euclidean_dist

crispy.scms.euclidean_dist(X, G)

Compute the Euclidean distances and differences between data points and walkers.

This function calculates pairwise Euclidean distances between points in X and G and returns both the distances and the differences in their coordinates.

Parameters:

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

• G (ndarray) – Coordinates of the walkers, shape (m, D, 1), where m is the number of walkers.

Returns:

• distances (ndarray) – Pairwise Euclidean distances between each point in G and each point in X, shape (m, n).

• diff (ndarray) – Pairwise coordinate differences between points in G and points in X, shape (m, n, D).

Notes

• This function is useful for calculating distances and displacements required in SCMS-based ridge detection.

Examples

Compute pairwise distances and differences:

>>> 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))  # Walker positions
>>> distances, diff = scms.euclidean_dist(data, walkers)
>>> print(distances.shape)  # Should be (10, 100)
>>> print(diff.shape)  # Should be (10, 100, 3)