GestureRecognitionToolkit
Version: 0.1.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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- t -
test() :
GestureRecognitionPipeline
TestInstanceResult() :
GRT::TestInstanceResult
TestResult() :
GRT::TestResult
ThreadPool() :
GRT::ThreadPool
ThresholdCrossingDetector() :
ThresholdCrossingDetector
TimeDomainFeatures() :
TimeDomainFeatures
Timer() :
Timer
timerReached() :
Timer
TimeseriesBuffer() :
TimeseriesBuffer
TimeSeriesClassificationData() :
TimeSeriesClassificationData
TimeSeriesClassificationSampleTrimmer() :
TimeSeriesClassificationSampleTrimmer
TimeSeriesPositionTracker() :
TimeSeriesPositionTracker
toString() :
Util
tql2() :
EigenvalueDecomposition
trackingData() :
RangeTracker
train() :
DecisionStump
,
GestureRecognitionPipeline
,
HMM
,
LDA
,
MLBase
,
RadialBasisFunction
,
WeakClassifier
train_() :
AdaBoost
,
ANBC
,
BAG
,
BernoulliRBM
,
Clusterer
,
ClusterTree
,
DecisionTree
,
DTW
,
FiniteStateMachine
,
GaussianMixtureModels
,
GMM
,
HierarchicalClustering
,
HMM
,
KMeans
,
KMeansFeatures
,
KMeansQuantizer
,
KNN
,
LinearRegression
,
LogisticRegression
,
MinDist
,
MLBase
,
MLP
,
MultidimensionalRegression
,
ParticleClassifier
,
RandomForests
,
RBMQuantizer
,
RegressionTree
,
SelfOrganizingMap
,
Softmax
,
SOMQuantizer
,
SVM
,
SwipeDetector
TrainingDataRecordingTimer() :
GRT::TrainingDataRecordingTimer
TrainingResult() :
GRT::TrainingResult
trainModel() :
KMeans
transpose() :
MatrixFloat
tred2() :
EigenvalueDecomposition
Tree() :
Tree
triggerSearchTimeout() :
ThresholdCrossingDetector
trimTimeSeries() :
TimeSeriesClassificationSampleTrimmer
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