cupyx.scipy.stats.entropy#

cupyx.scipy.stats.entropy(pk, qk=None, base=None, axis=0)[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=axis).

If qk is not None, then compute the Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=axis).

This routine will normalize pk and qk if they don’t sum to 1.

Parameters:

  • pk (ndarray) – Defines the (discrete) distribution. pk[i] is the (possibly unnormalized) probability of event i.

  • qk (ndarray, 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).

  • axis (int, optional) – The axis along which the entropy is calculated. Default is 0.

Returns:

The calculated entropy.

Return type:

S (cupy.ndarray)