def simulate(self,B_,s_0,T,sHist=None):
'''
Simulates planners policies for T periods
'''
Para,Pi,beta = self.Para,self.Pi,self.beta
Uc = Para.Uc
if sHist == None:
sHist = mc_sample_path(Pi,s_0,T)
cHist,nHist,Bhist,TauHist,muHist = np.zeros((5,T))
RHist = np.zeros(T-1)
#time0
mu,cHist[0],nHist[0],_ = self.time0_allocation(B_,s_0)
TauHist[0] = self.Tau(cHist[0],nHist[0])[s_0]
Bhist[0] = B_
muHist[0] = mu
#time 1 onward
for t in range(1,T):
c,n,x,Xi = self.time1_allocation(mu)
Tau = self.Tau(c,n)
u_c = Uc(c,n)
s = sHist[t]
Eu_c = Pi[sHist[t-1]].dot(u_c)
cHist[t],nHist[t],Bhist[t],TauHist[t] = c[s],n[s],x[s]/u_c[s],Tau[s]
RHist[t-1] = Uc(cHist[t-1],nHist[t-1])/(beta*Eu_c)
muHist[t] = mu
return cHist,nHist,Bhist,TauHist,sHist,muHist,RHist
def simulate(self,B_,s_0,T,sHist=None):
'''
Simulates Ramsey plan for T periods
'''
Para,Pi = self.Para,self.Pi
Uc = Para.Uc
cf,nf,xprimef = self.policies
if sHist == None:
sHist = mc_sample_path(Pi,s_0,T)
cHist,nHist,Bhist,TauHist,muHist = np.zeros((5,T))
RHist = np.zeros(T-1)
#time0
cHist[0],nHist[0],xprime = self.time0_allocation(B_,s_0)
TauHist[0] = self.Tau(cHist[0],nHist[0])[s_0]
Bhist[0] = B_
muHist[0] = 0.
#time 1 onward
for t in range(1,T):
s,x = sHist[t],xprime[sHist[t]]
c,n,xprime = np.empty(self.S),nf[s](x),np.empty(self.S)
for shat in range(self.S):
c[shat] = cf[shat](x)
for sprime in range(self.S):
xprime[sprime] = xprimef[s,sprime](x)
Tau = self.Tau(c,n)[s]
u_c = Uc(c,n)
Eu_c = Pi[sHist[t-1]].dot(u_c)
muHist[t] = self.Vf[s](x,1)
RHist[t-1] = Uc(cHist[t-1],nHist[t-1])/(self.beta*Eu_c)
cHist[t],nHist[t],Bhist[t],TauHist[t] = c[s],n,x/u_c[s],Tau
return cHist,nHist,Bhist,TauHist,sHist,muHist,RHist
def mc_sample_path(P, init, sample_size):
return mc_tools.mc_sample_path(P, init, sample_size)
def mc_sample_path(P, init, sample_size):
return mc_tools.mc_sample_path(P, init, sample_size)