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
andji
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