def plotIterationAcc(itr,tau):
newInstanceD = {}
wt = np.ones(300) # create a random weight vector
for i in range(0,300):
wt[i] = np.random.rand()
for i in range(0,itr):
wt, newInstanceD = computeWeight(superBagsDict,Y,wt)
# wt, newInstanceD = computeWeightDelta(superBagsDict,Y,wt)
pX=[]
nX=[]
nY=[]
pY=[]
FinalTweets = []
cntP=1
cntN=1
# Algorithm1
for k in newInstanceD.keys():
# print "super",k
# print "***************************************************************8"
for i in newInstanceD[k].keys():
# print "bag",i
# print "########################"
for j in newInstanceD[k][i].keys():
# print "art",j
# print "====================="
if Y[k-1]==1:
pX.append(cntP)
pY.append(newInstanceD[k][i][j])
cntP=cntP+1
else:
nX.append(cntN)
nY.append(newInstanceD[k][i][j])
cntN=cntN+1
if newInstanceD[k][i][j]>=tau and Y[k-1]==1:
#print "article: ",k,i,j
#print "prob: ",newInstanceD[k][i][j]
#print "sentence: ", sentenceDict[k][i][j]
item = []
item.append(k)
item.append(i)
item.append(j)
FinalTweets.append(item)
print "size of finaltweets",len(FinalTweets)
acc=findCosineScore(FinalTweets)
print "Accuracy=",acc
return acc
superBagsDict = pickle.load(handle)
with open('../input/tfSentence.pickle',
'rb') as handle: # '../input/dataSentences.pickle'
sentenceDict = pickle.load(handle)
# print superBagsDict[1].keys()
# print superBagsDict[2].keys()
newInstanceD = {}
wt = np.ones(300) # create a random weight vector
for i in range(0, 300):
wt[i] = np.random.rand()
eta = 1.2
for i in range(0, 2):
wt, newInstanceD = computeWeight(eta, superBagsDict, Y, wt)
pX = []
nX = []
nY = []
pY = []
FinalTweets = []
tau = 0.85
cntP = 1
cntN = 1
# Algorithm1
for k in newInstanceD.keys():
# print "super",k
# print "***************************************************************8"
for i in newInstanceD[k].keys():