import numpy as np from .metrics import r2_score class LinearRegression:     def __init__(self):         """初始化Linear Regression模型"""         self.coef_ = None         self.intercept_ = None         self._theta = None     def fit_normal(self, X_train, y_train):         """根据训练数据集X_train, y_train训练Linear Regression模型"""         assert X_train.shape[0] == y_train.shape[0], \             "the size of X_train must be equal to the size of y_train"         X_b = np.hstack([np.ones((len(X_train), 1)), X_train])         self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)         self.intercept_ = self._theta[0]         self.coef_ = self._theta[1:]         return self     def fit_gd(self, X_train, y_train, eta=0.01, n_iters=1e4):         """根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""         assert X_train.shape[0] == y_train.shape[0], \             "the size of X_train must be equal to the size of y_train"         def J(theta, X_b, y):             try:                 return np.sum((y - X_b.dot(theta)) ** 2) / len(y)             except:                 return float('inf')         def dJ(theta, X_b, y):             # res = np.empty(len(theta))             # res[0] = np.sum(X_b.dot(theta) - y)             # for i in range(1, len(theta)):             #     res[i] = (X_b.dot(theta) - y).dot(X_b[:, i])             # return res * 2 / len(X_b)             return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(X_b)         def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):             theta = initial_theta             cur_iter = 0             while cur_iter < n_iters:                 gradient = dJ(theta, X_b, y)                 last_theta = theta                 theta = theta - eta * gradient                 if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):                     break                 cur_iter += 1             return theta         X_b = np.hstack([np.ones((len(X_train), 1)), X_train])         initial_theta = np.zeros(X_b.shape[1])         self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)         self.intercept_ = self._theta[0]         self.coef_ = self._theta[1:]         return self     def fit_sgd(self, X_train, y_train, n_iters=5, t0=5, t1=50):         """根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""         assert X_train.shape[0] == y_train.shape[0], \             "the size of X_train must be equal to the size of y_train"         assert n_iters >= 1         def dJ_sgd(theta, X_b_i, y_i):             return X_b_i * (X_b_i.dot(theta) - y_i) * 2.         def sgd(X_b, y, initial_theta, n_iters, t0=5, t1=50):             def learning_rate(t):                 return t0 / (t + t1)             theta = initial_theta             m = len(X_b)             for cur_iter in range(n_iters):                 indexes = np.random.permutation(m)                 X_b_new = X_b[indexes]                 y_new = y[indexes]                 for i in range(m):                     gradient = dJ_sgd(theta, X_b_new[i], y_new[i])                     theta = theta - learning_rate(cur_iter * m + i) * gradient             return theta         X_b = np.hstack([np.ones((len(X_train), 1)), X_train])         initial_theta = np.random.randn(X_b.shape[1])         self._theta = sgd(X_b, y_train, initial_theta, n_iters, t0, t1)         self.intercept_ = self._theta[0]         self.coef_ = self._theta[1:]         return self     def predict(self, X_predict):         """给定待预测数据集X_predict，返回表示X_predict的结果向量"""         assert self.intercept_ is not None and self.coef_ is not None, \             "must fit before predict!"         assert X_predict.shape[1] == len(self.coef_), \             "the feature number of X_predict must be equal to X_train"         X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])         return X_b.dot(self._theta)     def score(self, X_test, y_test):         """根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""         y_predict = self.predict(X_test)         return r2_score(y_test, y_predict)     def __repr__(self):         return "LinearRegression()"