如何實現pymc

我試圖讓我圍繞着如何使用pymc複製由道格拉斯·哈伯德電子表格在他的書中How to Measure Anything 如何實現pymc

我的嘗試是在Python實現蒙特卡洛函數頭簡單蒙特卡洛功能：

import numpy as np 
import pandas as pd 
from pymc import DiscreteUniform, Exponential, deterministic, Poisson, Uniform, Normal, Stochastic, MCMC, Model 


maintenance_saving_range = DiscreteUniform('maintenance_saving_range', lower=10, upper=21) 
labour_saving_range = DiscreteUniform('labour_saving_range', lower=-2, upper=9) 
raw_material_range = DiscreteUniform('maintenance_saving_range', lower=3, upper=10) 
production_level_range = DiscreteUniform('maintenance_saving_range', lower=15000, upper=35000) 



@deterministic(plot=False) 
def rate(m = maintenance_saving_range, l = labour_saving_range, r=raw_material_range, p=production_level_range): 
    return (m + l + r) * p 

model = Model([rate, maintenance_saving_range, labour_saving_range, raw_material_range, production_level_range]) 
mc = MCMC(model)

不幸的是，我發現了一個錯誤：ValueError: A tallyable PyMC object called maintenance_saving_range already exists. This will cause problems for some database backends.

我有什麼錯？

來源

2016-09-05 elksie5000

PyMC可能有自己的對象叫做maintenance_saving_range。嘗試重命名您的變量，例如my_maintenance_saving_range。 –

你說得對。我只是笨拙的複製粘貼代碼。 – elksie5000

啊，這是一個複製和粘貼錯誤。

我打了三個同名的發行版。

下面是可用的代碼。

import numpy as np 
import pandas as pd 
from pymc import DiscreteUniform, Exponential, deterministic, Poisson, Uniform, Normal, Stochastic, MCMC, Model 
%matplotlib inline 
import matplotlib.pyplot as plt 

maintenance_saving_range = DiscreteUniform('maintenance_saving_range', lower=10, upper=21) 
labour_saving_range = DiscreteUniform('labour_saving_range', lower=-2, upper=9) 
raw_material_range = DiscreteUniform('raw_material_range', lower=3, upper=10) 
production_level_range = DiscreteUniform('production_level_range', lower=15000, upper=35000) 


@deterministic(plot=False, name="rate") 
def rate(m = maintenance_saving_range, l = labour_saving_range, r=raw_material_range, p=production_level_range): 
    #out = np.empty(10000) 
    out = (m + l + r) * p 
    return out 
model = Model([rate, maintenance_saving_range, labour_saving_range, raw_material_range]) 
mc = MCMC(model) 
mc.sample(iter=10000)

來源

2016-09-06 10:42:10 elksie5000

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