def sample_emcee(self, nIterations):
ndim = self.nNodes*36
self.nwalkers = 2 * ndim
self.theta0 = np.zeros(ndim)
p0 = [self.theta0 + 1e-4*np.random.randn(ndim) for i in range(self.nwalkers)]
self.sampler = emcee.EnsembleSampler(self.nwalkers, ndim, self.logLikelihood)
loop = 0
self.chain = np.zeros((nIterations,self.nwalkers,ndim))
for result in self.sampler.sample(p0, iterations=nIterations, storechain=False):
out, _ = tr.lrBoundedForward(result[0], self.low, self.up)
self.chain[loop,:,:] = out
ps.util.progressbar(loop, nIterations, text='sampling')
loop += 1
def logLikelihood(self, thetaIn):
theta, lnJactheta = tr.lrBoundedForward(thetaIn, self.low, self.up)
matrix = theta.reshape((self.nNodes,self.nParties,self.nParties))
self.matrix = np.zeros((self.nTimes,self.nParties,self.nParties))
for i in range(self.nParties):
for j in range(self.nParties):
tmp = interp.UnivariateSpline(self.timeNodes, matrix[:,i,j])
self.matrix[:,i,j] = tmp(self.obsTime)
out = np.zeros((self.nTimes,self.nParties))
out[0,:] = self.initialCondition
for i in range(self.nTimes-1):
out[i+1,:] = out[i,:] + ( self.obsTime[i+1] - self.obsTime[i] ) * self.matrix[i,:,:].dot(out[i,:])
logL = np.sum( (out[1:,:] - self.party[1:,:])**2 / 0.1**2 )
#print(logL)
return logL + np.sum(lnJactheta)
