def computeCorr(self):
'compute correlation among items'
print 'Computing item correlation...'
for i1 in self.dao.testSet_i:
for i2 in self.dao.item:
if i1 <> i2:
if self.itemSim.contains(i1,i2):
continue
sim = qmath.similarity(self.dao.sCol(i1),self.dao.sCol(i2),self.sim)
self.itemSim.set(i1,i2,sim)
print 'item '+i1+' finished.'
print 'The item correlation has been figured out.'
def computeCorr(self):
'compute correlation among users'
print 'Computing user correlation...'
for u1 in self.dao.testSet_u:
for u2 in self.dao.user:
if u1 <> u2:
if self.userSim.contains(u1,u2):
continue
sim = qmath.similarity(self.dao.row(u1),self.dao.row(u2),self.sim)
self.userSim.set(u1,u2,sim)
print 'user '+u1+' finished.'
print 'The user correlation has been figured out.'
def computeCorr(self):
'compute correlation among items'
print('Computing item correlation...')
for i1 in self.data.testSet_i:
for i2 in self.data.item:
if i1 != i2:
if self.itemSim.contains(i1,i2):
continue
sim = qmath.similarity(self.data.sCol(i1),self.data.sCol(i2),self.sim)
self.itemSim.set(i1,i2,sim)
self.topItems[i1] = sorted(self.itemSim[i1].iteritems(),key = lambda d:d[1],reverse=True)
print('item '+i1+' finished.')
print('The item correlation has been figured out.')
def computeCorr(self):
'compute correlation among users'
print 'Computing user similarities...'
for idx,u1 in enumerate(self.data.testSet_u):
for u2 in self.data.user:
if u1 <> u2:
if self.userSim.contains(u1,u2):
continue
sim = qmath.similarity(self.data.sRow(u1),self.data.sRow(u2),self.sim)
self.userSim.set(u1,u2,sim)
self.topUsers[u1]=sorted(self.userSim[u1].iteritems(), key=lambda d: d[1], reverse=True)
if idx%100==0:
print 'progress:',idx,'/',len(self.data.testSet_u)
print 'The user similarities have been calculated.'
def computeCorr(self):
'compute correlation among users'
print 'Computing user correlation...'
for u1 in self.data.testSet_u:
for u2 in self.data.user:
if u1 <> u2:
if self.userSim.contains(u1, u2):
continue
sim = qmath.similarity(self.data.sRow(u1),
self.data.sRow(u2), self.sim)
self.userSim.set(u1, u2, sim)
self.topUsers[u1] = sorted(self.userSim[u1].iteritems(),
key=lambda d: d[1],
reverse=True)
print 'user ' + u1 + ' finished.'
print 'The user correlation has been figured out.'
def computeCorr(self):
'compute correlation among items'
print 'Computing item similarities...'
for idx, i1 in enumerate(self.data.testSet_i):
for i2 in self.data.item:
if i1 <> i2:
if self.itemSim.contains(i1, i2):
continue
sim = qmath.similarity(self.data.sCol(i1),
self.data.sCol(i2), self.sim)
self.itemSim.set(i1, i2, sim)
self.topItems[i1] = sorted(self.itemSim[i1].iteritems(),
key=lambda d: d[1],
reverse=True)
if idx % 100 == 0:
print 'progress:', idx, '/', len(self.data.testSet_i)
print 'The item similarities have been calculated.'
def computeUCorr(self):
'compute correlation among users'
print 'Computing user correlation...'
for u1 in self.dao.testSet_u:
for u2 in self.dao.user:
if u1 <> u2:
if self.userSim.contains(u1, u2):
continue
sim = qmath.similarity(self.dao.sRow(u1),
self.dao.sRow(u2), self.sim)
if sim >= self.v:
self.userSim.set(u1, u2, 1)
else:
self.userSim.set(u1, u2, 0)
tcount = 0
for i in range(len(self.userSim[u1])):
us = list(self.userSim[u1].iteritems())
if us[i][1] == 1:
tcount += 1
print 'user ' + u1 + ' finished.'
print 'The user correlation has been figured out.'
