rapidlib-gs v1.0.1
Link to C++ documentation
JavaScript documentation:
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