parameters: (self, X)
return_statement: return v.shape[1] * log_logistic(fe_ - fe)
docstring: Compute the pseudo-likelihood of X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} shape (n_samples, n_features)
              Values of the visible layer. Must be all-boolean (not checked).
  
          Returns
          -------
          pseudo_likelihood : array-like, shape (n_samples,)
              Value of the pseudo-likelihood (proxy for likelihood).
  
          Notes
          -----
          This method is not deterministic: it computes a quantity called the
          free energy on X, then on a randomly corrupted version of X, and
          returns the log of the logistic function of the difference.
docstring_summary: Compute the pseudo-likelihood of X.
docstring_tokens: Compute the pseudo - likelihood of X .
function: def score_samples(self, X):
          """Compute the pseudo-likelihood of X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} shape (n_samples, n_features)
              Values of the visible layer. Must be all-boolean (not checked).
  
          Returns
          -------
          pseudo_likelihood : array-like, shape (n_samples,)
              Value of the pseudo-likelihood (proxy for likelihood).
  
          Notes
          -----
          This method is not deterministic: it computes a quantity called the
          free energy on X, then on a randomly corrupted version of X, and
          returns the log of the logistic function of the difference.
          """
          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)
function_tokens: 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 )