IshitaTakeshi/PCANet

import pcanet as net

# Arguments are basically passed as tuple in the form (height, width) but int is also allowed. 
# If int is given, the parameter will be converted into (size, size) implicitly.
pcanet = net.PCANet(
    image_shape=28,
    filter_shape_l1=2, step_shape_l1=1, n_l1_output=3,  # parameters for the 1st layer
    filter_shape_l2=2, step_shape_l2=1, n_l2_output=3,  # parameters for the 2nd layer
    filter_shape_pooling=2, step_shape_pooling=2        # parameters for the pooling layer
)

# Check whether all pixels can be considered. Raise ValueError if the structure is not valid.
# Calling this function is optional. PCANet works without this line.
pcanet.validate_structure()

pcanet.fit(images_train)  # Train PCANet

# Trained PCANet behaves as a transformer from images into features.
# `images` is a 3d array in the form (n_images, height, width), who are transformed into feature vectors.
X_train = pcanet.transform(images_train)
X_test = pcanet.transform(images_test)

# Fit any models you like
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)