def score_samples(self, X):
        check_is_fitted(self, "components_")

        v = check_array(X, accept_sparse='csr')
        rng = check_random_state(self.random_state)

        # Randomly corrupt one feature in each sample in v.
        ind = (np.arange(v.shape[0]),
               rng.randint(0, v.shape[1], v.shape[0]))
        if issparse(v):
            data = -2 * v[ind] + 1
            v_ = v + sp.csr_matrix((data.A.ravel(), ind), shape=v.shape)
        else:
            v_ = v.copy()
            v_[ind] = 1 - v_[ind]

        fe = self._free_energy(v)
        fe_ = self._free_energy(v_)
        return v.shape[1] * log_logistic(fe_ - fe)
Wait for the given command to end
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/rbm.py/BernoulliRBM.score_samples--crossover with--dataset/ETHPy150Open panoptes/POCS/panoptes/camera/camera.py/AbstractGPhotoCamera.wait_for_command