Helion is a data-driven framework that models the regularities of user-driven home automation, generates natural home automation scenarios, and provides stakeholders with tools to use the scenarios and obtain actionable outcomes.
NOTE: We are currently working on deanonymizing the data. We will pubilish the code and the dataset by the conference date i.e., May 18, 2020.
1. Download necessary files:
- MITLM - MIT Language Modeling Toolkit
- Python Daemon 1.5.5 - Library to implement a well-behaved Unix daemon process.
- Brain Files - The language model server script which reads/writes JSON documents to named pipes.
2. Untar MITLM and Python Daemon:
Execute the following command to un-tar the the MITLM and python daemon packages:
$ tar-xzf <package_name.tar.gz>
3. Add proper directories to your path:
Execute the Following commands or add them to your .bash_profile to set up your $PATH. Be sure to replace the paths with local paths on your machine
export PYTHONPATH=$PYTHONPATH:/Path/to/Python/Daemon/python-daemon-1.5.5/ export PYTHONPATH=$PYTHONPATH:/Path/to/Python/Daemon/python-daemon-1.5.5/daemon/ export PYTHONPATH=$PYTHONPATH:/Path/to/Python/Daemon/python-daemon-1.5.5/daemon/version/ export PATH=$PATH:/Path/to/MITLM/mitlm-0.4.1/ export PATH=$PATH:/Path/to/Brain/Files/
4. Build MITLM:
Navigate to the mitlm-0.4.1/ directory and execute the following commands:
$ ./configure $ make
After both of these commands have been executed, you should be able to see the estimate-ngram, evaluate-ngram, and interpolate-ngram executables in the mitlm-0.4.1/ directory.
5. Instantiate the Brain:
Navigate to the folder where your training and vocabulary files are located. Then instantiate the brain by running:
$ braind data/helion.train data/helion.vocab
For more detailed instructions on how to interact and instantiate the Brain, see the README.