import?random?as?rand
import?math?as?math
from?point?import?Point
#import?pkg_resources
#pkg_resources.require（“matplotlib“）
import?numpy?as?np
from?mpl_toolkits.mplot3d?import?Axes3D
import?matplotlib.pyplot?as?plt

class?clustering:
????#define?creat?function?initialization
????def?__init__（self?geo_locs_?k_）:
????????self.geo_locations?=?geo_locs_
????????self.k?=?k_
????????self.clusters?=?[]??#clusters?of?nodes
????????self.means?=?[]?????#means?of?clusters
????????self.debug?=?False??#debug?flag
????#this?method?returns?the?next?random?node
????def?next_random（self?index?points?clusters）:
????????#pick?next?node?that?has?the?maximum?distance?from?other?nodes
????????dist?=?{}
????????for?point_1?in?points:
????????????if?self.debug:
????????????????print?‘point_1:?%f?%f‘?%?（point_1.latit?point_1.longit）?
????????????#compute?this?node?distance?from?all?other?points?in?cluster
????????????for?cluster?in?clusters.values（）:
????????????????point_2?=?cluster[0]
????????????????if?self.debug:
????????????????????print?‘point_2:?%f?%f‘?%?（point_2.latit?point_2.longit）
????????????????if?point_1?not?in?dist:?#if?the?point?doesnt?have?the?class?put?it?into?the?closest?class


????????????????????dist[point_1]?=?math.sqrt（math.pow（point_1.latit?-?point_2.latit2.0）?+?math.pow（point_1.longit?-?point_2.longit2.0））?############because?the?point?only?have?two?dimensions


????????????????else:


????????????????????dist[point_1]?+=?math.sqrt（math.pow（point_1.latit?-?point_2.latit2.0）?+?math.pow（point_1.longit?-?point_2.longit2.0））#calculate?the?sum?distance?of?every?clustering




????????if?self.debug:
????????????for?key?value?in?dist.items（）:
????????????????print?“（%f?%f）?==>?%f“?%?（key.latitkey.longitvalue）
????????#now?let‘s?return?the?point?that?has?the?maximum?distance?from?previous?nodes
????????count_?=?0
????????max_?=?0
????????#?find?the?max?distance
????????for?key?value?in?dist.items（）:?#put?the?value?and?key?of?the?dict?into?a?list
????????????if?count_?==?0:
????????????????max_?=?value
????????????????max_point?=?key
????????????????count_?+=?1
????????????else:
????????????????if?value?>?max_:
????????????????????max_?=?value
????????????????????max_point?=?key
????????return?max_point
????#this?method?computes?the?initial?means
????def?initial_means（self?points）:
????????#pick?the?first?node?at?random
????????point_?=?rand.choice（points）#choose?one?point?in?random
????????if?self.debug:
????????????print?‘point#0:?%f?%f‘?%?（point_.latit?point_.longit）
????????clusters?=?dict（）?#creat?an?empty?dict
????????clusters.setdefault（0?[]）.append（point_）#setdefault（）:if?key?doesnt?in?dictcreat?the?key?and?set?to?default
????????points.remove（point_）#remove?the?center?of?clustering
????????#now?let‘s?pick?k-1?more?random?points
????????for?i?in?range（1?self.k）:
????????????point_?=?self.next_random（i?points?clusters）#call?child?function?i:the?index?of?clustering?center?points:clustering?sample?clusters:clustering?cent

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????7894??2018-01-15?11:49??k-medoids-master\clustering.py
?????文件????????1057??2018-01-15?10:01??k-medoids-master\main.py
?????文件?????????177??2014-08-07?16:11??k-medoids-master\point.py
?????文件????????1022??2014-08-07?16:11??k-medoids-master\README.md