Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mljs/knn
A k-nearest neighboor classifier algorithm.
https://github.com/mljs/knn
Last synced: about 2 months ago
JSON representation
A k-nearest neighboor classifier algorithm.
- Host: GitHub
- URL: https://github.com/mljs/knn
- Owner: mljs
- License: mit
- Created: 2015-07-12T08:36:34.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-02-13T14:56:38.000Z (over 1 year ago)
- Last Synced: 2024-07-13T04:46:32.095Z (2 months ago)
- Language: JavaScript
- Size: 1.24 MB
- Stars: 135
- Watchers: 11
- Forks: 28
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# knn
[![NPM version][npm-image]][npm-url]
[![npm download][download-image]][download-url]
A General purpose k-nearest neighbor classifier algorithm based on the k-d tree Javascript library develop by Ubilabs:
- [k-d trees](https://github.com/ubilabs/kd-tree-javascript)
## Installation
`$ npm i ml-knn`
## API
### new KNN(dataset, labels[, options])
Instantiates the KNN algorithm.
**Arguments**:
- `dataset` - A matrix (2D array) of the dataset.
- `labels` - An array of labels (one for each sample in the dataset).
- `options` - Object with the options for the algorithm.
**Options**:
- `k` - number of nearest neighbors (Default: number of labels + 1).
- `distance` - distance function for the algorithm (Default: euclidean distance).
**Example**:
```js
var train_dataset = [
[0, 0, 0],
[0, 1, 1],
[1, 1, 0],
[2, 2, 2],
[1, 2, 2],
[2, 1, 2],
];
var train_labels = [0, 0, 0, 1, 1, 1];
var knn = new KNN(train_dataset, train_labels, { k: 2 }); // consider 2 nearest neighbors
```
### predict(newDataset)
Predict the values of the dataset.
**Arguments**:
- `newDataset` - A matrix that contains the dataset.
**Example**:
```js
var test_dataset = [
[0.9, 0.9, 0.9],
[1.1, 1.1, 1.1],
[1.1, 1.1, 1.2],
[1.2, 1.2, 1.2],
];
var ans = knn.predict(test_dataset);
console.log(ans);
// classification result:
// ans = [ 0, 0, 1, 1 ]
// Based on the training data, the first two points of the test dataset are classified as "0" (type 0, perhaps),
// the third and fourth data points are classified as "1".
```
### toJSON()
Returns an object representing the model. This function is automatically called if `JSON.stringify(knn)` is used.
Be aware that the serialized model takes about 1.3 times the size of the input dataset (it actually is the dataset in a tree structure). Stringification can fail if the resulting string is too large.
### KNN.load(model[, distance])
Loads a model previously exported by `knn.toJSON()`. If a custom distance function was provided, it must be passed again.
## External links
Check this cool blog post for a detailed example:
https://hackernoon.com/machine-learning-with-javascript-part-2-da994c17d483
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/ml-knn.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-knn
[download-image]: https://img.shields.io/npm/dm/ml-knn.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-knn