generated from LufsX/Experiment-LogReg
108 lines
3.7 KiB
Python
108 lines
3.7 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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"""
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id = "202210423217"
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name = "吴俊煯"
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# 这里写自己的代码实现
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def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.0):
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m, n = X.shape
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weights = np.zeros(n)
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bias = 0
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m = len(X) # 重新赋值m，以确保它表示样本数量（在添加正则化时可能需要）
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for _ in range(iterations):
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linear_output = np.dot(X, weights) + bias # 向量化操作
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y_predicted = 1 / (1 + np.exp(-linear_output))
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error = y - y_predicted
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# 计算梯度（不包括正则化项）
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gradient = np.dot(X.T, error) / m
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# 加上正则化项的梯度（注意这里是对weights的每个元素都加上-regularization*weight[i]）
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gradient += -regularization * weights
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# 更新权重
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weights += learning_rate * gradient
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bias += learning_rate * np.sum(error) / m
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return weights, bias
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def predict_logreg(inX, weights, bias):
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# 确保 inX 是一维数组
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if inX.ndim == 2 and inX.shape[1] == 1:
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inX = inX.flatten()
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elif inX.ndim > 1:
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raise ValueError("Input data should be one-dimensional or a column vector.")
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# 计算线性输出
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linear_output = np.dot(inX, weights) + bias
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# 应用sigmoid函数（如果需要的话）
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# sigmoid_output = 1 / (1 + np.exp(-linear_output))
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# 这里可以返回线性输出或sigmoid输出，取决于您的需求
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return linear_output
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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 = []
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with open(filepath, 'r') as file:
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for line in file:
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parts = [part.strip() for part in line.split()]
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features = [float(part) if part != '?' else 0 for part in parts[:-1]]
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label = int(parts[-1]) - 1 # 假设标签是{1, 2}，转换为{0, 1}
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data.append(features + [label])
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data_array = np.array(data)
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X = data_array[:, :-1]
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y = data_array[:, -1]
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return X, y
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def evaluate_model(X, y, weights, bias):
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predictions = [predict_logreg(x, weights, bias) for x in X]
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accuracy = np.mean(predictions == y)
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return accuracy
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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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# 数据标准化（可选，但通常有助于模型性能）
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X_train_mean = np.mean(X_train, axis=0)
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X_train_std = np.std(X_train, axis=0)
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X_train = (X_train - X_train_mean) / X_train_std
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X_test = (X_test - X_train_mean) / X_train_std # 使用训练集的标准差来标准化测试集
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# 训练逻辑回归模型
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weights, bias = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, regularization=0.1)
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# 评估模型性能
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accuracy = evaluate_model(X_test, y_test, weights, bias)
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print(f"测试集准确率: {accuracy:.2f}")
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# 测试单个数据（可选）
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y_pred = predict_logreg(X_test[11], weights, bias)
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print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
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