Class BagDimlp

Inheritance Relationships

Base Type

Class Documentation

class BagDimlp : public Dimlp

Implements a bagging ensemble of Dimlp neural networks.

Public Functions

inline float *GetGlobalOut()

Gets the global output of the ensemble.

Returns:

Pointer to the global output array.

void MakeDataSets(DataSet &masterTrain, DataSet &masterClass, int nbPat)

Creates datasets for each Dimlp network in the ensemble using bootstrap sampling.

Parameters:

  • masterTrain – The master training dataset.

  • masterClass – The master class dataset.

  • nbPat – Number of patterns in the dataset.

void TrainAll(DataSet &test, DataSet &testTar, const std::string &genericWeightsFile, const std::string &accuracyFile, int seed = 0)

Trains all Dimlp networks in the ensemble.

Parameters:

  • test – The test dataset.

  • testTar – The test target dataset.

  • genericWeightsFile – Path to the file for saving weights.

  • accuracyFile – Path to the file for saving accuracy.

  • seed – Seed for random number generation.

void DefNetsWithWeights(const std::string &prefix)

Initializes networks with pre-trained weights.

Parameters:

prefix – Prefix for the weight files.

std::shared_ptr<VirtualHyp> MakeGlobalVirt(int nbBins, int nbIn, int multiple)

Creates a global virtual hyperplane for all networks in the ensemble.

Parameters:

  • nbBins – Number of bins.

  • nbIn – Number of input neurons.

  • multiple – Multiplication factor.

Returns:

Shared pointer to the global virtual hyperplane.

virtual void ForwardOneExample1(DataSet &data, int index) override

Performs forward pass using the bagged ensemble of Dimlp networks for a single example.

This method iterates over all the Dimlp networks in the ensemble (VectDimlp), performs a forward pass on each network, and averages their outputs to produce a final global output.

Parameters:

  • data – The dataset.

  • index – Index of the example.

virtual void ForwardOneExample1() override

Performs forward pass using the bagged ensemble of Dimlp networks for a single example.

This method iterates over all the Dimlp networks in the ensemble (VectDimlp), performs a forward pass on each network, and averages their outputs to produce a final global output.

void ComputeAcc(DataSet &data, DataSet &target, float *accuracy, int tW, const std::string &predFile)

Computes the accuracy of the ensemble on the given dataset.

Parameters:

  • data – The dataset.

  • target – The target dataset.

  • accuracy – Pointer to store the computed accuracy.

  • toWrite – Flag to indicate whether to write the predictions to a file.

  • predFile – Path to the file for saving predictions.

BagDimlp(float eta, float mu, float flat, float errParam, float accuracyParam, float deltaErrParam, int discrLevels, int showErrParam, int nbEpochsParam, int nbLayers, std::vector<int> nbNeurons, int nbDimlpNets, const std::string &weightFile, int seed = 0)

Constructs a BagDimlp object.

Parameters:

  • eta – Learning rate.

  • mu – Momentum.

  • flat – Flat spot elimination parameter.

  • errParam – Error threshold parameter.

  • accuracyParam – Accuracy threshold parameter.

  • deltaErrParam – Delta error threshold parameter.

  • discrLevels – Number of discretization levels.

  • showErrParam – Frequency of error display.

  • nbEpochsParam – Number of training epochs.

  • nbLayers – Number of layers.

  • nbNeurons – Vector containing the number of neurons in each layer.

  • nbDimlpNets – Number of Dimlp networks in the ensemble.

  • weightFile – Path to the file for saving weights.

  • seed – Seed for random number generation.

BagDimlp(int discrLevels, int nbLayers, std::vector<int> nbNeurons, int nbDimlpNets, const std::string &weightFile, int seed = 0)

Constructs a BagDimlp object with specified discretization levels, layers, and neurons.

Parameters:

  • discrLevels – Number of discretization levels.

  • nbLayers – Number of layers.

  • nbNeurons – Vector containing the number of neurons in each layer.

  • nbDimlpNets – Number of Dimlp networks in the ensemble.

  • weightFile – Path to the file for saving weights.

  • seed – Seed for random number generation.

virtual void ForwardOneExample1(float *ex)

Forwards one example through the network.

Parameters:

ex – The example to forward.