我想使用pymc3来估计霍奇金·赫x黎(Hodgkin Huxley)神经元模型中的未知参数和状态。我在pymc中的代码基于http://healthyalgorithms.com/2010/10/19/mcmc-in-python-how-to-stick-a-statistical-model-on-a-system-dynamics-model-in-pymc/，并且执行得很好。
#parameter priors
@deterministic
def HH(priors in here)
#model equations
#return numpy arrays that somehow contain the probability distributions as elements
return V,n,m,h
#Make V deterministic in one line. Seems to be the magic that makes this work.
V = Lambda('V', lambda HH=HH: HH[0])
#set up the likelihood
A = Normal('A',mu=V,tau=2.0,value=voltage_data,observed = True)
#run the sampling...
在pymc3中，我无法使用Lambda技巧。这是我的尝试之一:
import numpy as np
import theano.tensor as tt
from pymc3 import Model, Normal, Uniform, Deterministic, sample, NUTS, Metropolis, find_MAP
import scipy
#observed data
T = 10
dt = 0.02
voltage_data_file = 'noise_measured.dat'
voltage_data = np.loadtxt(voltage_data_file)
voltage_data = voltage_data[0:T]
current_data_file = 'current.dat'
current_data = np.loadtxt(current_data_file)
current_data = current_data[0:T]
#functions needed later
def x_inf(V,vx,dvx):
return 0.5*(1 + np.tanh((V - vx)/dvx))
def tau(V,vx_t,dvx_t,tx_0,tx_1):
return tx_0 + 0.5*tx_1*(1 + np.tanh((V- vx_t)/dvx_t))
#Model
NaKL_Model = Model()
with NaKL_Model:
#priors
g_Naa = Uniform('g_Naa',0.0,150.0)
E_Na = Uniform('E_Na',30.0,70.0)
g_K = Uniform('g_K',0.0,150.0)
E_K = Uniform('E_K',-100.0,-50.0)
g_L = Uniform('g_L',0.1,1.0)
E_L = Uniform('E_L',-90.0,-50.0)
vm = Uniform('vm',-60.0,-30.0)
vm_t = Uniform('vm_t',-60.0,-30.0)
dvm = Uniform('dvm',10.0,20.0)
dvm_t = Uniform('dvm_t',10.0,20.0)
tmm_0 = Uniform('tmm_0',0.05,0.25)
tm_1 = Uniform('tm_1',0.1,1.0)
vh = Uniform('vh',-80.0,-40.0)
vh_t = Uniform('vh_t',-80.0,-40.0)
dvh = Uniform('dvh',-30.0,-10.0)
dvh_t = Uniform('dvh_t',-30.0,-10.0)
th_0 = Uniform('th_0',0.5,1.5)
th_1 = Uniform('th_1',5.0,9.0)
vn = Uniform('vn',-70.0,-40.0)
vn_t = Uniform('vn_t',-70.0,-40.0)
dvn = Uniform('dvn',10.0,40.0)
dvn_t = Uniform('dvn_t',10.0,40.0)
tn_0 = Uniform('tn_0',0.5,1.5)
tn_1 = Uniform('tn_1',3.0,7.0)
#Initial Conditions
V_0 = Uniform('V_0',-100.0,50.0)
n_0 = Uniform('n_0',0.0,1.0)
m_0 = Uniform('m_0',0.0,1.0)
h_0 = Uniform('h_0',0.0,1.0)
def HH(i,V_current,n_current,m_current,h_current,g_Naa=g_Naa,E_Na=E_Na,g_K=g_K,E_K=E_K,g_L=g_L,E_L=E_L,vm=vm,vm_t=vm_t,dvm=dvm,dvm_t=dvm_t,tmm_0=tmm_0,tm_1=tm_1,vh=vh,vh_t=vh_t,dvh=dvh,dvh_t=dvh_t,th_0=th_0,th_1=th_1,vn=vn,vn_t=vn_t,dvn=dvn,dvn_t=dvn_t,tn_0=tn_0,tn_1=tn_1):
V_new = V_current + dt*(g_L*(E_L - V_current) + g_K*n_current**4*(E_K - V_current) + g_Naa*m_current**3*h_current*(E_Na - V_current) + current_data[i])
n_new = n_current + dt*(x_inf(V_current,vn,dvn)-n_current)/tau(V_current,vn_t,dvn_t,tn_0,tn_1)
m_new = m_current + dt*(x_inf(V_current,vm,dvm)-m_current)/tau(V_current,vm_t,dvm_t,tmm_0,tm_1)
h_new = h_current + dt*(x_inf(V_current,vh,dvh)-h_current)/tau(V_current,vh_t,dvh_t,th_0,th_1)
return V_new,n_new,m_new,h_new
V_state = [V_0]
n_state = [n_0]
m_state = [m_0]
h_state = [h_0]
#A = [Normal('A0',mu=V_0,tau=2.0,observed = voltage_data[0])]
for i in range(1,T):
V_state.append(Deterministic('V' + str(i), HH(i-1,V_state[i-1],n_state[i-1],m_state[i-1],h_state[i-1])[0]))
n_state.append(Deterministic('V' + str(i), HH(i-1,V_state[i-1],n_state[i-1],m_state[i-1],h_state[i-1])[1]))
m_state.append(Deterministic('V' + str(i), HH(i-1,V_state[i-1],n_state[i-1],m_state[i-1],h_state[i-1])[2]))
h_state.append(Deterministic('V' + str(i), HH(i-1,V_state[i-1],n_state[i-1],m_state[i-1],h_state[i-1])[3]))
#A.append(Normal('A' + str(i),mu=V_state[i],sd=2.0,observed = voltage_data[0]))
A = Normal('A',mu=V_state,sd=2.0,observed = voltage_data)
start = find_MAP()
#step = NUTS(scaling=start)
step = Metropolis(start=start)
trace = sample(100,step)
我在这些论坛上看到的唯一另一个示例是:Simple Dynamical Model in PyMC3
但是，这无助于回答我的问题，因为那里没有提议的解决方案。我的解决方案也不起作用-我没有收到错误，但是当我运行脚本时，采样似乎永远不会开始。无论如何，我的解决方案似乎效率很低，因为我必须运行python for循环来定义所有确定性分布。我不确定pymc3是否能识别我将它们全部放入纯python列表中的方式。如果我的函数HH()可以返回一个numpy数组或某种theano对象，则可能会有所帮助。但是我对如何实现它迷失了。
最佳答案
在最后两行中稍作更改后，您的方法对我有用:
step = Metropolis()
trace = sample(100, step, start=start)
不过，这需要一些时间，因此，如果您想更快地看到结果，可以将
T减小。这是a notebook that shows it in action。