def ml_k_cross_validation(class1_data, class2_data, p1, p2, k, n1, n2):
test_results_ml_class1 = []
test_results_ml_class2 = []
accuracies = []
for i in range(0, k, 1):
print('Cross:' + str(i + 1))
class1_testing_points_count = int(n1 / k)
class1_training_points_count = int(n1 - n1 / k)
class1_start = int(n1 * i / k)
class1_end = int((i + 1) * n1 / k)
class2_testing_points_count = int(n2 / k)
class2_training_points_count = int(n2 - n2 / k)
class2_start = int(n2 * i / k)
class2_end = int((i + 1) * n2 / k)
# print("start:", class1_start, "\tend:", class1_end)
# print("start:", class2_start, "\tend:", class2_end)
class1_test_points = class1_data[:, class1_start: class1_end]
class1_train_points = class1_data[:, 0:class1_start]
class1_train_points = np.append(class1_train_points, class1_data[:, class1_end:], axis=1)
class2_test_points = class2_data[:, class2_start: class2_end]
class2_train_points = class2_data[:, 0:class2_start]
class2_train_points = np.append(class2_train_points, class2_data[:, class2_end:], axis=1)
# estimated mean and cov using ML
x1_ml_estimated_mean = ml.estimate_mean_ml(class1_train_points, len(class1_train_points[0]))
x1_ml_estimated_cov = ml.estimate_cov_ml(class1_train_points, x1_ml_estimated_mean,
class1_training_points_count)
x2_ml_estimated_mean = ml.estimate_mean_ml(class2_train_points, len(class2_train_points[0]))
x2_ml_estimated_cov = ml.estimate_cov_ml(class2_train_points, x2_ml_estimated_mean,
class2_training_points_count)
ml_class1_accuracy, ml_class2_accuracy, total_accuracy = test_classifier(class1_test_points, class2_test_points,
x1_ml_estimated_cov,
x2_ml_estimated_cov,
x1_ml_estimated_mean,
x2_ml_estimated_mean,
class1_testing_points_count,
class2_testing_points_count, p1, p2)
# print(ml_class1_accuracy, ml_class2_accuracy)
test_results_ml_class1 = np.append(test_results_ml_class1, ml_class1_accuracy)
test_results_ml_class2 = np.append(test_results_ml_class2, ml_class2_accuracy)
accuracies = np.append(accuracies, total_accuracy)
print('\nML Average Accuracy:', np.mean(accuracies))
return test_results_ml_class1, test_results_ml_class2
def bl_expected_mean(x1_training_points, x2_training_points, sigma_x1,
sigma_x2, m1, m2):
w1_plot_points = [[], [], []]
w1_plot_points_index = []
w1_mean_values = [[], [], []]
w1_mean_difference = []
w1_ml_mean_difference = []
w1_ml_cov_difference = []
sigma0 = np.identity(len(x1_training_points[0]))
m1_0 = np.array([[1], [1], [1]])
w2_plot_points = [[], [], []]
w2_plot_points_index = []
w2_mean_values = [[], [], []]
w2_mean_difference = []
w2_ml_mean_difference = []
w2_ml_cov_difference = []
m2_0 = np.array([[1], [1], [1]])
for n in range(1, len(x1_training_points[0]), 1):
# for class 1
w1_plot_points_index = np.append(w1_plot_points_index, n)
x1_ml_estimated_mean = ml.estimate_mean_ml(x1_training_points, n)
x1_ml_estimated_cov = ml.estimate_cov_ml(x1_training_points,
x1_ml_estimated_mean, n)
w1_ml_mean_difference = np.append(
w1_ml_mean_difference,
np.absolute(np.linalg.norm(x1_ml_estimated_mean - m1)))
w1_ml_cov_difference = np.append(
w1_ml_cov_difference,
np.absolute(np.linalg.norm(x1_ml_estimated_cov - sigma_x1)))
m1_0 = estimate_mean_bl(x1_training_points, m1_0, sigma0, sigma_x1, n)
w1_mean_values = np.append(w1_mean_values, m1_0, axis=1)
w1_plot_points = np.append(w1_plot_points,
x1_training_points[:, [n]],
axis=1)
w1_mean_difference = np.append(w1_mean_difference,
np.absolute(np.linalg.norm(m1_0 - m1)))
for n in range(1, len(x2_training_points[0]), 1):
# for class 2
w2_plot_points_index = np.append(w2_plot_points_index, n)
x2_ml_estimated_mean = ml.estimate_mean_ml(x2_training_points, n)
x2_ml_estimated_cov = ml.estimate_cov_ml(x2_training_points,
x2_ml_estimated_mean, n)
w2_ml_mean_difference = np.append(
w2_ml_mean_difference,
np.absolute(np.linalg.norm(x2_ml_estimated_mean - m2)))
w2_ml_cov_difference = np.append(
w2_ml_cov_difference,
np.absolute(np.linalg.norm(x2_ml_estimated_cov - sigma_x2)))
m2_0 = estimate_mean_bl(x2_training_points, m2_0, sigma0, sigma_x2, n)
w2_mean_values = np.append(w2_mean_values, m2_0, axis=1)
w2_plot_points = np.append(w2_plot_points,
x2_training_points[:, [n]],
axis=1)
w2_mean_difference = np.append(w2_mean_difference,
np.absolute(np.linalg.norm(m2_0 - m2)))
return m1_0, m2_0,
np.set_printoptions(suppress=True)
instances_count = class1_instances_count + class2_instances_count
###############################################################################
###############################################################################
"""
Create the ML Classifier
"""
# estimate parameter for both classes
class1_ml_est_mean = ml.estimate_mean_ml(class1_data, class1_instances_count)
class2_ml_est_mean = ml.estimate_mean_ml(class2_data, class2_instances_count)
class1_ml_est_cov = ml.estimate_cov_ml(class1_data, class1_ml_est_mean, class1_instances_count)
class2_ml_est_cov = ml.estimate_cov_ml(class2_data, class2_ml_est_mean, class2_instances_count)
# print("\nBEFORE DIAGONALIZING:")
# print('\nClass 1 mean using ML:')
# print(class1_ml_est_mean)
# print('\nClass 2 mean using ML:')
# print(class2_ml_est_mean)
# print('\nClass 1 cov using ML:')
# print(class1_ml_est_cov)
# print('\nClass 2 cov using ML:')
# print(class2_ml_est_cov)
# discriminant function for ML
plt.plot_disc_func(class1_ml_est_mean, class2_ml_est_mean, class1_ml_est_cov,
class2_ml_est_cov, class1_data, class2_data, p1, p2, 1, 2, 'ML')
def fd_k_cross_validation(class1_data, class2_data, k, n1, n2, w, p1, p2):
test_results_fd_class1 = []
test_results_fd_class2 = []
accuracies = []
for i in range(0, k, 1):
print('Cross:' + str(i + 1))
class1_testing_points_count = int(n1 / k)
class1_training_points_count = int(n1 - n1 / k)
class1_start = int(n1 * i / k)
class1_end = int((i + 1) * n1 / k)
class2_testing_points_count = int(n2 / k)
class2_training_points_count = int(n2 - n2 / k)
class2_start = int(n2 * i / k)
class2_end = int((i + 1) * n2 / k)
class1_test_points = class1_data[:, class1_start: class1_end]
class1_train_points = class1_data[:, 0:class1_start]
class1_train_points = np.append(class1_train_points, class1_data[:, class1_end:], axis=1)
class2_test_points = class2_data[:, class2_start: class2_end]
class2_train_points = class2_data[:, 0:class2_start]
class2_train_points = np.append(class2_train_points, class2_data[:, class2_end:], axis=1)
class1_ml_est_mean = ml.estimate_mean_ml(class1_train_points, len(class1_train_points[0]))
class1_ml_est_cov = ml.estimate_cov_ml(class1_train_points, class1_ml_est_mean,
class1_training_points_count)
class2_ml_est_mean = ml.estimate_mean_ml(class2_train_points, len(class2_train_points[0]))
class2_ml_est_cov = ml.estimate_cov_ml(class2_train_points, class2_ml_est_mean,
class2_training_points_count)
fd_mean1 = w.transpose() @ class1_ml_est_mean
fd_mean2 = w.transpose() @ class2_ml_est_mean
fd_cov1 = w.transpose() @ class1_ml_est_cov @ w
fd_cov2 = w.transpose() @ class2_ml_est_cov @ w
class1_test_points = np.array(class1_test_points).transpose()
class2_test_points = np.array(class2_test_points).transpose()
class1_true = 0
class1_false = 0
class2_true = 0
class2_false = 0
for x in class1_test_points:
x_test = w.transpose() @ x
classification = fd.classify(x_test, fd_mean1, fd_mean2, fd_cov1, fd_cov2, p1, p2)
if classification == 1:
class1_true = class1_true + 1
else:
class1_false = class1_false + 1
for x in class2_test_points:
x_test = w.transpose() @ x
classification = fd.classify(x_test, fd_mean1, fd_mean2, fd_cov1, fd_cov2, p1, p2)
if classification == 2:
class2_true = class2_true + 1
else:
class2_false = class2_false + 1
class1_accuracy = (class1_true / len(class1_test_points)) * 100
class2_accuracy = (class2_true / len(class2_test_points)) * 100
test_results_fd_class1 = np.append(test_results_fd_class1, class1_accuracy)
test_results_fd_class2 = np.append(test_results_fd_class2, class2_accuracy)
accuracy = (class1_true + class2_true) * 100 / (len(class1_test_points) + len(class2_test_points))
accuracies = np.append(accuracies, accuracy)
print(accuracy)
print('\nFisher\'s Disc. Average Accuracy:', np.mean(accuracies))
return test_results_fd_class1, test_results_fd_class2
def bl_k_cross_validation(class1_data, class2_data, p1, p2, k, n1, n2):
test_results_bl_class1 = []
test_results_bl_class2 = []
accuracies = []
for i in range(0, k, 1):
print('Cross:' + str(i + 1))
class1_testing_points_count = int(n1 / k)
class1_training_points_count = int(n1 - n1 / k)
class1_start = int(n1 * i / k)
class1_end = int((i + 1) * n1 / k)
class2_testing_points_count = int(n2 / k)
class2_training_points_count = int(n2 - n2 / k)
class2_start = int(n2 * i / k)
class2_end = int((i + 1) * n2 / k)
# print("start:", class1_start, "\tend:", class1_end)
# print("start:", class2_start, "\tend:", class2_end)
class1_test_points = class1_data[:, class1_start: class1_end]
class1_train_points = class1_data[:, 0:class1_start]
class1_train_points = np.append(class1_train_points, class1_data[:, class1_end:], axis=1)
class2_test_points = class2_data[:, class2_start: class2_end]
class2_train_points = class2_data[:, 0:class2_start]
class2_train_points = np.append(class2_train_points, class2_data[:, class2_end:], axis=1)
class1_ml_est_mean = ml.estimate_mean_ml(class1_train_points, len(class1_train_points[0]))
class1_ml_est_cov = ml.estimate_cov_ml(class1_train_points, class1_ml_est_mean,
class1_training_points_count)
class2_ml_est_mean = ml.estimate_mean_ml(class2_train_points, len(class2_train_points[0]))
class2_ml_est_cov = ml.estimate_cov_ml(class2_train_points, class2_ml_est_mean,
class2_training_points_count)
# Estimating the means using BL
class1_bl_initial_mean = np.ones((len(class1_data), 1))
class1_bl_initial_cov = np.identity(len(class1_data))
class2_bl_initial_mean = np.ones((len(class2_data), 1))
class2_bl_initial_cov = np.identity(len(class2_data))
class1_bl_est_mean = bl.estimate_mean_bl(class1_train_points, class1_bl_initial_mean, class1_bl_initial_cov,
class1_ml_est_cov, len(class1_train_points[0]))
class2_bl_est_mean = bl.estimate_mean_bl(class2_train_points, class2_bl_initial_mean, class2_bl_initial_cov,
class2_ml_est_cov, len(class2_train_points[0]))
bl_class1_accuracy, bl_class2_accuracy, total_accuracy = test_classifier(class1_test_points, class2_test_points,
class1_ml_est_cov, class2_ml_est_cov,
class1_bl_est_mean, class2_bl_est_mean,
class1_testing_points_count,
class2_testing_points_count, p1, p2)
# print(bl_class1_accuracy, bl_class2_accuracy)
test_results_bl_class1 = np.append(test_results_bl_class1, bl_class1_accuracy)
test_results_bl_class2 = np.append(test_results_bl_class2, bl_class2_accuracy)
accuracies = np.append(accuracies, total_accuracy)
print('\nBL Average Accuracy:', np.mean(accuracies))
return test_results_bl_class1, test_results_bl_class2