"""
list_acc = []
X = np.hstack((self.Xs.T, self.Xt.T))
X = np.dot(X, np.diag(1 / (np.sum(X ** 2, axis=0) ** 0.5)))
m, n = X.shape
ns, nt = len(self.Xs), len(self.Xt)
e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1))))
C = len(np.unique(Ys))
H = np.eye(n) - 1 / n * np.ones((n, n))
M = e * e.T * C
Y_tar_pseudo = None
for t in range(self.T):
N = 0
if Y_tar_pseudo is not None and len(Y_tar_pseudo) == nt:
for c in range(1, C + 1):
e = np.zeros((n, 1))
tt = Ys == c
e[np.where(tt == True)] = 1 / len(Ys[np.where(self.Ys == c)])
yy = Y_tar_pseudo == c
ind = np.where(yy == True)
inds = [item + ns for item in ind]
e[tuple(inds)] = -1 / len(Y_tar_pseudo[np.where(Y_tar_pseudo == c)])
e[np.isinf(e)] = 0
N = N + np.dot(e, e.T)
M += N
M = M / np.linalg.norm(M, "fro")
K = kernel(self.kernel_type, X, None, gamma=self.gamma)
n_eye = m if self.kernel_type == "primal" else n
a, b = np.linalg.multi_dot([K, M, K.T]) + self.lamb * np.eye(n_eye), np.linalg.multi_dot([K, H, K.T])
w, V = scipy.linalg.eig(a, b)
ind = np.argsort(w)
A = V[:, ind[:self.dim]]
Z = np.dot(A.T, K)
Z = np.dot(Z, np.diag(1 / (np.sum(Z ** 2, axis=0) ** 0.5)))
Xs_new, Xt_new = Z[:, :ns].T, Z[:, ns:].T
clf = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1)
clf.fit(Xs_new, self.Ys.ravel())
Y_tar_pseudo = clf.predict(Xt_new)
After Change
:return: acc, y_pred, list_acc
"""
list_acc = []
X = np.hstack((Xs.T, Xt.T))
X /= np.linalg.norm(X, axis=0)
m, n = X.shape
ns, nt = len(Xs), len(Xt)
e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1))))
C = len(np.unique(Ys))
H = np.eye(n) - 1 / n * np.ones((n, n))
M = e * e.T * C
Y_tar_pseudo = None
for t in range(self.T):
N = 0
if Y_tar_pseudo is not None and len(Y_tar_pseudo) == nt:
for c in range(1, C + 1):
e = np.zeros((n, 1))
tt = Ys == c
e[np.where(tt == True)] = 1 / len(Ys[np.where(self.Ys == c)])
yy = Y_tar_pseudo == c
ind = np.where(yy == True)
inds = [item + ns for item in ind]
e[tuple(inds)] = -1 / len(Y_tar_pseudo[np.where(Y_tar_pseudo == c)])
e[np.isinf(e)] = 0
N = N + np.dot(e, e.T)
M += N
M = M / np.linalg.norm(M, "fro")
K = kernel(self.kernel_type, X, None, gamma=self.gamma)
n_eye = m if self.kernel_type == "primal" else n
a, b = np.linalg.multi_dot([K, M, K.T]) + self.lamb * np.eye(n_eye), np.linalg.multi_dot([K, H, K.T])
w, V = scipy.linalg.eig(a, b)
ind = np.argsort(w)
A = V[:, ind[:self.dim]]
Z = np.dot(A.T, K)
Z /= np.linalg.norm(Z, axis=0)
Xs_new, Xt_new = Z[:, :ns].T, Z[:, ns:].T
clf = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1)
clf.fit(Xs_new, Ys.ravel())
