Template
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import numpy as np
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# 学号和姓名
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id = "202210423224"
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name = "吴杨洋"
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def sigmoid(z):
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"""
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Sigmoid 激活函数。
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"""
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, num_iter=1000, learning_rate=0.01, 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(num_iter):
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z = np.dot(X, weights)
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h = sigmoid(z)
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error = y - h
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# 计算梯度并添加正则化项
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gradient = np.dot(X.T, error) / m + (lambda_reg / m) * weights
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weights += learning_rate * gradient
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return weights
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def predict_logreg(X, 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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- X (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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- numpy array: 预测的分类标签。
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"""
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probabilities = sigmoid(np.dot(X, weights))
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return (probabilities >= 0.5).astype(int)
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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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- X (np.array): 特征数据。
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- y (np.array): 标签数据。
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"""
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data = np.genfromtxt(filepath, delimiter=',', dtype=str, filling_values=np.nan)
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# 处理缺失值和类型转换
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data = np.where(data == '?', np.nan, data).astype(float)
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data = data[~np.isnan(data).any(axis=1)] # 删除含有NaN的行
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# 提取特征和标签
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X = np.delete(data, [22, 24, 25, 26, -1], axis=1) # 删除不需要的列
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y = np.array([1 if row[22] in ['2', '3'] else 0 for row in data]) # 生成标签
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X = np.insert(X, 0, 1, axis=1) # 添加偏置项
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return X, y
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def standardize(X):
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mean = X.mean(axis=0)
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std = X.std(axis=0)
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return (X - mean) / std, mean, std
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def main():
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# 加载训练集和测试集
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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
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X_train, mean, std = standardize(X_train)
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X_test = (X_test - mean) / std
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# 训练对率回归模型
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weights = train_logreg(X_train, y_train, num_iter=1000, learning_rate=0.01, lambda_reg=0.1)
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# 测试模型
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y_pred_test = predict_logreg(X_test, weights)
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accuracy_test = np.mean(y_pred_test == y_test)
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print(f"Test Accuracy: {accuracy_test:.2f}")
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if __name__ == "__main__":
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main() |