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rte_train_1_without_weightlearning.py
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rte_train_1_without_weightlearning.py
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from xml.etree.ElementTree import parse
import os
import nltk
import math
import numpy as np
from nltk.corpus import wordnet
from nltk.tag import StanfordPOSTagger
###########################################ICANTUSEPOSTAGGER
#from nltk.tag.stanford import POSTagger
#############
#Change path#
#############
java_path ="/usr/bin/java.exe"
###########################################ICANTUSEPOSTAGGER
#st=POSTagger('C:\\Users\\CGR\\Desktop\\stanford-postagger-2015-04-20\\stanford-postagger-2015-04-20\\models\\english-bidirectional-distsim.tagger','C:\\Users\\CGR\\Desktop\\stanford-postagger-2015-04-20\\stanford-postagger-2015-04-20\\stanford-postagger.jar',encoding='UTF-8')
st = StanfordPOSTagger('/home/jsrang02/english-bidirectional-distsim.tagger', '/home/jsrang02/stanford-postagger.jar')
os.environ['JAVAHOME'] = java_path
print("Please Input RTE Set Number:")
key=input()
#############
#Change path#
#############
if key=="1":
tree = parse("/home/jsrang02/RTE/dev/dev.xml")
elif key=="2":
tree = parse("/home/jsrang02/RTE/dev/dev2.xml")
elif key=="3":
tree = parse("/home/jsrang02/RTE/dev/dev3.xml")
root = tree.getroot()
true_similarity=[]
number_true=0
false_similarity=[]
number_false=0
prob_syn = 0.99
prob_hyp = 0.98
prob_ant = 0.97
prob_sim = 0.9
prob_lem = 0.99
numb_syn = 0
numb_hyp = 0
numb_ant = 0
numb_sim = 0
output = open('/home/jsrang02/RTE/output.txt', 'w')
def howSimilarSyn(synonymSet_h, synonymSet_t):
buf = 0
for k in range(len(synonymSet_h)):
for n in range(len(synonymSet_t)):
##############################modifying function######################
#if synonymSet_h[k].wup_similarity(synonymSet_t[n]!=None):
# x.append(synonymSet_h[k].wup_similarity(SynonymSet_t[n]))
if synonymSet_h[k].wup_similarity(synonymSet_t[n])!=None:
if buf<synonymSet_h[k].wup_similarity(synonymSet_t[n]):
buf=synonymSet_h[k].wup_similarity(synonymSet_t[n])
return buf
for m in root.findall("pair"):
hypothesis=m.findtext("h").casefold()
tokenized_hypothesis=nltk.word_tokenize(hypothesis)
# tagged_tokenized_hypothesis=nltk.pos_tag(tokenized_hypothesis) #nltk tagger
tagged_tokenized_hypothesis=st.tag(tokenized_hypothesis) #stanfordnlp tagger
text=m.findtext("t").casefold()
tokenized_text=nltk.word_tokenize(text)
# tagged_tokenized_text=nltk.pos_tag(tokenized_text) #nltk tagger
tagged_tokenized_text=st.tag(tokenized_text) #stanfordnlp tagger
output.write("newhypo:\n")
for i in range(len(tokenized_hypothesis)):
output.write(tagged_tokenized_hypothesis[i][0])
output.write(tagged_tokenized_hypothesis[i][1])
output.write("newtext:\n")
for j in range(len(tokenized_text)):
output.write(tagged_tokenized_text[j][0])
output.write(tagged_tokenized_text[j][1])
output.write("value:\n")
output.write(m.get("entailment"))
output.write("\n")
Similarity=1
text_flag=True
hypothesis_flag=True
flag=True
#########################
#Negative word processig#
#########################
for i in range(len(tokenized_text)):
tagged_tokenized_text[i]=list(tagged_tokenized_text[i])
tagged_tokenized_text[i].append("")
for i in range(len(tokenized_text)):
if (tokenized_text[i]=="never"):
if text_flag==True:
text_flag=False
else:
text_flag=True
elif (tokenized_text[i]=="n't")|(tokenized_text[i]=="not"):
for j in range(len(tokenized_text)-i-1):
if(tagged_tokenized_text[i+j][1] in ["VB","VBD","VBG","VBN","VBP","VBZ"]):
tagged_tokenized_text[i+j][2]="~"
elif(tokenized_text[i]=="no"):
for j in range(len(tokenized_text)-i-1):
if(tagged_tokenized_text[i+j][1] in ["NN","NNS","NNP","NNPS"]):
tagged_tokenized_text[i+j][2]="~"
###############################################################################
for i in range(len(tokenized_hypothesis)):
tagged_tokenized_hypothesis[i]=list(tagged_tokenized_hypothesis[i])
tagged_tokenized_hypothesis[i].append("")
for i in range(len(tokenized_hypothesis)):
synonymSet_hypothesis=None
synonymSet_text=None
Match=False
if (tokenized_hypothesis[i]=="never"):
if hypothesis_flag==True:
hypothesis_flag=False
else:
hypothesis_flag=True
if (tokenized_hypothesis[i]=="n't")|(tokenized_hypothesis[i]=="not"):
for j in range(len(tokenized_hypothesis)-i-1):
if(tagged_tokenized_hypothesis[i+j][1] in ["VB","VBD","VBG","VBN","VBP","VBZ"]):
tagged_tokenized_hypothesis[i+1][2]="~"
elif(tokenized_hypothesis[i]=="no"):
for j in range(len(tokenized_hypothesis)-i-1):
if(tagged_tokenized_hypothesis[i+j][1] in ["NN","NNS","NNP","NNPS"]):
tagged_tokenized_hypothesis[i+j][2]="~"
######################
#End of Preprocessing#
######################
#Noun case
elif (tagged_tokenized_hypothesis[i][1]=="NN")|(tagged_tokenized_hypothesis[i][1]=="NNS")|(tagged_tokenized_hypothesis[i][1]=="NNP")|(tagged_tokenized_hypothesis[i][1]=="NNPS"):
#there is not same word.
if tokenized_hypothesis[i] not in tokenized_text:
synonymSet_hypothesis=wordnet.synsets(tokenized_hypothesis[i],pos=wordnet.NOUN)
for j in range(len(tokenized_text)):
if (tagged_tokenized_text[j][1]=="NN")|(tagged_tokenized_text[j][1]=="NNS")|(tagged_tokenized_text[j][1]=="NNP")|(tagged_tokenized_text[j][1]=="NNPS"):
if Match==True:
break
for k in range(len(synonymSet_hypothesis)):
if Match==True:
break
if(tagged_tokenized_hypothesis[i][2]==tagged_tokenized_text[j][2]):
for lemma in synonymSet_hypothesis[k].lemmas(): #synonym
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match = True
Similarity = Similarity * prob_syn
numb_syn = numb_syn + 1
break
######
for n in range(len(synonymSet_hypothesis[k].hyponyms())): #hyponym
for lemma in synonymSet_hypothesis[k].hyponyms()[n].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * prob_hyp
numb_hyp = numb_hyp + 1
break
else: #Similarity
for lemma in synonymSet_hypothesis[k].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * max(prob_syn+prob_lem-1,0)
numb_syn = numb_syn+1
if flag==True:
flag=False
else:
flag=True
break
else:
for n in range(len(synonymSet_hypothesis[k].hyponyms())):
for lemma in synonymSet_hypothesis[k].hyponyms()[n].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * max(prob_hyp+prob_lem-1,0)
numb_hyp = numb_hyp+1
if flag==True:
flag=False
else:
flag=True
break
if Match==False:
for j in range(len(tokenized_text)):
if (tagged_tokenized_text[j][1]=="NN")|(tagged_tokenized_text[j][1]=="NNS")|(tagged_tokenized_text[j][1]=="NNP")|(tagged_tokenized_text[j][1]=="NNPS"):
for k in range(len(synonymSet_hypothesis)):
if Match==True:
break
for n in range(len(synonymSet_hypothesis[k].lemmas())):
for lemma in synonymSet_hypothesis[k].lemmas()[n].antonyms(): #antonym
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * prob_ant
if(tagged_tokenized_hypothesis[i][2]==tagged_tokenized_text[j][2]):
if flag==True:
flag=False
else:
flag=True
break
if Match==False: #not matched
buf=0
for j in range(len(tokenized_text)):
if(tagged_tokenized_text[j][1]!="DT")&(tagged_tokenized_text[j][1]!="TO")&(tagged_tokenized_text[j][1]!=".")&(tagged_tokenized_text[j][1]!=",")&(tagged_tokenized_text[j][1]!=":")&(tagged_tokenized_text[j][1]!="POS")&(tagged_tokenized_text[j][1]!="IN"):
synonymSet_text=wordnet.synsets(tokenized_text[j],pos=wordnet.NOUN)
buf = howSimilarSyn(synonymSet_hypothesis, synonymSet_text)
if(buf!=0):
Similarity = Similarity * max(prob_sim+buf-1,0)
numb_sim = numb_sim+buf
#verb case
elif(tagged_tokenized_hypothesis[i][1]=="VB")|(tagged_tokenized_hypothesis[i][1]=="VBD")|(tagged_tokenized_hypothesis[i][1]=="VBG")|(tagged_tokenized_hypothesis[i][1]=="VBN")|(tagged_tokenized_hypothesis[i][1]=="VBP")|(tagged_tokenized_hypothesis[i][1]=="VBZ"):
if tokenized_hypothesis[i] not in tokenized_text:
synonymSet_hypothesis=wordnet.synsets(tokenized_hypothesis[i],pos=wordnet.VERB)
for j in range(len(tokenized_text)):
if Match==True:
break
if(tagged_tokenized_text[j][1]=="VB")|(tagged_tokenized_text[j][1]=="VBD")|(tagged_tokenized_text[j][1]=="VBG")|(tagged_tokenized_text[j][1]=="VBN")|(tagged_tokenized_text[j][1]=="VBP")|(tagged_tokenized_text[j][1]=="VBZ"):
for k in range(len(synonymSet_hypothesis)):
if Match==True:
break
if(tagged_tokenized_hypothesis[i][2]==tagged_tokenized_text[j][2]):
for lemma in synonymSet_hypothesis[k].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity*prob_syn
numb_syn = numb_syn+1
if (((tagged_tokenized_hypothesis[i][1]=="VBN")&(tagged_tokenized_text[j][1]!="VBN"))|((tagged_tokenized_hypothesis[i][1]!="VBN")&(tagged_tokenized_text[j][1]=="VBN"))): #This statement means case, "T : I give a book to giyoung, H : I was given book from giyoung"
if flag==True:
flag=False
else:
flag=True
break
for n in range(len(synonymSet_hypothesis[k].hyponyms())):
for lemma in synonymSet_hypothesis[k].hyponyms()[n].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity*prob_hyp
numb_hyp = numb_hyp+1
if (((tagged_tokenized_hypothesis[i][1]=="VBN")&(tagged_tokenized_text[j][1]!="VBN"))|((tagged_tokenized_hypothesis[i][1]!="VBN")&(tagged_tokenized_text[j][1]=="VBN"))):
if flag==True:
flag=False
else:
flag=True
break
else:
for lemma in synonymSet_hypothesis[k].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity*max(prob_syn+prob_lem-1,0)
numb_syn = numb_syn+1
if (not(((tagged_tokenized_hypothesis[i][1]=="VBN")&(tagged_tokenized_text[j][1]!="VBN"))|((tagged_tokenized_hypothesis[i][1]!="VBN")&(tagged_tokenized_text[j][1]=="VBN")))):
if flag==True:
flag=False
else:
flag=True
break
for n in range(len(synonymSet_hypothesis[k].hyponyms())):
for n in range(len(synonymSet_hypothesis[k].hyponyms())):
for lemma in synonymSet_hypothesis[k].hyponyms()[n].lemmas():
Match=True
Similarity = Similarity*max(prob_hyp+prob_lem-1,0)
numb_hyp = numb_hyp+1
if (not(((tagged_tokenized_hypothesis[i][1]=="VBN")&(tagged_tokenized_text[j][1]!="VBN"))|((tagged_tokenized_hypothesis[i][1]!="VBN")&(tagged_tokenized_text[j][1]=="VBN")))):
if flag==True:
flag=False
else:
flag=True
break
if Match==False:
for j in range(len(tokenized_text)):
if Match==True:
break
if(tagged_tokenized_text[j][1]=="VB")|(tagged_tokenized_text[j][1]=="VBD")|(tagged_tokenized_text[j][1]=="VBG")|(tagged_tokenized_text[j][1]=="VBN")|(tagged_tokenized_text[j][1]=="VBP")|(tagged_tokenized_text[j][1]=="VBZ"):
for k in range(len(synonymSet_hypothesis)):
if Match==True:
break
if(tagged_tokenized_hypothesis[i][2]==tagged_tokenized_text[j][2]):
for n in range(len(synonymSet_hypothesis[k].lemmas())):
for lemma in synonymSet_hypothesis[k].lemmas()[n].antonyms():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity*prob_ant
numb_ant = numb_ant+1
if (not(((tagged_tokenized_hypothesis[i][1]=="VBN")&(tagged_tokenized_text[j][1]!="VBN"))|((tagged_tokenized_hypothesis[i][1]!="VBN")&(tagged_tokenized_text[j][1]=="VBN")))):
if flag==True:
flag=False
else:
flag=True
break
else:
for n in range(len(synonymSet_hypothesis[k].lemmas())):
for lemma in synonymSet_hypothesis[k].lemmas()[n].antonyms():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity*max(prob_ant+prob_lem-1,0)
numb_ant = numb_ant+1
if (((tagged_tokenized_hypothesis[i][1]=="VBN")&(tagged_tokenized_text[j][1]!="VBN"))|((tagged_tokenized_hypothesis[i][1]!="VBN")&(tagged_tokenized_text[j][1]=="VBN"))):
if flag==True:
flag=False
else:
flag=True
break
if Match==False:
#print(h_t[i])
buf=0
for j in range(len(tokenized_text)):
if(tagged_tokenized_text[j][1]!="CC")&(tagged_tokenized_text[j][1]!="DT")&(tagged_tokenized_text[j][1]!="TO")&(tagged_tokenized_text[j][1]!=".")&(tagged_tokenized_text[j][1]!=",")&(tagged_tokenized_text[j][1]!=":")&(tagged_tokenized_text[j][1]!="POS")&(tagged_tokenized_text[j][1]!="IN"):
#print(t_t_t[j])
synonymSet_text=wordnet.synsets(tokenized_text[j],pos=wordnet.VERB)
buf = howSimilarSyn(synonymSet_hypothesis, synonymSet_text)
if(buf!=0):
Similarity=Similarity*max(prob_sim+buf-1,0)
numb_sim = numb_sim+1
elif(tagged_tokenized_hypothesis[i][1]!="CC")&(tagged_tokenized_hypothesis[i][1]!="TO")&(tagged_tokenized_hypothesis[i][1]!="IN")&(tagged_tokenized_hypothesis[i][1]!="DT")&(tagged_tokenized_hypothesis[i][1]!=".")&(tagged_tokenized_hypothesis[i][1]!=",")&(tagged_tokenized_hypothesis[i][1]!=":")&(tagged_tokenized_hypothesis[i][1]!="POS"):
#print("ooooooooo")
if tokenized_hypothesis[i] not in tokenized_text:
buf=0
synonymSet_hypothesis=wordnet.synsets(tokenized_hypothesis[i],pos=wordnet.ADJ)
if wordnet.synsets(tokenized_hypothesis[i],pos=wordnet.ADV)!=[]:
synonymSet_hypothesis=synonymSet_hypothesis+wordnet.synsets(tokenized_hypothesis[i],pos=wordnet.ADV)
for j in range(len(tokenized_text)):
if Match==True:
break
if(tagged_tokenized_text[j][1]!="CC")&(tagged_tokenized_text[j][1]!="DT")&(tagged_tokenized_text[j][1]!="TO")&(tagged_tokenized_text[j][1]!=".")&(tagged_tokenized_text[j][1]!=",")&(tagged_tokenized_text[j][1]!=":")&(tagged_tokenized_text[j][1]!="POS")&(tagged_tokenized_text[j][1]!="IN")&(tagged_tokenized_text[j][1]!="NN")&(tagged_tokenized_text[j][1]!="NNS")&(tagged_tokenized_text[j][1]!="NNP")&(tagged_tokenized_text[j][1]!="NNPS")&(tagged_tokenized_text[j][1]!="VB")&(tagged_tokenized_text[j][1]!="VBD")&(tagged_tokenized_text[j][1]!="VBG")&(tagged_tokenized_text[j][1]!="VBN")&(tagged_tokenized_text[j][1]!="VBP")&(tagged_tokenized_text[j][1]!="VBZ"):
for k in range(len(synonymSet_hypothesis)):
if Match==True:
break
if(tagged_tokenized_hypothesis[i][2]==tagged_tokenized_text[j][2]):
for lemma in synonymSet_hypothesis[k].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * prob_syn
numb_syn = numb_syn+1
break
for n in range(len(synonymSet_hypothesis[k].hyponyms())):
for lemma in synonymSet_hypothesis[k].hyponyms()[n].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * prob_hyp
numb_hyp = numb_hyp+1
break
else:
for lemma in synonymSet_hypothesis[k].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * max(prob_lem+prob_syn-1,0)
numb_syn = numb_syn+1
if flag==True:
flag=False
else:
flag=True
break
for n in range(len(synonymSet_hypothesis[k].hyponyms())):
for lemma in synonymSet_hypothesis[k].hyponyms()[n].lemmas():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * max(prob_hyp+prob_lem-1,0)
numb_hyp = numb_hyp+1
if flag==True:
flag=False
else:
flag=True
break
if Match==False:
for j in range(len(tokenized_text)):
if(tagged_tokenized_text[j][1]!="CC")&(tagged_tokenized_text[j][1]!="DT")&(tagged_tokenized_text[j][1]!="TO")&(tagged_tokenized_text[j][1]!=".")&(tagged_tokenized_text[j][1]!=",")&(tagged_tokenized_text[j][1]!=":")&(tagged_tokenized_text[j][1]!="POS")&(tagged_tokenized_text[j][1]!="IN")&(tagged_tokenized_text[j][1]!="NN")&(tagged_tokenized_text[j][1]!="NNS")&(tagged_tokenized_text[j][1]!="NNP")&(tagged_tokenized_text[j][1]!="NNPS")&(tagged_tokenized_text[j][1]!="VB")&(tagged_tokenized_text[j][1]!="VBD")&(tagged_tokenized_text[j][1]!="VBG")&(tagged_tokenized_text[j][1]!="VBN")&(tagged_tokenized_text[j][1]!="VBP")&(tagged_tokenized_text[j][1]!="VBZ"):
for k in range(len(synonymSet_hypothesis)):
if Match==True:
break
for n in range(len(synonymSet_hypothesis[k].lemmas())):
for lemma in synonymSet_hypothesis[k].lemmas()[n].antonyms():
if (len(wordnet.synsets(tokenized_text[j]))!=0):
if(len(set(wordnet.synsets(tokenized_text[j])[0].lemma_names()).intersection(lemma.name()))>0):
Match=True
Similarity = Similarity * prob_ant
numb_ant = numb_ant+1
if(tagged_tokenized_hypothesis[i][2]==tagged_tokenized_text[j][2]):
if flag==True:
flag=False
else:
flag=True
break
if Match==False:
for j in range(len(tokenized_text)):
if(tagged_tokenized_text[j][1]!="CC")&(tagged_tokenized_text[j][1]!="DT")&(tagged_tokenized_text[j][1]!="TO")&(tagged_tokenized_text[j][1]!=".")&(tagged_tokenized_text[j][1]!=",")&(tagged_tokenized_text[j][1]!=":")&(tagged_tokenized_text[j][1]!="POS")&(tagged_tokenized_text[j][1]!="IN"):#&(t_t_t[j][1]!="NN")&(t_t_t[j][1]!="NNS")&(t_t_t[j][1]!="NNP")&(t_t_t[j][1]!="NNPS")&(t_t_t[j][1]!="VB")&(t_t_t[j][1]!="VBD")&(t_t_t[j][1]!="VBG")&(t_t_t[j][1]!="VBN")&(t_t_t[j][1]!="VBP")&(t_t_t[j][1]!="VBZ"):
synonymSet_text=wordnet.synsets(tokenized_text[j],pos=wordnet.ADJ)
if wordnet.synsets(tokenized_text[j],pos=wordnet.ADV)!=[]:
synonymSet_text=synonymSet_text+wordnet.synsets(tokenized_text[j],pos=wordnet.ADV)
buf = howSimilarSyn(synonymSet_hypothesis, synonymSet_text)
if(buf!=0):
Similarity = Similarity * max(prob_sim + buf -1, 0)
numb_sim = numb_sim+1
if ((text_flag==hypothesis_flag)&(flag==False))|((text_flag!=hypothesis_flag)&(flag==True)):
Similarity=0
#print(t)
#print(h)
if key=="1":
print(m.get("value"))
elif (key=="2")|(key=="3"):
print(m.get("entailment"))
print(Similarity)
output.write("%lf\n" %Similarity)
#test
#print("stop:5")
#if input()=="5":
# break
if key=="1":
if(m.get("value")=="TRUE"):
true_similarity.append(Similarity)
number_true=number_true+1
else:
false_similarity.append(Similarity)
number_false=number_false+1
elif (key=="2")|(key=="3"):
if(m.get("entailment")=="YES"):
true_similarity.append(Similarity)
number_true=number_true+1
else:
false_similarity.append(Similarity)
number_false=number_false+1
printparam = "Syn: %lf, Hyp = %lf, Ant = %lf, Sim = %lf"
print (printparam % (numb_syn, numb_hyp, numb_ant, numb_sim))
numb_syn = 0
numb_hyp = 0
numb_ant = 0
numb_sim = 0
#############################
##############weight learning
#############################
# if (Similarity!=0):
# if key=="1":
# if(m.get("value")=="TRUE"):
# prob_syn = prob_syn
#############################
#########eof weight learning#
#############################
print("RTE"+key+" set")
print("Result:")
################################
#Normalization and set treshold#
################################
print(" Average of True:")
sum_true=0
sum_false=0
for i in true_similarity:
sum_true=sum_true+i
average_true=sum_true/number_true
print(average_true)
print(" Average of False:")
for i in false_similarity:
sum_false=sum_false+i
average_false=sum_false/number_false
print(average_false)
squre_sum_ture=0
squre_sum_false=0
print(" Distribution of True:")
for i in true_similarity:
squre_sum_ture=squre_sum_ture+(i*i)
distribution_true=(squre_sum_ture/number_true)-(average_true*average_true)
print(distribution_true)
print(" Distribution of False:")
for i in false_similarity:
squre_sum_false=squre_sum_false+(i*i)
distribution_false=(squre_sum_false/number_false)-(average_false*average_false)
print(distribution_false)
print(" Treshold:")
Tres=((math.sqrt(distribution_true)*average_false)+(math.sqrt(distribution_false)*average_true))/(math.sqrt(distribution_false)+math.sqrt(distribution_true))
print(Tres)
if key=="1":
data=open('/home/jsrang02/RTE/tres/Treshold1.txt','w')
elif key=="2":
data=open('/home/jsrang02/RTE/tres/Treshold2.txt','w')
elif key=="3":
data=open('/home/jsrang02/RTE/tres/Treshold3.txt','w')
data.write("%lf" %Tres)
data.close()
output.close()