# ----------------house data------------------- m, o, n, p = generate_data() print(len(n)) print(len(p)) ratio = 0.7 i, j, ii, jj = [], [], [], [] random.shuffle(o) lens = int(len(o) * ratio) X_train = [t[1] for t in o[:lens]] y_train = [t[0] for t in o[:lens]] X_test = [t[1] for t in o[lens:]] y_test = [t[0] for t in o[lens:]] print("=========My Model=============") model = MulticlassPerceptron(epoch=50, early_stopping=True) model.fit(X_train, y_train, m) model.model_analysis(X_test, y_test) print("=======skit-learn==========") model = Perceptron(max_iter=50) model.fit(X_train, y_train) predicted = model.predict(X_test) errors = 0 for i in range(len(predicted)): if predicted[i] != y_test[i]: errors += 1 print("accuracy:", str(1 - errors * 1.0 / len(predicted))) # # --------------------------------------------- # # # # -----------------position data------------------