medpy.metric.histogram.jensen_shannon#
- medpy.metric.histogram.jensen_shannon(h1, h2)[source]#
Jensen-Shannon divergence.
A symmetric and numerically more stable empirical extension of the Kullback-Leibler divergence.
The Jensen Shannon divergence between two histograms \(H\) and \(H'\) of size \(m\) is defined as:
\[d_{JSD}(H, H') = \frac{1}{2} d_{KL}(H, H^*) + \frac{1}{2} d_{KL}(H', H^*)\]with \(H^*=\frac{1}{2}(H + H')\).
Attributes:
semimetric
Attributes for normalized histograms:
\(d(H, H')\in[0, 1]\)
\(d(H, H) = 0\)
\(d(H, H') = d(H', H)\)
Attributes for not-normalized histograms:
\(d(H, H')\in[0, \infty)\)
\(d(H, H) = 0\)
\(d(H, H') = d(H', H)\)
Attributes for not-equal histograms:
not applicable
- Parameters:
- h1sequence
The first histogram.
- h2sequence
The second histogram, same bins as
h1.
- Returns:
- jensen_shannonfloat
Jensen-Shannon divergence.