def barycenter_kneighbors_graph(X, n_neighbors, reg=0.001, n_jobs=1):
 knn = NearestNeighbors((n_neighbors + 1), n_jobs=n_jobs).fit(X)
 X = knn._fit_X
 n_samples = X.shape[0]
 ind = knn.kneighbors(X, return_distance=False)[:, 1:]
 data = barycenter_weights(X, X[ind], reg=reg)
 indptr = np.arange(0, ((n_samples * n_neighbors) + 1), n_neighbors)
 return csr_matrix((data.ravel(), ind.ravel(), indptr), shape=(n_samples, n_samples))

def barycenter_kneighbors_graph ( X , n_neighbors , reg = 0.001 , n_jobs = 1 ) : knn = NearestNeighbors ( ( n_neighbors + 1 ) , n_jobs = n_jobs ) . fit ( X ) X = knn . _fit_X n_samples = X . shape [ 0 ] ind = knn . kneighbors ( X , return_distance = False ) [ : , 1 : ] data = barycenter_weights ( X , X [ ind ] , reg = reg ) indptr = np . arange ( 0 , ( ( n_samples * n_neighbors ) + 1 ) , n_neighbors ) return csr_matrix ( ( data . ravel ( ) , ind . ravel ( ) , indptr ) , shape = ( n_samples , n_samples ) )
computes the barycenter weighted graph of k-neighbors for points in x parameters x : {array-like .