确定性python脚本以非确定性方式运行 [英] Deterministic python script behaves in non-deterministic way
问题描述
我有一个不使用随机化的脚本，当我运行它时会给我不同的答案.我希望每次运行脚本时答案都是一样的.该问题似乎仅针对某些(病态)输入数据才会发生.
I have a script which uses no randomisation that gives me different answers when I run it. I expect the answer to be the same, every time I run the script. The problem appears to only happen for certain (ill-conditioned) input data.
该代码段来自一种算法，用于为线性系统计算特定类型的控制器，并且主要由线性代数(矩阵求逆，Riccati方程，特征值)组成.
The snippet comes from an algorithm to compute a specific type of controller for a linear system, and it mostly consists of doing linear algebra (matrix inversions, Riccati equation, eigenvalues).
显然，这对我来说是一个大问题，因为我现在不相信自己的代码可以给我正确的结果.我知道条件不佳的数据的结果可能是错误的，但我希望始终是错误的.为什么答案在我的Windows机器上并不总是相同?为什么要使用Linux& Windows机器不能给出相同的结果?
Obviously, this is a major worry for me, as I now cannot trust my code to give me the right results. I know the result can be wrong for poorly conditioned data, but I expect consistently wrong. Why is the answer not always the same on my Windows machine? Why do the Linux & Windows machine not give the same results?
我正在使用Python 2.7.9 (default, Dec 10 2014, 12:24:55) [MSC v.1500 32 bit (Intel)] on win 32，具有Numpy版本1.8.2和Scipy 0.14.0. (Windows 8，64位).
I'm using Python 2.7.9 (default, Dec 10 2014, 12:24:55) [MSC v.1500 32 bit (Intel)] on win 32, with Numpy version 1.8.2 and Scipy 0.14.0. (Windows 8, 64bit).
代码在下面.我也尝试在两台Linux机器上运行代码，并且脚本始终给出相同的答案(但是机器给出的答案不同).其中一个正在运行Python 2.7.8，Numpy 1.8.2和Scipy 0.14.0.第二个是运行带有Numpy 1.6.1和Scipy 0.12.0的Python 2.7.3.
The code is below. I've also tried running the code on two Linux machines, and there the script always gives the same answer (but the machines gave differing answers). One was running Python 2.7.8, with Numpy 1.8.2 and Scipy 0.14.0. The second was running Python 2.7.3 with Numpy 1.6.1 and Scipy 0.12.0.
我三次求解Riccati方程，然后打印答案.我每次都希望得到相同的答案，而是得到序列'1.75305103767e-09; 3.25501787302e-07; 3.25501787302e-07'.
I solve the Riccati equation three times, and then print the answers. I expect the same answer every time, instead I get the sequence '1.75305103767e-09; 3.25501787302e-07; 3.25501787302e-07'.
import numpy as np
import scipy.linalg
matrix = np.matrix
A = matrix([[ 0.00000000e+00, 2.96156260e+01, 0.00000000e+00,
-1.00000000e+00],
[ -2.96156260e+01, -6.77626358e-21, 1.00000000e+00,
-2.11758237e-22],
[ 0.00000000e+00, 0.00000000e+00, 2.06196064e+00,
5.59422224e+01],
[ 0.00000000e+00, 0.00000000e+00, 2.12407340e+01,
-2.06195974e+00]])
B = matrix([[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ -342.35401351, -14204.86532216, 31.22469724],
[ 1390.44997337, 342.33745324, -126.81720597]])
Q = matrix([[ 5.00000001, 0. , 0. , 0. ],
[ 0. , 5.00000001, 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]])
R = matrix([[ -3.75632852e+04, -0.00000000e+00, 0.00000000e+00],
[ -0.00000000e+00, -3.75632852e+04, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 4.00000000e+00]])
counter = 0
while counter < 3:
counter +=1
X = scipy.linalg.solve_continuous_are(A, B, Q, R)
print(-3449.15531628 - X[0,0])
我的numpy配置如下print np.show_config()
My numpy config is as below print np.show_config()
lapack_opt_info:
libraries = ['mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md', 'mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md']
library_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/lib/ia32', 'C:/Program Files (x86)/Intel/Composer XE 2013 SP1/compiler/lib/ia32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/include']
blas_opt_info:
libraries = ['mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md']
library_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/lib/ia32', 'C:/Program Files (x86)/Intel/Composer XE 2013 SP1/compiler/lib/ia32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/include']
openblas_info:
NOT AVAILABLE
lapack_mkl_info:
libraries = ['mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md', 'mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md']
library_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/lib/ia32', 'C:/Program Files (x86)/Intel/Composer XE 2013 SP1/compiler/lib/ia32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/include']
blas_mkl_info:
libraries = ['mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md']
library_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/lib/ia32', 'C:/Program Files (x86)/Intel/Composer XE 2013 SP1/compiler/lib/ia32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/include']
mkl_info:
libraries = ['mkl_blas95', 'mkl_lapack95', 'mkl_intel_c', 'mkl_intel_thread', 'mkl_core', 'libiomp5md']
library_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/lib/ia32', 'C:/Program Files (x86)/Intel/Composer XE 2013 SP1/compiler/lib/ia32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['c:/Program Files (x86)/Intel/Composer XE 2013 SP1/mkl/include']
None
(编辑以缩小问题范围)
(edits to trim the question down)
推荐答案
通常，Windows上的linalg库在机器精度级别的不同运行上给出不同的答案.我从未听说过为什么仅在Windows上或主要在Windows上发生这种解释的原因.
In general, linalg libraries on Windows give different answers on different runs at machine precision level. I never heard of an explanation why this happens only or mainly on Windows.
如果矩阵不适，那么inv很大程度上将是数字噪声.在Windows上，连续运行时的噪声并不总是相同的，在其他操作系统上，噪声可能总是相同的，但可能会有所不同，具体取决于线性代数库的详细信息，线程选项，缓存使用情况等等.
If your matrix is ill conditioned, then the inv will be largely numerical noise. On Windows the noise is not always the same in consecutive runs, on other operating systems the noise might be always the same but can differ depending on the details of the linear algebra library, on threading options, cache usage and so on.
我已经在Windows上看到了几个示例并将其张贴到scipy邮件列表上，我使用的主要是带有ATLAS BLAS/LAPACK的官方32位二进制文件.
I've seen on and posted to the scipy mailing list several examples for this on Windows, I was using mostly the official 32 bit binaries with ATLAS BLAS/LAPACK.
唯一的解决方案是使计算结果在很大程度上不取决于浮点精度问题和数值噪声，例如正则化矩阵逆，使用广义逆，pinv，重新参数化或类似方法.
The only solution is to make the outcome of your calculation not depend so much on floating point precision issues and numerical noise, for example regularize the matrix inverse, use generalized inverse, pinv, reparameterize or similar.
这篇关于确定性python脚本以非确定性方式运行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
