369d08e119950f356bee2408f6880f7bad1a60d4,pymc3/distributions/multivariate.py,Multinomial,__init__,#Multinomial#Any#Any#,483

Before Change

                n *= tt.ones(m)
            self.n = tt.shape_padright(n)
            self.p = p if p.ndim > 1 else tt.shape_padleft(p)
            lst = list(lst for _ in range(m))
        else:
            // n is a scalar, p is a 1d array
            self.n = tt.as_tensor_variable(n)
            self.p = tt.as_tensor_variable(p)

        self.mean = self.n * self.p
        mode = tt.cast(tt.round(self.mean), "int32")
        diff = self.n - tt.sum(mode, axis=-1, keepdims=True)
        inc_bool_arr = tt.as_tensor_variable(lst) < diff
        mode = tt.inc_subtensor(mode[inc_bool_arr.nonzero()], 1)
        dec_bool_arr = tt.as_tensor_variable(lst) < -diff
        mode = tt.inc_subtensor(mode[dec_bool_arr.nonzero()], -1)
        self.mode = mode

    def _random(self, n, p, size=None):
        original_dtype = p.dtype

After Change

        diff = self.n - tt.sum(mode, axis=-1, keepdims=True)
        inc_bool_arr = tt.abs_(diff) > 0
        mode = tt.inc_subtensor(mode[inc_bool_arr.nonzero()],
                                diff[inc_bool_arr.nonzero()])
        self.mode = mode

    def _random(self, n, p, size=None):

Instances