copulas.univariate.gaussian_kde module

GaussianKDE module.

class copulas.univariate.gaussian_kde.GaussianKDE(sample_size=None, random_state=None, bw_method=None, weights=None)[source]

Bases: ScipyModel

A wrapper for gaussian Kernel density estimation.

It was implemented in scipy.stats toolbox. gaussian_kde is slower than statsmodels but allows more flexibility.

When a sample_size is provided the fit method will sample the data, and mask the real information. Also, ensure the number of entries will be always the value of sample_size.

Parameters:: sample_size (int) – amount of parameters to sample

BOUNDED = 0

MODEL_CLASS: alias of gaussian_kde

PARAMETRIC = 0

cumulative_distribution(X)[source]

Compute the cumulative distribution value for each point in X.

Parameters:: X (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1).
Returns:: Cumulative distribution values for points in X.
Return type:: numpy.ndarray
Raises:: NotFittedError – if the model is not fitted.

percent_point(U, method='chandrupatla')[source]

Compute the inverse cumulative distribution value for each point in U.

Parameters:

U (numpy.ndarray) – Values for which the cumulative distribution will be computed. It must have shape (n, 1) and values must be in [0,1].
method (str) – Whether to use the chandrupatla or bisect solver.

Returns:

Inverse cumulative distribution values for points in U.

Return type:

numpy.ndarray

Raises:

NotFittedError – if the model is not fitted.

probability_density(X)[source]

Compute the probability density for each point in X.

Parameters:: X (numpy.ndarray) – Values for which the probability density will be computed. It must have shape (n, 1).
Returns:: Probability density values for points in X.
Return type:: numpy.ndarray
Raises:: NotFittedError – if the model is not fitted.

sample(n_samples=1)[source]

Sample values from this model.

Argument:

n_samples (int):: Number of values to sample

Returns:: Array of shape (n_samples, 1) with values randomly sampled from this model distribution.
Return type:: numpy.ndarray
Raises:: NotFittedError – if the model is not fitted.