def gibbs(self, v):
        check_is_fitted(self, "components_")
        if not hasattr(self, "random_state_"):
            self.random_state_ = check_random_state(self.random_state)
        h_ = self._sample_hiddens(v, self.random_state_)
        v_ = self._sample_visibles(h_, self.random_state_)

        return v_
Perform one Gibbs sampling step.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/rbm.py/BernoulliRBM.gibbs