
# Guided Combinatorial Search

Implementation of emprical evalution as reported in the paper.

# Requirements

- Anaconda (available at https://www.anaconda.com/)

Once Anaconda is installed, to set up and activate the anaconda environment, use the included environment.yml file with Anaconda. This will create a working environment called GuidedSearch.

    >>> conda env create -f environment.yml
    >>> conda activate GuidedSearch


# Running experiments

Video Summarization

To reproduce the results related to video summarization, open Video Summarization.ipynb with Jupyter Notebook and run each cell.

Maximum Cut

To reproduce the results for the Max-Cut problem, run the max_cut.py file. Here are the commands:

For ER graphs:

    >>> python max_cut.py --model ER --n 10000 --p 0.001

For the BA graphs:

    >>> python max_cut.py --model BA --n 10000 --m 2

For Watts_Strogatz graphs:

    >>> python max_cut.py --model Watts_Strogatz --n 10000 --k 10 --p 0.001

To generate the plots, please use the notebook Max_Cut Plot generation.ipynb to create the plots.

# Additional details on Jupyter Notebook

With your environment activated, launch Jupyter Notebook by typing:
    >>> jupyter notebook

A browser window will open showing the Jupyter Notebook interface. Navigate to the directory where your .ipynb files are located.


