Metadata-Version: 2.1
Name: route-optimization-constrained
Version: 0.1.2
Summary: Python library for Route Optimization Constrained
Home-page: https://github.com/ustropo/uttlv
Author: Tri Basuki Kurniawan
Author-email: tribasuki@thelorry.com
License: MIT
Download-URL: https://github.com/ustropo/uttlv/archive/v0.3.1.tar.gz
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown

## Route Optimization Constrained
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Implementation of Clustering with Constrained Algorithm. 
Route Optimization Constrained can be treated as an optimization problem. 

### Installation
Requirement Python >= 3.6, Numpy >= 1.13
* install from PyPI
```shell
pip install route-optimization-constrained
```

### Methods
* Constrained Clustering Algorithm: Route Optimization Constrained algorithms


### Usage:
```python
# setup
from route-optimization-constrained import RouteOptimizationConstraint
```

if __name__ == "__main__":
    X = []
    n_points = 1000
    random_state = 42
    random.seed(random_state)
    np.random.seed(random_state)
    X = np.random.rand(n_points, 2)
    demands = np.ones((n_points, 1))
    n_clusters = 4
    n_iters = 100
    max_size = [n_points / n_clusters] * n_clusters
    max_size = [0.25, 0.5, 0.1, 0.15]

    roc = RouteOptimizationConstraint(n_clusters, max_size, n_iters)
    roc.fit(X, demands)
    labels = roc.labels_
    centers = roc.cluster_centers_


## Copyright
Copyright (c) 2020 Jing Wang. Released under the MIT License. 

Third-party copyright in this distribution is noted where applicable.

### Reference
* [Clustering with Capacity and Size Constraints: A Deterministic
Approach](http://web.eecs.umich.edu/~mayankb/docs/ClusterCap.pdf)
* [Deterministic Annealing, Clustering and Optimization](https://thesis.library.caltech.edu/2858/1/Rose_k_1991.pdf)
* [Deterministic Annealing, Constrained Clustering, and Opthiieation](https://authors.library.caltech.edu/78353/1/00170767.pdf)
* [Shrinkage Clustering](https://www.researchgate.net/publication/322668506_Shrinkage_Clustering_A_fast_and_size-constrained_clustering_algorithm_for_biomedical_applications)
* [Clustering with size constraints](https://www.researchgate.net/publication/268292668_Clustering_with_Size_Constraints)
* [Data Clustering with Cluster Size Constraints Using a Modified k-means Algorithm](https://core.ac.uk/download/pdf/61217069.pdf)
* [KMeans Constrained Clustering Inspired by Minimum Cost Flow Problem](https://github.com/joshlk/k-means-constrained)
* [Same Size Kmeans Heuristics Methods](https://elki-project.github.io/tutorial/same-size_k_means)
* [Google's Operations Research tools's
`SimpleMinCostFlow`](https://developers.google.com/optimization/flow/mincostflow)
* [Cluster KMeans Constrained](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2000-65.pdf)


