数据集
- Dataset1.txt
328 个同学的身高、体重、性别数据（78 个女生、250 个男生） - Dataset2.txt
124 个同学的数据（40 女、84 男） - Dataset3.txt
90 个同学的数据（16 女，74 男）
百度网盘 提取码：8plu
基于压缩近邻法练的分类问题
压缩近邻法的做法是：
- 对初始训练集R，将其划分为两个部分A和B，初始A样本集合为空。
- 从R中随机选择一个样本放入A中，其它样本放入B中，用其对B中的每一个样本进行分类。若样本i能够被正确分类，则将其放回到B中；否则将其加入到A中；
- 重复上述过程，直到B中所有样本都能正确分类为止。
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 19 22:27:40 2020
@author: lihuanyu
"""
#%%数据预处理
import numpy as np
from gain_xy import gain_xy
from sklearn.metrics import accuracy_score
import csv
X_train,y_train,X_test1,y_test1,X_test2,y_test2 = gain_xy()
#%%
import numpy as np
from math import sqrt
from collections import Counter
class KNNClassifier:
#定义K近邻的值必须大于1
def __init__(self,k):
self.k = k
self.x_train_fit = None
self.y_train_fit = None
def fit(self,x_train,y_train):
self.x_train_fit = x_train
self.y_train_fit = y_train
return self
#算出每个点与其他训练集的距离
def _predict(self,x):
distance = []
for x_train in self.x_train_fit:
distance.append(sqrt(np.sum((x_train - x) ** 2)))
nearst = np.argsort(distance)
topk_y = []
for j in nearst[:self.k]:
topk_y.append(self.y_train_fit[j])
votes = Counter(topk_y)
result = votes.most_common(1)[0][0]
return result
#批量预测
def predict(self,X_test,y_test):
y_predict = [self._predict(i) for i in X_test]
print("准确率为",accuracy_score(np.array(y_predict),y_test))
#%%KNN预测
knn = KNNClassifier(k=5)
knn.fit(X_train,y_train)
knn.predict(X_test1,y_test1)
knn.predict(X_test2,y_test2)
#%%压缩处理
k=1
Store = [X_train[0]] #新样本集
Store_y = [y_train[0]]
Grabbag = [i for i in X_train[1:]]
Grabbag_y = [i for i in y_train[1:]]
for x_t,y_t in zip(Grabbag,Grabbag_y):
distance = []
for x,y in zip(Store,Store_y):
#print(x,y)
distance.append(sqrt(np.sum((x - x_t) ** 2)))
nearst = np.argsort(distance)
topk_y = [Store_y[t] for t in nearst[:k]]
votes = Counter(topk_y)
result = votes.most_common(1)[0][0]
if result == y:
Store.append(x_t)
Store_y.append(y_t)
print(len(Store),len(Store_y))
#%%结果
X_train1 = np.array([i for i in Store])
y_train1 = np.array([i for i in Store_y])
import matplotlib.pyplot as plt
plt.scatter(X_train1[y_train1==0,0],X_train1[y_train1==0,1],color='red')
plt.scatter(X_train1[y_train1==1,0],X_train1[y_train1==1,1],color='blue')
knn = KNNClassifier(k=5)
knn.fit(X_train1,y_train1)
knn.predict(X_test1,y_test1)
knn.predict(X_test2,y_test2)结果
test1K近邻准确率为 0.9032258064516129
test2K近邻准确率为 0.8444444444444444
test1压缩近邻准确率为 0.8548387096774194
test压缩近邻准确率为 0.8压缩之后样本点分布如下图所示：
