1c758ba9d6c14d9b9d3599e44c3508e2692d8d35,pyprob/distributions/empirical.py,Empirical,expectation,#Empirical#Any#,84

Before Change

            return util.fast_np_random_choice(self.values, self._weights_numpy_cumsum)

    def expectation(self, func):
        if self.length == 0:
            raise RuntimeError("Empirical distribution instance is empty.")
        if self._uniform_weights:
            return util.to_tensor(sum(map(func, self.values)) / self.length)
        else:
            ret = 0.

After Change

                ret += util.to_tensor(func(self._shelf[str(i)]), dtype=torch.float64) * self._categorical.probs[i].double()
        else:
            for i in range(self._length):
                ret += util.to_tensor(func(self._values[i]), dtype=torch.float64) * self._categorical.probs[i].double()
        return util.to_tensor(ret)

    def map(self, func):
        self._check_finalized()

Instances