From 1308465b8855c517fcb63f983f083e33358fc823 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=90=B4=E6=9D=A8=E6=B4=8B?= <2445091977@qq.com>
Date: Sat, 2 Nov 2024 21:08:35 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

diff --git a/function.py b/function.py
index 6e2ab41..f189af5 100644
--- a/function.py
+++ b/function.py
@@ -14,21 +14,17 @@ def sigmoid(z):
     return 1 / (1 + np.exp(-z))
 
 
-def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_reg=0.01):
+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):
-        dataIndex = np.random.permutation(m)
-        for i in range(0, m, batch_size):
-            end = min(i + batch_size, m)
-            batch_X = X[dataIndex[i:end]]
-            batch_y = y[dataIndex[i:end]]
-            z = np.dot(batch_X, weights)
-            h = 1 / (1 + np.exp(-z))
-            error = batch_y - h
-            gradient = np.dot(batch_X.T, error) / len(batch_y)
-            reg_term = 2 * lambda_reg * weights
-            gradient -= reg_term / len(batch_y)
+        for i in range(m):
+            z = np.dot(X[i], weights)
+            h = sigmoid(z)
+            error = y[i] - h
+            gradient = X[i] * error
+            reg_term = lambda_reg * weights
+            gradient += reg_term
             weights += learning_rate * gradient
     return weights
 
@@ -45,7 +41,7 @@ def predict_logreg(inX, weights):
     - int: 预测的分类标签。
     """
     z = np.dot(inX, weights)
-    prob = 1 / (1 + np.exp(-z))
+    prob = sigmoid(z)
     return 1 if prob >= 0.5 else 0