shift_wakers_multiproc#

crispy.scms.shift_wakers_multiproc(G, X, h, d, c, mask, ncpu)[source]#

Shift walkers towards density ridges using the SCMS algorithm with multiprocessing.

This function parallelizes the SCMS walker-shifting process for improved efficiency on large datasets. It divides the walkers into chunks and processes them concurrently across multiple CPUs.

Parameters:

  • G (ndarray) – Initial coordinates of the walkers, shape (m, D, 1), where m is the number of walkers and D is the dimensionality.

  • X (ndarray) – Coordinates of the data points, shape (n, D, 1), where n is the number of data points.

  • h (float) – Smoothing bandwidth for the Gaussian kernel.

  • d (int) – Target dimensionality of the ridge subspace.

  • c (ndarray) – Weighted Gaussian values computed for the data points and walkers, shape (m, n).

  • mask (ndarray of bool) – Boolean mask indicating valid (True) data points for each walker. Shape is (m, n).

  • ncpu (int) – Number of CPUs to use for parallel processing. If set to None, defaults to the number of available CPUs.

Returns:

  • G_updated (ndarray) – Updated coordinates of the walkers after the SCMS shift, shape (m, D, 1).

  • error (ndarray) – Convergence error for each walker, shape (m,). The error represents the displacement of each walker and is used to determine convergence.

Notes

  • The walkers (G) are divided into chunks, and each chunk is processed independently on a separate CPU.

  • Internally, this function calls shift_walkers for each chunk, ensuring consistency with the SCMS algorithm.

  • Multiprocessing is particularly beneficial when the number of walkers or data points is large.

Examples

Perform a parallel SCMS shift for walkers:

>>> import numpy as np
>>> from crispy import scms
>>> data = np.random.random((100, 3, 1))  # 3D data points
>>> walkers = np.random.random((10, 3, 1))  # Initial walker positions
>>> c = np.random.random((10, 100))  # Weighted Gaussian values
>>> mask = np.random.choice([True, False], size=(10, 100))  # Boolean mask
>>> h = 1.0
>>> d = 1
>>> ncpu = 4  # Use 4 CPUs
>>> G_updated, error = scms.shift_wakers_multiproc(walkers, data, h, d, c, mask, ncpu)
On this page

This Page