https://github.com/frgfm/pair-assignment
Optimisation solutions for pair assignment problems
https://github.com/frgfm/pair-assignment
cost-optimization optimisation pairing-functions
Last synced: 8 months ago
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Optimisation solutions for pair assignment problems
- Host: GitHub
- URL: https://github.com/frgfm/pair-assignment
- Owner: frgfm
- License: mit
- Created: 2018-07-08T14:48:39.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-01-27T19:20:36.000Z (over 6 years ago)
- Last Synced: 2024-12-29T17:41:26.457Z (10 months ago)
- Topics: cost-optimization, optimisation, pairing-functions
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pair assignment
This repository was made for pair assignments in constrained environments.
Input is mostly considered as a symmetric cost matrix of shape N x N where N is the number of agents.
## Installation
This module was developed using only Python and numpy
```bash
git clone https://github.com/frgfm/pair-assignment.git
cd pair-assignment
pip install requirements.txt
```
## Usage
If you prefer running the simulation directly without entering a shell:
```bash
python main.py
```
If you favor using objects in Python, you can use the classes and functions from the game.py file
```python
# Generate random cost matrix
nb_agents = 13
cost_matrix = np.random.rand(nb_agents, nb_agents)
for row_idx in range(cost_matrix.shape[0]):
for col_idx in range(row_idx, cost_matrix.shape[1]):
if row_idx == col_idx:
cost_matrix[row_idx, col_idx] = 0
else:
cost_matrix[col_idx, row_idx] = cost_matrix[row_idx, col_idx]
# Brute Force
start_time = datetime.now()
bf_assignment, bf_score = bf_assign(cost_matrix)
print('Brute force score: %s (computed in %s)' % (np.sum([cost_matrix[couple] for couple in bf_assignment]), datetime.now() - start_time))
# Approx method
start_time = datetime.now()
ap_assignment = approx_assign(cost_matrix)
print('Approx method score: %s (computed in %s)' % (np.sum([cost_matrix[couple] for couple in ap_assignment]), datetime.now() - start_time))
```
Either way, it should simulate a game with an output similar to:
```bash
Brute force score: 1.2715180930424301 (computed in 0:00:01.583963)
Approx method score: 1.9992776643053922 (computed in 0:00:00)
```
## TODO
- [x] Build a baseline exact assignment method (brute force most likely)
- [x] Draft a first approximation method to boost computation speed
- [ ] Explore other solutions