def outofsample_extensions(method=None, dataset=None):
np.random.seed(1)
sklearn.utils.check_random_state(1)
train_data, train_labels, test_data, test_labels = dataset_loader(dataset, seed=1)
# Learn a new space using Isomap
isomap = Isomap(n_components=10, n_neighbors=20)
train_data_isomap = np.float32(isomap.fit_transform(train_data))
sigma = mean_data_distance(np.float32(train_data))
if method == 'kernel-regression':
# Use kernel regression to provide baseline out-of-sample extensions
proj = KernelRidge(kernel='rbf', gamma=(1.0 / sigma ** 2))
proj.fit(np.float64(train_data), np.float64(train_data_isomap))
acc = evaluate_svm(proj.predict(train_data), train_labels,
proj.predict(test_data), test_labels)
elif method == 'cK-ISOMAP-10d' or method == 'cK-ISOMAP-20d':
# Use the SEF to provide out-of-sample extensions
if method == 'cK-ISOMAP-10d':
dims = 10
else:
dims = 20
proj = KernelSEF(train_data, train_data.shape[1], output_dimensionality=dims)
proj.cuda()
loss = proj.fit(data=train_data, target_data=train_data_isomap, target='copy',
epochs=100, batch_size=256, verbose=True, learning_rate=0.00001, regularizer_weight=0.001)
acc = evaluate_svm(proj.transform(train_data), train_labels,
proj.transform(test_data), test_labels)
print("Method: ", method, " Test accuracy: ", 100 * acc, " %")
评论列表
文章目录