import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np

def generateData():
    X = []
    y = []
    for i in range(0, 100):
        tem_x = []
        tem_x.append(i)
        X.append(tem_x)
        tem_y = []
        tem_y.append(i + 2.128 + np.random.uniform(-15,15))
        y.append(tem_y)
        plt.scatter(X, y, alpha=0.6)
    return X,y

def residual_sum_of_squares(y_true, y_pred):
    # 計算殘差
    residuals = y_true - y_pred    
    # 計算殘差平方
    squared_residuals = residuals ** 2   
    # 計算殘差平方和
    rss = np.sum(squared_residuals)    
    return rss

if __name__ == '__main__':
    np.random.seed(0)
    X,y = generateData()
    print(len(X))
    X_train,X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
    random_state=0)
    regressor = LinearRegression()
    regressor.fit(X_train, y_train)
    y_result = regressor.predict(X_test)
    plt.plot(X_test, y_result, color='red',alpha=0.6, linewidth=3,
    label='Predicted Line')
    plt.show()
    
    # 提取模型的斜率和截距
    slope = regressor.coef_[0]
    intercept = regressor.intercept_

    # 輸出擬合函數(shù)
    print(f"擬合函數(shù): y = {slope}x + {intercept}")
    
    # 計算殘差平方和
    rss = residual_sum_of_squares(y_test, y_result)
    print("Residual Sum of Squares:", rss)