route-optimization-constrained 0.1.2

Python library for Route Optimization Constrained

Route Optimization Constrained

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

pip install route-optimization-constrained

Methods

• Constrained Clustering Algorithm: Route Optimization Constrained algorithms

Usage:

# 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
• Deterministic Annealing, Clustering and Optimization
• Deterministic Annealing, Constrained Clustering, and Opthiieation
• Shrinkage Clustering
• Clustering with size constraints
• Data Clustering with Cluster Size Constraints Using a Modified k-means Algorithm
• KMeans Constrained Clustering Inspired by Minimum Cost Flow Problem
• Same Size Kmeans Heuristics Methods
• Google's Operations Research tools's SimpleMinCostFlow
• Cluster KMeans Constrained