- scipy.stats.entropy(pk, qk=None, base=None)[source]¶
Calculate the entropy of a distribution for given probability values.
If only probabilities pk are given, the entropy is calculated as S = -sum(pk * log(pk), axis=0).
If qk is not None, then compute a relative entropy (also known as Kullback-Leibler divergence or Kullback-Leibler distance) S = sum(pk * log(pk / qk), axis=0).
This routine will normalize pk and qk if they don’t sum to 1.
pk : sequence
Defines the (discrete) distribution. pk[i] is the (possibly unnormalized) probability of event i.
qk : sequence, optional
Sequence against which the relative entropy is computed. Should be in the same format as pk.
base : float, optional
The logarithmic base to use, defaults to e (natural logarithm).
S : float
The calculated entropy.