Reproducible Activities Recognition From Smartphone Sensors

This package contains code to run random forest or svm classification on a dataset created for the study of activities recognition from smartphone sensors.

This package performs multiple tasks sequentially:

1. Download the dataset (if not already available)

2. Train a model (random forest or svm) to recognize activities

3. Predict the activities of the testing set

4. Evaluate the performances and save the analysis in the output folder

To run the script using the default configuration just follow the Installation and use one of the following commands.

It will train an svm model using predefined parameters and save the analysis in a results folder.

Installed from Github

(activities) $ python run.py

Installed from PyPI

$ rrgp

Documentation

• Installation
  • Using Github
  • Using PyPI
• User Guide
  • Code installed from Github
  • Package installed from PyPI
  • Results with the SVM model
  • Results with the random forest model
  • Customizing the parameters
  • Adding a new model
  • Using custom Data
• Troubleshooting
• Python API
  • Database
  • Pre-processor
  • Machine Learning Algorithm
  • Analysis

Indices and tables

• Index

• Module Index

• Search Page