def cross_validate_parameters(dataset_name, train_set, test_set):
dims = [i for i in range(1, 7)]
sigmas = [0.001, 0.01, 0.1, 1, 2, 5]
accuracies = []
best_accu_and_dim = 0, 0
max = 0
for dim in dims:
clf_poly = Perceptron.Perceptron(dataset_name,
train_set[:, :-1],
train_set[:, -1],
_kernel_=2, epochs=10, _dim_=dim)
accuracy, accuracy_index = tests.best_epoch(clf_poly, test_set, 10)
# clf_poly.fit()
# predicted = clf_poly.predict_set(test_set[:, :-1], test_set[:, -1])
# accuracy = clf_poly.accuracy(test_set[:, -1], predicted)
accuracies.append((accuracy, dim))
for accu, dim in accuracies:
if accu > max:
max = accu
best_accu_and_dim = accu, dim
accuracies = []
best_accu_and_sigma = 0, 0
max = 0
for sigma in sigmas:
clf_gaussian = Perceptron.Perceptron(dataset_name,
train_set[:, :-1],
train_set[:, -1],
_kernel_=3, epochs=10, _sigma_=sigma)
accuracy, accuracy_index = tests.best_epoch(clf_gaussian, test_set, 10)
# clf_gaussian.fit()
# predicted = clf_gaussian.predict_set(test_set[:, :-1], test_set[:, -1])
# accuracy = clf_gaussian.accuracy(test_set[:, -1], predicted)
accuracies.append((accuracy, sigma))
for accu, sigma in accuracies:
if accu > max:
max = accu
best_accu_and_sigma = accu, sigma
print("Best accuracy with associated dim: ", best_accu_and_dim, "\n",
"Best accuracy with associated sigma :", best_accu_and_sigma)
return best_accu_and_dim, best_accu_and_sigma
