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在这里,我的目标是估计阻尼谐振子的参数( Gamma 和欧米茄),由下式给出
dX^2/dt^2+gamma*dX/dt+(2*pi*omega)^2*X=0. (We can add white gaussian noise to the system.)
import pymc
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt;
from scipy.integrate import odeint
#import data
xdata = sio.loadmat('T.mat')['T'][0] #time
ydata1 = sio.loadmat('V1.mat')['V1'][0] # V2=dV1/dt, (X=V1),
ydata2 = sio.loadmat('V2.mat')['V2'][0] # dV2/dt=-(2pi*omega)^2*V1-gama*V2
#time span for solving the equations
npts= 500
dt=0.01
Tspan=5.0
time = np.linspace(0,Tspan,npts+1)
#initial condition
V0 = [1.0, 1.0]
# Priors for unknown model parameters
sigma = pymc.Uniform('sigma', 0.0, 100.0)
gama= pymc.Uniform('gama', 0.0, 20.0)
omega=pymc.Uniform('omega',0.0, 20.0)
#Solve the equations
@pymc.deterministic
def DHOS(gama=gama, omega=omega):
V1= np.zeros(npts+1)
V2= np.zeros(npts+1)
V1[0] = V0[0]
V2[0] = V0[1]
for i in range(1,npts+1):
V1[i]= V1[i-1] + dt*V2[i-1];
V2[i] = V2[i-1] + dt*(-((2*np.pi*omega)**2)*V1[i-1]-gama*V2[i-1]);
return [V1, V2]
#or we can use odeint
#@pymc.deterministic
#def DHS( gama=gama, omega=omega):
# def DOS_func(y, time):
# V1, V2 = y[0], y[1]
# dV1dt = V2
# dV2dt = -((2*np.pi*omega)**2)* V1 -gama*V2
# dydt = [dV1dt, dV2dt]
# return dydt
# soln = odeint(DOS_func,V0, time)
# V1, V2 = soln[:,0], soln[:,1]
# return V1, V2
# value of outcome (observations)
V1 = pymc.Lambda('V1', lambda DHOS=DHOS: DHOS[0])
V2 = pymc.Lambda('V2', lambda DHOS=DHOS: DHOS[1])
# liklihood of observations
Yobs1 = pymc.Normal('Yobs1', mu=V1, tau=1.0/sigma**2, value=ydata1, observed=True)
Yobs2 = pymc.Normal('Yobs2', mu=V2, tau=1.0/sigma**2, value=ydata2, observed=True)
import pymc
import DampedOscil_model
MDL = pymc.MCMC(DampedOscil_model, db='pickle')
MDL.sample(iter=1e4, burn=1e2, thin=2)
gama_trace=MDL.trace('gama')[- 1000:]
omega_trace=MDL.trace('omega')[-1000:]
gama=MDL.gama.value
omega=MDL.omega.value
import matplotlib.pyplot as plt
import scipy.io as sio
import pandas as pd
import numpy as np
import pymc3 as pm
import theano.tensor as tt
import theano
from pymc3 import Model, Normal, HalfNormal, Uniform
from pymc3 import NUTS, find_MAP, sample, Slice, traceplot, summary
from pymc3 import Deterministic
from scipy.optimize import fmin_powell
#import data
xdata = sio.loadmat('T.mat')['T'][0] #time
ydata1 = sio.loadmat('V1.mat')['V1'][0] # V2=dV1/dt, (X=V1),
ydata2 = sio.loadmat('V2.mat')['V2'][0] # dV2/dt=-(2pi*omega)^2*V1-gama*V2
#time span for solving the equations
npts= 500
dt=0.01
Tspan=5.0
time = np.linspace(0,Tspan,npts+1)
niter=10000
burn=niter//2;
with pm.Model() as model:
#Priors for unknown model parameters
sigma = pm.HalfNormal('sigma', sd=1)
gama= pm.Uniform('gama', 0.0, 20.0)
omega=pm.Uniform('omega',0.0, 20.0)
#initial condition
V0 = [1.0, 1.0]
#Solve the equations
# do I need to use theano.tensor here?!
@theano.compile.ops.as_op(itypes=[tt.dscalar, tt.dscalar],otypes=[tt.dvector])
def DHOS(gama=gama, omega=omega):
V1= np.zeros(npts+1)
V2= np.zeros(npts+1)
V1[0] = V0[0]
V2[0] = V0[1]
for i in range(1,npts+1):
V1[i]= V1[i-1] + dt*V2[i-1];
V2[i] = V2[i-1] + dt*(-((2*np.pi*1)**2)*V1[i-1]-gama*V2[i-1]);
return V1,V2
V1 = pm.Deterministic('V1', DHOS[0])
V2 = pm.Deterministic('V2', DHOS[1])
start = pm.find_MAP(fmin=fmin_powell, disp=True)
step=pm.NUTS()
trace=pm.sample(niter, step, start=start, progressbar=False)
traceplot(trace);
Summary=pm.df_summary(trace[-1000:])
gama_trace = trace.get_values('gama', burn)
omega_trace = trace.get_values('omega', burn)
TypeError: 'FromFunctionOp' object does not support indexing
@pymc.deterministic
def DOS(gama=gama, omega=omega):
V1= np.zeros(npts+1)
V2= np.zeros(npts+1)
V1[0] = V0[0]
V2[0] = V0[1]
for i in range(1,npts+1):
V1[i]= V1[i-1] + dt*V2[i-1];
V2[i] = V2[i-1] + dt*(-((2*np.pi*omega)**2)*V1[i-1]-gama*V2[i-1]);
return [V1, V2]
V1 = pymc.Lambda('V1', lambda DOS=DOS: DOS[0])
V2 = pymc.Lambda('V2', lambda DOS=DOS: DOS[1])
最佳答案
我建议并已成功实现,使用“黑匣子”方法与 PYMC3 连接。在这种情况下,这意味着自己计算对数似然,然后使用 PYMC3 对其进行采样。这需要以 Theano 和 PYMC3 可以与它们交互的方式编写您的函数。
这是在 notebook 中概述的在 PYMC3 页面上,以 cython 为例。
这是需要完成的工作的简短示例。
首先,您可以加载数据并设置所需的任何参数,例如时间步长等。
import pymc3 as pm
import numpy as np
import theano
import theano.tensor as tt
#import data
xdata = sio.loadmat('T.mat')['T'][0] #time
ydata1 = sio.loadmat('V1.mat')['V1'][0] # V2=dV1/dt, (X=V1),
ydata2 = sio.loadmat('V2.mat')['V2'][0] # dV2/dt=-(2pi*omega)^2*V1-gama*V2
#time span for solving the equations
npts= 500
dt=0.01
Tspan=5.0
time = np.linspace(0,Tspan,npts+1)
#initial condition
V0 = [1.0, 1.0]
def DHOS(theta):
gama,omega=theta
V1= np.zeros(npts+1)
V2= np.zeros(npts+1)
V1[0] = V0[0]
V2[0] = V0[1]
for i in range(1,npts+1):
V1[i]= V1[i-1] + dt*V2[i-1];
V2[i] = V2[i-1] + dt*(-((2*np.pi*omega)**2)*V1[i-1]-gama*V2[i-1]);
return [V1, V2]
def my_loglike(theta,data,sigma):
"""
A Gaussian log-likelihood function for a model with parameters given in theta
"""
model = DHOS(theta) #V1 and V2 from the DHOS function
#Here data = [ydata1,ydata2] to compare with model
#sigma is either the same shape as model or a scalar
#which corresponds to the uncertainty on the data.
return -(0.5)*sum((data - model)**2/sigma**2)
# define a theano Op for our likelihood function
class LogLike(tt.Op):
"""
Specify what type of object will be passed and returned to the Op when it is
called. In our case we will be passing it a vector of values (the parameters
that define our model) and returning a single "scalar" value (the
log-likelihood)
"""
itypes = [tt.dvector] # expects a vector of parameter values when called
otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)
def __init__(self, loglike, data, sigma):
"""
Initialise the Op with various things that our log-likelihood function
requires. Below are the things that are needed in this particular
example.
Parameters
----------
loglike:
The log-likelihood (or whatever) function we've defined
data:
The "observed" data that our log-likelihood function takes in
x:
The dependent variable (aka 'x') that our model requires
sigma:
The noise standard deviation that our function requires.
"""
# add inputs as class attributes
self.likelihood = loglike
self.data = data
self.sigma = sigma
def perform(self, node, inputs, outputs):
# the method that is used when calling the Op
theta, = inputs # this will contain my variables
# call the log-likelihood function
logl = self.likelihood(theta, self.data, self.sigma)
outputs[0][0] = array(logl) # output the log-likelihood
ndraws = 10000 # number of draws from the distribution
nburn = 1000 # number of "burn-in points" (which we'll discard)
# create our Op
logl = LogLike(my_loglike, rdat_sim, 10)
# use PyMC3 to sampler from log-likelihood
with pm.Model():
gama= pm.Uniform('gama', 0.0, 20.0)
omega=pm.Uniform('omega',0.0, 20.0)
# convert m and c to a tensor vector
theta = tt.as_tensor_variable([gama,omega])
# use a DensityDist (use a lamdba function to "call" the Op)
pm.DensityDist('likelihood', lambda v: logl(v), observed={'v': theta})
trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)
_ = pm.traceplot(trace)
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