knn_application

python counter类:

python

>>> from collections import Counter
>>> s = "hello pinsily"
>>> d = Counter(s)
>>> d
Counter({'l': 3, 'i': 2, 'h': 1, 'e': 1, 'o': 1, ' ': 1, 'p': 1, 'n': 1, 's': 1, 'y': 1})

most_common(n)
返回数量最多的前 n 个元素

python

>>> d.most_common(3)
[('l', 3), ('i', 2), ('h', 1)]

代码实现：

python

import numpy as np 
from math import sqrt
import matplotlib.pyplot as plt
import warnings
from matplotlib import style
from collections import Counter
style.use('fivethirtyeight')
dataset = {'k':[[1,2],[2,3],[3,1]], 'r':[[6,5],[7,7],[8,6]]}
new_features = [5,7]
for i in dataset:
    for ii in dataset[i]:
        plt.scatter(ii[0],ii[1],s=100,color=i)

def k_nearest_neighbors(data, predict, k=3):
    if len(data) >= k:
        warnings.warn('K is set to a value less than total voting groups!')
        
    distances = []
    for group in data:
        for features in data[group]:
            euclidean_distance = np.linalg.norm(np.array(features)-np.array(predict))  #欧几里得距离
            distances.append([euclidean_distance,group])

    votes = [i[1] for i in sorted(distances)[:k]]
    vote_result = Counter(votes).most_common(1)[0][0] #不使用[0][0],得到的是[('r', 3)]. [0][0]得到元组中第一个元素
    return vote_result

result = k_nearest_neighbors(dataset,new_features,k=3)
print(result)
plt.scatter(new_features[0],new_features[1],s=50,color=result)#预测的数据用小红点表示
plt.show()

运行结果：

然后用这个代码来跑下癌症预测，代码如下

python

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import warnings
from collections import Counter
import pandas as pd
import random

def k_nearest_neighbors(data, predict, k=3):
    if len(data) >= k:
        warnings.warn('K is set to a value less than total voting groups!')
        
    distances = []
    for group in data:
        for features in data[group]:
            euclidean_distance = np.linalg.norm(np.array(features)-np.array(predict))
            distances.append([euclidean_distance,group])

    votes = [i[1] for i in sorted(distances)[:k]]
    vote_result = Counter(votes).most_common(1)[0][0] #不使用[0][0],得到的是[('r', 3)]. [0][0]得到元组中第一个元素
    return vote_result

df = pd.read_csv('breast-cancer-wisconsin.txt')
df.replace('?',-99999,inplace=True)
df.drop(['id'],1,inplace=True)
full_data = df.astype(float).values.tolist()

test_size = 0.2
train_set = {2:[], 4:[]}#良性恶性两个lable
test_set = {2:[], 4:[]}
train_data = full_data[:-int(test_size*len(full_data))]
test_data = full_data[-int(test_size*len(full_data)):]   #最后20%
correct = 0
total = 0
for i in train_data:
    train_set[i[-1]].append(i[:-1]) #去掉label，将属性填入

for i in test_data:
    test_set[i[-1]].append(i[:-1])

for group in test_set:
    for data in test_set[group]:
        vote = k_nearest_neighbors(train_set, data, k=5)
        if group == vote:
            correct += 1
        total += 1
print('Accuracy:', correct/total)

准确度很高！