0.

Returns:

ndarray

Array of normalized weights sampled from a normal distribution.

Examples:

Example 1: Generate normalized weights from a chi-square distribution with default parameters

>>> weights = chisquare_distribution(3)
>>> print(weights)

Example 2: Generate normalized weights from a chi-square distribution with explicit parameters

>>> weights = chisquare_distribution(3, 5)
>>> print(weights)

Laplace

pysensmcda.criteria.random_distribution.laplace_distribution.laplace_distribution(size: int, loc: float = 0.0, scale: float = 1.0) → ndarray

Generate a set of normalized weights sampled from a laplace distribution.

Parameters:

sizeint

Number of weights to generate.

locfloat, optional, default=0.0

The position of distribution peak

scalefloat, optional, default=1.0

The exponential decay. Must be non-negative

Returns:

ndarray

Array of normalized weights sampled from a laplace distribution.

Examples

Example 1: Generate normalized weights from a laplace distribution with default parameters

>>> weights = laplace_distribution(3)
>>> print(weights)

Example 2: Generate normalized weights from a laplace distribution with explicit parameters

>>> weights = laplace_distribution(3, 5, 2)
>>> print(weights)

Normal

pysensmcda.criteria.random_distribution.normal_distribution.normal_distribution(size: int, loc: float = 0.0, scale: float = 1.0) → ndarray

Generate a set of normalized weights sampled from a normal distribution.

Parameters:

sizeint

Number of weights to generate.

locfloat, optional, default=0.0

Mean of the normal distribution.

scalefloat, optional, default=1.0

Standard deviation of the normal distribution.

Returns:

ndarray

Array of normalized weights sampled from a normal distribution.

Examples:

Example 1: Generate normalized weights from a normal distribution with default parameters

>>> weights = normal_distribution(3)
>>> print(weights)

Example 2: Generate normalized weights from a normal distribution with explicit parameters

>>> weights = normal_distribution(3, 5, 2)
>>> print(weights)

Random

pysensmcda.criteria.random_distribution.random_distribution.random_distribution(size: int) → ndarray

Generate a set of normalized weights sampled from a random distribution ( from half-open interval [0.0, 1.0) ).

Parameters:

sizeint

Number of weights to generate.

Returns:

ndarray

Array of normalized weights sampled from a random distribution.

Example

>>> weights = random_distribution(3)
>>> print(weights)

Triangular

pysensmcda.criteria.random_distribution.triangular_distribution.triangular_distribution(size: int, left: float = 0.0, mode: float = 0.5, right: float = 1.0) → ndarray

Generate a set of normalized weights sampled from a triangular distribution.

Parameters:

sizeint

Number of weights to generate.

leftfloat, optional, default=0.0

The lower bound of the triangular distribution.

modefloat, optional, default=0.5

The mode of the triangular distribution.

rightfloat, optional, default=1.0

The upper bound of the triangular distribution.

Returns:

ndarray

Array of normalized weights sampled from a triangular distribution.

Examples:

Example 1: Generate normalized weights from a triangular distribution with default parameters

>>> weights = triangular_distribution(3)
>>> print(weights)

Example 2: Generate normalized weights from a triangular distribution with explicit parameters

>>> weights = triangular_distribution(3, 2, 5, 6)
>>> print(weights)

Uniform

pysensmcda.criteria.random_distribution.uniform_distribution.uniform_distribution(size: int, low: float = 0.0, high: float = 1.0) → ndarray

Generate a set of normalized weights sampled from a uniform distribution.

Parameters:

sizeint

Number of weights to generate.

lowfloat, optional, default=0.0

Lower bound of the uniform distribution.

highfloat, optional, default=1.0

Upper bound of the uniform distribution.

Returns:

ndarray

Array of normalized weights sampled from a uniform distribution.

Examples:

Example 1: Generate normalized weights from a uniform distribution with default parameters

>>> weights = uniform_distribution(3)
>>> print(weights)

Example 2: Generate normalized weights from a uniform distribution with explicit parameters

>>> weights = uniform_distribution(3, 2, 5)
>>> print(weights)