def KEDM(param, A):
N = param.N
d = param.d
K = ktools.K_base(param)
###########################################################################
MaxIter = 500
verbosity = True
###########################################################################
output = KEDM_OUT()
tau_list = ktools.generalsampler(param, 'basis')
trn_list = ktools.generalsampler(param, 'trn_sample')
tst_list = ktools.generalsampler(param, 'tst_sample')
log_list = ktools.generalsampler(param, 'log_sample')
D, W, ei = ktools.generateData(param, trn_list, A)
G = []
con = []
for k in range(K):
G.append(cvx.Variable((N, N), PSD=True))
con.append(cvx.sum(G[k], axis=0) == 0)
for t in log_list:
weights = ktools.W(t, tau_list, param)
weights = weights / np.linalg.norm(weights)
G_tot = ktools.G_t(G, weights, True)
con.append(G_tot >> 0)
cost = 0
for i_t, t in enumerate(trn_list):
weights = ktools.W(t, tau_list, param)
G_tot = ktools.G_t(G, weights, True)
con.append(G_tot >> 0)
D_G = ktools.gram2edm(G_tot, N, True)
W_vec = np.diag(W[i_t].flatten())
alpha = (np.linalg.norm(np.matmul(W_vec, D[i_t].flatten())))**(-2)
cost += alpha * cvx.norm(cvx.matmul(W_vec, cvx.vec(D[i_t] - D_G)))**2
obj = cvx.Minimize(cost)
prob = cvx.Problem(obj, con)
try:
prob.solve(solver=cvx.CVXOPT, verbose=False, normalize=True)
except Exception as message:
print(message)
output.status = str(prob.status)
if str(prob.status) == 'optimal':
G = ktools.rankProj(G, param)
eo = ktools.testError(param, tau_list, tst_list, G, A)
output.G = G
output.tau_list = tau_list
output.trn_list = trn_list
output.tst_list = tst_list
output.ei = ei
output.eo = eo
output.A = A
return output
def __init__(self):
self.N = 6
self.d = 2
self.P = 3
self.omega = 2 * np.pi
self.mode = 1
self.T_trn = np.array([-1, 1])
self.T_tst = np.array([-1, 1])
self.N_trn = ktools.K_base(self)
self.K = ktools.K_base(self)
self.N_tst = 500
self.Nr = 5
self.n_del = 0
self.sampling = 1
self.delta = 0.99
self.std = 1
self.maxIter = 5
self.n_del_init = 0
self.bipartite = False
self.N0 = 3
self.Pr = 0.9
self.path = '../../../results/kedm/python3/'
def KEDM(param, A):
N = param.N
d = param.d
K = ktools.K_base(param)
###########################################################################
MaxIter = 500
verbosity = True
###########################################################################
output = KEDM_OUT()
tau_list = ktools.generalsampler(param, 'basis')
trn_list = ktools.generalsampler(param, 'trn_sample')
tst_list = ktools.generalsampler(param, 'tst_sample')
log_list = ktools.generalsampler(param, 'log_sample')
D, W, ei = ktools.generateData(param, trn_list, A)
G = []
con = []
for k in range(K):
G.append(cvx.Variable((N, N), PSD=True))
con.append(cvx.sum(G[k],axis = 0) == 0)
for t in log_list:
weights = ktools.W(t, tau_list, param)
weights = weights/np.linalg.norm(weights)
G_tot = ktools.G_t(G, weights, True)
con.append(G_tot>>0)
cost = 0
for i_t, t in enumerate(trn_list):
weights = ktools.W(t, tau_list, param)
G_tot = ktools.G_t(G, weights, True)
con.append(G_tot >> 0)
D_G = ktools.gram2edm(G_tot, N, True)
W_vec = np.diag(W[i_t].flatten())
alpha = (np.linalg.norm( np.matmul(W_vec, D[i_t].flatten()) ) )**(-2)
cost += alpha*cvx.norm( cvx.matmul(W_vec, cvx.vec(D[i_t]-D_G) ) )**2
obj = cvx.Minimize(cost)
prob = cvx.Problem(obj,con)