def _updateH(self):
self.rhoh = self.ah + self.M.sum(3)
self.tauh[:,:,:] = (self.bh + (self.Eg[:,sp.newaxis,sp.newaxis] * self.Ew * self.Et[:,sp.newaxis,:]).sum(1))[:,:,sp.newaxis]
self.Eh = Gamma(self.rhoh, self.tauh).expectation()
print 'Eh:'
print self.Eh
def _updateG(self):
Ew = sp.transpose(self.Ew, (0,2,1))
self.rhog = self.alphag / self.Kg + self.M.sum((1,2,3))
self.taug = self.alphag * self.cg + (self.Eh[:,:,:,sp.newaxis] * Ew[:,:,sp.newaxis,:] * self.Et[:,:,sp.newaxis,sp.newaxis]).sum((1,2,3))
self.Eg = Gamma(self.rhog, self.taug).expectation()
print 'Eg:'
print self.Eg
def _updateW(self):
self.rhow = self.aw + self.M.sum(2)
self.rhow = sp.transpose(self.rhow, (0,2,1))
self.tauw[:,:,:] = (self.bw + (self.Eg[:,sp.newaxis,sp.newaxis] * self.Eh * self.Et[:,:,sp.newaxis]).sum(2))[:,sp.newaxis,:]
self.Ew = Gamma(self.rhow, self.tauw).expectation()
# print 'rhow:'
# print self.rhow
# print 'tauw:'
# print self.tauw
print 'Ew:'
print self.Ew
def _updateG_brute(self):
self.rhog[:] = self.alphag
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.rhog[g] += self.M[g,j,t,f]
self.taug[:] = self.alphag / self.cg
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.taug[g] += self.Eh[g,j,t] * self.Ew[g,f,j] * self.Et[g,j]
self.Eg = Gamma(self.rhog, self.taug).expectation()
def _updateTheta_brute(self):
self.rhot[:,:] = self.alphat
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.rhot[g,j] += self.M[g,j,t,f]
self.taut[:,:] = self.alphat / self.Kt
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.taut[g,j] += self.Eg[g] * self.Eh[g,j,t] * self.Ew[g,f,j]
self.Et = Gamma(self.rhot, self.taut).expectation()
def _updateH_brute(self):
self.rhoh[:,:,:] = self.ah
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.rhoh[g,j,t] += self.M[g,j,t,f]
self.tauh[:,:,:] = self.bh
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.tauh[g,j,t] += self.Eg[g] * self.Ew[g,f,j] * self.Et[g,j]
self.Eh = Gamma(self.rhoh, self.tauh).expectation()
def _updateW_brute(self):
self.rhow[:,:,:] = self.aw
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.rhow[g,f,j] += self.M[g,j,t,f]
self.tauw[:,:,:] = self.bw
for g in xrange(self.Kg):
for j in xrange(self.Kt):
for t in xrange(self.nT):
for f in xrange(self.nF):
self.tauw[g,f,j] += self.Eg[g] * self.Eh[g,j,t] * self.Et[g,j]
self.Ew = Gamma(self.rhow, self.tauw).expectation()
def _updateTheta(self):
# Ew = sp.transpose(self.Ew, (0,2,1))
# self.rhot = self.alphat + self.M.sum((2,3))
# self.taut = self.alphat / self.ct + (self.Eh[:,:,:,sp.newaxis] * Ew[:,:,sp.newaxis,:] * self.Et[:,:,sp.newaxis,sp.newaxis]).sum((2,3))
self.rhot = self.alphat / self.Kt + self.M.sum( (2,3) )
for g in xrange(self.Kg):
Eh_g = self.Eh[g]
Ew_g = self.Ew[g].T
self.taut[g,:] = self.alphat * self.ct + (Eh_g[:,:,sp.newaxis] * Ew_g[:,sp.newaxis,:]).sum((1,2))
# self.taut[g,:] = (self.Eg[g] * Eh_g[:,:,sp.newaxis] * Ew_g[:,sp.newaxis,:]).sum((1,2))
self.Et = Gamma(self.rhot, self.taut).expectation()
print 'Et:'
print self.Et
def _compute_expectations(self):
self.Ew = Gamma(self.rhow, self.tauw).expectation()
self.Eh = Gamma(self.rhoh, self.tauh).expectation()
self.Et = Gamma(self.rhot, self.taut).expectation()
self.Eg = Gamma(self.rhog, self.taug).expectation()