- Find some crap;
- Cut the crap;
- Go to step 1.
- boot: Boot code: stuff needed before anything else
- bore: BORE (best or rest)
- cocomo: Software Effort and Risk Estimation
- cocomo: vlow low nom high vhigh xhigh
- cocomoeg: Test Cases for COCOMO
- column: Defining columns
- column: Thresholds are from http://goo.gl/25bAh9
- columneg: Column stuff (demos)
- columns: Handling Columns of Data
- config: Configuration Control
- de: Simple Differential Evolution
- lib: General stuff
- libeg: General stuff (demos)
- ranker: add in scorerd. abcd mre, lift
LEANER uses a standard UNIX environment (with git, make, python 2.7+, bash, awk, etc). To install and test, do the following:
git clone https://github.com/ai-se/leaner.git
make test
Work in progress. Started Dec 17 2014. But I got a cool logo!
this section needs work
- settings. specified locally per file, held in a global for (a) printing (b) tuning purposes. not passed down (tedious for long chains of sub-calls)
- extensive use of iterators, list comprehensions, decorators. read up on it!
- write less classes: o is good
- N-1 globals better than N. we have only one "the"
- many file.py has fileeg.py. each eg starts with @go, fired on loading
- 2 spaces, "self" ==> "i"
this section needs work
- Decades of research, data mining is simple. Much to much made of the complexity of data mining when the truth is, it is much simpler than that (particulalry in the arena of statistical comparisons).
- Many books offer small examples of different kinds of data miners. Wanted something different- something that draws all the methods together. Seeing a new synthesis. Not some increasing focus on one discipline but building tools that work on many fields. Look closer at the pieces top build a new toolkit whose whole is more than the parts.
- My students can, after 6 to 12 weeks, build and modify start of the art devices. A research career marked by many masters with journal publications (novel results, short time). Do you want to be that productive? You can!
- Thou shalt not click.
- Not enough to use these tools, need to look inside them (at least once). The shepperd results.
- Mix and match. e.g. data mining and moea closer than you might expect from reading the literature.
- Fun!
Copyright (c) 2015 Tim Menzies
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