crispy.scms

Subspace Constrained Mean Shift (SCMS) algorithm for density ridge estimation.

This module provides functions to identify density ridges in high-dimensional data using the SCMS algorithm, including support for parallel computation and filtering to improve efficiency. Core functionalities include walker initialization, Gaussian kernel evaluation, ridge-shifting processes, and multiprocessing utilities.

Functions

chunk_data	Divide data into chunks for multiprocessing.
euclidean_dist	Compute the Euclidean distances and differences between data points and walkers.
find_ridge	Identify density ridges in data using the Subspace Constrained Mean Shift (SCMS) algorithm.
shift_particles	Shift walkers toward density ridges using the Subspace Constrained Mean Shift (SCMS) algorithm.
shift_wakers_multiproc	Shift walkers towards density ridges using the SCMS algorithm with multiprocessing.
shift_walkers	Shift walkers towards density ridges using the Subspace Constrained Mean Shift (SCMS) algorithm.
vectorized_gaussian	Compute Gaussian kernel values for data points relative to walker positions.
wgauss_n_filtered_points	Compute weighted Gaussian values for data points relative to walker positions, filtering out distant points to optimize computation.
wgauss_n_filtered_points_multiproc	Compute weighted Gaussian values for data points relative to walker positions in parallel, filtering out distant points to optimize computation.