pylocating¶

docs
tests
package

Detect the accurate object position disturbed by noise using Artificial Intelligence algorithms.

• Free software: GPLv2 license

pip install pylocating

tox

Federated Particles¶

Two separated environments contain a different number of particles. All the particles are PSOParticle (they follow a standard PSO model). The initial position of particles are around the beacons.

/path/to/pylocating$ scripts/bestfitnessgraph.sh federated_particles 20 10

arguments:

• 20: the first environment contains 20 particles
• 10: the second environment contains 10 particles

FollowBest Particles¶

Two connected environments contain a different number of particles. The first environment contains GlobalBestPSOParticle particles (the same of PSOParticle, but in this case the best fitness is the best found by all environments instead of the best found inside the environment itself). The second environment contains FollowBestParticle; they are special particles that only search around the globally found best position in that moment.

/path/to/pylocating$ scripts/bestfitnessgraph.sh followbest_particles 20 10

arguments:

• 20: the first environment contains 20 particles
• 10: the second environment contains 10 particles

Start from beacon sphere surface¶

One single environment contains all particles. Them are equally distributed around the beacons on the sphere surface with center the beacon itself and radius the distance measured. 3/4 of all particles are PSOParticle. The rest are FollowBestParticle.

/path/to/pylocating$ scripts/bestfitnessgraph.sh start_from_sphere_surface 16

arguments:

• 16: the environment contains 16 particles.

note: the number of particle should be divisible for 4 (the number of

beacons).

Benchmarks 1 - config 1¶

Evaluate distance error as a function of swarm size:

• error introduced: 3
• inertial weight: 1
• cognition: 2
• social: 2
• number of particles: range [10, 300]
• max particle velocity: 0.5
• interations per particle: 60

There is only one environmnent where all PSOParticle is connected.

The virtual space where the 4 beacons and the point is inserted is defined by:

• center: [1000, 1000, 1000]
• side length: 100

Every time the benchmark is started, their position are chosen randomly inside this cube. The distance error introduced is fixed and moved every time in a different position in the space. The benchmark is executed 100 times:

examples/benchmark_1.config1.sh 100

At the end of execution, you can see /tmp/benchmark_1.config1.jpg file generated.

Benchmarks 1 - config 2¶

Evaluate distance error as a function of social parameter:

• error introduced: 3
• inertial weight: 1
• cognition: 2
• social: range [0, 10]
• number of particles: 100
• max particle velocity: 5
• interations per particle: 60

There is only one environmnent where all PSOParticle is connected.

The virtual space where the 4 beacons and the point is inserted is defined by:

• center: [1000, 1000, 1000]
• side length: 100

Every time the benchmark is started, their position are chosen randomly inside this cube. The distance error introduced is fixed and moved every time in a different position in the space. The benchmark is executed 100 times:

examples/benchmark_1.config2.sh 100

At the end of execution, you can see /tmp/benchmark_1.config2.jpg file generated.

Benchmarks 2 - config 1¶

Evaluate distance error as a function of FollowBestParticle swarm size:

• error introduced: 3
• inertial weight: 1
• cognition: 2
• social: 2
• number of particles: range 80
• number of particles: range [1, 20]
• max particle velocity: 5
• interations per particle: 60

There are two environmnents:

• the first where PSOParticle are inserted
• the second where FollowBestParticle are inserted

The two environmnents are connected together.

The virtual space where the 4 beacons and the point is inserted is defined by:

• center: [1000, 1000, 1000]
• side length: 100

Every time the benchmark is started, their position are chosen randomly inside this cube. The distance error introduced is fixed and moved every time in a different position in the space. The benchmark is executed 100 times:

examples/benchmark_2.config1.sh 100

At the end of execution, you can see /tmp/benchmark_2.config1.jpg file generated.

Bug reports¶

When reporting a bug please include:

• Your operating system name and version.
• Any details about your local setup that might be helpful in troubleshooting.
• Detailed steps to reproduce the bug.

Documentation improvements¶

pylocating could always use more documentation, whether as part of the official pylocating docs, in docstrings, or even on the web in blog posts, articles, and such.

Feature requests and feedback¶

The best way to send feedback is to file an issue at https://github.com/hachreak/pylocating/issues.

If you are proposing a feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible, to make it easier to implement.
• Remember that this is a volunteer-driven project, and that contributions are welcome :)

Development¶

To set up pylocating for local development:

1. Fork pylocating on GitHub.

2. Clone your fork locally:

   git clone git@github.com:your_name_here/pylocating.git
   

3. Create a branch for local development:

   git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

4. When you’re done making changes, run all the checks, doc builder and spell checker with tox one command:

   tox
   

5. Commit your changes and push your branch to GitHub:

   git add .
   git commit -m "Your detailed description of your changes."
   git push origin name-of-your-bugfix-or-feature
   

6. Submit a pull request through the GitHub website.

tox -e envname -- py.test -k test_myfeature

detox

0.1.0 (2015-11-11)¶

• First release on PyPI.