85317dad0531c04e41c94d30e0667032f7d17aff,skopt/samples/sobol.py,Sobol,generate,#Sobol#Any#Any#Any#,190

Before Change

        for j in range(n_samples):
            r[j, 0:n_dim], seed = self._sobol(n_dim, seed)
        if self.randomize:
            return space.inverse_transform(_random_shift(r, random_state))
        return space.inverse_transform(r)

    def _sobol(self, dim_num, seed):
        Generates a new quasirandom Sobol vector with each call.

After Change

        rng = check_random_state(random_state)
        space = Space(dimensions)
        n_dim = space.n_dims
        transformer = space.get_transformer()
        space.set_transformer("normalize")
        r = np.full((n_samples, n_dim), np.nan)
        if self.min_skip == self.max_skip:
            seed = self.min_skip
        else:
            seed = rng.randint(self.min_skip, self.max_skip)
        for j in range(n_samples):
            r[j, 0:n_dim], seed = self._sobol(n_dim, seed)
        if self.randomize:
            r = space.inverse_transform(_random_shift(r, random_state))
        r = space.inverse_transform(r)
        space.set_transformer(transformer)
        return r

    def _sobol(self, dim_num, seed):
        Generates a new quasirandom Sobol vector with each call.

Instances