/
debug.py
61 lines (53 loc) · 1.75 KB
/
debug.py
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import clusters
import nn
from Manhattan_distance import Manhattan_distance
from K_clustering import kcluster
from difference_K_clusters import difference_kcluster
# blognames,words,data=clusters.readfile('blogdata.txt')
# clust=clusters.hcluster(data)
# clust1=clusters.hcluster(data,distance=Manhattan_distance)
#
# reload(clusters)
# clusters.printclust(clust,labels=blognames)
# clusters.printclust(clust1,labels=blognames)
# print data[0]
# print len(data[0])
# print data[1]
# print len(data[1])
# print Manhattan_distance(data[0],data[1])
# bestmatches,dis,clusters=kcluster(data)
# print dis
# print clusters
# dis_K=difference_kcluster(data)
# print dis_K
# import nn
# mynet=nn.searchnet('nn,db')
# # mynet.maketables()
# wWorld,wRiver,wBank =101,102,103
# uWorldBank,uRiver,uEarth =201,202,203
# mynet.generatehiddennode([wWorld,wBank],[uWorldBank,uRiver,uEarth])
# for c in mynet.con.execute('select * from wordhidden'):print c
#
# for c in mynet.con.execute('select * from hiddenurl'):print c
#
# mynet.trainquery([wWorld,wBank],[uWorldBank,uRiver,uEarth],uWorldBank)
# mynet.getresult([wWorld,wBank],[uWorldBank,uRiver,uEarth])
#
# s=[1,4,3,2,7,3,6,3,2,4,5,3]
# from improve_schedulecost import improve_schedulecost
# money=improve_schedulecost(s)
# print 'totalprice='+str(money)
from annealing_algorithm import annealing_algorithm
from optimization import schedulecost
from optimization import people
from optimization import printschedule
domain=[(0,9)]*(len(people)*2)
# s,costf=annealing_algorithm(domain,schedulecost)
# printschedule(s)
# print costf
from genetic_algorithm import genetic_algorithm
s=genetic_algorithm(domain,schedulecost)
printschedule(s)
# from optimization import geneticoptimize
# s=geneticoptimize(domain,schedulecost)
# printschedule(s)