Attributes

dataset : ndarray

The dataset with which :None:None:`gaussian_kde` was initialized.

dim : int

Number of dimensions.

num_dp : int

Number of datapoints.

factor : float

The bandwidth factor, obtained from :None:None:`kde.covariance_factor`, with which the covariance matrix is multiplied.

covariance : ndarray

The covariance matrix of dataset, scaled by the calculated bandwidth (:None:None:`kde.factor`).

inv_cov : ndarray

The inverse of covariance.

Methods

Parameters

dataset : array-like

Datapoints to estimate from. In case of univariate data this is a 1-D array, otherwise a 2D array with shape (# of dims, # of data).

bw_method : str, scalar or callable, optional

The method used to calculate the estimator bandwidth. This can be 'scott', 'silverman', a scalar constant or a callable. If a scalar, this will be used directly as :None:None:`kde.factor`. If a callable, it should take a GaussianKDE instance as only parameter and return a scalar. If None (default), 'scott' is used.

Representation of a kernel-density estimate using Gaussian kernels.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

matplotlib.mlab.GaussianKDE matplotlib.pyplot.violinplot matplotlib.axes._axes.Axes.violinplot

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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