Table of Contents
The NormDat_test.txt and NormDat_train.txt files contain 30.000 randomized and normalized examples, each having the first column with the true label and 23 particle physics features.
This is used by a ML class in Bari for testing different Machine Learning algorithms and their performance
Darwin MacBook-Pro.local 21.4.0 Darwin Kernel Version 21.4.0: Fri Mar 18 00:47:26 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T8101 arm64
- Brunella D'Anzi (University and INFN Bari)
- Francesco Sivo (University and INFN Bari)
$ pip install numpy
$ pip install sklearn
$ pip install matplotlib
$ pip install pandasIn the ML_COURSEBARI\ directory of this repository:
$ python ml_exercise.pyThe command will produce .png files:
- ROC curves
ROC_results.png - Loss function vs Trainsize
numbertrainingsamples_loss_mse.png - MLP score vs Trainsize
numbertrainingsamples_MLP_score.png
Examples of the plot produced are stored in the Plots_NNtraining\ directory for the usage of a Multi Layer Perceptron (MLP) and a Boosted Decision Tree (BDT) implemented by using scikit-learn package.
$ python NN_backpropagation_bdanzi.pyThis python code has been written to implement the backpropagation process in Neural Network training not using already available open-source packages.
The command will produce the .png file:
- Loss function vs Epochs This code represents a home-made (no usage of numpy, scikit-learn,keras) Neural Network having one hidden layer.