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Meu_KNeighbors_Classifier.py
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Meu_KNeighbors_Classifier.py
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import numpy as np
from math import sqrt
import warnings
from collections import Counter
import pandas as pd
import pickle
def k_nearest_neighbors(data, predict, k=2):
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]
confidence = Counter(votes).most_common(1)[0][1] / k
return vote_result, confidence
df = pd.read_csv('data/STN_DATA_DESGASTE_MEDIA.csv')
df.replace('?', -99999, inplace=True)
df.drop(['id'], 1, inplace=True)
full_data = np.array(df, dtype=np.float64)
pickle_in = open('classifier.pickle', 'rb')
train_set = pickle.load(pickle_in)
best = 0
np.random.shuffle(full_data)
test_size = 1
# train_set = {1:[],2:[]}
test_set= {1:[],2:[]}
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_set[i[-1]].append(i[:-1])
correct = 0
total = 0
confidences=[[]]
for group in test_set:
for data in test_set[group]:
vote, confidence = k_nearest_neighbors(train_set, data)
if group == vote:
correct += 1
else:
confidences.append([group,vote,confidence])
total += 1
if correct/total > best:
best = correct/total
print(best)
print(confidences)
## with open('classifier.pickle', 'wb') as f:
## pickle.dump(train_set, f)