def s3vm_moon():
my_random_generator = random.Random()
my_random_generator.seed(0)
# dense moons data set
X_train_l, L_train_l, X_train_u, X_test, L_test = examples.get_moons_data(
my_random_generator)
print("Unique labels: ", np.unique(L_test))
print("X_train_l: ", X_train_l)
print("L_train_l: ", L_train_l)
print("X_train_u: ", X_train_u)
print("X_test: ", X_test)
print("L_test: ", L_test)
t_start = time.time()
# the parameter estimate_r for the balance ratio has to be provided explicitly, since the estimation
# is bad due to only 5 labeled patterns in the training set.
model = QN_S3VM(
X_train_l,
L_train_l,
X_train_u,
my_random_generator,
lam=0.0009765625,
lamU=1,
kernel_type="RBF",
sigma=0.5,
estimate_r=0.0,
)
preds_train = model.train()
t_end = time.time()
elapsed_time = t_end - t_start
preds = model.getPredictions(X_test)
error = examples.classification_error(preds, L_test)
print("Time needed to compute the model: ", elapsed_time, " seconds")
print("Classification error of QN-S3VM: ", error)
X_draw = np.concatenate((X_train_l, X_train_u), axis=0)
y_draw = np.hstack((L_train_l, np.zeros(len(X_train_u))))
disp(X_draw, y_draw)
disp(X_draw, preds_train)
disp(np.array(X_test), np.array(preds))
return
# sparse text data set
X_train_l, L_train_l, X_train_u, X_test, L_test, kw = examples.get_text_data(
my_random_generator)
t_start = time.time()
model = QN_S3VM(X_train_l,
L_train_l,
X_train_u,
my_random_generator,
lam=0.00390625,
lamU=1)
model.train()
t_end = time.time()
elapsed_time = t_end - t_start
preds = model.getPredictions(X_test)
error = examples.classification_error(preds, L_test)
print("Time needed to compute the model: ", elapsed_time, " seconds")
print("Classification error of QN-S3VM: ", error)
# dense gaussian data set
X_train_l, L_train_l, X_train_u, X_test, L_test = examples.get_gaussian_data(
my_random_generator)
t_start = time.time()
model = QN_S3VM(X_train_l,
L_train_l,
X_train_u,
my_random_generator,
lam=1,
lamU=1)
model.train()
t_end = time.time()
if __name__ == "__main__":
import random, examples, time
my_random_generator = random.Random()
my_random_generator.seed(0)
# sparse text data set
X_train_l, L_train_l, X_train_u, X_test, L_test, kw = examples.get_text_data(my_random_generator)
t_start = time.time()
model = QN_S3VM(X_train_l, L_train_l, X_train_u, my_random_generator, lam=0.00390625, lamU=1)
model.train()
t_end = time.time()
elapsed_time = t_end - t_start
preds = model.getPredictions(X_test)
error = examples.classification_error(preds,L_test)
print "Time needed to compute the model: ", elapsed_time, " seconds"
print "Classification error of QN-S3VM: ", error
# dense gaussian data set
X_train_l, L_train_l, X_train_u, X_test, L_test = examples.get_gaussian_data(my_random_generator)
t_start = time.time()
model = QN_S3VM(X_train_l, L_train_l, X_train_u, my_random_generator, lam=1, lamU=1)
model.train()
t_end = time.time()
elapsed_time = t_end - t_start
preds = model.getPredictions(X_test)
error = examples.classification_error(preds,L_test)
print "Time needed to compute the model: ", elapsed_time, " seconds"
print "Classification error of QN-S3VM: ", error