scipy.spatial.distance.jensenshannon¶
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scipy.spatial.distance.
jensenshannon
(p, q, base=None)[source]¶ Compute the Jensen-Shannon distance (metric) between two 1-D probability arrays. This is the square root of the Jensen-Shannon divergence.
The Jensen-Shannon distance between two probability vectors p and q is defined as,
\[\sqrt{\frac{D(p \parallel m) + D(q \parallel m)}{2}}\]where \(m\) is the pointwise mean of \(p\) and \(q\) and \(D\) is the Kullback-Leibler divergence.
This routine will normalize p and q if they don’t sum to 1.0.
- Parameters
- p(N,) array_like
left probability vector
- q(N,) array_like
right probability vector
- basedouble, optional
the base of the logarithm used to compute the output if not given, then the routine uses the default base of scipy.stats.entropy.
- Returns
- jsdouble
The Jensen-Shannon distance between p and q
- .. versionadded:: 1.2.0
Examples
>>> from scipy.spatial import distance >>> distance.jensenshannon([1.0, 0.0, 0.0], [0.0, 1.0, 0.0], 2.0) 1.0 >>> distance.jensenshannon([1.0, 0.0], [0.5, 0.5]) 0.46450140402245893 >>> distance.jensenshannon([1.0, 0.0, 0.0], [1.0, 0.0, 0.0]) 0.0