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https://github.com/kortirso/elixir_learn_kit
Elixir library for machine learning
https://github.com/kortirso/elixir_learn_kit
classification elixir elixir-library gaussian-naive-bayes knn-classifier linear-regression machine-learning phoenix prediction
Last synced: about 1 month ago
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Elixir library for machine learning
- Host: GitHub
- URL: https://github.com/kortirso/elixir_learn_kit
- Owner: kortirso
- Created: 2018-09-29T17:51:46.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-01-09T08:14:21.000Z (almost 6 years ago)
- Last Synced: 2024-10-03T09:49:35.245Z (about 1 month ago)
- Topics: classification, elixir, elixir-library, gaussian-naive-bayes, knn-classifier, linear-regression, machine-learning, phoenix, prediction
- Language: Elixir
- Size: 67.4 KB
- Stars: 32
- Watchers: 2
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
README
# LearnKit
Elixir package for machine learning
Available preprocessing methods:
- Normalization
Available algorithms for prediction:
- Linear Regression
- Polynomial Regression
Available algorithms for classification:
- K-Nearest Neighbours
- Gaussian Naive Bayes
## Installation
If [available in Hex](https://hex.pm/docs/publish), the package can be installed
by adding `learn_kit` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[
{:learn_kit, "~> 0.1.6"}
]
end
```
### Normalization
Normalize data set with minimax normalization
```elixir
alias LearnKit.Preprocessing
Preprocessing.normalize([[1, 2], [3, 4], [5, 6]])
```
Or normalize data set with selected type
```elixir
Preprocessing.normalize([[1, 2], [3, 4], [5, 6]], [type: "z_normalization"])
```
options - array of options
Additionally you can prepare coefficients for normalization
```elixir
Preprocessing.coefficients([[1, 2], [3, 4], [5, 6]], "minimax")
```
type - method of normalization, one of the [minimax|z_normalization], required
And then normalize 1 feature with predefined coefficients
```elixir
Preprocessing.normalize_feature([1, 2], [{1, 5}, {2, 6}], "minimax")
```
type - method of normalization, one of the [minimax|z_normalization], required
### Linear Regression
Initialize predictor with data:
```elixir
alias LearnKit.Regression.Linear
predictor = Linear.new([1, 2, 3, 4], [3, 6, 10, 15])
```
Fit data set with least squares method:
```elixir
predictor = predictor |> Linear.fit
```
Fit data set with gradient descent method:
```elixir
predictor = predictor |> Linear.fit([method: "gradient descent"])
```
Predict using the linear model:
```elixir
predictor |> Linear.predict([4, 8, 13])
```
samples - array of variables, required
Returns the coefficient of determination R^2 of the prediction:
```elixir
predictor |> Linear.score
```
### K-Nearest Neighbours classification
Initialize classifier with data set consists from labels and features:
```elixir
alias LearnKit.Knn
classifier =
Knn.new
|> Knn.add_train_data({:a1, [-1, -1]})
|> Knn.add_train_data({:a1, [-2, -1]})
|> Knn.add_train_data({:a2, [1, 1]})
```
Predict label for new feature:
```elixir
Knn.classify(classifier, [feature: [-1, -2], k: 3, weight: "distance", normalization: "minimax"])
```
feature - new feature for prediction, required
k - number of nearest neighbors, optional, default - 3
algorithm - algorithm for calculation of distances, one of the [brute], optional, default - "brute"
weight - method of weighted neighbors, one of the [uniform|distance], optional, default - "uniform"
normalization - method of normalization, one of the [none|minimax|z_normalization], optional, default - "none"
### Gaussian Naive Bayes classification
Initialize classifier with data set consists from labels and features:
```elixir
alias LearnKit.NaiveBayes.Gaussian
classifier =
Gaussian.new
|> Gaussian.add_train_data({:a1, [-1, -1]})
|> Gaussian.add_train_data({:a1, [-2, -1]})
|> Gaussian.add_train_data({:a2, [1, 1]})
```
Normalize data set:
```elixir
classifier = classifier |> Gaussian.normalize_train_data("minimax")
```
type - method of normalization, one of the [none|minimax|z_normalization], optional, default - "none"
Fit data set:
```elixir
classifier = classifier |> Gaussian.fit
```
Return probability estimates for the feature:
```elixir
classifier |> Gaussian.predict_proba([1, 2])
```
feature - new feature for prediction, required
Return exact prediction for the feature:
```elixir
classifier |> Gaussian.predict([1, 2])
```
feature - new feature for prediction, required
Returns the mean accuracy on the given test data and labels:
```elixir
classifier |> Gaussian.score
```
## Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/kortirso/elixir_learn_kit.
## License
The package is available as open source under the terms of the [MIT License](http://opensource.org/licenses/MIT).
## Disclaimer
Use this package at your own peril and risk.
## Documentation
Documentation can be generated with [ExDoc](https://github.com/elixir-lang/ex_doc)
and published on [HexDocs](https://hexdocs.pm). Once published, the docs can
be found at [https://hexdocs.pm/learn_kit](https://hexdocs.pm/learn_kit).