K-Nearest neighbor classifier

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

• k-d trees
  

Methodsnew NaiveBayes()

Constructor that takes no arguments.

Example

var knn = new KNN();
train(trainingSet, predictions)

Train the Naive Bayes model to the given training set, predictions, and some options.

Arguments

• trainingSet - A matrix of the training set.
• trainingLabels - An array of value for each case in the training set.
• options - Object with the options for the training.
  

Options

• k - number of nearest neighbor (Default, number of label + 1).
• distance - distance function for the algorithm, the argument is a function, not an String (by default is euclidean, you can use the functions of this repository distance).
  

Example

var trainingSet = [[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];knn.train(trainingSet, predictions);
predict(dataset)

Predict the values of the dataset.

Arguments

• dataset - A matrix that contains the dataset.
  

Example

var dataset = [[0, 0, 0],               [2, 2, 2]];var ans = knn.predict(dataset);
export()

Exports the actual K-Nearest Neighbor model to an Javascript Object.

load(model)

Returns a new K-Nearest Neighbor Classifier with the given model.

Arguments

• model - Javascript Object generated from export() function.
  

Authors

• Jefferson Hernandez
  

License

MIT