generated from LufsX/Experiment-LogReg
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
import numpy as np
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from sklearn.preprocessing import StandardScaler, PolynomialFeatures
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id = "202210423124"
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name = "刘子豪"
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def train_logreg(X, y, learning_rate=0.005, iterations=5000, reg_lambda=0.001):
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"""
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对率回归训练函数。
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Args:
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- X (numpy array): 特征数据。
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- y (numpy array): 标签数据。
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- learning_rate (float): 学习率。
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- iterations (int): 迭代次数。
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- reg_lambda (float): 正则化强度。
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Returns:
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- numpy array: 训练得到的权重。
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"""
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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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z = np.dot(X, weights)
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predictions = 1 / (1 + np.exp(-z)) # 计算sigmoid
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errors = y - predictions
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gradient = np.dot(X.T, errors) / m
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# 加入正则化
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gradient -= reg_lambda * weights / m
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weights += learning_rate * gradient
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return weights
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def predict_logreg(inX, weights):
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"""
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对率回归预测函数。
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Args:
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- inX (numpy array): 输入待分类的样本。
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- weights (numpy array): 训练得到的权重。
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Returns:
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- int: 预测的分类标签。
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"""
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z = np.dot(inX, weights)
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probability = 1 / (1 + np.exp(-z))
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return 1 if probability >= 0.5 else 0
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def load_data(filepath):
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"""
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从文件中加载数据。
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Args:
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- filepath (str): 数据文件路径。
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Returns:
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- np.array: 特征数据。
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- np.array: 标签数据。
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"""
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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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line_data = line.strip().split()
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if line_data:
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try:
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data.append([float(x) if x !='?' else np.nan for x in line_data])
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except ValueError as e:
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print(f"Error processing line: {line}. Error: {e}")
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data = np.array(data)
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data = np.nan_to_num(data, nan=np.nanmean(data, axis=0)) # 将缺失值用平均值代替
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X = data[:, :-1] # 特征
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y = data[:, -1] # 标签
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return X, y
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if __name__ == "__main__":
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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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# 特征标准化
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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# 使用 PolynomialFeatures 生成多项式特征
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poly = PolynomialFeatures(degree=2, include_bias=False)
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X_train_poly = poly.fit_transform(X_train)
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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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X_test = scaler.transform(X_test) # 使用同一标准化参数
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X_test_poly = poly.transform(X_test) # 应用相同的多项式特征转换
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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, reg_lambda=0.01)
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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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