def run_pcanet_normal(transformer_params,
images_train, images_test, y_train, y_test):
model = PCANet(**transformer_params)
model.validate_structure()
t1 = timeit.default_timer()
model.fit(images_train)
t2 = timeit.default_timer()
train_time = t2 - t1
t1 = timeit.default_timer()
X_train = model.transform(images_train)
t2 = timeit.default_timer()
transform_time = t2 - t1
X_test = model.transform(images_test)
y_test, y_pred = run_classifier(X_train, X_test, y_train, y_test)
accuracy = accuracy_score(y_test, y_pred)
return model, accuracy, train_time, transform_time
train_set, valid_set, test_set = load_mnist()
images_train, y_train = train_set
images_test, y_test = test_set
images_train, y_train = shuffle(images_train, y_train, random_state=0)
images_train, y_train = images_train[:n_train], y_train[:n_train]
images_test, y_test = shuffle(images_test, y_test, random_state=0)
images_test, y_test = images_test[:n_test], y_test[:n_test]
pcanet = PCANet(image_shape=28,
filter_shape_l1=2,
step_shape_l1=1,
n_l1_output=4,
filter_shape_l2=2,
step_shape_l2=1,
n_l2_output=4,
block_shape=2)
pcanet.validate_structure()
pcanet.fit(images_train)
X_train = pcanet.transform(images_train)
X_test = pcanet.transform(images_test)
model = RandomForestClassifier(n_estimators=100, random_state=1234, n_jobs=-1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("accuracy: " + str(accuracy))
images = random.shuffle(images)
print(images)
print(num_examples)
print(images.shape)
pcanet = PCANet(image_shape=(60, 80),
filter_shape_l1=2,
step_shape_l1=1,
n_l1_output=4,
filter_shape_l2=2,
step_shape_l2=1,
n_l2_output=4,
block_shape=2)
print("has excuted PCANET function")
pcanet.validate_structure()
print("has excuted pcanet.validate_structure")
pcanet.fit(images)
print("has excuted pacnet.fit")
X_train = pcanet.transform(images)
print("has excuted pcanet.transform")
print(X_train)
print(X_train.shape)