SingularityNET Unsupervised Language Learning project
- Anaconda 3 Python 3.6
- numpy, pandas, scipy, scikit-learn, cython
- SparseSVD Anaconda package
- Jupyter notebook
Download source code
Fork https://github.com/singnet/language-learning.git to your_repo
$ git clone https://github.com/your_repo/language-learning.git
Anaconda virtual environment
We use Ubuntu 16.04 LTS and
miniconda3version of Anaconda. Please check Anaconda guides
Create virtual environment
$ cd ~/language-learning $ conda env create -f conda-env-ull.yml
ull environment includes necessary packages, matplotlib and Jupyter notebook.
You can add packages and update environment at your own risk.
conda-env-ull-cli.yml-- simplified for CLI: no Jupyter notebook, matplotlib.
conda-env-ull-dev.yml-- development environment with extended package set.
Update with new environment file from Github repository recommended:
$ cd ~/language-learning $ git pull $ conda env update -n ull -f conda-env-ull.yml --prune
--prune key would force remove packages not specified in the
If you have added come packages to the environment, you would rather let them prune and add after the update. Otherwise vwrsion conflicts might occur.
You might need to reinstall Grammar Tester after environment update.
language-learning directory run:
$ source activate ull $ pip install .
If for some reason you are not using virtual environment or using Python 2.x along with Python 3.x make sure you
$ pip3 install .
opencog-ull package will be installed to your virtual environment.
Command line scripts from
src/cli-scripts are copied to
/bin subdirectory in your virtual environment.
To uninstall the package type:
$ pip uninstall opencog-ull
Running command line scripts
Command line scripts (which are located in
src/cli-scripts) can be run from any location. In activated virtual
environment type the name of the script you need to run.
Calling library functions from within your code
If you are going to use grammar tester from within your own code see
src/samples for use cases.
Running on a local machine (with opencog-ull istalled)
$ cd ~/language-learning $ source activate ull $ jupyter notebook
Check sample notebooks in /notebooks directory.
Runing Jupyter notebooks on a server
$ ssh -L 8000:localhost:8888 email@example.com $ screen sh-4.3$ cd language-learning sh-4.3$ source activate ull (ull) sh-4.3$ jupyter notebook --no-browser --port=8888 #... [...NotebookApp] The Jupyter Notebook is running at: [...NotebookApp] http://localhost:8888/?token=(copy_this_token)