print("Prediction: x1 = 0, x2 = 1, ... -> y = %d" %
(tm.predict(np.array([[0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0]]))))
print("Prediction: x1 = 0, x2 = 0, ... -> y = %d" %
(tm.predict(np.array([[0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0]]))))
print("Prediction: x1 = 1, x2 = 1, ... -> y = %d" %
(tm.predict(np.array([[1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0]]))))
print("\nLet's try to get clauses....")
NUM_FEATURES = len(X_train[0])
print('Num Clauses:', NUM_CLAUSES)
print('Num Classes: ', len(CLASSES), ' : ', CLASSES)
print('Num Features: ', NUM_FEATURES)
for cur_clause in range(NUM_CLAUSES):
for cur_cls in CLASSES:
this_clause = ''
for f in range(NUM_FEATURES * 2):
action = tm.ta_action(int(cur_cls), cur_clause, f)
if action == 1:
if this_clause != '':
this_clause += 'AND '
if f < NUM_FEATURES:
this_clause += 'F' + str(f) + ' '
else:
this_clause += '-|F' + str(f - NUM_FEATURES) + ' '
print('CLASS :', cur_cls, ' - CLAUSE ', cur_clause, ' : ', this_clause)
print('\n\n')
#print('\nsplits ready:',x_train.shape, x_test.shape)
tm = MultiClassTsetlinMachine(NUM_CLAUSES, T, s)
tm.fit(x_train, y_train, epochs=TRAIN_EPOCHS, incremental=True)
print('\nfit done')
result[r] = 100 * (tm.predict(x_test) == y_test).mean()
feature_vector = np.zeros(NUM_FEATURES * 2)
for cur_cls in CLASSES:
for cur_clause in range(NUM_CLAUSES):
if cur_clause % 2 == 0:
clause_type = 'positive'
else:
clause_type = 'negative'
this_clause = ''
for f in range(0, NUM_FEATURES):
action_plain = tm.ta_action(int(cur_cls), cur_clause, f)
action_negated = tm.ta_action(int(cur_cls), cur_clause,
f + NUM_FEATURES)
feature_vector[f] = action_plain
feature_vector[f + NUM_FEATURES] = action_negated
feature_count_plain[f] += action_plain
feature_count_negated[f] += action_negated
if action_plain == 0 and action_negated == 0:
feature_count_ignore += 1
feature_count_contradiction += action_plain and action_negated
if (cur_cls % 2 == 0):
feature_count_plain_positive[f] += action_plain
feature_count_negated_positive[f] += action_negated
else:
feature_count_plain_negative[f] += action_plain
feature_count_negated_negative[f] += action_negated
print(
"#%d AccuracyTrain: %.2f%% AccuracyTest: %.2f%% F1-Score: %.2f%% Training: %.2fs Testing: %.2fs"
% (i + 1, result2, result1, f1 * 100, stop_training - start_training,
stop_testing - start_testing))
#%%%%%%%%%%%%%%%%% To extrat the clause with its literal of particular class %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tm1.set_state(ta_state)
#Save trained model with highest testing accuracy
np.savez_compressed("laptopModel.npz", ta_state)
#Load trained TM to evaulate results
ta_state = np.load("laptopModel.npz")['arr_0']
tm1.set_state(ta_state)
print(tm1.predict(X_final4[1990:1991, :]))
number_of_features = 4212
print("\nClass 2 Positive Clauses:\n")
for j in range(
0, 300, 2
): #0 is negative, 1 is neutral and 2 is positive class (change accordingly)
print("Clause #%d: " % (j), end=' ')
l = []
for k in range(number_of_features * 2):
if tm1.ta_action(1, j, k) == 1:
if k < number_of_features:
l.append(" x%d" % (k))
else:
continue
print(" ∧ ".join(l))