o װi@s2ddlZddlZddlmZddZdddZdS)N)specialcCs2|j|dd}|jjdkr||}|S||}|S)z4Normalize, preserving floating point precision of x.T)axiskeepdimsf)sumdtypekind)xrx_sumr T/home/ubuntu/.local/lib/python3.10/site-packages/cupyx/scipy/stats/_distributions.py _normalizes  r cCs|jjdks|dur|jjdkrtd|jjdvrtjntj}|j|dd}t||}|dur6t |}n|j |j kr@t d|j|dd}t||}t ||}tj||d}|durd|t|}|S) aCalculate 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. Args: 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: S (cupy.ndarray): The calculated entropy. cNzcomplex dtype not supportedefF)copyzqk and pk must have same shape.)r)rr TypeErrorcharcupyfloat32float64astyper rentrshape ValueErrorrel_entrrmathlog)pkqkbaser float_typevecsr r r entropys      r#)NNr)rr cupyx.scipyrr r#r r r r s