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.')
# %%
# Results
original_pred = model_svm.predict(x_test)
pred_ae = model_svm.predict(adversarial_examples)
y_ae = and_gen.get_y(adversarial_examples)
for xx, ae, p, p_ae, yy_ae in zip(x_test, adversarial_examples, original_pred,
pred_ae, y_ae):
if p != p_ae:
print(f'from [{xx[0]: .4f}, {xx[1]: .4f}] = {p} to ' +
f'[{ae[0]: .4f}, {ae[1]: .4f}] = {p_ae}; True y = {yy_ae}')
matches = np.equal(y_ae, pred)
count = len(matches[matches == False])
print(f'\nFound {count} Adversarial Examples out of ' +
f'{len(y_ae)}. {count / len(y_ae) * 100.0:.4f}% successful rate')
# %%
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(x_min, x_max, h))
Z = model_svm.predict(np.c_[xx.ravel(), yy.ravel()])
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.')
# %%
# Results
original_pred = model_svm.predict(x_test)
pred_ae = model_svm.predict(adversarial_examples)
y_ae = and_gen.get_y(adversarial_examples)
matches = np.equal(y_ae, pred_ae)
ind_misclassified = np.where(matches == False)[0]
# Print all misclassified samples
# for i in ind_misclassified:
# print(
# f'from [{x_test[i][0]: .4f}, {x_test[i][1]: .4f}] = {original_pred[i]}'
# + f' to [{adversarial_examples[i][0]: .4f}, '
# + f'{adversarial_examples[i][1]: .4f}] = {pred_ae[i]};'
# + f' True y = {y_ae[i]}')
missclassified = len(ind_misclassified)
print(f'Misclassified = {missclassified}')
[0.5010, 0.5020], # 1
[0.5020, 0.5020], # 1
# out of training range
# positive
[0.5003, 1.0000], # 1
[1.0000, 0.5001], # 1
[1.4584, 1.0000], # 1
[1.0000, 1.4010], # 1
# negative
[-0.7756, 0.0000], # 0
[1.4500, 0.0000], # 0
[0.0000, -0.9839], # 0
[0.0000, 1.4690] # 0
])
y_ae = and_gen.get_y(x_ae)
print(*y_ae, sep=', ')
# %%
def print_misclassified_samples(x, y, pred):
ind_mis = np.where(np.logical_xor(y, pred) == True)
for xx, yy, pp in zip(x[ind_mis], y[ind_mis], pred[ind_mis]):
print(f'[{xx[0]: .4f}, {xx[1]: .4f}] y = {yy} pred = {pp}')
# %%
# Do NOT change the initial predictions
pred_ae = tuple(model_svm1.predict(x_ae))
print('Misclassified samples:')
print_misclassified_samples(x_ae, y_ae, np.array(pred_ae))
# %% [markdown]