Commit 03416c72 authored by Michael Zbyszyński's avatar Michael Zbyszyński

Update README.md

parent bc883960
# Link to [C++ documentation](http://doc.gold.ac.uk/eavi/rapidmix/docs_cpp/annotated.html)
# 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
* * *
## C++ Library
RapidLib is a lightweight library for interactive machine learning, inspired by [Wekinator](http://www.wekinator.org/).
It currently features classification (using kNN), regression (multilayer perceptron), and series classification (using dynamic time warping).
**[Full C++ documentation can be found here](http://doc.gold.ac.uk/eavi/rapidmix/docs_cpp/annotated.html).**
## Javascript
RapidLib has also been transpiled to JavaScript using Emscripten. It can be used in a browser through a script tag.
```javascript
<script src="https://www.doc.gold.ac.uk/eavi/rapidmix/RapidLib.js"></script>
```
It can also be used in Node.js via npm:
```javascript
npm install rapidlib
```
**[Full JavaScript documentation can be found here] (http://doc.gold.ac.uk/eavi/rapidmix/docs_js/)**
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