Question

无法判断我是否在使用pymc的错误功能做错了，或者这是一个错误。通过蒙版数组的Impute将1e20值传递给缺少的元素，而效率低下的方法Impute似乎传回正确的样本。以下是一个小例子。

import numpy as np
import pymc as py

disasters_array = np.random.random((3,3))
disasters_array[1,1]=None

# The inefficient way, using the Impute function:
D = py.Impute('D', py.Normal, disasters_array, mu=.5, tau=1E5)

# The efficient way, using masked arrays:
# Generate masked array. Where the mask is true,
# the value is taken as missing.
print disasters_array
masked_values = np.ma.masked_invalid(disasters_array)

# Pass masked array to data stochastic, and it does the right thing
disasters = py.Normal('disasters', mu=.5, tau=1E5, value=masked_values, observed=True)
@py.deterministic
def test(disasters=disasters, D=D):
    print D
    print disasters

mcmc = py.MCMC(py.Model(set([test,disasters])))

输出：

原始矩阵：

[[ 0.23507836  0.2024624   0.90518228]
 [ 0.95816            **nan**  0.43145808]
 [ 0.99566308  0.25431568  0.25464137]]

D带有插补：

[[array(0.23507836309832741) array(0.20246240248367342)
  array(0.9051822818081371)]
 [array(0.9581599997650212) **array(0.5005324083232756)**
  array(0.43145807852698237)]
 [array(0.9956630757864052) array(0.2543156788973996)
  array(0.25464136701826867)]]

Masked Array方法：

[[  2.35078363e-01   2.02462402e-01   9.05182282e-01]
 [  9.58160000e-01   **1.00000000e+20**   4.31458079e-01]
 [  9.95663076e-01   2.54315679e-01   2.54641367e-01]]

pymc imput传回1e20

0 个答案: