parameters: (self, X)
return_statement: return np.array(nX).T
docstring: Transform a new matrix using the built clustering
  
          Parameters
          ----------
          X : array-like, shape = [n_samples, n_features] or [n_features]
              A M by N array of M observations in N dimensions or a length
              M array of M one-dimensional observations.
  
          Returns
          -------
          Y : array, shape = [n_samples, n_clusters] or [n_clusters]
              The pooled values for each feature cluster.
docstring_summary: Transform a new matrix using the built clustering
docstring_tokens: Transform a new matrix using the built clustering
function: def transform(self, X):
          """
          Transform a new matrix using the built clustering
  
          Parameters
          ----------
          X : array-like, shape = [n_samples, n_features] or [n_features]
              A M by N array of M observations in N dimensions or a length
              M array of M one-dimensional observations.
  
          Returns
          -------
          Y : array, shape = [n_samples, n_clusters] or [n_clusters]
              The pooled values for each feature cluster.
          """
          check_is_fitted(self, "labels_")
  
          pooling_func = self.pooling_func
          X = check_array(X)
          nX = []
          if len(self.labels_) != X.shape[1]:
              raise ValueError("X has a different number of features than "
                               "during fitting.")
  
          for l in np.unique(self.labels_):
              nX.append(pooling_func(X[:, self.labels_ == l], axis=1))
          return np.array(nX).T
function_tokens: def transform ( self , X ) : check_is_fitted ( self , "labels_" ) pooling_func = self . pooling_func X = check_array ( X ) nX = [ ] if len ( self . labels_ ) != X . shape [ 1 ] : raise ValueError ( "X has a different number of features than " "during fitting." ) for l in np . unique ( self . labels_ ) : nX . append ( pooling_func ( X [ : , self . labels_ == l ] , axis = 1 ) ) return np . array ( nX ) . T