/
kmeans.py
131 lines (98 loc) · 3.59 KB
/
kmeans.py
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from random import randrange
import math
import pandas
import matplotlib
import matplotlib.pyplot as plt
import glob
import os
import time
os.chdir('C:/Users/user/Desktop/iPython/')
folders = [ item for item in glob.glob('*') if os.path.isdir(item) ]
print folders
#folders.remove('.ipynb_checkpoints')
folders = map(lambda x:int(x) , folders)
new_folder = int(sorted(folders)[len(folders)-1]) + 1
os.mkdir(str(new_folder))
os.chdir(str(new_folder) + '/')
files = [ item for item in glob.glob('*.jpg')]
for item in files :
os.remove(item)
num_points = 60000
X = [ { 'x':randrange(1000) , 'y':randrange(1000) } for i in range(num_points) ]
for i in X:
pass#print "%s %s" % (i['x'], i['y'])
K = 10
color = matplotlib.colors.cnames.keys()
centroid = [ X[randrange(len(X))] for i in range(K) ]
lastCoord = [ {'x':0,'y':0} for i in range(K) ]
count = 0
last_clust_avg = [ 0 for i in range(K) ]
content = '''
Report
Parameters
K ''' + str(K) + '''
Number of Points Used ''' + str(num_points) + '''
Colors''' + ''.join([ '\n\t\t\tCluster ' + str(k+1) + '\t' + str(color[k]).capitalize() for k in range(len(color[:K])) ]) + '''
'''
start = time.time()
while True :
loop_start = time.time()
for point in X:
minDist = 10000000
cluster = 0
for i in range(K) :
dist = math.sqrt((point['x'] - centroid[i]['x'])**2 + (point['y'] - centroid[i]['y'])**2)
if dist < minDist :
minDist = dist
cluster = i
point.update( { 'cluster':str(cluster)} )
cluster_avg = [ 0 for i in range(K)]
cluster_xsum = [ 0 for i in range(K)]
cluster_ysum = [ 0 for i in range(K)]
for i in X:
for j in range(K) :
if i['cluster'] == str(j):
cluster_avg[j] += 1
cluster_xsum[j] += i['x']
cluster_ysum[j] += i['y']
if count == 0 :
pass
else :
#print '' .join([ str(cluster_avg[x]) + ' ' + str(last_clust_avg[x]) for x in range(K-1)])
print "Iteration Number " + str(count) + ' : ' + ''.join([ '\n\tCluster' + str(x+1) + ' - ' + str( float((cluster_avg[x] - last_clust_avg[x])) * 100 / last_clust_avg[x] )[:4] + ' % ' for x in range(K) ])
print '\n\tTime for Iteration\t'+str(time.time()-loop_start)+'\n'
content += "\n\tIteration Number " + str(count) + ' : ' + ''.join([ '\n\t\tCluster' + str(x+1) + '\t' + str( float((cluster_avg[x] - last_clust_avg[x])) * 100 / last_clust_avg[x] )[:4] + ' % ' for x in range(K) ])
content += '\n\tTime for Iteration\t'+str(time.time()-loop_start)+'\n'
for i in range(K) :
cluster_xsum[i] = cluster_xsum[i] / cluster_avg[i]
cluster_ysum[i] = cluster_ysum[i] / cluster_avg[i]
centroid[i]['x'] = cluster_xsum[i]
centroid[i]['y'] = cluster_ysum[i]
fig = plt.figure()
add = fig.add_subplot(111)
for i in range(K) :
points = [ {'x':point['x'] ,'y':point['y'] } for point in X if point['cluster'] == str(i) ]
frame = pandas.DataFrame(points)
add.scatter(frame.x,frame.y , label='Cluster ' + str(i+1) , s=75 , c=color[i])
plt.legend()
#plt.show()
fig.set_size_inches(20,20)
fig.savefig( str(count) + '.jpg',dpi=100)
for i in range(K) :
last_clust_avg[i] = cluster_avg[i]
completed = True
for i in range(K) :
if lastCoord[i]['x'] == cluster_xsum[i] and lastCoord[i]['y'] == cluster_ysum[i] :
pass
else :
completed = False
if completed :
print "ENDED!"
content += '\nTotal elasped time\t' + str(time.time() - start)
with open('report.txt','w') as f :
f.write(content)
break
for i in range(K) :
lastCoord[i]['x'] = cluster_xsum[i]
lastCoord[i]['y'] = cluster_ysum[i]
count += 1