C# Class BP_LDA.LDA_Learn

Afficher le fichier Open project: jvking/bp-lda

Méthodes publiques

Méthode	Description
BackPropagation_LDA ( SparseMatrix Xt, SparseMatrix Dt, DNNRun_t DNNRun, paramModel_t paramModel, Grad_t Grad ) : void
ComputeCrossEntropy ( SparseMatrix Xt, DenseMatrix Phi, DenseMatrix theta_top ) : float
ComputeCrossEntropy ( SparseMatrix Xt, DenseMatrix Phi, DenseMatrix theta_pool, int nHidLayerEffective ) : float
ComputeInverseDocumentFrequency ( SparseMatrix InputData ) : DenseColumnVector
ComputeNumberOfErrors ( SparseMatrix Dt, DenseMatrix y ) : int
ComputeNumberOfErrors ( SparseMatrix Dt, SparseMatrix y ) : int
ComputeRegularizedCrossEntropy ( SparseMatrix Xt, DenseMatrix Phi, DenseMatrix theta_top, DenseColumnVector b ) : float
ComputeSupervisedLoss ( SparseMatrix Dt, DenseMatrix y, string OutputType ) : float
ComputeSupervisedLoss ( SparseMatrix Dt, SparseMatrix y, string OutputType ) : float
DumpingFeature_BP_LDA ( SparseMatrix InputData, paramModel_t paramModel, int BatchSize_normal, string FeatureFileName, string DataName ) : void
ExternalEvaluation ( string ExternalEvalToolPath, string ResultFile, string TestLabelFile, int epoch, string EvalDataName ) : void
ForwardActivation_LDA ( SparseMatrix Xt, DNNRun_t DNNRun, paramModel_t paramModel, bool flag_IsTraining ) : void
ModelInit_LDA_Feedforward ( paramModel_t paramModel ) : void
PrecomputeLearningRateSchedule ( int nBatch, int nEpoch, double LearnRateStart, double LearnRateEnd, double Accuracy ) : double[]
PredictingOutput_BP_sLDA ( SparseMatrix TestData, paramModel_t paramModel, int BatchSize_normal, string ScoreFileName ) : void
SMD_Update ( DenseMatrix X, DenseMatrix Grad, DenseRowVector LearningRatePerCol, float eta ) : float
Testing_BP_LDA ( SparseMatrix TestData, paramModel_t paramModel ) : float
Testing_BP_LDA ( SparseMatrix TestData, paramModel_t paramModel, int BatchSize_normal ) : float
Testing_BP_sLDA ( SparseMatrix TestData, SparseMatrix TestLabel, paramModel_t paramModel ) : float
Testing_BP_sLDA ( SparseMatrix TestData, SparseMatrix TestLabel, paramModel_t paramModel, int BatchSize_normal, string ScoreFileName, string EvalDataName ) : float
TrainingBP_LDA ( SparseMatrix TrainData, SparseMatrix TestData, paramModel_t paramModel, paramTrain_t paramTrain, string ModelFile, string ResultFile ) : void
TrainingBP_sLDA ( SparseMatrix TrainData, SparseMatrix TrainLabel, SparseMatrix TestData, SparseMatrix TestLabel, SparseMatrix ValidData, SparseMatrix ValidLabel, paramModel_t paramModel, paramTrain_t paramTrain, string ModelFile, string ResultFile ) : void

Method Details

BackPropagation_LDA() public static méthode

public static BackPropagation_LDA ( SparseMatrix Xt, SparseMatrix Dt, DNNRun_t DNNRun, paramModel_t paramModel, Grad_t Grad ) : void
Xt	LinearAlgebra.SparseMatrix
Dt	LinearAlgebra.SparseMatrix
DNNRun	DNNRun_t
paramModel	paramModel_t
Grad	Grad_t
Résultat	void

ComputeCrossEntropy() public static méthode

public static ComputeCrossEntropy ( SparseMatrix Xt, DenseMatrix Phi, DenseMatrix theta_top ) : float
Xt	LinearAlgebra.SparseMatrix
Phi	LinearAlgebra.DenseMatrix
theta_top	LinearAlgebra.DenseMatrix
Résultat	float

ComputeCrossEntropy() public static méthode

public static ComputeCrossEntropy ( SparseMatrix Xt, DenseMatrix Phi, DenseMatrix theta_pool, int nHidLayerEffective ) : float
Xt	LinearAlgebra.SparseMatrix
Phi	LinearAlgebra.DenseMatrix
theta_pool	LinearAlgebra.DenseMatrix
nHidLayerEffective	int
Résultat	float

ComputeInverseDocumentFrequency() public static méthode

public static ComputeInverseDocumentFrequency ( SparseMatrix InputData ) : DenseColumnVector
InputData	LinearAlgebra.SparseMatrix
Résultat	LinearAlgebra.DenseColumnVector

ComputeNumberOfErrors() public static méthode

public static ComputeNumberOfErrors ( SparseMatrix Dt, DenseMatrix y ) : int
Dt	LinearAlgebra.SparseMatrix
y	LinearAlgebra.DenseMatrix
Résultat	int

ComputeNumberOfErrors() public static méthode

public static ComputeNumberOfErrors ( SparseMatrix Dt, SparseMatrix y ) : int
Dt	LinearAlgebra.SparseMatrix
y	LinearAlgebra.SparseMatrix
Résultat	int

ComputeRegularizedCrossEntropy() public static méthode

public static ComputeRegularizedCrossEntropy ( SparseMatrix Xt, DenseMatrix Phi, DenseMatrix theta_top, DenseColumnVector b ) : float
Xt	LinearAlgebra.SparseMatrix
Phi	LinearAlgebra.DenseMatrix
theta_top	LinearAlgebra.DenseMatrix
b	LinearAlgebra.DenseColumnVector
Résultat	float

ComputeSupervisedLoss() public static méthode

public static ComputeSupervisedLoss ( SparseMatrix Dt, DenseMatrix y, string OutputType ) : float
Dt	LinearAlgebra.SparseMatrix
y	LinearAlgebra.DenseMatrix
OutputType	string
Résultat	float

ComputeSupervisedLoss() public static méthode

public static ComputeSupervisedLoss ( SparseMatrix Dt, SparseMatrix y, string OutputType ) : float
Dt	LinearAlgebra.SparseMatrix
y	LinearAlgebra.SparseMatrix
OutputType	string
Résultat	float

DumpingFeature_BP_LDA() public static méthode

public static DumpingFeature_BP_LDA ( SparseMatrix InputData, paramModel_t paramModel, int BatchSize_normal, string FeatureFileName, string DataName ) : void
InputData	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
BatchSize_normal	int
FeatureFileName	string
DataName	string
Résultat	void

ExternalEvaluation() public static méthode

public static ExternalEvaluation ( string ExternalEvalToolPath, string ResultFile, string TestLabelFile, int epoch, string EvalDataName ) : void
ExternalEvalToolPath	string
ResultFile	string
TestLabelFile	string
epoch	int
EvalDataName	string
Résultat	void

ForwardActivation_LDA() public static méthode

public static ForwardActivation_LDA ( SparseMatrix Xt, DNNRun_t DNNRun, paramModel_t paramModel, bool flag_IsTraining ) : void
Xt	LinearAlgebra.SparseMatrix
DNNRun	DNNRun_t
paramModel	paramModel_t
flag_IsTraining	bool
Résultat	void

ModelInit_LDA_Feedforward() public static méthode

public static ModelInit_LDA_Feedforward ( paramModel_t paramModel ) : void
paramModel	paramModel_t
Résultat	void

PrecomputeLearningRateSchedule() public static méthode

public static PrecomputeLearningRateSchedule ( int nBatch, int nEpoch, double LearnRateStart, double LearnRateEnd, double Accuracy ) : double[]
nBatch	int
nEpoch	int
LearnRateStart	double
LearnRateEnd	double
Accuracy	double
Résultat	double[]

PredictingOutput_BP_sLDA() public static méthode

public static PredictingOutput_BP_sLDA ( SparseMatrix TestData, paramModel_t paramModel, int BatchSize_normal, string ScoreFileName ) : void
TestData	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
BatchSize_normal	int
ScoreFileName	string
Résultat	void

SMD_Update() public static méthode

public static SMD_Update ( DenseMatrix X, DenseMatrix Grad, DenseRowVector LearningRatePerCol, float eta ) : float
X	LinearAlgebra.DenseMatrix
Grad	LinearAlgebra.DenseMatrix
LearningRatePerCol	LinearAlgebra.DenseRowVector
eta	float
Résultat	float

Testing_BP_LDA() public static méthode

public static Testing_BP_LDA ( SparseMatrix TestData, paramModel_t paramModel ) : float
TestData	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
Résultat	float

Testing_BP_LDA() public static méthode

public static Testing_BP_LDA ( SparseMatrix TestData, paramModel_t paramModel, int BatchSize_normal ) : float
TestData	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
BatchSize_normal	int
Résultat	float

Testing_BP_sLDA() public static méthode

public static Testing_BP_sLDA ( SparseMatrix TestData, SparseMatrix TestLabel, paramModel_t paramModel ) : float
TestData	LinearAlgebra.SparseMatrix
TestLabel	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
Résultat	float

Testing_BP_sLDA() public static méthode

public static Testing_BP_sLDA ( SparseMatrix TestData, SparseMatrix TestLabel, paramModel_t paramModel, int BatchSize_normal, string ScoreFileName, string EvalDataName ) : float
TestData	LinearAlgebra.SparseMatrix
TestLabel	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
BatchSize_normal	int
ScoreFileName	string
EvalDataName	string
Résultat	float

TrainingBP_LDA() public static méthode

public static TrainingBP_LDA ( SparseMatrix TrainData, SparseMatrix TestData, paramModel_t paramModel, paramTrain_t paramTrain, string ModelFile, string ResultFile ) : void
TrainData	LinearAlgebra.SparseMatrix
TestData	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
paramTrain	paramTrain_t
ModelFile	string
ResultFile	string
Résultat	void

TrainingBP_sLDA() public static méthode

public static TrainingBP_sLDA ( SparseMatrix TrainData, SparseMatrix TrainLabel, SparseMatrix TestData, SparseMatrix TestLabel, SparseMatrix ValidData, SparseMatrix ValidLabel, paramModel_t paramModel, paramTrain_t paramTrain, string ModelFile, string ResultFile ) : void
TrainData	LinearAlgebra.SparseMatrix
TrainLabel	LinearAlgebra.SparseMatrix
TestData	LinearAlgebra.SparseMatrix
TestLabel	LinearAlgebra.SparseMatrix
ValidData	LinearAlgebra.SparseMatrix
ValidLabel	LinearAlgebra.SparseMatrix
paramModel	paramModel_t
paramTrain	paramTrain_t
ModelFile	string
ResultFile	string
Résultat	void