更新 function.py

function.py

@@ -3,6 +3,8 @@ import numpy as np
 import pandas as pd
 from sklearn.impute import SimpleImputer
 from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report
 
 """
 实验说明:
@@ -34,83 +36,69 @@ def sigmoid(x):
     return 1 / (1 + np.exp(-x))
 
 # train_logreg函数，用于训练逻辑回归模型
-def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.01):
+def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.1):
-    m, n = X.shape  # 获取样本数量和特征数量
+    m, n = X.shape
-    weights = np.zeros(n)  # 初始化权重为0
+    weights = np.zeros(n)
-    for _ in range(iterations):  # 迭代指定次数
+    for _ in range(iterations):
-        linear_model = np.dot(X, weights)  # 计算线性模型的输出
+        linear_model = np.dot(X, weights)
-        y_predicted = sigmoid(linear_model)  # 将线性输出转换为概率
+        y_predicted = sigmoid(linear_model)
-        gradient = np.dot(X.T, (y_predicted - y)) / m + regularization * weights  # 计算梯度
+        gradient = np.dot(X.T, (y_predicted - y)) / m + regularization * weights
-        weights -= learning_rate * gradient  # 更新权重
+        weights -= learning_rate * gradient
-    return weights  # 返回训练好的权重
+    return weights
 
 # predict_logreg函数，用于使用训练好的权重进行预测
 def predict_logreg(inX, weights):
-    linear_output = np.dot(inX, weights)  # 计算线性输出
+    linear_output = np.dot(inX, weights)
-    probability = sigmoid(linear_output)  # 将线性输出转换为概率
+    probability = sigmoid(linear_output)
-    return 1 if probability > 0.5 else 0  # 返回预测结果，大于0.5为1，否则为0
+    return 1 if probability > 0.5 else 0
 
 # preprocess_data函数，用于预处理数据
 def preprocess_data(X, y):
-    # 合并目标变量的类别，将2和3都转换为0
     y[y == 2] = 0
     y[y == 3] = 0
-
-    # 删除特征3和特征24~28
     X = np.delete(X, [3], axis=1)
     X = X[:, :23]
-
+    return X, y
-    return X, y  # 返回预处理后的数据
 
 # load_data函数，用于加载数据
 def load_data(filepath):
-    # 使用Pandas读取数据，可以更好地处理缺失值和字符串
     try:
         data = pd.read_csv(filepath, header=None, sep=r'\s+', on_bad_lines='skip')
     except pd.errors.ParserError as e:
         print(f"Error parsing file: {e}")
         return None, None
-
-    # 将'?'替换为np.nan
     data.replace('?', np.nan, inplace=True)
-
-    # 使用SimpleImputer填充缺失值
     imputer = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)
     data.iloc[:, :-1] = imputer.fit_transform(data.iloc[:, :-1])
-
-    # 将所有列转换为数值类型
     for col in data.columns[:-1]:
         data[col] = pd.to_numeric(data[col], errors='coerce')
-
+    X = data.iloc[:, :-1].values
-    X = data.iloc[:, :-1].values  # 特征数据
+    y = data.iloc[:, -1].values.astype(int)
-    y = data.iloc[:, -1].values.astype(int)  # 目标变量
-
-    # 预处理数据
     X, y = preprocess_data(X, y)
-
+    return X, y
-    return X, y  # 返回加载和预处理后的数据
 
 # main函数，程序的入口点
 def main():
-    # 假设data文件夹和function.py在同一目录下
+    X, y = load_data("./data/horse-colic.data")
-    X_train, y_train = load_data("./data/horse-colic.data")
+    if X is not None:
-    X_test, y_test = load_data("./data/horse-colic.test")
+        # 分割数据集为训练集和测试集
-
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    if X_train is not None and X_test is not None:
+        
         # 特征缩放
         scaler = StandardScaler()
         X_train = scaler.fit_transform(X_train)
         X_test = scaler.transform(X_test)
-
+        
         # 训练模型
         weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, regularization=0.1)
-
+        
         # 预测测试集
         y_pred = [predict_logreg(x, weights) for x in X_test]
-
+        
         # 计算准确率
-        accuracy = np.mean(y_pred == y_test)
+        accuracy = accuracy_score(y_test, y_pred)
         print(f"准确率: {accuracy}")
+        print(classification_report(y_test, y_pred))
 
 if __name__ == "__main__":
-    main()  # 调用main函数，运行程序
+    main()