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import numpy as npfrom sklearn import datasetsclass LogisticRegression:    def __init__(self):        self._theta = None    def sigmoid(self, t):        return 1 / (1 + np.exp(-t))    def fit(self, X_train, y_train, eta=0.01, n_iters=100000):        """        训练函数  设置学习率和最大迭代次数        :param X_train: 训练集        :param y_train:        :param eta: 学习率        :param n_iters: 最大迭代次数        :return:        """        def loss(theta, X_b, y):            """            loss function            交叉熵            """            y_hat = self.sigmoid(X_b.dot(theta))            return np.sum(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat))        def dloss(theta, X_b, y):                        return X_b.T.dot(self.sigmoid(X_b.dot(theta) - y)) / len(y)        def grandient_descent(X_b, y, init_theta, eta, n_iters, threshold=0.000001):            theta = init_theta            now_iter = 0            while now_iter < n_iters:                gradient = dloss(theta, X_b, y)                last_theta = theta                theta = theta - eta * gradient                if (abs(loss(theta, X_b, y) - loss(last_theta, X_b, y)) < threshold):                    break                now_iter += 1            return theta        X_b = np.hstack([np.ones((len(X_train), 1)), X_train])        # X_b = X_train        init_theta = np.zeros(X_b.shape[1])        self._theta = grandient_descent(X_b, y_train, init_theta, eta, n_iters)        return self    def predict_prob(self, X_pre):        X_b = np.hstack([np.ones((len(X_pre), 1)), X_pre])        return self.sigmoid(X_b.dot(self._theta))    def predict(self, X_test):        prob = self.predict_prob(X_test)        return np.array(prob >= 0.5, dtype='int')    def score(self, X_test, y_test):        y_pre = self.predict(X_test)        correct = [1 if (a == b) else 0 for (a, b) in zip(y_pre, y_test)]        rate = (sum(correct) / len(correct))        return int(rate * 100)if __name__ == '__main__':    iris = datasets.load_iris()    X = iris.data    y = iris.target    X = X[y < 2, :2]    y = y[y < 2]    lr = LogisticRegression()    lr.fit(X, y)        print(X.shape)

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