kalepy.kernels

Kernal basis functions for KDE calculations, used by kalepy.kde.KDE class.

Contents:

  • Kernel : class performing the numerical/mathematical functions of a KDE using a particular kernel-function.

  • Distribution : base class for kernel-function functionality

  • Gaussian(Distribution) : class for Gaussian kernel functions

  • Box_Asym(Distribution) : class for box (top-hat) kernel functions

  • Parabola(Distribution) : class for parabolic (Epanechnikov) kernel functions

  • get_distribution_class : returns the appropriate Distribution subclass matching the given string specification.

  • get_all_distribution_classes : returns a list of active Distribution subclasses (used for testing).

class kalepy.kernels.Kernel(distribution, bandwidth, covariance, helper=False, chunk=100000.0)

Bases: object

property FINITE
__init__(distribution, bandwidth, covariance, helper=False, chunk=100000.0)

Initialize self. See help(type(self)) for accurate signature.

property bandwidth
property covariance
density(points, data, weights=None, reflect=None, params=None)

Calculate the Density Function using this Kernel.

Parameters

points ((D, N), 2darray of float,) – N points at which to evaluate the density function over D parameters (dimensions). Locations must be specified for each dimension of the data, or for each of target params dimensions of the data.

property distribution
property matrix
property matrix_inv
property norm
resample(data, weights=None, size=None, keep=None, reflect=None, squeeze=True)
kalepy.kernels.get_distribution_class(arg=None)