Kernel Function

A kernel function is a specific type of mathematical funciton that is particularly useful in certain machine learning and pattern recognition contexts. The density_sketch performs approximate kernel density estimation which, unsurprisingly, relies on the use of such a kernel function.

The library provides an abstract base class KernelFunction and an example implementation of a Gaussian (also known as a Radial Basis Function) kernel. Custom classes must override the base class and provide a floating point value as a score indicating the similarity of two input vectors.

class KernelFunction

A generic base class from which user-defined kernels must inherit.

__call__(self, a: object, b: object) float

A method to evaluate a kernel with given inputs a and b.

  • a (numpy array) – An input vector

  • b (numpy array) – An input vector


A vector similarity score

Return type:


class GaussianKernel(bandwidth: float = 1.0)

Bases: KernelFunction

Implements a basic Gaussian kernel


bandwidth (float) – The kernel bandwidth, default 1.0