把这段代码import numpy as npfrom scipysparse import from sklearnmetricspairwise import pairwise_distancesdef construct_SX kwargs n_samples n_features = npshapeX k=kwargsneighbour_size t = kwargst_param

import numpy as np from scipy.sparse import * from sklearn.metrics.pairwise import pairwise_distances

def construct_S(X, varargin):

n_samples, n_features = np.shape(X)

k = varargin['neighbour_size']

t = varargin['t_param']

# compute pairwise euclidean distances
D = pairwise_distances(X)
D **= 2

# sort the distance matrix D in ascending order
Dsorted = np.sort(D, axis=1)
idxSorted = np.argsort(D, axis=1)
idx = idxSorted[:, 0:k+1]
dist = Dsorted[:, 0:k+1]

# compute the pairwise heat kernel distances
Weight = np.exp(-dist/(t))
G = np.zeros((n_samples*(k+1),3))
G[:, 0] = np.tile(np.arange(n_samples), (k+1, 1)).reshape(-1)
G[:, 1] = np.ravel(idx, order='F')
G[:, 2] = np.ravel(Weight, order='F')

# build the sparse affinity matrix W
S = csc_matrix((G[:, 2], (G[:, 0], G[:, 1])), shape=(n_samples, n_samples))
bigger = S.transpose() > S
S = S - S.multiply(bigger) + S.transpose().multiply(bigger)
print(S)
return S