import experiments as exp
import modifier as mdf
import re
paths=['swSims.p','swSimsL.p','dmSims.p','dmSimsL.p',\
'lengthSims.p']
names=['swAlign','swAlign-Length','directMatch',\
'directMatchL','lengthOnly']
netpath='netdata.p'
(edges,ciss,proms)=exp.getPickle(netpath)
filt1=[]
filt2=[]
#ratio=0.99
#filt1=mdf.random_remove(edges.keys(),ratio)
'''
for cis in ciss.keys():
entry=ciss[cis]
target=entry.promname
target= re.sub(r'p\d?$','',target).upper()
source=entry.tfname.upper()
if not (source,target) in filt1:
filt1.append((source,target))
for elem in filt1:
print elem[0],'R',elem[1]
import experiments as exp
from yxtools import dictInsert
import math
import matplotlib.pyplot as plt
import operator
(edges,ciss,proms)=exp.getPickle('netdata.p')
transactions=exp.getTS(edges,ciss,proms)
rules=exp.getAssoc(transactions,min_s=2,\
min_c=0.5)
# Determine the relationship between support and
# size of frequent itemset
(perfs,maks)=exp.evalCisCollection(rules,weighted=True)
'''
y=[[],[],[],[],[]]
for i in perfs.keys():
for elem in perfs[i]:
y[i-1].append(elem)
'''
x=[]
for key in maks.keys():
x.append(len(maks[key][0])+1)
plt.hist(x)
plt.show()
