print(f'Positive mean = [{mu_train_c1[0]:.4f}, {mu_train_c1[1]:.4f}]')
print(f'Negative mean = [{mu_train_c0[0]:.4f}, {mu_train_c0[1]:.4f}]')
# %%
# Generating Adversarial Examples from test set
# This implementation uses multiple iterations to update x, until all of them
# match the target classes.
# scale down the parameter
epsilon = 0.0006 / np.average(x_norms)
adversarial_examples = np.copy(x_test) # make a clone
pred = pred_test
# Aimed to generate labels which completely opposite to the true labels,
# not the prediction from SVM.
targets = and_gen.get_not_y(y_test)
epoch = 1
while np.array_equal(pred, targets) == False:
adversarial_examples = adversarial.moving_mean(
x=adversarial_examples,
y=pred,
targets=targets,
means={0: mu_train_c0, 1: mu_train_c1},
epsilon=epsilon,
verbose=0,
epoch=epoch)
pred = model_svm.predict(adversarial_examples)
epoch += 1
print(f'Completed after {epoch} epoch.')
max_epoch = 2000
ind_train_c0 = np.where(y_train == 0)
x_train_c0 = x_train[ind_train_c0]
mu_train_c0 = np.mean(x_train_c0, axis=0)
ind_train_c1 = np.where(y_train == 1)
x_train_c1 = x_train[ind_train_c1]
mu_train_c1 = np.mean(x_train_c1, axis=0)
print('Negative mean =', mu_train_c0)
print('Positive mean =', mu_train_c1)
x_ae = np.copy(x_test)
pred_ae = np.copy(pred_test)
targets = get_not_y(y_test)
epoch = 1
while np.array_equal(pred_ae, targets) == False and epoch <= max_epoch:
x_ae = adversarial.moving_mean(x=x_ae,
y=pred_ae,
targets=targets,
means={
0: mu_train_c0,
1: mu_train_c1
},
epsilon=epsilon,
verbose=0,
epoch=epoch)
pred_ae = model.predict(x_ae)
epoch += 1