machine_learning Directory Reference

Directory dependency graph for machine_learning:

Directories
	envelope
	keras
	pytorch

Files
	createData.C
	Plot the variables.
	PyTorch_Generate_CNN_Model.py
	RBatchGenerator_filters_vectors.py
	RBatchGenerator_NumPy.py
	Example of getting batches of events from a ROOT dataset as Python generators of numpy arrays.
	RBatchGenerator_PyTorch.py
	Example of getting batches of events from a ROOT dataset into a basic PyTorch workflow.
	RBatchGenerator_TensorFlow.py
	Example of getting batches of events from a ROOT dataset into a basic TensorFlow workflow.
	tmva001_RTensor.C
	This tutorial illustrates the basic features of the RTensor class, RTensor is a std::vector-like container with additional shape information.
	tmva002_RDataFrameAsTensor.C
	This tutorial shows how the content of an RDataFrame can be converted to an RTensor object.
	tmva003_RReader.C
	This tutorial shows how to apply with the modern interfaces models saved in TMVA XML files.
	tmva004_RStandardScaler.C
	This tutorial illustrates the usage of the standard scaler as preprocessing method.
	tmva100_DataPreparation.py
	This tutorial illustrates how to prepare ROOT datasets to be nicely readable by most machine learning methods.
	tmva101_Training.py
	This tutorial show how you can train a machine learning model with any package reading the training data directly from ROOT files.
	tmva102_Testing.py
	This tutorial illustrates how you can test a trained BDT model using the fast tree inference engine offered by TMVA and external tools such as scikit-learn.
	tmva103_Application.C
	This tutorial illustrates how you can conveniently apply BDTs in C++ using the fast tree inference engine offered by TMVA.
	TMVA_CNN_Classification.C
	TMVA Classification Example Using a Convolutional Neural Network
	TMVA_CNN_Classification.py
	TMVA Classification Example Using a Convolutional Neural Network
	TMVA_Higgs_Classification.C
	Classification example of TMVA based on public Higgs UCI dataset
	TMVA_Higgs_Classification.py
	Classification example of TMVA based on public Higgs UCI dataset
	TMVA_RNN_Classification.C
	TMVA Classification Example Using a Recurrent Neural Network
	TMVA_RNN_Classification.py
	TMVA Classification Example Using a Recurrent Neural Network
	TMVA_SOFIE_GNN.py
	TMVA_SOFIE_GNN_Application.C
	TMVA_SOFIE_GNN_Parser.py
	TMVA_SOFIE_Inference.py
	This macro provides an example of using a trained model with Keras and make inference using SOFIE directly from Numpy This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before this one.
	TMVA_SOFIE_Keras.C
	This macro provides a simple example for the parsing of Keras .h5 file into RModel object and further generating the .hxx header files for inference.
	TMVA_SOFIE_Keras_HiggsModel.C
	This macro run the SOFIE parser on the Keras model obtaining running TMVA_Higgs_Classification.C You need to run that macro before this one
	TMVA_SOFIE_Models.py
	Example of inference with SOFIE using a set of models trained with Keras.
	TMVA_SOFIE_ONNX.C
	This macro provides a simple example for the parsing of ONNX files into RModel object and further generating the .hxx header files for inference.
	TMVA_SOFIE_PyTorch.C
	This macro provides a simple example for the parsing of PyTorch .pt file into RModel object and further generating the .hxx header files for inference.
	TMVA_SOFIE_RDataFrame.C
	This macro provides an example of using a trained model with Keras and make inference using SOFIE and RDataFrame This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before to generate the trained Keras model Then you need to run the macro TMVA_SOFIE_Keras_HiggsModel.C to generate the corresponding header file using SOFIE.
	TMVA_SOFIE_RDataFrame.py
	Example of inference with SOFIE and RDataFrame, of a model trained with Keras.
	TMVA_SOFIE_RDataFrame_JIT.C
	This macro provides an example of using a trained model with Keras and make inference using SOFIE and RDataFrame This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before this one.
	TMVA_SOFIE_RSofieReader.C
	This macro provides an example of using a trained model with Keras and make inference using SOFIE with the RSofieReader class This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before to generate the trained Keras model
	TMVAClassification.C
	This macro provides examples for the training and testing of the TMVA classifiers.
	TMVAClassificationApplication.C
	This macro provides a simple example on how to use the trained classifiers within an analysis module
	TMVAClassificationCategory.C
	This macro provides examples for the training and testing of the TMVA classifiers in categorisation mode.
	TMVAClassificationCategoryApplication.C
	This macro provides a simple example on how to use the trained classifiers (with categories) within an analysis module
	TMVACrossValidation.C
	This macro provides an example of how to use TMVA for k-folds cross evaluation.
	TMVACrossValidationApplication.C
	This macro provides an example of how to use TMVA for k-folds cross evaluation in application.
	TMVACrossValidationRegression.C
	This macro provides an example of how to use TMVA for k-folds cross evaluation.
	TMVAGAexample.C
	This executable gives an example of a very simple use of the genetic algorithm of TMVA
	TMVAGAexample2.C
	This executable gives an example of a very simple use of the genetic algorithm of TMVA.
	TMVAMinimalClassification.C
	Minimal self-contained example for setting up TMVA with binary classification.
	TMVAMulticlass.C
	This macro provides a simple example for the training and testing of the TMVA multiclass classification
	TMVAMulticlassApplication.C
	This macro provides a simple example on how to use the trained multiclass classifiers within an analysis module
	TMVAMultipleBackgroundExample.C
	This example shows the training of signal with three different backgrounds Then in the application a tree is created with all signal and background events where the true class ID and the three classifier outputs are added finally with the application tree, the significance is maximized with the help of the TMVA genetic algorithm.
	TMVARegression.C
	This macro provides examples for the training and testing of the TMVA classifiers.
	TMVARegressionApplication.C
	This macro provides a simple example on how to use the trained regression MVAs within an analysis module