def cross_validate(X_train, Y_train, folds, k_list):
classifier = KNearestNeighbor()
classifier.train(X_train, Y_train)
k_to_accuracies = classifier.cross_validate(folds, k_list)
for k in k_list:
accuracies = k_to_accuracies[k]
plt.scatter([k] * len(accuracies), accuracies)
accuracies_mean = np.array([np.mean(v) for k, v in sorted(k_to_accuracies.items())])
accuracies_std = np.array([np.std(v) for k, v in sorted(k_to_accuracies.items())])
plt.errorbar(k_list, accuracies_mean, yerr=accuracies_std)
plt.title("Cross-Validation on k")
plt.xlabel('k')
plt.ylabel('Cross-Validation accuracy')
plt.show()
def k_classifier(X_train, Y_train, X_test, Y_test, k):
classifier = KNearestNeighbor()
classifier.train(X_train, Y_train)
Y_test_predictions = classifier.predict(X_test, k=k, distance_type='euclidean_no_loop')
accuracy = classifier.eval_accuracy(Y_test, Y_test_predictions)
return [accuracy, Y_test_predictions]
def compare_distances(X_train, Y_train, X_test):
classifier = KNearestNeighbor()
classifier.train(X_train, Y_train)
classifier.eval_distances(X_test)
def compare_time(X_train, Y_train, X_test):
classifier = KNearestNeighbor()
classifier.train(X_train, Y_train)
classifier.eval_time(X_test)
def predict_with_distances(X_train, Y_train, X_test, Y_test):
classifier = KNearestNeighbor()
classifier.train(X_train, Y_train)
Y_test_predictions = classifier.predict(X_test, k=1, distance_type='manhattan_two_loops')
classifier.eval_accuracy(Y_test, Y_test_predictions)
Y_test_predictions = classifier.predict(X_test, k=1, distance_type='manhattan_one_loop')
classifier.eval_accuracy(Y_test, Y_test_predictions)
Y_test_predictions = classifier.predict(X_test, k=1, distance_type='manhattan_no_loop')
classifier.eval_accuracy(Y_test, Y_test_predictions)
Y_test_predictions = classifier.predict(X_test, k=1, distance_type='euclidean_two_loops')
classifier.eval_accuracy(Y_test, Y_test_predictions)
Y_test_predictions = classifier.predict(X_test, k=1, distance_type='euclidean_one_loop')
classifier.eval_accuracy(Y_test, Y_test_predictions)
Y_test_predictions = classifier.predict(X_test, k=1, distance_type='euclidean_no_loop')
classifier.eval_accuracy(Y_test, Y_test_predictions)
