plot_confusion_matrix(conf, 1, "Closed form")
acc1 = np.ones(EPOCHS_NO) * acc
print("-------------------")
# ------------------------------------------------------------------------------
# ------ Gradient optimization of linear model
grad_model = LinearClassifier()
acc2 = np.zeros(EPOCHS_NO)
ep = 1
while ep <= EPOCHS_NO:
grad_model.update_params(X_train, T_train, LEARNING_RATE)
acc, conf = evaluate(grad_model, X_test, L_test)
acc2[ep - 1] = acc
if ep % REPORT_EVERY == 0:
print("[Linear-grad] Epoch %4d; Accuracy on test set: %f" % (ep, acc))
ep = ep + 1
print(conf)
plot_confusion_matrix(conf, 2, "Linear model - gradient")
print("-------------------")
# ------------------------------------------------------------------------------
