惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
DataBreaches.Net
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Google DeepMind News
Google DeepMind News
博客园 - 聂微东
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
IT之家
IT之家
博客园 - 【当耐特】
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
B
Blog
爱范儿
爱范儿
阮一峰的网络日志
阮一峰的网络日志
云风的 BLOG
云风的 BLOG
Vercel News
Vercel News
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
H
Help Net Security
J
Java Code Geeks
aimingoo的专栏
aimingoo的专栏
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog RSS Feed
Blog — PlanetScale
Blog — PlanetScale
S
SegmentFault 最新的问题
Apple Machine Learning Research
Apple Machine Learning Research

all models are wrong

-> Going both ways in R <- finding homologous probes using biomaRt Profiling in R More Segment HMMs Python and Numpy integers MICROCOSMOGRAPHIA ACADEMICA Pebl The Pirate Bay Trial Latex, Beamer, Python, Beauty
Nasty Python Things
2009-03-27 · via all models are wrong

March 27, 2009

So I seem to keep writing commands that look like this:

delta[t][q] = max(
    [delta[tau][j] +
        pylab.log(
            pylab.array([
                output_dist(Q=q,L=(t-tau),Y=Y[tau+1:t]),
                duration_dist(Q=q,L=(t-tau)),
                transition_dist[q,j]]).prod())            
    for j in self.state_range])

Is this bad? The above is the max of a list. The list is made up using a list comprehension, where each element is the log of a product of a 1D array plus a bit. Each element of each array is a call to a function associated with my model. The trouble is, if I break it down into some for loops, then I start having to invent temporary names for my variables, which seems clunky.

Any opinions?

M