https://github.com/johnstonskj/rml-knn

K-Nearest Neighbor implementation in Racket.
https://github.com/johnstonskj/rml-knn

Last synced: about 1 month ago
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K-Nearest Neighbor implementation in Racket.

• Host: GitHub
• URL: https://github.com/johnstonskj/rml-knn
• Owner: johnstonskj
• License: mit
• Created: 2018-06-28T23:14:47.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-03-25T16:20:29.000Z (10 months ago)
• Last Synced: 2024-11-15T00:52:58.895Z (2 months ago)
• Language: Racket
• Size: 32.2 KB
• Stars: 1
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-racket - rml-knn - This Package is part of a set of packages implementing machine learning capabilities for Racket. This particular package implements the K-Nearest Neighbor approach for classification. (Machine Learning)

README

        
# Racket Machine Learning - *k*-Nearest Neighbor

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This package implements a *k*-NN approach for the Racket Machine Learning

package set, based on an article by 

[Tony Baker](https://spin.atomicobject.com/2013/05/06/k-nearest-neighbor-racket/). 

The `classifier` module provides a relatively simple classification approach by

determining the Euclidean distance between an individual and a set of pre-

classified training data. This package relies on the 

[rml-core](https://github.com/johnstonskj/rml-core) package and provides a

*classifier* for use with the `rml/classify` module.

# Modules

* `classifier` - Support for classifying an individual against a trained data set.

# Examples

```scheme

(require rml/data

         rml/individual

         rml/results 

         rml-knn/classifier)

; construct dataset ...

(define iris (make-individual "sepal-length" 6.3

                              "sepal-width" 2.5

                              "petal-length" 4.9

                              "petal-width" 1.5

                              "classification" "Iris-versicolor"))

(define C (make-result-matrix dataset))

(record-result C

  (hash-ref iris "classification")

  (first ((make-knn-classifier 5) dataset iris)))

```

The function `make-knn-classifier` returns the classification function 

itself, this conforms to the `classifier/c` contract from the `rml/classify` 

module.

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