更新 function.py

function.py

@@ -1,30 +1,61 @@
 import numpy as np
 
+"""
+请在这里填写学号和姓名，不填写即成绩无效。
+"""
 id = "202210423224"
 name = "吴杨洋"
 
+
 def sigmoid(z):
+    """
+    Sigmoid 激活函数。
+    """
     return 1 / (1 + np.exp(-z))
 
+
 def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01):
     m, n = X.shape
     weights = np.zeros(n)
     for j in range(numIter):
-        z = np.dot(X, weights)
+        for i in range(m):
+            z = np.dot(X[i], weights)
             h = sigmoid(z)
-        error = y - h
+            error = y[i] - h
-        gradient = np.dot(X.T, error) / m
+            gradient = X[i] * error
             reg_term = lambda_reg * weights
-        gradient += reg_term / m
+            gradient += reg_term
-        weights -= learning_rate * gradient
+            weights += learning_rate * gradient
     return weights
 
+
 def predict_logreg(inX, weights):
+    """
+    对率回归预测函数。
+
+    Args:
+    - inX (numpy array): 输入待分类的样本。
+    - weights (numpy array): 训练得到的权重。
+
+    Returns:
+    - int: 预测的分类标签。
+    """
     z = np.dot(inX, weights)
     prob = sigmoid(z)
     return 1 if prob >= 0.5 else 0
 
+
 def load_data(filepath):
+    """
+    从文件中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。
+
+    Args:
+    - filepath (str): 数据文件路径。
+
+    Returns:
+    - X (np.array): 特征数据。
+    - y (np.array): 标签数据。
+    """
     file = open(filepath)
     fr = file.readlines()
     data_list = []
@@ -47,26 +78,34 @@ def load_data(filepath):
     data_number = len(y)
     y_train = np.array([1 if y[j] == '2' or y[j] == '3' else 0 for j in range(data_number)])
     X = np.array(data_list)
-    X = np.insert(X, 0, 1, axis=1)  # 添加偏置项，应该在第一列添加
+    X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
-    return X, y_train
+    return X_train, y_train
+
 
 def standardize(X):
     mean = X.mean(axis=0)
     std = X.std(axis=0)
     return (X - mean) / std, mean, std
 
+
 def main():
+    # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")
+    # 加载测试集
     X_test, y_test = load_data("./data/horse-colic.test")
 
+    # 标准化特征数据
     X_train, mean, std = standardize(X_train)
     X_test = (X_test - mean) / std
 
+    # 训练对率回归模型
     weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1)
 
+    # 测试单个数据
     y_pred_test = [predict_logreg(x, weights) for x in X_test]
     accuracy_test = np.mean(y_pred_test == y_test)
     print(f"Test Accuracy: {accuracy_test:.2f}")
 
+
 if __name__ == "__main__":
     main()