def log_logistic(X, out=None):
 is_1d = (X.ndim == 1)
 X = np.atleast_2d(X)
 X = check_array(X, dtype=np.float64)
 (n_samples, n_features) = X.shape
 if (out is None):
  out = np.empty_like(X)
 _log_logistic_sigmoid(n_samples, n_features, X, out)
 if is_1d:
  return np.squeeze(out)
 return out

def log_logistic ( X , out = None ) : is_1d = ( X . ndim == 1 ) X = np . atleast_2d ( X ) X = check_array ( X , dtype = np . float64 ) ( n_samples , n_features ) = X . shape if ( out is None ) : out = np . empty_like ( X ) _log_logistic_sigmoid ( n_samples , n_features , X , out ) if is_1d : return np . squeeze ( out ) return out
compute the log of the logistic function .