From fcb50d0d206f2956e8be3665fb5e52ae5a64c2e1 Mon Sep 17 00:00:00 2001
From: wzy <675163717@qq.com>
Date: Thu, 31 Oct 2024 17:19:03 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/function.py b/function.py
index be4bf3f..8d51b11 100644
--- a/function.py
+++ b/function.py
@@ -1,6 +1,7 @@
 # function.py
 import numpy as np
 import random
+import pandas as pd
 
 """
 实验说明:
@@ -27,8 +28,8 @@ name = "万子玉"
 
 
 # 这里写自己的代码实现
-def sigmoid(z):
-    return 1 / (1 + np.exp(-z))
+def sigmoid(inx):
+    return 1 / (1 + np.exp(-inx))
 
 def train_logreg(X, y, learning_rate=0.001, iterations=500, batch_size=16, initial_weights=None):
     X = np.insert(X, 0, 1, axis=1)  # 添加偏置项
@@ -140,6 +141,22 @@ def load_data(filepath):
     y = [row[-1] for row in data]
     
     return np.array(X), np.array(y)
+    
+    data = pd.read_csv(filepath, header=None, delimiter=' ')
+    return data
+
+# 标准化函数
+def regularize(xMat):
+    inMat = xMat.copy()
+    inMeans = np.mean(inMat, axis=0)
+    inVar = np.std(inMat, axis=0)
+    inMat = (inMat - inMeans) / inVar
+    return inMat
+
+def classify(inx, weights):
+    p = sigmoid(np.dot(inx, weights))
+    return 1 if p >= 0.5 else 0
+
     """
     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**。