Python 化学网络的贝叶斯分析

我试着做一个简单的反应网络，看看反应速率k1和k2的灵敏度。所以我的反应分别是A-->B-->C和k1和k2

import numpy as np
import pymc3 as pm
from matplotlib.pyplot import figure, scatter, legend, plot
from scipy.integrate import solve_ivp
from sys import exit
Nt = 11
time  = 10
tt = np.linspace(0,time,Nt+1)
y0 = [1,0,0]


k1_0, k2_0 = 1, 0.5
def equat(t,c):
    da_dt = -k1_0*c[0]
    db_dt = k1_0*c[0] - k2_0*c[1]
    dc_dt = k2_0*c[1]
    return da_dt, db_dt, dc_dt

c_est = solve_ivp(equat, t_span = [0,time], t_eval = tt, y0 = y0)  

niter = 10
with pm.Model() as reak_model:
    k1 = pm.Normal('k1', mu = 0, sd = 100)
    k2 = pm.Normal('k2', mu=0, sd=100, shape = 1)
    sigma = pm.HalfNormal('sigma', sd=1)
    def equat_2(t,c):
        da_dt = -k1*c[0]
        db_dt = k1*c[0] - k1*c[1]
        dc_dt = k1*c[1]
        return da_dt, db_dt, dc_dt
    c = solve_ivp(equat_2, t_span = [0,time], t_eval = tt, y0 = y0)
    likelihood = pm.Normal('y', mu=c.y, sd=sigma, observed=c_est.y)
    trace = pm.sample(niter, chains = 1)
pm.traceplot(trace, varnames=['k1','k2'])

 ValueError: setting an array element with a sequence.

我得到这个错误。因为我是贝叶斯的新手，我想知道它是否和分布有关

Traceback (most recent call last):

  File "<ipython-input-95-26ff8f25faea>", line 1, in <module>
    runfile('/Daft regresion.py', wdir='/Final code')

  File "/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py", line 786, in runfile
    execfile(filename, namespace)

  File "python3.7/site-packages/spyder_kernels/customize/spydercustomize.py", line 110, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "Daft regression.py", line 53, in <module>
    c = solve_ivp(equat_2, t_span = [0,time], t_eval = tt, y0 = y0)

  File "/python3.7/site-packages/scipy/integrate/_ivp/ivp.py", line 454, in solve_ivp
    solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)

  File "/python3.7/site-packages/scipy/integrate/_ivp/rk.py", line 99, in __init__
    self.f = self.fun(self.t, self.y)

  File "/python3.7/site-packages/scipy/integrate/_ivp/base.py", line 139, in fun
    return self.fun_single(t, y)

  File "/python3.7/site-packages/scipy/integrate/_ivp/base.py", line 21, in fun_wrapped
    return np.asarray(fun(t, y), dtype=dtype)

  File "/python3.7/site-packages/numpy/core/numeric.py", line 501, in asarray
    return array(a, dtype, copy=False, order=order)

ValueError: setting an array element with a sequence.

回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
运行文件（'/Daft regresion.py'，wdir='/Final code'）
文件“/python3.7/site-packages/spyder_-kernels/customize/spyderrcustomize.py”，第786行，在runfile中
execfile（文件名、命名空间）
文件“python3.7/site packages/spyder_kernels/customize/spydercustomize.py”，第110行，在execfile中
exec（编译（f.read（），文件名，'exec'），命名空间）
文件“Daft regression.py”，第53行，在
c=求解ivp（等式2，t_span=[0，时间]，t_eval=tt，y0=y0）
文件“/python3.7/site packages/scipy/integrate/_ivp/ivp.py”，第454行，在solve_ivp中
解算器=方法（乐趣、t0、y0、tf、矢量化=矢量化，**选项）
文件“/python3.7/site packages/scipy/integrate/_ivp/rk.py”，第99行，在__
self.f=self.fun（self.t，self.y）
文件“/python3.7/site packages/scipy/integrate/_ivp/base.py”，第139行，in fun
回归自我。快乐单身（t，y）
文件“/python3.7/site packages/scipy/integrate/_ivp/base.py”，第21行，用fun_包装
返回np.asarray（fun（t，y），dtype=dtype）
asarray中的文件“/python3.7/site packages/numpy/core/numeric.py”，第501行
返回数组（a，数据类型，copy=False，order=order）
ValueError:使用序列设置数组元素。

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编辑：显然我需要实现Theano包而不是solve_ivp。仍然感谢您的帮助。

添加了错误