This repository is supposed to contain all my GNU Guile or Scheme machine learning algorithm implementations.
zelphir.kaltstahl
459e0564a1
remove todo, usage of car is OK here
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4 年之前 | |
|---|---|---|
| old-racket-code | 5 年之前 | |
| scripts | 5 年之前 | |
| test | 4 年之前 | |
| utils | 4 年之前 | |
| .gitignore | 5 年之前 | |
| LICENSE | 7 年之前 | |
| README.org | 5 年之前 | |
| columns.csv | 7 年之前 | |
| data-point.scm | 5 年之前 | |
| data_banknote_authentication.csv | 7 年之前 | |
| dataset.scm | 4 年之前 | |
| decision-tree.scm | 4 年之前 | |
| metrics.scm | 4 年之前 | |
| notes.org | 4 年之前 | |
| prediction.scm | 5 年之前 | |
| pruning.scm | 4 年之前 | |
| split-quality-measure.scm | 5 年之前 | |
| todo.org | 4 年之前 | |
| tree.scm | 4 年之前 | |
| utils.scm | 5 年之前 |
README.org
Tests
You can run the tests by running the script run-tests.bash in the scripts/ directory as follows:
# from the root directory of this project:
bash scripts/run-tests.bash
Usage (outdated example)
This example is outdated and still for the older Racket code.
(define shuffled-dataset (shuffle dataset))
(define small-dataset
(data-range shuffled-dataset
0
;; take only a fifth of the data to make this example run faster
(exact-floor (/ (dataset-length shuffled-dataset)
5))))
;; be sure to collect all garbage, apparently this should be called thrice
(collect-garbage)
(collect-garbage)
(collect-garbage)
;; requires a ~time~ macro
(time
;; ~for/list~ -- a Racketism, needs to be rewritten
(for/list ([i (in-range 1)])
(mean
(evaluate-algorithm #:dataset (shuffle dataset)
#:n-folds 10
#:feature-column-indices (list 0 1 2 3)
#:label-column-index 4
#:max-depth 5
#:min-data-points 24
#:min-data-points-ratio 0.02
#:min-impurity-split (expt 10 -7)
#:stop-at-no-impurity-improvement #t
#:random-seed 0))))
;; be sure to collect all garbage, apparently this should be called thrice
(collect-garbage)
(collect-garbage)
(collect-garbage)
(time
;; ~for/list~ -- a Racketism, needs to be rewritten
(for/list ([i (in-range 1)])
;; run with the whole dataset as an example, no random seed
(define tree (fit #:train-data dataset
#:feature-column-indices (list 0 1 2 3)
#:label-column-index 4
#:max-depth 5
#:min-data-points 12
#:min-data-points-ratio 0.02
#:min-impurity-split (expt 10 -7)
#:stop-at-no-impurity-improvement #t))
'done))
Approach
Data representation
- A dataset is currently represented by a list of vectors. Rows are represented by vectors.