Density Sketch

Builds a coreset from the given set of input points. Provides density estimate at a given point.

Based on the following paper: Zohar Karnin, Edo Liberty “Discrepancy, Coresets, and Sketches in Machine Learning”

Inspired by the following implementation:

Requires the use of a KernelFunction to compute the distance between two vectors.

class density_sketch

Static Methods:

deserialize(bytes: bytes, kernel: _datasketches.KernelFunction) _datasketches.density_sketch

Reads a bytes object and returns the corresponding density_sketch

Non-static Methods:

__init__(self, k: int, dim: int, kernel: _datasketches.KernelFunction) None

Creates a new density sketch

  • k (int) – controls the size and error of the sketch

  • dim (int) – dimension of the input data

  • kernel (KernelFunction) – instance of a kernel

property dim

The configured parameter dim


Returns an approximate density at the given point


Returns True if the sketch is empty, otherwise False


Returns True if the sketch is in estimation mode, otherwise False

property k

The configured parameter k


Merges the provided sketch into this one

property n

The length of the input stream

property num_retained

The number of retained items (samples) in the sketch


Serializes the sketch into a bytes object


Produces a string summary of the sketch


Updates the sketch with the given vector