text: import numpy as np
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
  
  from adaboost import Adaboost
  
  def accuracy(y_true, y_pred):
      accuracy = np.sum(y_true == y_pred) / len(y_true)
      return accuracy
  
  data = datasets.load_breast_cancer()
  X = data.data
  y = data.target
  
  y[y == 0] = -1
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)
  
  # Adaboost classification with 5 weak classifiers
  clf = Adaboost(n_clf=5)
  clf.fit(X_train, y_train)
  y_pred = clf.predict(X_test)
  
  acc = accuracy(y_test, y_pred)
  print ("Accuracy:", acc)
  