From 389e27f1c6782336e5da3fd8819f17503c2ab44a Mon Sep 17 00:00:00 2001
From: HTQ17double <100686379+HTQ17double@users.noreply.github.com>
Date: Wed, 13 Sep 2023 19:38:26 +0800
Subject: [PATCH 1/2] Add files via upload

---
 tree.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/tree.py b/tree.py
index 0e624aa..2335eb9 100644
--- a/tree.py
+++ b/tree.py
@@ -4,6 +4,7 @@
 import operator
 import treePlotter
 from collections import Counter
+from copy import deepcopy
 
 
 pre_pruning = True
@@ -29,7 +30,7 @@ def read_dataset(filename):
     for line in all_lines[0:]:
         line = line.strip().split(',')  # 以逗号为分割符拆分列表
         dataset.append(line)
-    return dataset, labels
+    return dataset, labels#这里的label是特征
 
 
 def read_testset(testfile):
@@ -55,10 +56,10 @@ def cal_entropy(dataset):
     labelCounts = {}
     # 给所有可能分类创建字典
     for featVec in dataset:
-        currentlabel = featVec[-1]
+        currentlabel = featVec[-1] #类别标签
         if currentlabel not in labelCounts.keys():
             labelCounts[currentlabel] = 0
-        labelCounts[currentlabel] += 1
+        labelCounts[currentlabel] += 1# 各个类的总个数
     Ent = 0.0
     for key in labelCounts:
         p = float(labelCounts[key]) / numEntries
@@ -184,6 +185,7 @@ def ID3_createTree(dataset, labels, test_dataset):
     print(u"此时最优索引为：" + (bestFeatLabel))
 
     ID3Tree = {bestFeatLabel: {}}
+    labels_for_post_pruning = deepcopy(labels)
     del (labels[bestFeat])
     # 得到列表包括节点所有的属性值
     featValues = [example[bestFeat] for example in dataset]
@@ -224,7 +226,7 @@ def ID3_createTree(dataset, labels, test_dataset):
 
     if post_pruning:
         tree_output = classifytest(ID3Tree,
-                                   featLabels=['年龄段', '有工作', '有自己的房子', '信贷情况'],
+                                   featLabels=labels_for_post_pruning,
                                    testDataSet=test_dataset)
         ans = []
         for vec in test_dataset:

From c8d5319d8a83998dd0b3d0883380a3640417edce Mon Sep 17 00:00:00 2001
From: HTQ17double <100686379+HTQ17double@users.noreply.github.com>
Date: Wed, 13 Sep 2023 19:42:06 +0800
Subject: [PATCH 2/2] Add files via upload

---
 tree.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tree.py b/tree.py
index 2335eb9..1a38696 100644
--- a/tree.py
+++ b/tree.py
@@ -30,7 +30,7 @@ def read_dataset(filename):
     for line in all_lines[0:]:
         line = line.strip().split(',')  # 以逗号为分割符拆分列表
         dataset.append(line)
-    return dataset, labels#这里的label是特征
+    return dataset, labels
 
 
 def read_testset(testfile):
@@ -56,10 +56,10 @@ def cal_entropy(dataset):
     labelCounts = {}
     # 给所有可能分类创建字典
     for featVec in dataset:
-        currentlabel = featVec[-1] #类别标签
+        currentlabel = featVec[-1] 
         if currentlabel not in labelCounts.keys():
             labelCounts[currentlabel] = 0
-        labelCounts[currentlabel] += 1# 各个类的总个数
+        labelCounts[currentlabel] += 1 
     Ent = 0.0
     for key in labelCounts:
         p = float(labelCounts[key]) / numEntries
@@ -227,7 +227,7 @@ def ID3_createTree(dataset, labels, test_dataset):
     if post_pruning:
         tree_output = classifytest(ID3Tree,
                                    featLabels=labels_for_post_pruning,
-                                   testDataSet=test_dataset)
+                                   testDataSet=test_dataset)# 这里传入的数据集的特征集合是变化后的，所以应该传入变化的特征集
         ans = []
         for vec in test_dataset:
             ans.append(vec[-1])