def main():
print("+--------------------------------------+")
print("| Machine Learning Project, Part 2 |")
print("+--------------------------------------+")
print("| Author: Rodrigo Castiel |")
print("+--------------------------------------+")
# Set seed for deterministic execution.
random.seed(0)
np.random.seed(0)
# Load training and test data.
loader = DataLoader()
loader.load("data/segmentation")
x_train, w_train = loader.training_data()
# Build and train classifiers with the optimal hyper-parameters.
gaussian_mle = train_gaussian_mle(x_train, w_train)
knn_classifier = train_knn_classifier(x_train, w_train)
combined_max_classifier = train_combined_classifier(x_train, w_train)
classifiers = [gaussian_mle, knn_classifier, combined_max_classifier]
# Evaluate estimators.
evaluate_cross_validation(classifiers, x_train, w_train)
evaluate_accuracy_on_test_set(loader, classifiers)
# Perform Friedman's Test.
perform_friedman_test(classifiers, x_train, w_train)
def main():
print("+--------------------------------------+")
print("| Machine Learning Project, Part 1 |")
print("+--------------------------------------+")
print("| Author: Rodrigo Castiel |")
print("+--------------------------------------+")
print()
# Set seed for deterministic execution.
random.seed(42)
np.random.seed(42)
# Load training and test data.
loader = DataLoader()
loader.load("data/segmentation")
x_data, w_data = loader.test_data()
K = len(np.unique(w_data))
shape_view = [0, 1, 3, 5, 6, 7, 8]
rgb_view = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
full_view = shape_view + rgb_view
if "FULL" in args.views:
## FULL VIEW ---------------------------------------------------------------
print("+--------------------+")
print("| FULL VIEW |")
print("+--------------------+")
evaluate_kcm_f_gh(K, num_times, x_data[:, full_view], w_data)
evaluate_k_means(K, x_data[:, full_view], w_data)
if "RGB" in args.views:
## RGB VIEW ----------------------------------------------------------------
print("+--------------------+")
print("| RGB VIEW |")
print("+--------------------+")
evaluate_kcm_f_gh(K, num_times, x_data[:, rgb_view], w_data)
evaluate_k_means(K, x_data[:, rgb_view], w_data)
if "SHAPE" in args.views:
## SHAPE VIEW --------------------------------------------------------------
print("+--------------------+")
print("| SHAPE VIEW |")
print("+--------------------+")
evaluate_kcm_f_gh(K, num_times, x_data[:, shape_view], w_data)
evaluate_k_means(K, x_data[:, shape_view], w_data)