def testRLS(self):
print
print
print
print
print "Testing the cross-validation routines of the RLS module."
print
print
floattype = float64
m, n = 100, 300
Xtrain = random.rand(m, n)
ylen = 1
Y = mat(zeros((m, ylen), dtype=floattype))
Y = mat(random.rand(m, 1))
basis_vectors = [0,3,7,8]
def complement(indices, m):
compl = range(m)
for ind in indices:
compl.remove(ind)
return compl
#hoindices = [45, 50, 55]
hoindices = [0, 1, 2]
hocompl = complement(hoindices, m)
#bk = LinearKernel.Kernel()
#bk = GaussianKernel.Kernel()
bk = GaussianKernel.createKernel(**{'train_features':Xtrain[basis_vectors], 'gamma':'0.001'})
rk = RsetKernel.createKernel(**{'base_kernel':bk, 'basis_features':Xtrain[basis_vectors], 'train_features':Xtrain})
rpool = {}
rpool['train_features'] = Xtrain
bk2 = GaussianKernel.createKernel(**{'train_features':Xtrain, 'gamma':'0.001'})
K = np.mat(bk2.getKM(Xtrain))
Kho = K[ix_(hocompl, hocompl)]
Yho = Y[hocompl]
#rpool = {}
#rpool['train_labels'] = Y
#rpool['kernel_matrix'] = K[basis_vectors]
#rpool['basis_vectors'] = basis_vectors
#dualrls = RLS.createLearner(**rpool)
rpool = {}
rpool['train_labels'] = Y
rpool['train_features'] = Xtrain
rpool['basis_vectors'] = Xtrain[basis_vectors]
primalrls = RLS.createLearner(**rpool)
testkm = K[ix_(hocompl, hoindices)]
Xhocompl = Xtrain[hocompl]
testX = Xtrain[hoindices]
rpool = {}
rpool['train_labels'] = Yho
rpool['train_features'] = Xhocompl
rk = RsetKernel.createKernel(**{'base_kernel':bk, 'basis_features':Xtrain[basis_vectors], 'train_features':Xhocompl})
rpool['kernel_obj'] = rk
dualrls_naive = RLS.createLearner(**rpool)
rpool = {}
rpool['train_labels'] = Yho
rpool['train_features'] = Xhocompl
primalrls_naive = RLS.createLearner(**rpool)
rsaK = K[:, basis_vectors] * la.inv(K[ix_(basis_vectors, basis_vectors)]) * K[basis_vectors]
rsaKho = rsaK[ix_(hocompl, hocompl)]
rsa_testkm = rsaK[ix_(hocompl, hoindices)]
loglambdas = range(-5, 5)
for j in range(0, len(loglambdas)):
regparam = 2. ** loglambdas[j]
print
print "Regparam 2^%1d" % loglambdas[j]
print (rsa_testkm.T * la.inv(rsaKho + regparam * eye(rsaKho.shape[0])) * Yho).T, 'Dumb HO (dual)'
dumbho = np.squeeze(np.array(rsa_testkm.T * la.inv(rsaKho + regparam * eye(rsaKho.shape[0])) * Yho))
dualrls_naive.solve(regparam)
predho1 = np.squeeze(dualrls_naive.getModel().predict(testX))
print predho1.T, 'Naive HO (dual)'
#dualrls.solve(regparam)
#predho2 = np.squeeze(dualrls.computeHO(hoindices))
#print predho2.T, 'Fast HO (dual)'
for predho in [dumbho, predho1]:#, predho2]:
self.assertEqual(dumbho.shape, predho.shape)
for row in range(predho.shape[0]):
#for col in range(predho.shape[1]):
# self.assertAlmostEqual(dumbho[row,col],predho[row,col])
self.assertAlmostEqual(dumbho[row],predho[row])
