GestureRecognitionToolkit  Version: 0.2.5
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
Classes
RBMQuantizer.h File Reference

The SOMQuantizer module quantizes the N-dimensional input vector to a 1-dimensional discrete value. This value will be between [0 K-1], where K is the number of clusters used to create the quantization model. Before you use the SOMQuantizer, you need to train a quantization model. To do this, you select the number of clusters you want your quantizer to have and then give it any training data in the following formats: More...

#include "../../CoreModules/FeatureExtraction.h"
#include "../../CoreAlgorithms/BernoulliRBM/BernoulliRBM.h"
#include "../../DataStructures/TimeSeriesClassificationData.h"
#include "../../DataStructures/ClassificationDataStream.h"
#include "../../DataStructures/UnlabelledData.h"

Go to the source code of this file.

Classes

class  RBMQuantizer
 

Detailed Description

The SOMQuantizer module quantizes the N-dimensional input vector to a 1-dimensional discrete value. This value will be between [0 K-1], where K is the number of clusters used to create the quantization model. Before you use the SOMQuantizer, you need to train a quantization model. To do this, you select the number of clusters you want your quantizer to have and then give it any training data in the following formats:

Author
Nicholas Gillian ngill.nosp@m.ian@.nosp@m.media.nosp@m..mit.nosp@m..edu
Version
1.0

Definition in file RBMQuantizer.h.