Metadata-Version: 2.1
Name: cimren-wkmeans-geo
Version: 1.3.2
Summary: Weighted KMeans Clustering for Geolocational Problem
Home-page: https://github.com/emrahcimren/wkmeans-geo
Author: cimren
Author-email: cimren.1@gmail.com
License: MIT
Description: Weighted KMeans Clustering for Geolocational Problem
        ====================================================
        
        Repo for weighted k means clustering for specifically geo locational problems. 
        
        For an example and mathematical explanation:
        
        https://emrahcimren.github.io/data%20science/Greenfield-Analysis-with-Weighted-Clustering/
        
        Prerequisites
        -------------
        
        Install environment.yml for prerequisites.
        
        ```
        conda env create -f environment.yml
        ```
        
        To recreate environment.yml
        
        ```
        conda env export > environment.yml
        ```
        
        To create requirements.txt from environment.yml
        
        ```
        pip freeze > requirements.txt
        ```
        
        Installation
        ------------
        
        ```
        pip install cimren-wkmeans-geo
        ```
        
        Inputs
        ------
        
        *input_locations* is a pandas dataframe with the following format.
        
        LOCATION_NAME | LATITUDE | LONGITUDE | WEIGHT | VOLUME |
        --- | --- | --- | --- | --- 
        LOC 0 | -27.0065 | 170.583 | 1 | 10
        
        *number_of_clusters*: Number of clusters to be created
        
        *minimum_elements_in_a_cluster*: Minimum elements in a cluster
        
        *maximum_elements_in_a_cluster*: Maximum elements in a cluster
        
        *maximum_volume_in_a_cluster*: Maximum volume that can fit in a cluster; if set to None, then it is disabled
        
        *maximum_iteration*: How many maximum number of steps the algorithm takes to stop if it does not find the solution
        
        *enable_minimum_maximum_elements_in_a_cluster*: True/False to enable minimum and maximum cluster size
        
        *objective_range*: Acceptable difference between objectives at each iteration
        
        Data
        ----
        Package has a sample data set
        
        ```
        from wkmeans_geo.src import data
        data.locations_test
        data.number_of_clusters
        data.minimum_elements_in_a_cluster
        data.maximum_elements_in_a_cluster
        data.maximum_volume_in_a_cluster
        data.maximum_iteration
        data.enable_minimum_maximum_elements_in_a_cluster
        data.objective_range
        ```
        
        How to use
        ----------
        
        ```
        from wkmeans_geo.src import data
        from wkmeans_geo import wkmeans_clustering as wkc
        clusters, locations_with_clusters = wkc.calculate_clusters(
                                               data.locations_test,
                                               data.number_of_clusters,
                                               data.minimum_elements_in_a_cluster,
                                               data.maximum_elements_in_a_cluster,
                                               data.maximum_volume_in_a_cluster,
                                               data.maximum_iteration,
                                               data.objective_range,
                                               data.enable_minimum_maximum_elements_in_a_cluster)
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
