From d5a373a8f9d06c1a90f96fdb1a48bebbefabf5eb Mon Sep 17 00:00:00 2001
From: Dengddd <3149942468@qq.com>
Date: Wed, 30 Oct 2024 10:52:26 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 70 ++++++++++++++++++-----------------------------------
 1 file changed, 24 insertions(+), 46 deletions(-)

diff --git a/function.py b/function.py
index 6ebd621..228d143 100644
--- a/function.py
+++ b/function.py
@@ -26,47 +26,6 @@ name = "邓龙树"
 
 import numpy as np
 
-def sigmoid(z):
-    """计算sigmoid函数"""
-    return 1 / (1 + np.exp(-z))
-
-def train_logreg(X, y, learning_rate=0.01, iterations=1000):
-    """
-    对率回归训练函数。
-
-    Args:
-    - X (numpy array): 特征数据。
-    - y (numpy array): 标签数据。
-    - learning_rate (float): 学习率，默认值为0.01。
-    - iterations (int): 迭代次数，默认值为1000。
-
-    Returns:
-    - numpy array: 训练得到的权重。
-    """
-    m, n = X.shape
-    weights = np.zeros(n)  # 初始化权重
-    for _ in range(iterations):
-        z = np.dot(X, weights)
-        predictions = sigmoid(z)
-        error = predictions - y
-        gradient = np.dot(X.T, error) / m
-        weights -= learning_rate * gradient  # 更新权重
-    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。
-    """
-    probability = sigmoid(np.dot(inX, weights))
-    return 1 if probability >= 0.5 else 0  # 二分类阈值为0.5
-
 def load_data(filepath):
     """
     从文件中加载数据。
@@ -78,17 +37,36 @@ def load_data(filepath):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-    # 使用dtype=str先将所有数据加载为字符串
-    data = np.loadtxt(filepath, delimiter=' ', dtype=str)
+    # 读取每一行作为字符串
+    with open(filepath, 'r') as file:
+        lines = file.readlines()
 
-    # 替换'?'为np.nan，并转换为浮点数
-    data[data == '?'] = np.nan
-    data = data.astype(float)
+    # 分割每一行并填充缺失的列
+    max_cols = 0
+    rows = []
+    for line in lines:
+        # 分割每一行
+        cols = line.strip().split(' ')
+        # 记录最大列数
+        max_cols = max(max_cols, len(cols))
+        rows.append(cols)
+
+    # 将所有行填充到相同的列数
+    for row in rows:
+        while len(row) < max_cols:
+            row.append('nan')
+
+    # 转换为numpy数组
+    data = np.array(rows, dtype=float)
+
+    # 用np.nan替换字符串'nan'
+    data[data == 'nan'] = np.nan
 
     X = data[:, :-1]  # 假设最后一列是标签
     y = data[:, -1]
     return X, y
 
+
 if __name__ == "__main__":
     # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")