| Name | pykp JSON |
| Version |
1.2.0
JSON |
| download |
| home_page | None |
| Summary | Tooling for sampling and solving instances of the 0-1 Knapsack Problem. |
| upload_time | 2024-11-18 12:32:24 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | MIT |
| keywords |
knapsack
optimisation
sampling
algorithms
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# PyKP
PyKP is a free and open-source library for sampling and solving instances of the knapsack problem. It provides tools to define knapsack instances, solve them eficiently, and analyse computational complexity metrics. You can also use `pykp` to randomly sample knapsack problem instances based on specified distributions.
## Features
- Define knapsack problem instances with custom items, weights, and values.
- Solve knapsack instances using branch-and-bound and other methods.
- Compute optimal and feasible solutions for different knapsack configurations.
- Analyse computational complexity metrics.
- Generate synthetic knapsack instances with custom weight, density, and solution value ranges.
## Installation
PyKP support Python version 3.12 and higher. To install PyKP, run
```
pip install pykp
```
## Usage
### Defining and Solving a Knapsack Problem
To start, define a knapsack problem with a set of items and solve it using the Knapsack class.
```python
import numpy as np
from pykp import Knapsack
from pykp import Item
# Define items for the knapsack
items = np.array([
Item(value=10, weight=5),
Item(value=15, weight=10),
Item(value=7, weight=3)
])
# Initialise a Knapsack instance
capacity = 15
knapsack = Knapsack(items=items, capacity=capacity)
knapsack.solve()
# Display the optimal solution
print("Optimal Solution Value:", knapsack.optimal_nodes[0].value)
```
### Generating Knapsack Instances with Sampler
The `Sampler` class allows you to generate knapsack instances based on specific ranges for item densities (value/weight ratio) and optimal solution values.
```python
from pykp import Sampler
# Initialise a Sampler instance with desired ranges
sampler = Sampler(
num_items=5,
normalised_capacity=0.6,
density_range=(0.5, 1.5),
solution_value_range=(100, 200)
)
# Generate a sampled knapsack instance
sampled_knapsack = sampler.sample()
print("Sampled Knapsack Capacity:", sampled_knapsack.capacity)
```
### Analysing Knapsack Solutions
The package provides methods to analyse the optimal solutions and other feasible arrangements.
```python
# Display a summary of the knapsack solutions
print(sampled_knapsack.summary())
```
## License
This project is licensed under the MIT License.
## Contributing
Contributions are welcome. Please fork the repository and submit a pull request.
## Contact
For questions or feedback, please reach out at hrs.andrabi@gmail.com.
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"description": "# PyKP\n\nPyKP is a free and open-source library for sampling and solving instances of the knapsack problem. It provides tools to define knapsack instances, solve them eficiently, and analyse computational complexity metrics. You can also use `pykp` to randomly sample knapsack problem instances based on specified distributions.\n\n## Features\n\n- Define knapsack problem instances with custom items, weights, and values.\n- Solve knapsack instances using branch-and-bound and other methods.\n- Compute optimal and feasible solutions for different knapsack configurations.\n- Analyse computational complexity metrics.\n- Generate synthetic knapsack instances with custom weight, density, and solution value ranges.\n\n## Installation\n\nPyKP support Python version 3.12 and higher. To install PyKP, run\n\n```\npip install pykp\n```\n\n## Usage\n\n### Defining and Solving a Knapsack Problem\n\nTo start, define a knapsack problem with a set of items and solve it using the Knapsack class.\n\n```python\nimport numpy as np\nfrom pykp import Knapsack\nfrom pykp import Item\n\n# Define items for the knapsack\nitems = np.array([\n Item(value=10, weight=5),\n Item(value=15, weight=10),\n Item(value=7, weight=3)\n])\n\n# Initialise a Knapsack instance\ncapacity = 15\nknapsack = Knapsack(items=items, capacity=capacity)\nknapsack.solve()\n\n# Display the optimal solution\nprint(\"Optimal Solution Value:\", knapsack.optimal_nodes[0].value)\n```\n\n### Generating Knapsack Instances with Sampler\n\nThe `Sampler` class allows you to generate knapsack instances based on specific ranges for item densities (value/weight ratio) and optimal solution values.\n\n```python\nfrom pykp import Sampler\n\n# Initialise a Sampler instance with desired ranges\nsampler = Sampler(\n num_items=5,\n normalised_capacity=0.6,\n density_range=(0.5, 1.5),\n solution_value_range=(100, 200)\n)\n\n# Generate a sampled knapsack instance\nsampled_knapsack = sampler.sample()\nprint(\"Sampled Knapsack Capacity:\", sampled_knapsack.capacity)\n```\n\n### Analysing Knapsack Solutions\n\nThe package provides methods to analyse the optimal solutions and other feasible arrangements.\n\n```python\n# Display a summary of the knapsack solutions\nprint(sampled_knapsack.summary())\n```\n\n## License\n\nThis project is licensed under the MIT License.\n\n## Contributing\n\nContributions are welcome. Please fork the repository and submit a pull request.\n\n## Contact\n\nFor questions or feedback, please reach out at hrs.andrabi@gmail.com.\n",
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