distribution module¶

The notions of distribution and composed distributions¶

Overview¶

The abstract Distribution class implements the concept of probability distribution. It is enriched by concrete classes such as OTDistribution and SPDistribution.

Similarly, the abstract ComposedDistribution class implements the concept of composed probability distribution from a list of probability distributions. It inherits from Distribution.

Construction¶

The Distribution of a given uncertain variable is built from a recognized distribution name, a variable dimension, a set of parameters and optionally a standard representation of these parameters.

The ComposedDistribution of a list of given uncertain variables is built from a list of Distribution objects implementing the probability distributions of these variables and from a copula name.

Note

A copula is a mathematical function used to define the dependence between random variables from their cumulative density functions. See more.

Capabilities¶

From a Distribution, we can easily get statistics, such as Distribution.mean, Distribution.standard_deviation, numerical Distribution.range and mathematical Distribution.support, or plot the cumulative and probability density functions, either for a given marginal (Distribution.plot()) or for all marginals (Distribution.plot_all()).

We can also compute the cumulative density function (Distribution.cdf()) for the different marginals of the random variable, as well as the inverse cumulative density function (Distribution.inverse_cdf()).

Lastly, we can get realizations of the random variable by means of the Distribution.get_sample() method.

Note

As ComposedDistribution inherits from Distribution, it has the same capabilities.

class gemseo.uncertainty.distributions.distribution.ComposedDistribution(distributions, copula='independent_copula')[source]¶

Bases: gemseo.uncertainty.distributions.distribution.Distribution

Composed Distribution.

Constructor.

Parameters

distributions (list(Distribution)) – list of distributions.
copula (str) – copula name. Default: INDEPENDENT_COPULA.

AVAILABLE_COPULA = ['independent_copula']¶

COMPOSED = 'Composed'¶

INDEPENDENT_COPULA = 'independent_copula'¶

get_sample(n_samples=1)[source]¶

Get sample.

Parameters: n_samples (int) – number of samples.
Returns: samples
Return type: list(array)

property mean¶

Get the mean of the random variable.

Returns: mean of the random variable.
Return type: array

property standard_deviation¶

Get the standard deviation of the random variable.

Returns: standard deviation of the random variable.
Return type: array

class gemseo.uncertainty.distributions.distribution.Distribution(variable, distribution, parameters, dimension, standard_parameters=None)[source]¶

Bases: object

Distribution.

Constructor

Parameters

variable (str) – variable name.
distribution (str) – distribution name.
parameters (tuple or dict) – distribution parameters.
dimension (int) – variable dimension.
standard_parameters (dict) – standard parameters.

LOC = 'loc'¶

LOWER = 'lower'¶

MATHEMATICAL_LOWER_BOUND = 'math_l_b'¶

MATHEMATICAL_UPPER_BOUND = 'math_u_b'¶

MODE = 'mode'¶

MU = 'mu'¶

NUMERICAL_LOWER_BOUND = 'num_l_b'¶

NUMERICAL_UPPER_BOUND = 'num_u_b'¶

RATE = 'rate'¶

SIGMA = 'sigma'¶

UPPER = 'upper'¶

cdf(vector)[source]¶

Evaluate the cumulative density function of the random variable marginals for a given instance.

Parameters: vector (array) – instance of the random variable.
Returns: cdf values
Return type: array

get_sample(n_samples=1)[source]¶

Get several samples.

Parameters: n_samples (int) – number of samples.
Returns: samples
Return type: array

inverse_cdf(vector)[source]¶

Evaluate the inverse of the cumulative density function of the random variable marginals for a given unit vector .

Parameters: vector (array) – vector of values comprised between 0 and 1 with same dimension as the random variable.
Returns: inverse cdf values
Return type: array

property mean¶

Get the mean of the random variable.

Returns: mean of the random variable.
Return type: array

plot(index=0, show=True, save=False, prefix=None)[source]¶

Plot the probability density function and the cumulative density function of the random variable.

Parameters: prefix (str) – If not None, start the filename with a prefix. Default: None.

plot_all(show=True, save=False, prefix=None)[source]¶

Plot the probability density function and the cumulative density function of the random variable.

Parameters: prefix (str) – If not None, start the filename with a prefix. Default: None.

property range¶

Get the numerical range for the different components.

Returns: numerical range.
Return type: list(array)

property standard_deviation¶

Get the standard deviation of the random variable.

Returns: standard deviation of the random variable.
Return type: array

property support¶

Get the mathematical support for the different components.

Returns: mathematical support.
Return type: list(array)