Notes in Machine Learning with Python(2)

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Notes in Machine Learning with Python(2)

Environment Problem

Regression

  • python list : xs*ys means every element in xs times ys by the order of index

Matplotlib

  • plt.scatter() —-> scatter

Classification

K Nearest Neighbors Application

  • dataset
  • numpy.reshape :
    example_measures 
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    ## a：将要被重塑的类数组或数组

    ## newshape：整数值或整数元组。新的形状应该兼容于原始形状。如果是一个整数值，表示一个一维数组的长度；

    ## 如果是元组，一个元素值可以为-1，此时该元素值表示为指定，此时会从数组的长度和剩余的维度中推断出

    `
  • lib - warnings :
    is set to a value less than total voting groups!')
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    `
  • numpy.linalg.norm : np.linalg.norm(np.array(features) - np.array(predict))

  • python dictionary:

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    dataset = {'k':[[1,2],[2,3],[3,1]], 'r':[[6,5],[7,7],[8,6]]}
      new_features = [5,7]
      for group in dataset:
          for features in data[group]:
              euclidean_distance = np.linalg.norm(np.array(features) - np.array(predict))
              distances.append([euclidean_distance, group])

  • Lib - Counters : from collections import Counter

    • vote_result = Counter(votes).most_common(1)[0][0]
    • It gives us a list of tuple,the ‘1’ in here determines the numbers of the most common tuples
    • tuples:(the most common element,numbers of the most common)

  • use the [-num] of list flexbily

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    test_size = 0.2
      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_set[i[-1]].append(i[:-1])