classification.py

# classification.py
# Classification algorithms for the DewCrypt model.

from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import SGDClassifier

def mini_batch_sgd_classifier(input_data, labels):
    """
    Mini-Batch SGD Classifier algorithm as described in the paper.
    """
    clf = SGDClassifier(max_iter=1000, tol=1e-3)
    clf.fit(input_data, labels)
    return clf.predict(input_data)

def random_forest_classifier(input_data, labels):
    """
    Random Forest Classifier algorithm as described in the paper.
    """
    clf = RandomForestClassifier(n_estimators=100)
    clf.fit(input_data, labels)
    return clf.predict(input_data)

if __name__ == "__main__":
    # Test the classification functions
    sample_data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    sample_labels = np.array([0, 1, 0])
    print("SGD Classifier Predictions:", mini_batch_sgd_classifier(sample_data, sample_labels))
    print("Random Forest Classifier Predictions:", random_forest_classifier(sample_data, sample_labels))