def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting2_split()
learner = KronRLS(X1 = X1_train, X2 = X2_train, Y = Y_train)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.settingB_split()
learner = KronRLS(X1 = X1_train, X2 = X2_train, Y = Y_train)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1, X2, Y = davis_data.load_davis()
Y = Y.ravel(order='F')
learner = KronRLS(X1 = X1, X2 = X2, Y = Y)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.in_sample_loo()
perf = cindex(Y, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.settingD_split()
kernel1 = GaussianKernel(X1_train, gamma=0.01)
kernel2 = GaussianKernel(X2_train, gamma=10**-9)
K1_train = kernel1.getKM(X1_train)
K1_test = kernel1.getKM(X1_test)
K2_train = kernel2.getKM(X2_train)
K2_test = kernel2.getKM(X2_test)
learner = KronRLS(K1 = K1_train, K2 = K2_train, Y = Y_train)
log_regparams = range(-15, 15)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(K1_test, K2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
