https://github.com/komax/spanningtree-crossingnumber

Spanning Tree with Low Crossing Number
https://github.com/komax/spanningtree-crossingnumber

Last synced: 5 days ago
JSON representation

Spanning Tree with Low Crossing Number

• Host: GitHub
• URL: https://github.com/komax/spanningtree-crossingnumber
• Owner: komax
• License: mit
• Created: 2012-10-31T12:57:29.000Z (about 12 years ago)
• Default Branch: master
• Last Pushed: 2014-01-26T14:30:51.000Z (almost 11 years ago)
• Last Synced: 2024-11-08T09:51:13.005Z (about 2 months ago)
• Language: Python
• Size: 22.9 MB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: MIT-LICENSE

Awesome Lists containing this project

README

        
# Spanning Tree with Low Crossing Number

This Repository implements experiments for finding a [Spanning Tree with Low Crossing Number](

http://arxiv.org/abs/cs/0310034)

- The input is: set of points P (n=|P|) and optional a set of lines L

- An algorithm for this problem outputs: set of edges F forming a spanning tree for P with crossing number t

- t is the maximum crossing number of any line in L

This project is released under the MIT license.

## Installation/Prerequisites

* [Python 2.7](http://www.python.org/)

* [NumPy](http://www.numpy.org/) for efficient computation

* [matplotlib](http://matplotlib.org/) for plotting

* [Gurobi](http://www.gurobi.com/) solving LPs

1. Install the above libraries as they are not preinstalled

        $ sudo apt-get install python-numpy python-matplotlib

2. Check if the current Gurobi license is activated. A correct prompt looks

   like this

        $ gurobi.sh 

        Python 2.7.2 (default, Nov 21 2011, 12:59:35) 

        [GCC 4.2.4 (Ubuntu 4.2.4-1ubuntu4)] on linux2

        Type "help", "copyright", "credits" or "license" for more information.

        Gurobi Interactive Shell (linux64), Version 5.1.0

        Copyright (c) 2013, Gurobi Optimization, Inc.

        Type "help()" for help

    

        gurobi> 

3. Clone the repository

        $ git clone https://github.com/komax/spanningtree-crossingnumber

## Types of Experiments

Two command line tools were developed:

1. Executation of a single experiment

2. Generation of csv files

An experiments consists of these parameters

* generation of uniform or grid data

* reading data from a file

* controlling sampling of lines

* selecting a solver

* specification of the output

The outputs for an single experiment are

* Standard Output in form of text information

* dumping the graph as png file

* open the GUI from matplotlib to browse the spanning tree

The csv tool dumps only the text information (crossing number, size of the

points, number of lines, ...)

## Usage

An experiment has the following flags:

    $ ./experiment.py -h

    usage: spanning_tree_with_low_crossing.py [-h]

                                              [-s {hp_lp,fekete_lp,cc_lp,mult_weight,opt,all}]

                                              [-d DIMENSIONS] [-n NUMBER]

                                              [-g {uniform,grid}] [-i INPUT]

                                              [-l {all,stabbing,random}] [-m MEAN]

                                              [-p] [-r] [-v]

    run an parameterized experiment to compute a spanning tree with low crossing

    number

    optional arguments:

      -h, --help            show this help message and exit

      -s {hp_lp,fekete_lp,cc_lp,mult_weight,opt,all}, --solver {hp_lp,fekete_lp,cc_lp,mult_weight,opt,all}

                            choose an algorithm computing a feasible solution

      -d DIMENSIONS, --dimensions DIMENSIONS

                            number of dimensions of point set

      -n NUMBER, --number NUMBER

                            quantify how many points you want

      -g {uniform,grid}, --generate {uniform,grid}

                            how should the point set be sampled

      -i INPUT, --input INPUT

                            specify a input file

      -l {all,stabbing,random}, --linesampling {all,stabbing,random}

                            structure of the line set

      -m MEAN, --mean MEAN  run experiment m times and returns averaged results

      -p, --plot            plots the computed solution into a widget

      -r, --record          record computed solution into a png

      -v, --verbose         adds verbose outputs to STDOUT

An example usage is

    $  ./experiment.py -n 16 -g grid -l stabbing -s mult_weight -r -v

    Start generating graph...

    Graph has been created.

    Sampling of lines started...

    Sampling of lines finished.

    Start now computing a spanning tree...

    Computing a spanning tree finished.

    CPU time (in sec) 2.64830684662

    crossing number=5

    iterations=15

    number of points=16

    number of lines=120

    all crossings=330

    average crossing number=2.75

    Start now plotting...

    Closed plot.

The flags the flags for generating a csv file are almost the same, except the

specification for range 4..20 and stepping by `--increment 2`:

    $ ./csv_experiment.py -f 4 -t 20 --increment 2

``-c`` generates a human readable header prepending the csv file. Use this flag

for understanding the file format or consult the generated results in the

directory `results`