copulas.univariate.gaussian_kde module¶

GaussianKDE module.

class copulas.univariate.gaussian_kde.GaussianKDE(*args, **kwargs)[source]¶

Bases: copulas.univariate.base.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 scipy.stats._kde.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(*args, **kwargs)¶