# function.py
import numpy as np

"""
实验说明:
- 自行编写实现数据加载与预测病马死亡的对率回归算法。

注意事项:
- 在 function.py 中实现对率回归算法。
- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。

测评环境:
- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
- OS: Ubuntu 22.04.4 LTS
- Python: 3.10.12
- numpy: 2.1.1
"""


"""
请在这里填写学号和姓名，不填写即成绩无效。
"""
id = "202210423204"
name = "陈垣豪"


def load_data(filepath):
    """加载数据并处理缺失值."""
    data = np.genfromtxt(filepath, delimiter=' ', dtype=float, missing_values='?', filling_values=np.nan)
    nan_columns = np.isnan(data)
    mean_values = np.nanmean(data, axis=0)
    data[nan_columns] = np.take(mean_values, np.where(nan_columns)[1])
    X = data[:, :-1]
    y = data[:, -1]
    return X, y

def train_logreg(X, y, learning_rate=0.01, iterations=1000):
    """训练逻辑回归模型，使用随机梯度下降."""
    n_samples, n_features = X.shape
    weights = np.zeros(n_features + 1)  # 增加一位用于偏置
    X = np.c_[X, np.ones((n_samples, 1))]  # 增加偏置项

    for _ in range(iterations):
        random_index = np.random.randint(n_samples)
        X_i = X[random_index]
        y_i = y[random_index]

        linear_model = np.dot(X_i, weights)
        dw = (linear_model - y_i) * X_i
        weights -= learning_rate * dw

    return weights

def predict_logreg(inX, weights):
    """使用线性模型预测结果."""
    inX = np.append(inX, 1)  # 增加偏置项
    linear_model = np.dot(inX, weights)
    return 1.0 if linear_model >= 0 else 2.0

if __name__ == "__main__":
    # 示例：加载数据并训练模型
    X_train, y_train = load_data("./data/horse-colic.data")
    weights = train_logreg(X_train, y_train, learning_rate=0.001, iterations=5000)

    # 测试单个数据
    X_test, y_test = load_data("./data/horse-colic.test")
    y_pred = predict_logreg(X_test[11], weights)
    
    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")


    # 训练逻辑回归模型
    weights = train_logreg(X_train, y_train, learning_rate=0.001, iterations=5000)

    # 测试单个数据
    X_test, y_test = load_data("./data/horse-colic.test")
    X_test = scaler.transform(X_test)  # 同样标准化测试集
    y_pred = predict_logreg(X_test[11], weights)
    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")