Metadata-Version: 2.1
Name: k-mxt-w3
Version: 0.0.2
Summary: k-mxt and k-mxt-w clustering algorithms
Home-page: https://github.com/stacy-s/k_mxt_w
Author: Anastasia Stepanova
Author-email: stacey.stepanova@gmail.com
License: UNKNOWN
Description: # k-mxt-w3
        The k-mxt-w3 library contains an implementation of the k-mxt and k-mxt-w algorithms.
        Using clustering algorithms can identify clusters on a dataset.
        
        ## Installation
        ```bash
        pip install k-mxt-w3
        ```
        
        ## Parameters
         * The larger the parameter k, the more vertices will be in each cluster, and the number of clusters will be less.
         * The eps parameter indicates the maximum distance between the vertices at which these vertices are connected.
         
         ## Usage
         ```python
        import k_mxt_w3.clustering_algorithms
        import k_mxt_w3.clusters_data
        import k_mxt_w3.data
        import pandas as pd
        
        filename = 'dataset.csv'
        df = pd.read_csv(filename)                                          # reading csv-file data
        dataset = k_mxt_w3.data.DataPropertyImportSpace(df=df)
        features_list = ['column_x']                                        # list of the dataset columns
        x, y, features = dataset.get_data(name_latitude_cols='latitude',    # name of the column containing the latitude values
                                           name_longitude_cols='longitude',  # name of the column containing the longitude values
                                           features_list=features_list)      # list of others
        clusters = k_mxt_w3.clusters_data.ClustersDataSpaceFeaturesEuclidean(x_init=x, 
                                                                             y_init=y,
                                                                             features_init=features)  # creating an object 
                                                                                                      # containing clusters
        alg = k_mxt_w3.clustering_algorithms.K_MXT_gauss(k=5, eps=0.05, clusters_data=clusters)       # creating an object 
                                                                                                       # with k=5, eps=0.05
        alg()                               # run the clustering algorithm
        print(clusters.cluster_numbers)     # print cluster number for each vertex
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
