`i0ddlZddlZddlmZdZddZdS)N)specialcj||d}|jjdkr||z}n||z }|S)z4Normalize, preserving floating point precision of x.T)axiskeepdimsf)sumdtypekind)xrx_sums t/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupyx/scipy/stats/_distributions.py _normalizers? EEtdE + +Ew|s U  I HcD|jjdks||jjdkrtd|jjdvr tjn tj}||d}t||}|tj |}n[|j |j krtd||d}t||}tj ||}t j||}||tj|z}|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)r r TypeErrorcharcupyfloat32float64astyperrentrshape ValueErrorrel_entrrmathlog)pkqkbaser float_typevecss r entropyr&s2 x}r~"(-32F2F5666!#$!6!6DLJ :E * *B B  B zl2 8rx  >?? ? YYzY . . D ! !r2&& 4   A  TXd^^ Hr)NNr)rr cupyx.scipyrrr&rr r)sV    * * * * * * r