def tsetlin_with_log(clauses, T, s, x_train, y_train, x_val, y_val,
name_dataset):
tm = MultiClassTsetlinMachine(clauses, T, s)
log = Logger(name_dataset, x_train, 'mctm', clauses, T, s)
print(log.metadata)
print('predicting 400 epochs')
for i in range(400):
start = time()
tm.fit(x_train, y_train, epochs=1, incremental=True)
stop = time()
pred = tm.predict(x_val)
conf_matrix = confusion_matrix(np.asarray(y_val), pred)
print('sum predict:', sum(pred), 'sum validation:', sum(y_val))
accuracy = 100 * (pred == np.asarray(y_val)).mean()
print("#%d Accuracy: %.2f%% (%.2fs)" % (i + 1, accuracy, stop - start))
print('Confusion matrix:')
print(conf_matrix)
log.add_epoch(np.asarray(y_val), pred)
log.save_log()
print('done')
x_val = np.asarray(dataset[2], np.uint8)
y_val = dataset[3]
# Tsetlin machine
clauses = args.CLAUSES
mask = args.MASK
T = args.T
s = args.S
tm = MultiClassConvolutionalTsetlinMachine2D(clauses, T, s, (mask, mask))
log = Logger(dataset_name, x_train, "TM2D", clauses, T, s, (mask, mask))
print(x_train[0][0])
print(x_train.shape)
print('predicting 400 epochs')
for i in range(400):
start = time()
tm.fit(x_train, y_train, epochs=1, incremental=True)
stop = time()
pred = tm.predict(x_val)
conf_matrix = confusion_matrix(np.asarray(y_val), pred)
print('sum predict:', sum(pred), 'sum validation:', sum(y_val))
accuracy = 100 * (pred == np.asarray(y_val)).mean()
print("#%d Accuracy: %.2f%% (%.2fs)" % (i + 1, accuracy, stop - start))
print('Confusion matrix:')
print(conf_matrix)
log.add_epoch(np.asarray(y_val), pred)
log.save_log()
print('done')
def ctm(clauses, T, s, mask, image_array_train, label_array_train,
image_array_validation, label_array_validation, epochs):
# find max and min values for the train and validation data
image_array_train_min = np.amin(image_array_train)
image_array_train_max = np.amax(image_array_train)
image_array_validation_min = np.amin(image_array_validation)
image_array_validation_max = np.amax(image_array_validation)
print(image_array_train_min, image_array_train_max,
image_array_validation_min, image_array_validation_max)
tm = MultiClassConvolutionalTsetlinMachine2D(clauses, T, s, (mask, mask))
log_creator = Logger("CIFAR_random_samling_validation", "CTM", clauses, T,
s, (mask, mask))
max_acc = 0
print(
f"Running CTM with random sampling for {epochs} epochs, with the following parameters:"
)
print(f"Clauses: {clauses}, s: {s}, T: {T}, mask: {mask} x {mask}")
for i in range(epochs):
start = time()
# Generate matrix with size of the image array, with random values ranging from 0 to 255
random_train_matrix = np.random.randint(image_array_train_min,
image_array_train_max,
size=(image_array_train.shape))
random_validation_matrix = np.random.randint(
image_array_validation_min,
image_array_validation_max,
size=(image_array_validation.shape))
# Sampeled images
floaty_train_images = np.greater(random_train_matrix,
image_array_train)
floaty_validation_images = np.greater(random_validation_matrix,
image_array_validation)
# Fit
tm.fit(floaty_train_images,
label_array_train,
epochs=1,
incremental=True)
stop = time()
# Predict
pred_validation = tm.predict(floaty_validation_images)
pred_train = tm.predict(floaty_train_images)
# Get some nice logging in terminal
accuracy_validation = 100 * (
pred_validation == np.asarray(label_array_validation)).mean()
if max_acc < accuracy_validation:
max_acc = accuracy_validation
conf_matrix_validation = confusion_matrix(
np.asarray(label_array_validation), pred_validation)
print('sum predict:', sum(pred_validation), 'sum validation:',
sum(label_array_validation))
print(
"#%d Accuracy on training data: %.2f%% (%.2fs), best accuracy on training data: %.2f%% "
% (i + 1, accuracy_validation, stop - start, max_acc))
print('Confusion matrix:')
print(conf_matrix_validation)
# Save to logfile
log_creator.add_epoch(np.asarray(label_array_validation),
pred_validation, np.asarray(label_array_train),
pred_train)
log_creator.save_log()
print('done')
log = Logger(dataset_name, X_train_transformed, "2layerTsetlin", clauses, T, s)
print(type(X_train_transformed), X_train_transformed.shape)
print(type(Y_train), len(Y_train))
print("\nAccuracy over 250 epochs:\n")
max_accuracy = 0.0
for i in range(250):
start_training = time()
tm.fit(X_train_transformed, Y_train, epochs=1, incremental=True)
stop_training = time()
start_testing = time()
pred = tm.predict(X_test_transformed)
result = 100 * (pred == Y_test).mean()
stop_testing = time()
print("#%d Accuracy: %.2f%% Training: %.2fs Testing: %.2fs" %
(i + 1, result, stop_training - start_training,
stop_testing - start_testing))
conf_matrix = confusion_matrix(np.asarray(Y_test), pred)
print('sum predict:', sum(pred), 'sum validation:', sum(Y_test))
accuracy = 100 * (pred == np.asarray(Y_test)).mean()
print("#%d Accuracy: %.2f%% (%.2fs)" %
(i + 1, accuracy, stop_testing - start_training))
print('Confusion matrix:')
print(conf_matrix)
log.add_epoch(np.asarray(Y_test), pred)
log.save_log()
print('done')
