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a guest Sep 20th, 2019 88 Never
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  1. import numpy as np
  2. import math
  3.  
  4. def euclidian(test_case, train_case):
  5.     ans = 0
  6.     size = len(test_case)
  7.     for i in range(0, size - 1):
  8.         ans += (test_case[i] - train_case[i])**2
  9.     return ans
  10.  
  11. def sortFirst(entry):
  12.     return entry[0]
  13.  
  14. def predict(distances):
  15.     classes = []
  16.     for i in range(0, k):
  17.         classes.append(distances[i][1])
  18.     pred = np.bincount(classes).argmax()
  19.     return pred
  20.  
  21. def knn(test_case):
  22.     distances = []
  23.     for train_case in train_dataset:
  24.         ans = euclidian(test_case, train_case)
  25.         distances.append([ans, train_case[90]])
  26.     distances.sort(key=sortFirst)
  27.     pred = predict(distances)
  28.     if pred == test_case[90]:
  29.         return True
  30.     else:
  31.         return False
  32.  
  33. data = np.loadtxt('./movement_libras.data', delimiter=',')
  34. np.random.shuffle(data)
  35. test_size = math.ceil(len(data)/10)
  36. test_dataset = data[:test_size]
  37. train_dataset = data[test_size:]
  38. k = 7  
  39.  
  40. correct = 0
  41. wrong = 0
  42. for test_case in test_dataset:
  43.     if knn(test_case):
  44.         correct += 1
  45.     else:
  46.         wrong += 1
  47.  
  48. print('Utilizando ', test_size, ' dados como teste em uma base de ', len(data), ' dados.')
  49. print('Acertos: ', correct, '\nErros: ', wrong)
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