```import sys
import math
import numpy as np
import scipy.io as sio
import scipy.sparse as sp
from scipy.sparse.linalg import svds as SVD
from sklearn import svm
from sklearn.metrics import accuracy_score

class SFA:
'''
spectrual feature alignment
'''

def __init__(self, l=500, K=100, base_classifer=svm.SVC()):
self.l = l
self.K = K
self.m = 0
self.ut = None
self.phi = 1
self.base_classifer = base_classifer
self.ix = None
self._ix = None
return

def fit(self, Xs, Xt):

ix_s = np.argsort(np.sum(Xs, axis=0))
ix_t = np.argsort(np.sum(Xt, axis=0))

ix_s = ix_s[::-1][:self.l]
ix_t = ix_t[::-1][:self.l]
ix = np.intersect1d(ix_s, ix_t)
_ix = np.setdiff1d(range(Xs.shape[1]), ix)
self.ix = ix
self._ix = _ix
self.m = len(_ix)
self.l = len(ix)

X = np.concatenate((Xs, Xt), axis=0)
DI = (X[:, ix] > 0).astype('float')
DS = (X[:, _ix] > 0).astype('float')

# construct co-occurrence matrix DSxDI
M = np.zeros((self.m, self.l))
for i in range(X.shape[0]):
tem1 = np.reshape(DS[i], (1, self.m))
tem2 = np.reshape(DI[i], (1, self.l))
M += np.matmul(tem1.T, tem2)
M = M/np.linalg.norm(M, 'fro')
# #construct A matrix
# tem_1 = np.zeros((self.m, self.m))
# tem_2 = np.zeros((self.l, self.l))
# A1 = np.concatenate((tem_1, M.T), axis=0)
# A2 = np.concatenate((M, tem_2), axis=0)
# A = np.concatenate((A1, A2), axis=1)
# # compute laplace
# D = np.zeros((A.shape[0], A.shape[1]))
# for i in range(self.l+self.m):
# 	D[i,i] = 1.0/np.sqrt(np.sum(A[i,:]))
# L = (D.dot(A)).dot(D)
# ut, _, _ = np.linalg.svd(L)
M = sp.lil_matrix(M)
D1 = sp.lil_matrix((self.m, self.m))
D2 = sp.lil_matrix((self.l, self.l))
for i in range(self.m):
D1[i, i] = 1.0/np.sqrt(np.sum(M[1, :]).data[0])
for i in range(self.l):
D2[i, i] = 1.0/np.sqrt(np.sum(M[:, i]).T.data[0])
B = (D1.tocsr().dot(M.tocsr())).dot(D2.tocsr())
# print("Done.")
# print("Computing SVD...")
ut, s, vt = SVD(B.tocsc(), k=self.K)
self.ut = ut
return ut

def transform(self, X):
return np.concatenate((X, X[:, self._ix].dot(self.ut)), axis=1)

def fit_predict(self, Xs, Xt, X_test, Ys, Y_test):
ut = self.fit(Xs, Xt)
Xs = self.transform(Xs)
self.base_classifer.fit(Xs, Ys)
X_test = self.transform(X_test)
y_pred = self.base_classifer.predict(X_test)
acc = accuracy_score(Y_test, y_pred)
return acc
```