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

• k-d trees

$ npm i ml-knn

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 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 the values of the dataset.

Arguments:

• newDataset - A matrix that contains the dataset.

Example:

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".

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.

Loads a model previously exported by knn.toJSON(). If a custom distance function was provided, it must be passed again.

Check this cool blog post for a detailed example: https://hackernoon.com/machine-learning-with-javascript-part-2-da994c17d483

MIT