knn

A General purpose k-nearest neighbor classifier algorithm based on the k-d tree Javascript library develop by Ubilabs:

• k-d trees

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:

var dataset = [
  [0, 0, 0],
  [0, 1, 1],
  [1, 1, 0],
  [2, 2, 2],
  [1, 2, 2],
  [2, 1, 2]
];
var predictions = [0, 0, 0, 1, 1, 1];
var knn = new KNN(dataset, predictions);

predict(newDataset)

Predict the values of the dataset.

Arguments:

• newDataset - A matrix that contains the dataset.

Example:

var dataset = [[0, 0, 0], [2, 2, 2]];

var ans = knn.predict(dataset);

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