scipy.linalg。规范不同于sklearn.preprocessing.normalize吗?

from numpy.random import rand
from sklearn.preprocessing import normalize
from scipy.sparse import csr_matrix
from scipy.linalg import norm

w = (rand(1,10)<0.25)*rand(1,10)
x = (rand(1,10)<0.25)*rand(1,10)
w_csr = csr_matrix(w)
x_csr = csr_matrix(x)
(normalize(w_csr,axis=1,copy=False,norm='l2')*normalize(x_csr,axis=1,copy=False,norm='l2')).todense()

norm(w,ord='fro')*norm(x,ord='fro')
from numpy.random import rand
from sklearn.prep