e3fd56b08ed5c4d7247a147c3fab7c0fe59e9a0e,pymc3/distributions/mixture.py,Mixture,comp_dists,#Mixture#Any#,111

Before Change

        self._comp_dists = _comp_dists
        // Tests if the comp_dists can call random with non None size
        with _DrawValuesContextBlocker():
            if isinstance(self.comp_dists, (list, tuple)):
                try:
                    [comp_dist.random(size=23)
                     for comp_dist in self.comp_dists]
                    self._comp_dists_vect = True
                except Exception:
                    // The comp_dists cannot call random with non None size or
                    // without knowledge of the point so we assume that we will
                    // have to iterate calls to random to get the correct size
                    self._comp_dists_vect = False
            else:
                try:
                    self.comp_dists.random(size=23)
                    self._comp_dists_vect = True
                except Exception:
                    // The comp_dists cannot call random with non None size or
                    // without knowledge of the point so we assume that we will
                    // have to iterate calls to random to get the correct size
                    self._comp_dists_vect = False

    def _comp_logp(self, value):
        comp_dists = self.comp_dists

        try:

After Change

    @comp_dists.setter
    def comp_dists(self, comp_dists):
        self._comp_dists = comp_dists
        if isinstance(comp_dists, Distribution):
            self._comp_dist_shapes = to_tuple(comp_dists.shape)
            self._broadcast_shape = self._comp_dist_shapes
            self.comp_is_distribution = True
        else:
            // Now we check the comp_dists distribution shape, see what
            // the broadcast shape would be. This shape will be the dist_shape
            // used by generate samples (the shape of a single random sample)
            // from the mixture
            self._comp_dist_shapes = [to_tuple(d.shape) for d in comp_dists]
            // All component distributions must broadcast with each other
            try:
                self._broadcast_shape = np.broadcast(
                    *[np.empty(shape) for shape in self._comp_dist_shapes]
                ).shape
            except Exception:
                raise TypeError("Supplied comp_dists shapes do not broadcast "
                                "with each other. comp_dists shapes are: "
                                "{}".format(self._comp_dist_shapes))

            // We wrap the _comp_dist.random by adding the kwarg raw_size_,
            // which will be the size attribute passed to _comp_samples.
            // _comp_samples then calls generate_samples, which may change the
            // size value to make it compatible with scipy.stats.*.rvs
            self._generators = []
            for comp_dist in comp_dists:
                generator = Mixture._comp_dist_random_wrapper(comp_dist.random)
                self._generators.append(generator)
            self.comp_is_distribution = False

    @staticmethod
    def _comp_dist_random_wrapper(random):
        Wrap the comp_dists.random method to take the kwarg raw_size_ and
        use it"s value to replace the size parameter. This is needed because

Instances