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    ml-knn

    3.0.0¬†‚Äʬ†Public¬†‚Äʬ†Published

    knn

    NPM version build status npm download

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

    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

    Install

    npm i ml-knn

    DownloadsWeekly Downloads

    626

    Version

    3.0.0

    License

    MIT

    Unpacked Size

    32.1 kB

    Total Files

    8

    Last publish

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