我正在尝试将R代码转换为Python。R代码使用lsoda
函数,它是FORTRAN DOE
解算器的包装器。Python对应的似乎是solve_ivp
,它是FORTRAN ODEPACK
的包装器。我在Python中使用method='LSODA'
,这应该是R使用的等效方法。然而,我的结果是不同的高达1%的误差。在我的代码中没有什么是随机的,所以我相信我应该能够完全复制结果。你知道吗
知道吗?!你知道吗
这是R代码的一部分(之前的代码只是计算参数值:
val = c("A1" = 1, "A2" = 1, "A3" = 1, "A4" = 1, "A5" = 1, "A6" = 1, "A7" = 1)
hamberg_ode <- function(t,val,p) {
dA1 = p["ktr1"]*(1 - ((p["E_MAX"] * p["C_s_gamma"])/(p["EC_50_gamma"] + p["C_s_gamma"]))) - p["ktr1"]*val["A1"]
dA2 = p["ktr1"]*val["A1"] - p["ktr1"]*val["A2"]
dA3 = p["ktr1"]*val["A2"] - p["ktr1"]*val["A3"]
dA4 = p["ktr1"]*val["A3"] - p["ktr1"]*val["A4"]
dA5 = p["ktr1"]*val["A4"] - p["ktr1"]*val["A5"]
dA6 = p["ktr1"]*val["A5"] - p["ktr1"]*val["A6"]
dA7 = p["ktr2"]*(1 - ((p["E_MAX"] * p["C_s_gamma"])/(p["EC_50_gamma"] + p["C_s_gamma"]))) - p["ktr2"]*val["A7"]
cat(val["A1"], dA1, '\n')
list(c(dA1, dA2, dA3, dA4, dA5, dA6, dA7))
}
out = lsoda(val, times, hamberg_ode, p)
Python代码:
val = [1]*7
class hamberg_ode:
def __init__(self, p):
self.p = p
def f(self, t, val, p=None):
if p is None:
p = self.p
dA1=p["ktr1"]*(1 - ((p["E_MAX"] * p["C_s_gamma"]) /
(p["EC_50_gamma"] + p["C_s_gamma"]))) - p["ktr1"]*val[0]
dA2=p["ktr1"]*val[0] - p["ktr1"]*val[1]
dA3=p["ktr1"]*val[1] - p["ktr1"]*val[2]
dA4=p["ktr1"]*val[2] - p["ktr1"]*val[3]
dA5=p["ktr1"]*val[3] - p["ktr1"]*val[4]
dA6=p["ktr1"]*val[4] - p["ktr1"]*val[5]
dA7=p["ktr2"]*(1 - ((p["E_MAX"] * p["C_s_gamma"]) /
(p["EC_50_gamma"] + p["C_s_gamma"]))) - p["ktr2"]*val[6]
print(val[0], dA1)
return (dA1, dA2, dA3, dA4, dA5, dA6, dA7)
h_function = hamberg_ode(p).f
out = solve_ivp(h_function, (0, maxTime), val, t_eval=times, method='LSODA')
作为数字如何发散的示例,下面是两个代码的A1和dA1的几个第一个值: R
1至0.2289151
1至0.2289151
0.9997726至0.2287975
0.9997727至0.2287976
0.9995454至0.22868
0.9995455-0.2286801
0.9901534至0.2238221
0.9901523至0.2238215
0.9809609至0.2190673
0.9809587至0.2190662
0.9719626至0.214413
0.9719604至0.2144119
0.9493722至0.2027284
0.9493668至0.2027255
0.927996至0.1916717
0.9280039至0.1916758
0.9078033-0.1812272
0.9078049至0.181228
0.8887056-0.1713491
0.8887071-0.1713499
Python
1.0至0.22891514470392998
0.9868338969217406-0.2221050913875888
0.9872255785819792-0.22230768534978135
0.9744278526945864-0.2156881719597506
0.9748085754105683-0.2158850975024998
0.9069550726140441-0.18078845812498728
0.906362742770375-0.18048208061964116
0.8502494750308627-0.15145797661644517
0.8491489787959607至0.1508887542597866
0.8022897024620746-0.1266511977015548
0.8013657199203642-0.1261732756972218
0.7405758555885625-0.0947302422152
0.7400188154524862-0.09444211821383663
0.7145516960742005-0.08126947025955095
0.714884659705225-0.08144169282733643
正如@astoriko所指出的,scipy的默认相对公差(
rtol
)是1e-3,而R的默认相对公差(lsoda
)是1e-6。你知道吗相关问题 更多 >
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