knn algorithm

　　This is the first algorithm I came into contact with -----knn (proximity algorithm)

　　Self-understanding is to use the Pythagorean theorem to calculate the distance between point C and A and B. If the distance is smaller, it is considered that C tends to that value.

Python implements the knn process

　　

1  from numpy import *
 2  
3  #simulate training data---array 
4 with 4 rows and 6 columns train_data = array([
 5      [1, 2, 3, 4, 5, 6],      # A type 
6      [7, 5, 4, 1, 7, 5],      # B type 
7      [4, 7, 6, 5, 3, 8],      # C type 
8      [5, 2, 3, 4, 9, 4],      # D type 
9  ] )
 10  #Each line is a type, that is, 4 lines correspond to the following 4 types respectively 
11 labels = [ " A " , " B " , " C" , " D " ]
 12  
13  #Simulate test data 
14 test_data = array([1, 5, 8, 6, 4, 7 ])
 15  
16  
17  # Purpose: Calculate which category the test data belongs to through the knn algorithm 
18  def knn(test_data, train_data, labels):
 19      train_data_lines = train_data.shape[0] #Get    the number of lines of training data 
20      test_data = tile(test_data, (train_data_lines, 1)) #Organize the test data as much as the training data Number of rows, calculate "distance" 
21      dif_value = test_data - train_data #difference 22 square_value_1 = dif_value
      **2         #square
23      line_num = square_value_1.sum(axis=1)            #Sum on each line 
24      square_value_2 = line_num**0.5                        #Root 25 sortdistance
      = square_value_2.argsort() #sort in          descending order 26 return labels[sortdistance[0]]
 27 28 res = knn(test_data,train_data,labels)
 29 print (res)
      
 

It's that simple... lol, hope it helps.