[机器学习]09-基于四种近邻算法的鸢尾花数据集分类

基于四种不同的近邻判别方法对数据进行划分和分类

1. 最近邻：每个测试样本的类别由其最近的一个训练样本决定。

2. K近邻（KNN）：根据测试样本的9个最近邻的多数投票决定类别。相比NN更鲁棒，但对K值敏感。

3. 剪辑近邻：训练中迭代删除被KNN误分类的训练样本（剪辑噪声点）。测试中用剪辑后的训练集进行KNN分类。提升分类边界清晰度，减少过拟合。

4. 压缩近邻：训练中通过迭代将样本从Bag移到Store，仅保留对分类关键的样本。测试中用Store中的样本进行最近邻分类。显著减少存储样本量，提升效率。

程序代码：

import math
import random
import matplotlib
import numpy as np
from matplotlib import pyplot as plt
from sklearn import svmdata_dict = {}
train_data = {}
test_data = {}matplotlib.rcParams.update({'font.size': 7})with open('Iris数据txt版.txt', 'r') as file:for line in file:line = line.strip()data = line.split('\t')if len(data) >= 3:try:category = data[0]attribute1 = eval(data[1])attribute2 = eval(data[3])if category in ['2', '3']:if category not in data_dict:data_dict[category] = {'Length': [], 'Width': []}data_dict[category]['Length'].append(attribute1)data_dict[category]['Width'].append(attribute2)except ValueError:print(f"Invalid data in line: {line}")continue
for category, attributes in data_dict.items():print(f'种类: {category}')print(len(attributes["Length"]))print(len(attributes["Width"]))print(f'属性1: {attributes["Length"]}')print(f'属性2: {attributes["Width"]}')for category, attributes in data_dict.items():lengths = attributes['Length']widths = attributes['Width']train_indices = random.sample(range(len(lengths)), 45)test_indices = [i for i in range(len(lengths)) if i not in train_indices]train_data[category] = {'Length': [lengths[i] for i in train_indices],'Width': [widths[i] for i in train_indices]}test_data[category] = {'Length': [lengths[i] for i in test_indices],'Width': [widths[i] for i in test_indices]}print(len(train_data['2']['Length']))
print(train_data['2'])
print(len(test_data['2']['Length']))
print(test_data['2'])
print(len(train_data['3']['Length']))
print(train_data['3'])
print(len(test_data['3']['Length']))
print(test_data['3'])
'''
#plt.scatter(train_data['1']['Length'], train_data['1']['Width'], color='paleturquoise', label='Category 1')
plt.scatter(train_data['2']['Length'], train_data['2']['Width'], color='silver', label='Category 2')
plt.scatter(train_data['3']['Length'], train_data['3']['Width'], color='gold', label='Category 3')
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Basic Dataset Distribution')
plt.show()#最近邻法
all_count = 0
right_count = 0
plt.scatter(train_data['2']['Length'], train_data['2']['Width'], color='silver', label='Category 2')
plt.scatter(train_data['3']['Length'], train_data['3']['Width'], color='gold', label='Category 3')
for category, attributes in test_data.items():for i in range(len(attributes['Length'])):test_point = (attributes['Length'][i], attributes['Width'][i])min_distance = math.infn_category = Nonefor train_category, train_attributes in train_data.items():for j in range(len(train_attributes['Length'])):train_point = (train_attributes['Length'][j], train_attributes['Width'][j])distance = np.sqrt((train_point[0] - test_point[0]) ** 2 + (train_point[1] - test_point[1]) ** 2)if distance < min_distance:min_distance = distancen_category = train_categoryall_count += 1if n_category != category:marker = 'x'else:marker = 'o'right_count += 1if category == '2':color = 'gray'else:color = 'darkgoldenrod'plt.scatter(test_point[0], test_point[1], color=color, marker=marker, label=f'Category {category} (Test)')print("最近邻法准确率：",right_count/all_count)plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Nearest Neighbor Classifier with Test Points and Predictions')
plt.show()#k近邻法
kn = 9
all_count = 0
right_count = 0
plt.scatter(train_data['2']['Length'], train_data['2']['Width'], color='silver', label='Category 2')
plt.scatter(train_data['3']['Length'], train_data['3']['Width'], color='gold', label='Category 3')
for category, attributes in test_data.items():for i in range(len(attributes['Length'])):test_point = (attributes['Length'][i], attributes['Width'][i])test_label = [0,0]n_category = Nonedistances = []for train_category, train_attributes in train_data.items():for j in range(len(train_attributes['Length'])):train_point = (train_attributes['Length'][j], train_attributes['Width'][j])distances.append(np.sqrt((np.array(train_attributes['Length'][j]) - attributes['Length'][i]) ** 2 + (np.array(train_attributes['Width'][j]) - attributes['Width'][i]) ** 2))nearest_indices = np.argsort(distances)[:kn]print(nearest_indices)nearest_categories = [list(train_data.keys())[index // len(train_data['2']['Length'])] for index in nearest_indices]print(nearest_categories)for k in nearest_categories:if k == '2':test_label[0] += 1elif k == '3':test_label[1] += 1if test_label[0] > test_label[1]:n_category = '2'else:n_category = '3'all_count += 1if n_category != category:marker = 'x'else:marker = 'o'right_count += 1if category == '2':color = 'gray'else:color = 'darkgoldenrod'plt.scatter(test_point[0], test_point[1], color=color, marker=marker, label=f'Category {category} (Test)')print("K邻法准确率：", right_count / all_count)
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('K-Neighbors Classifier with Test Points and Predictions')
plt.show()'''
#剪辑近邻法kn = 9
max_iterations = 1500
correct_iterations = 1000
iteration_sum = 0
pop_sum = 0train_data_copy = {key: {'Length': value['Length'][:], 'Width': value['Width'][:]} for key, value in data_dict.items()}
print(train_data_copy)
iterations_without_error = 0while iterations_without_error < correct_iterations and iteration_sum < max_iterations:selected_category = random.choice(list(train_data_copy.keys()))selected_attributes = train_data_copy[selected_category]selected_index = random.randint(0, len(selected_attributes['Length']) - 1)test_point = (selected_attributes['Length'][selected_index], selected_attributes['Width'][selected_index])test_label = [0, 0]n_category = Nonedistances = []for train_category, train_attributes in train_data_copy.items():for j in range(len(train_attributes['Length'])):distance = np.sqrt((np.array(train_attributes['Length'][j]) - test_point[0]) ** 2 +(np.array(train_attributes['Width'][j]) - test_point[1]) ** 2)distances.append(distance)nearest_indices = np.argsort(distances)[:kn]nearest_categories = [list(data_dict.keys())[index // len(data_dict['2']['Length'])] for index in nearest_indices]for k in nearest_categories:if k == '2':test_label[0] += 1elif k == '3':test_label[1] += 1if test_label[0] > test_label[1]:n_category = '2'else:n_category = '3'if n_category != selected_category:train_data_copy[selected_category]['Length'].pop(selected_index)train_data_copy[selected_category]['Width'].pop(selected_index)pop_sum += 1iterations_without_error = 0else:iterations_without_error += 1iteration_sum += 1print("删除数据点数量：", pop_sum)
print("迭代次数：", iteration_sum)
#print(train_data_copy)plt.scatter(train_data['2']['Length'], train_data['2']['Width'], color='silver', label='Category 2')
plt.scatter(train_data['3']['Length'], train_data['3']['Width'], color='gold', label='Category 3')
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Basic Dataset Distribution')
plt.show()for category, attributes in train_data_copy.items():if category == '2':color = 'silver'label = 'Category 2'elif category == '3':color = 'gold'label = 'Category 3'plt.scatter(attributes['Length'], attributes['Width'], color=color, label=label)plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Remaining Training Dataset Distribution')
plt.show()test_data_copy = {}
rest_data = {}
for category, attributes in train_data_copy.items():lengthsx = attributes['Length']widthsx = attributes['Width']rest_indices = random.sample(range(len(lengthsx)), 40)test_indices_copy = [i for i in range(len(lengthsx)) if i not in rest_indices]test_data_copy[category] = {'Length': [lengthsx[i] for i in test_indices_copy],'Width': [widthsx[i] for i in test_indices_copy]}rest_data[category] = {'Length': [lengthsx[i] for i in rest_indices],'Width': [widthsx[i] for i in rest_indices]}
#print(test_data_copy['2'])
#print(test_data_copy['3'])
#print(rest_data['2'])
#print(rest_data['3'])
kn = 9
all_count = 0
right_count = 0
plt.scatter(rest_data['2']['Length'], rest_data['2']['Width'], color='silver', label='Category 2')
plt.scatter(rest_data['3']['Length'], rest_data['3']['Width'], color='gold', label='Category 3')
for category, attributes in test_data_copy.items():for i in range(len(attributes['Length'])):test_point = (attributes['Length'][i], attributes['Width'][i])test_label = [0,0]n_category = Nonedistances = []for train_category, rest_attributes in rest_data.items():for j in range(len(rest_attributes['Length'])):train_point = (rest_attributes['Length'][j], rest_attributes['Width'][j])distances.append(np.sqrt((np.array(rest_attributes['Length'][j]) - attributes['Length'][i]) ** 2 + (np.array(rest_attributes['Width'][j]) - attributes['Width'][i]) ** 2))nearest_ind = np.argsort(distances)[:kn]nearest_categories = [list(rest_data.keys())[index // len(rest_data['2']['Length'])] for index in nearest_ind]for k in nearest_categories:if k == '2':test_label[0] += 1elif k == '3':test_label[1] += 1if test_label[0] > test_label[1]:n_category = '2'else:n_category = '3'all_count += 1if n_category != category:marker = 'x'else:marker = 'o'right_count += 1if category == '2':color = 'gray'else:color = 'darkgoldenrod'plt.scatter(test_point[0], test_point[1], color=color, marker=marker, label=f'Category {category} (Test)')print("剪辑近邻法准确率：", right_count / all_count)
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Trimming Neighbors Classifier with Test Points and Predictions')
plt.show()#压缩近邻法Store = []
Bag = []# 将所有样本放入Garbbag
for category, attributes in train_data_copy.items():lengths = attributes['Length']widths = attributes['Width']for i in range(len(lengths)):Bag.append((lengths[i], widths[i], category))
random.shuffle(Bag)
#print(Bag)
#print(len(Bag))x = random.choice(Bag)
Bag.remove(x)
Store.append(x)max_iterations = 10000
correct_iterations = 1000
correct_sum = 0
iteration_sum = 0while correct_sum < correct_iterations and iteration_sum < max_iterations:# 从Garbbag中随机选择一个样本x = random.choice(Bag)Bag.remove(x)lengthx, widthx, catx = xmin_distance = math.infn_category = Nonefor i in Store:lengthy, widthy, caty = idistance = np.sqrt((lengthx - lengthy)**2 + (widthx - widthy)**2)if distance < min_distance:min_distance = distancen_category = catyif catx != n_category:Store.append(x)correct_sum = 0else:Bag.append(x)correct_sum += 1iteration_sum += 1print("Store数据点数量：",len(Store))
print("迭代次数：",iteration_sum)plt.scatter(rest_data['2']['Length'], rest_data['2']['Width'], color='silver', label='Category 2')
plt.scatter(rest_data['3']['Length'], rest_data['3']['Width'], color='gold', label='Category 3')
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Basic Dataset Distribution')
plt.show()lengths = [sample[0] for sample in Store]
widths = [sample[1] for sample in Store]
categories = [sample[2] for sample in Store]colors = ['silver' if category == '2' else 'gold' for category in categories]plt.scatter(lengths, widths, c=colors, label='Store Data')
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Compressed Data Points')
plt.show()test_data_copy = {}
for category, attributes in train_data_copy.items():lengthsx = attributes['Length']widthsx = attributes['Width']test_indices = random.sample(range(len(lengthsx)), 5)test_data_copy[category] = {'Length': [lengthsx[i] for i in test_indices],'Width': [widthsx[i] for i in test_indices]}plt.scatter(lengths, widths, c=colors, label='Store Data')
for category, attributes in test_data_copy.items():for i in range(len(attributes['Length'])):test_point = (attributes['Length'][i], attributes['Width'][i])test_label = [0,0]n_category = Nonemin_distance = math.inffor lengthz, widthz, catz in Store:distance = np.sqrt((lengthz - attributes['Length'][i]) ** 2 + (widthz - attributes['Width'][i]) ** 2)if distance < min_distance:min_distance = distancen_category = catzall_count += 1if n_category != category:marker = 'x'else:marker = 'o'right_count += 1if category == '2':color = 'gray'else:color = 'darkgoldenrod'plt.scatter(test_point[0], test_point[1], color=color, marker=marker, label=f'Category {category} (Test)')print("压缩近邻法准确率：", right_count / all_count)
plt.xlabel('Length')
plt.ylabel('Width')
plt.legend()
plt.title('Zipping Neighbors Classifier with Test Points and Predictions')
plt.show()

运行结果：

种类: 2
50
50
属性1: [7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7]
属性2: [4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1]
种类: 3
50
50
属性1: [6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
属性2: [6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1]
45
{'Length': [6.1, 6.0, 5.5, 7.0, 5.7, 6.1, 5.8, 6.4, 5.7, 5.8, 6.3, 5.5, 5.5, 6.6, 6.3, 6.7, 5.6, 6.9, 6.6, 6.1, 6.0, 5.4, 5.6, 5.1, 5.9, 5.7, 6.8, 5.9, 6.7, 6.1, 5.7, 6.4, 6.5, 6.0, 5.6, 5.0, 5.2, 5.5, 6.7, 5.5, 6.2, 5.8, 5.6, 6.3, 6.0], 'Width': [4.6, 4.0, 3.8, 4.7, 3.5, 4.7, 3.9, 4.5, 4.2, 4.1, 4.4, 4.0, 4.0, 4.4, 4.9, 4.7, 4.2, 4.9, 4.6, 4.7, 4.5, 4.5, 3.6, 3.0, 4.8, 4.1, 4.8, 4.2, 5.0, 4.0, 4.2, 4.3, 4.6, 4.5, 4.1, 3.5, 3.9, 4.4, 4.4, 3.7, 4.3, 4.0, 4.5, 4.7, 5.1]}
5
{'Length': [5.7, 4.9, 6.2, 5.6, 5.0], 'Width': [4.5, 3.3, 4.5, 3.9, 3.3]}
45
{'Length': [7.4, 6.4, 6.1, 5.8, 6.0, 6.5, 7.7, 7.6, 7.1, 6.9, 5.7, 6.3, 6.4, 7.3, 7.2, 7.2, 5.6, 6.7, 6.9, 7.7, 6.3, 6.0, 6.2, 6.1, 6.8, 6.3, 6.2, 6.8, 6.4, 6.7, 6.7, 6.4, 6.4, 7.2, 7.7, 7.7, 6.3, 6.5, 6.7, 5.8, 5.9, 6.7, 6.3, 6.3, 7.9], 'Width': [6.1, 5.3, 5.6, 5.1, 5.0, 5.5, 6.7, 6.6, 5.9, 5.1, 5.0, 4.9, 5.6, 6.3, 6.1, 5.8, 4.9, 5.2, 5.7, 6.7, 5.6, 4.8, 4.8, 4.9, 5.5, 5.6, 5.4, 5.9, 5.6, 5.8, 5.7, 5.3, 5.5, 6.0, 6.9, 6.1, 5, 5.2, 5.6, 5.1, 5.1, 5.7, 6.0, 5.1, 6.4]}
5
{'Length': [5.8, 6.5, 4.9, 6.5, 6.9], 'Width': [5.1, 5.8, 4.5, 5.1, 5.4]}
{'2': {'Length': [7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7], 'Width': [4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1]}, '3': {'Length': [6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9], 'Width': [6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1]}}
删除数据点数量： 6
迭代次数： 1203
剪辑近邻法准确率： 0.9285714285714286
Store数据点数量： 10
迭代次数： 1336
压缩近邻法准确率： 0.9583333333333334

进程已结束,退出代码0