EuclideanDistance.py

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

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]:


#plot1=[1,3]
#plot2=[2,5]

#euclidean_distance=sqrt((plot1[0]-plot2[0]**2 + plot1[1]-plot2[1])**2)
#print(euclidean_distance)

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.sqrt(np.sum((np.array(features)-np.array(predict))**2))
            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]]
    print(Counter(votes).most_common(1))
    vote_result=Counter(votes).most_common(1)[0][0]
    confidence=Counter(votes).most_common(1)[0][1]/k  
    print(vote_result,confidence)
    return vote_result,confidence

result=k_nearest_neighbors(dataset,new_features,k=3)
print(result)

[[plt.scatter(ii[0], ii[1], s=100, color=i) for ii in dataset[i]] for i in dataset]
plt.scatter(new_features[0],new_features[1],color=result)
plt.show()


accuracies=[]
for i in range(5):
    
    df=pd.read_csv("breast-cancer-wisconsin.data.txt")
    df.replace('?',-99999, inplace=True)
    df.drop(['id'],1,inplace=True)
    print(df.head())
    full_data=df.astype(float).values.tolist()

    random.shuffle(full_data)
    test_size=0.4
    train_set={2:[],4:[]}
    test_set={2:[],4:[]}
    train_data=full_data[:-int(test_size*len(full_data))]
    test_data=full_data[-int(test_size*len(full_data)):]

    for i in train_data:
        train_Set[i[-1]].append(i[:-1])
        
    for i in test_data:
        test_data=[i[-1]].append(i[:-1])

    correct=0
    total=0

    for group in test_set:
        for data in test_set[group]:
            vote,confidence=k_nearest_neighbors(train_set, data, k=5)
            if group == vote:
                correct+=1
            else:
                print(confidence)
            total+=1
    print('Accuracy:', correct/total)
    accuracies.append(correct/total)        
            
                                
    print(20*'#')
    print(full_data[:10])
print(sum(accuracies)/len(accuracies))