generated from LufsX/Experiment-LogReg
77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
# function.py
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import numpy as np
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"""
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实验说明:
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- 自行编写实现数据加载与预测病马死亡的对率回归算法。
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注意事项:
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- 在 function.py 中实现对率回归算法。
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- 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。
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- 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。
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测评环境:
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- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
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- OS: Ubuntu 22.04.4 LTS
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- Python: 3.10.12
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- numpy: 2.1.1
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"""
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"""
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请在这里填写学号和姓名，不填写即成绩无效。
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姓名请直接**填写中文**
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例如: name = "张三"
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"""
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id = "202210423113"
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name = "杨文强"
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# 这里写自己的代码实现
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def sigmoid(z):
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, learning_rate=0.01, iterations=1000):
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m, n = X.shape
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weights = np.zeros(n)
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for i in range(iterations):
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for j in range(m):
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z = np.dot(X[j], weights)
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h = sigmoid(z)
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error = y[j] - h
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weights += learning_rate * error * X[j]
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return weights
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def predict_logreg(inX, weights):
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z = np.dot(inX, weights)
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h = sigmoid(z)
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if h > 0.5:
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return 1
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else:
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return 0
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# 这里也写自己的代码实现
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def load_data(filepath):
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data = np.loadtxt(filepath, delimiter=' ')
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X = data[:, :-1]
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y = data[:, -1]
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return X, y
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if __name__ == "__main__":
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# 以下代码可用于测试，可自行随意修改
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# 加载训练集
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X_train, y_train = load_data("./data/horse-colic.data")
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# 加载测试集
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X_test, y_test = load_data("./data/horse-colic.test")
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# 训练对率回归模型
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weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
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# 测试单个数据
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y_pred = predict_logreg(X_test[11], weights)
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print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
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