Active Glossary, Statistics & ML algorithms.
Table of Contents
Some Libraries
I have got availability to 2 important library sources
- MGL Common Lisp machine learning library by Gábor Melis which includes parts originally contributed by Ravenpack International, and:
- CLML or CL-mlep its educational purposes so friendly fork.
The R's Psych around.
Though extended, to infinite and beyond, quite afterwards, my first interest was around usage of these tools in psychology. When moved into testing I had to gain literacy in R, here my R-notepad for a reference shortcut. Though, as being interested in personality, psychometric theory and experimental psychology the Personality Project (warn! no friendly crumbling) it's been a solid reference. Now that I feel in needed of steeping an integrative approach to tools, I have found easier, re-learn by breaking down e.g. this book, both concepts and data set though, with my own (elisp) approach in mind.
Glossary, basics.
Let's first work in some conceptual basis..
Average
Simple Average
(define (my-average data)
(/ (apply + data) (length data)))
(my-average '(3 5 7 9))
Standard Deviation
(define (my-standard-deviation sample)
(do ([x sample (cdr x)]
[s 0 (+ s (expt (- (car x) (/ (apply + sample) (length sample))) 2))])
((null? x)
(sqrt (/ s (- (length sample) 1))))))
(my-standard-deviation '(71.4286 79.8701 75.3247 77.2727 75.974))