def main():
class_means = np.array([[0, 0], [2.5, 2.5]])
class_variances = [np.eye(2), np.eye(2)]
num_components = 10
num_desired_points_per_class = 200
class_0, class_1 = DataGenerator.generate_gaussian_mixture(
class_means, class_variances, num_components, num_desired_points_per_class
)
combined_dataset = np.vstack((class_0, class_1))
combined_labels = np.hstack(
(np.zeros(num_desired_points_per_class, dtype=np.int32), np.ones(num_desired_points_per_class, dtype=np.int32))
)
ln = LinearModel()
ln.fit(train_data=combined_dataset, labels=combined_labels)
r_linear_model = ln.predict(test_data=combined_dataset, extra_params_dict={"threshold": 0.5})
print_accuracy(r_linear_model, combined_labels)
knn = KNN(num_neighbors=5)
knn.fit(train_data=combined_dataset, labels=combined_labels)
r_knn = knn.predict(test_data=combined_dataset)
print_accuracy(r_knn, combined_labels)
plot_results(
combined_dataset,
np.vstack((np.vstack((combined_labels[np.newaxis, :], r_linear_model[np.newaxis, :])), r_knn[np.newaxis, :])),
titles=["gt", "lin", "knn"],
)
return
def main():
class_means = np.array([[0, 0], [2.5, 2.5]])
class_variances = [np.eye(2), np.eye(2)]
num_components = 10
num_desired_points_per_class = 200
class_0, class_1 = DataGenerator.generate_gaussian_mixture(
class_means, class_variances, num_components,
num_desired_points_per_class)
combined_dataset = np.vstack((class_0, class_1))
combined_labels = np.hstack((np.zeros(num_desired_points_per_class,
dtype=np.int32),
np.ones(num_desired_points_per_class,
dtype=np.int32)))
ln = LinearModel()
ln.fit(train_data=combined_dataset, labels=combined_labels)
r_linear_model = ln.predict(test_data=combined_dataset,
extra_params_dict={'threshold': 0.5})
print_accuracy(r_linear_model, combined_labels)
knn = KNN(num_neighbors=5)
knn.fit(train_data=combined_dataset, labels=combined_labels)
r_knn = knn.predict(test_data=combined_dataset)
print_accuracy(r_knn, combined_labels)
plot_results(combined_dataset,
np.vstack((np.vstack(
(combined_labels[np.newaxis, :],
r_linear_model[np.newaxis, :])), r_knn[np.newaxis, :])),
titles=['gt', 'lin', 'knn'])
return
def main():
limit=20
folder_path = '../data/cancer micro array data/'
train_data_file = folder_path + '14cancer.xtrain'
train_labels_file = folder_path + '14cancer.ytrain'
test_data_file = folder_path + '14cancer.xtest'
test_labels_file = folder_path + '14cancer.ytest'
train_data = np.array(load_data(train_data_file), np.float64).T[:limit]
train_labels = np.array(load_data(train_labels_file), np.float64).T[:limit]
test_data = np.array(load_data(test_data_file), np.float64).T
test_labels = np.array(load_data(test_labels_file), np.float64).T
print train_data.shape, train_labels.shape, test_data.shape
ln = LinearModel()
ln.fit(train_data=train_data, labels=train_labels, extra_params_dict={'ridge_param':0.001})
ln.predict(test_data=test_data)
return
def main():
limit = 20
folder_path = '../data/cancer micro array data/'
train_data_file = folder_path + '14cancer.xtrain'
train_labels_file = folder_path + '14cancer.ytrain'
test_data_file = folder_path + '14cancer.xtest'
test_labels_file = folder_path + '14cancer.ytest'
train_data = np.array(load_data(train_data_file), np.float64).T[:limit]
train_labels = np.array(load_data(train_labels_file), np.float64).T[:limit]
test_data = np.array(load_data(test_data_file), np.float64).T
test_labels = np.array(load_data(test_labels_file), np.float64).T
print train_data.shape, train_labels.shape, test_data.shape
ln = LinearModel()
ln.fit(train_data=train_data,
labels=train_labels,
extra_params_dict={'ridge_param': 0.001})
ln.predict(test_data=test_data)
return
