scipy.spatial.distance.is_valid_dm#

scipy.spatial.distance.is_valid_dm(D, tol=0.0, throw=False, name='D', warning=False)[source]#

Return True if input array is a valid distance matrix.

Distance matrices must be 2-dimensional numpy arrays. They must have a zero-diagonal, and they must be symmetric.

Parameters:

Darray_like: The candidate object to test for validity.
tolfloat, optional: The distance matrix should be symmetric. tol is the maximum difference between entries ij and ji for the distance metric to be considered symmetric.
throwbool, optional: An exception is thrown if the distance matrix passed is not valid.
namestr, optional: The name of the variable to checked. This is useful if throw is set to True so the offending variable can be identified in the exception message when an exception is thrown.
warningbool, optional: Instead of throwing an exception, a warning message is raised.

Returns:

validbool: True if the variable D passed is a valid distance matrix.

Notes

Small numerical differences in D and D.T and non-zeroness of the diagonal are ignored if they are within the tolerance specified by tol.

Examples

>>> import numpy as np
>>> from scipy.spatial.distance import is_valid_dm

This matrix is a valid distance matrix.

>>> d = np.array([[0.0, 1.1, 1.2, 1.3],
...               [1.1, 0.0, 1.0, 1.4],
...               [1.2, 1.0, 0.0, 1.5],
...               [1.3, 1.4, 1.5, 0.0]])
>>> is_valid_dm(d)
True

In the following examples, the input is not a valid distance matrix.

Not square:

>>> is_valid_dm([[0, 2, 2], [2, 0, 2]])
False

Nonzero diagonal element:

>>> is_valid_dm([[0, 1, 1], [1, 2, 3], [1, 3, 0]])
False

Not symmetric:

>>> is_valid_dm([[0, 1, 3], [2, 0, 1], [3, 1, 0]])
False