https://github.com/felipexw/knn-java-library
Just a simple implementation of K-Nearest Neighbour algorithm.
https://github.com/felipexw/knn-java-library
data-science k-nearest-neighbor-classifier k-nearest-neighbours knn knn-algorithm knn-classification knn-classifier machine-learning supervised-learning supervised-learning-algorithms supervised-learning-classifiers supervised-machine-learning
Last synced: 8 days ago
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Just a simple implementation of K-Nearest Neighbour algorithm.
- Host: GitHub
- URL: https://github.com/felipexw/knn-java-library
- Owner: felipexw
- Created: 2016-08-29T14:11:40.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2016-09-08T13:27:55.000Z (about 9 years ago)
- Last Synced: 2024-04-10T14:50:54.487Z (over 1 year ago)
- Topics: data-science, k-nearest-neighbor-classifier, k-nearest-neighbours, knn, knn-algorithm, knn-classification, knn-classifier, machine-learning, supervised-learning, supervised-learning-algorithms, supervised-learning-classifiers, supervised-machine-learning
- Language: Java
- Homepage:
- Size: 348 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# A simple K-Nearest Neighbor (KNN) Java library #
### What is this repository for? ###
* Its a very simple implementation of K-Nearest Neighbor algorithm for Supervide Learning (user labeled data)
* __Version 1.0__
### Features presents ###
* The following similarities metrics are presents:
- Euclidian Distance
- Jaccard Distance
- Pearson Correlation
- Cosine Distance
- Minkowski Distance (to be done)
- Manhattan Distance (to be done)
- Mahalanobis Distance (to be done)
* A naive knn implementation with (or without) k-fold cross-validation.
### How to use ###
* Just clone the project
* Setup your project
* Init a ``SimpleKNNClassifier``
* Inject the ``SimilarityCalculator`` of your choice into ``SimpleKNNClassifier`` instance
* Fit with some labeled data
* Train the classifier (you can choose if you want to train using k-fold cross validation or not)
* Pass some data to ``predict()`` method and see the label predicted