old_contents: from sklearn.datasets import load_digits
  from sklearn.svm import LinearSVC
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from pcanet import PCANet
  
  
  digits = load_digits()
  images = digits.images
  y = digits.target
  
  pcanet = PCANet((2, 2), (1, 1), 2,
                  (2, 2), (1, 1), 2,
                  (2, 2))
  
  pcanet.fit(images)
  X = pcanet.transform(images)
  print("X.shape: " + str(X.shape))
  
  X_train, X_test, y_train, y_test = train_test_split(X, y)
  
  model = RandomForestClassifier()  # LinearSVC()
  model.fit(X_train, y_train)
  y_pred = model.predict(X_test)
  accuracy = accuracy_score(y_test, y_pred)
  print("accuracy: " + str(accuracy))
  
new_contents: from sklearn.datasets import load_digits
  from sklearn.svm import LinearSVC
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from pcanet import PCANet
  
  
  digits = load_digits()
  images = digits.images
  y = digits.target
  
  pcanet = PCANet((2, 2), (1, 1), 4,
                  (2, 2), (1, 1), 4,
                  (2, 2))
  
  pcanet.fit(images)
  X = pcanet.transform(images)
  print("X.shape: " + str(X.shape))
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1234)
  
  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))
  
subject: Fix random state