import numpy as np

"""
请在这里填写学号和姓名，不填写即成绩无效。
"""
id = "202210423224"
name = "吴杨洋"


def sigmoid(z):
    """
    Sigmoid 激活函数。
    """
    return 1 / (1 + np.exp(-z))


def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01):
    m, n = X.shape
    weights = np.zeros(n)
    for j in range(numIter):
        for i in range(m):
            z = np.dot(X[i], weights)
            h = sigmoid(z)
            error = y[i] - h
            gradient = X[i] * error
            reg_term = lambda_reg * weights
            gradient += reg_term
            weights += learning_rate * gradient
    return weights


def predict_logreg(inX, weights):
    """
    对率回归预测函数。

    Args:
    - inX (numpy array): 输入待分类的样本。
    - weights (numpy array): 训练得到的权重。

    Returns:
    - int: 预测的分类标签。
    """
    z = np.dot(inX, weights)
    prob = sigmoid(z)
    return 1 if prob >= 0.5 else 0


def load_data(filepath):
    """
    从文件中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。

    Args:
    - filepath (str): 数据文件路径。

    Returns:
    - X (np.array): 特征数据。
    - y (np.array): 标签数据。
    """
    file = open(filepath)
    fr = file.readlines()
    data_list = []
    y = []
    for data in fr:
        data = data.strip()
        data = data.split(' ')
        data_25 = data[24]
        y.append(data[22])
        del data[-1]
        del data[26]
        del data[25]
        del data[24]
        del data[22]
        del data[2]
        for i in data_25:
            data.append(i)
        data = [0 if data_elem == '?' else float(data_elem) for data_elem in data]
        data_list.append(data)
    data_number = len(y)
    y_train = np.array([1 if y[j] == '2' or y[j] == '3' else 0 for j in range(data_number)])
    X = np.array(data_list)
    X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
    return X_train, y_train


def standardize(X):
    mean = X.mean(axis=0)
    std = X.std(axis=0)
    return (X - mean) / std, mean, std


def main():
    # 加载训练集
    X_train, y_train = load_data("./data/horse-colic.data")
    # 加载测试集
    X_test, y_test = load_data("./data/horse-colic.test")

    # 标准化特征数据
    X_train, mean, std = standardize(X_train)
    X_test = (X_test - mean) / std

    # 训练对率回归模型
    weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1)

    # 测试单个数据
    y_pred_test = [predict_logreg(x, weights) for x in X_test]
    accuracy_test = np.mean(y_pred_test == y_test)
    print(f"Test Accuracy: {accuracy_test:.2f}")


if __name__ == "__main__":
    main()