Used nearest centroid algorithm, renamed accordingly

K_means.py → centroid.py

 import numpy as np
 from sklearn.cluster import KMeans
 
-# function that finds cluster of data points using K-means algorithm
-def km(X):
+# function that finds centroid of pre-labeled cluster of data points 
+def centroid(X):
     kmeans = KMeans(n_clusters=1)
     kmeans.fit(X)
     c = kmeans.cluster_centers_

main (K-means).py → main (nearest centroid).py

 # -*- coding: utf-8 -*-
-# K-means Text Color Chooser: Main
+# Nearest Centroid Text Color Chooser: Main
 import pygame
 import os
 import numpy as np
@@ -7,7 +7,7 @@ import scipy
 from matplotlib import pyplot
 from mpl_toolkits.mplot3d import Axes3D
 from random import randint
-from K_means import km
+from centroid import centroid
 from matrixToFile import matrixToFile
 
 # init
@@ -36,7 +36,7 @@ iconPic = pygame.image.load('icon.png')
 pygame.display.set_icon(iconPic)
 
 # title of app
-pygame.display.set_caption('K-means Text Color Chooser 1.1.1')
+pygame.display.set_caption('Nearest Centroid Text Color Chooser 1.2')
 
 # set fonts
 smallfont = pygame.font.SysFont('comicsansms', 25)
@@ -86,13 +86,13 @@ while True:
     text('press tab to show data', (255,255,255), smallfont, width - 275, 10)
 
     # make prediction, display prediction above color box
-    whitecluster = km(white)
-    blackcluster = km(black)
+    whitecent = centroid(white)
+    blackcent = centroid(black)
     y = np.reshape(color, (-1, 3))
     if len(points) > 1:
-            if scipy.spatial.distance.euclidean(y, whitecluster) < scipy.spatial.distance.euclidean(y, blackcluster):
+            if scipy.spatial.distance.euclidean(y, whitecent) < scipy.spatial.distance.euclidean(y, blackcent):
                 display.blit(predPic, (width / 3 - 80 / 2, 200))
-            elif scipy.spatial.distance.euclidean(y, whitecluster) > scipy.spatial.distance.euclidean(y, blackcluster):
+            elif scipy.spatial.distance.euclidean(y, whitecent) > scipy.spatial.distance.euclidean(y, blackcent):
                 display.blit(predPic, (width / 1.5 - 80 / 2, 200))
             else:
                 display.blit(predPic, (width / 1.5 - 80 / 2, 200))
@@ -161,9 +161,9 @@ while True:
                     for n in range(1,len(black)):
                         ax.scatter(black[n,0],black[n,1],black[n,2], color = "black")
                     ax.scatter(color[0], color[1], color[2], color = "blue")
-                    # display clusters
-                    ax.scatter(whitecluster[:, 0], whitecluster[:, 1], whitecluster[:, 2], marker='*', c='white', s=500)
-                    ax.scatter(blackcluster[:, 0], blackcluster[:, 1], blackcluster[:, 2], marker='*', c='black', s=500)
+                    # display centroids
+                    ax.scatter(whitecent[:, 0], whitecent[:, 1], whitecent[:, 2], marker='*', c='white', s=500)
+                    ax.scatter(blackcent[:, 0], blackcent[:, 1], blackcent[:, 2], marker='*', c='black', s=500)
                     # axis labels
                     ax.set_xlabel('R', fontsize=15)
                     ax.set_ylabel('G', fontsize=15)