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.
            for i in range(self.length):
                ret += util.to_tensor(func(self.values[i]), dtype=torch.float64) * self.weights[i]

After Change

            return self._get_value(index)

    def expectation(self, func):
        self._check_finalized()
        ret = 0.
        if self._on_disk:
            for i in range(self._length):
                ret += util.to_tensor(func(self._shelf[str(i)]), dtype=torch.float64) * self._categorical.probs[i].double()

Instances