This package has been deprecated

Author message:

Please use the rapidlib package instead.

rapidlib-gs

Link to C++ documentation

JavaScript documentation:

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prepTrainingSet(trainingSet)

Utility function to convert js objects into something emscripten likes

Parameters

trainingSet: Object, JS Object representing a training set

Returns: Module.TrainingSet

Class: Regression

Creates a set of regression objects using the constructor from emscripten

Module.RegressionCpp: function , constructor from emscripten

Regression.train(trainingSet)

Trains the models using the input. Starts training from the current state of the model: randomized or trained.

Parameters

trainingSet: Object, An array of training examples

Returns: Boolean, true indicates successful training

Regression.initialize()

Returns the model set to it's initial configuration.

Returns: Boolean, true indicates successful initialization

Regression.process(input)

Runs feed-forward regression on input

Parameters

input: Array, An array of features to be processed. Non-arrays are converted.

Returns: Array, output - One number for each model in the set

Class: Classification

Creates a set of classification objects using the constructor from emscripten

Module.ClassificationCpp: function , constructor from emscripten

Classification.train(trainingSet)

Trains the models using the input. Clears previous training set.

Parameters

trainingSet: Object, An array of training examples.

Returns: Boolean, true indicates successful training

Classification.initialize()

Returns the model set to it's initial configuration.

Returns: Boolean, true indicates successful initialization

Classification.process(input)

Does classifications on an input vector.

Parameters

input: Array, An array of features to be processed. Non-arrays are converted.

Returns: Array, output - One number for each model in the set

Class: ModelSet

Creates a set of machine learning objects using constructors from emscripten. Could be any mix of regression and classification.

ModelSet.loadJSON(url)

Trains the models using the input. Clears previous training set.

Parameters

url: string, JSON loaded from a model set description document.

Returns: Boolean, true indicates successful training

ModelSet.addNNModel(model)

Add a NN model to a modelSet. //TODO: this doesn't need it's own function

Parameters

model: , Add a NN model to a modelSet. //TODO: this doesn't need it's own function

ModelSet.addkNNModel(model)

Add a kNN model to a modelSet. //TODO: this doesn't need it's own function

Parameters

model: , Add a kNN model to a modelSet. //TODO: this doesn't need it's own function

ModelSet.process(input)

Applies regression and classification algorithms to an input vector.

Parameters

input: Array, An array of features to be processed.

Returns: Array, output - One number for each model in the set

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