generated from LufsX/Experiment-LogReg
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
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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"""
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id = "202210423228"
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name = "黎世浩"
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def load_data(filepath):
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data = np.genfromtxt(filepath, delimiter=' ', dtype=float, missing_values='?')
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X = data[:, :-1]
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y = data[:, -1]
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# 将缺失值填充为每列的平均值
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for i in range(X.shape[1]):
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X[:, i] = np.where(np.isnan(X[:, i]), np.mean(X[:, i]), X[:, i])
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return X, y.astype(int)
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def scale_features(X):
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mean = np.mean(X, axis=0)
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std = np.std(X, axis=0)
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std[std == 0] = 1
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scaled_X = (X - mean) / std
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return scaled_X
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def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
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m, n = X.shape
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weights = np.zeros(n)
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for _ in range(iterations):
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for i in range(m):
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x_i = X[i]
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y_i = y[i]
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prediction = 1 / (1 + np.exp(-np.dot(x_i, weights)))
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gradient = x_i * (prediction - y_i)
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weights -= learning_rate * (gradient + lambda_reg * weights)
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return weights
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def predict_logreg(inX, weights):
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prediction = 1 / (1 + np.exp(-np.dot(inX, weights)))
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return 1 if prediction > 0.5 else 0
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if __name__ == "__main__":
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X_train, y_train = load_data("./data/horse-colic.data")
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X_test, y_test = load_data("./data/horse-colic.test")
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X_train_scaled = scale_features(X_train)
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X_test_scaled = scale_features(X_test)
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weights = train_logreg(X_train_scaled, y_train, learning_rate=0.01, iterations=1000)
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# 测试模型
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y_pred = [predict_logreg(x, weights) for x in X_test_scaled]
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accuracy = np.mean(y_pred == y_test)
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print(f"Accuracy: {accuracy}") |