c6131064c6467e0927cfdb77b0488dc715aef75d,pymc3/distributions/discrete.py,Categorical,logp,#Categorical#Any#,732

Before Change

                               np.ndarray,
                               tt.TensorConstant,
                               tt.sharedvar.SharedVariable)):
            sumto1 = theano.gradient.zero_grad(
                tt.le(abs(tt.sum(p_, axis=-1) - 1), 1e-5))
            p = p_
        else:
            p = p_ / tt.sum(p_, axis=-1, keepdims=True)
            sumto1 = True

After Change

        p = p_ / tt.sum(p_, axis=-1, keepdims=True)

        if p.ndim > 1:
            pattern = (p.ndim - 1,) + tuple(range(p.ndim - 1))
            a = tt.log(p.dimshuffle(pattern)[value_clip])
        else:
            a = tt.log(p[value_clip])

Instances