Python implements simple K-Means classification

Write a simple K-Means classification through Python

The specific method is actually very simple:

  1. Generate several types of random data points
  2. Randomly generate K centers
  3. For each point point, find the center point with the closest distance, that is, the classification
    • Take the average of the data points in each classification set as the new center point coordinates
    • If the distance between all new center points and old center points is less than a certain threshold, the classification is complete; otherwise, iterate
import matplotlib . pyplot as plt
 import numpy as np
 import random
 from icecream import ic
 from collections import defaultdict
 from matplotlib . colors import BASE_COLORS

def  random_centers ( k , points ) : 
    for i in  range ( k ) : 
        # randomly generate k center points in the original possible coordinates 
        yield random . choice ( points [ : ,  0 ] ) , random . choice ( points [ : ,  1 ] )

def  mean ( points ) : 
    #all_x,all_y are lists 
    all_x , all_y =  [ x for x , y in points ] ,  [ y for x , y in points ] 
    return np . mean ( all_x ) , np . mean ( all_y )


def  distance ( p1 , p2 ) : 
    # Find the distance between two points 
    x1 , y1 = p1
    x2 , y2 = p2
     return np . sqrt ( ( x1 - x2 )  **  2  +  ( y1 - y2 ) ** 2 )

def  draw_points ( centers , centers_neighbor , colors ) : 
    # Traverse each center point 
    for i , c in  enumerate ( centers ) : 
        # Get the set of points covered by the center point 
        _points = centers_neighbor [ c ] 
        all_x , all_y =  [ x for x , y in _points ] ,  [ y for x , y in_points ] 
        # draw the corresponding points in color 
        plt . scatter ( all_x , all_y , c = colors [ i ] ) 
    plt . show ( )

def  kmeans ( k , points , centers = None ) : #Get 
    a list representing color information values 
    colors =  list ( BASE_COLORS . values ( ) ) #If 
    no centers are generated, randomly generate one 
    if  not centers : 
        centers =  list ( random_centers ( k = k , points = points ) ) #easy 
    to debug 
    ic ( centers ) 
    for i, c in  enumerate ( centers ) : #enumerate() combines an iterable data object (such as a list, tuple or string) into a sequence of indices 
        plt . scatter ( [ c [ 0 ] ] ,  [ c [ 1 ] ] , s = 90 , marker = '*' , c = colors [ i ] ) # draw a scatterplot

    plt . scatter ( * zip ( * points ) , c = 'black' ) 
    #defaultdict is that when the key in the dictionary does not exist but is searched, it returns not a keyError but a default value set corresponding to set( ) , that is, an empty set is returned when there is no key 
    centers_neighbor = defaultdict ( set )

    for p in points : 
        #min function returns a center point coordinate 
        closet_c =  min ( centers , key = lambda c : distance ( p , c ) )
         #Add points to the nearest center point set 
        centers_neighbor [ closet_c ] .add ( tuple ( p ) )

    #ic(centers_neighbor)

    draw_points ( centers , centers_neighbor , colors )

    new_centers =  [ ]

    for c in centers_neighbor : 
        # Calculate the average of all points contained in each center point as a new center point 
        new_c = mean ( centers_neighbor [ c ] ) 
        new_centers . append ( new_c )

    threshold =  0.1 
    distances_old_and_new =  [ distance ( c_old , c_new )  for c_old , c_new in  zip ( centers , new_centers ) ] 
    #ic(distances_old_and_new) 
    if  all ( c < threshold for c in distances_old_and_new ) : 
        return centers_neighbor
     else : 
        kmeans ( k , points, new_centers )

if __name__ ==  '__main__' : #Randomly 
    generate four sets of data 
    points0 = np . random . normal ( loc = 1 , size = ( 100 , 2 ) ) 
    points1 = np . random . normal ( loc = 2 , size = ( 100 ,  2 ) ) 
    points2 = np . random . normal( loc = 4 , size = ( 100 ,  2 ) ) 
    points3 = np . random . normal ( loc = 5 , size = ( 100 ,  2 ) )

    points = np . concatenate ( [ points0 , points1 , points2 , points3 ] )

    kmeans ( 3 , points = points , centers = None )
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Effect picture:

First iteration:
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Second iteration:

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Third iteration:

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Fourth iteration:

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Fifth iteration:

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Classification complete!

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