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- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Wed Dec 8 16:07:28 2021
-
- @author: pfaure
- """
- from numpy import arange
- from sklearn.neighbors import NearestNeighbors
- import numpy as np
-
- from myplotlib import print_1d_data, print_2d_data
- from mydatalib import extract_data_2d, scale_data, apply_mean_shift, evaluate
-
- path = './artificial/'
- dataset_name = "xclara"
- method_name = "mean-shift"
- save = True
-
- print("-----------------------------------------------------------")
- print(" Chargement du dataset : " + dataset_name)
- data = extract_data_2d(path + dataset_name)
- print_2d_data(data, dataset_name=dataset_name +
- "_brutes", stop=False, save=save)
-
- print("-----------------------------------------------------------")
- print(" Mise à l'échelle")
- data_scaled = scale_data(data)
- print_2d_data(data_scaled, dataset_name=dataset_name +
- "_scaled", stop=False, save=save)
-
- # Application de Affinity Propagation pour plusieurs valeurs de préférence
- # et evaluation de la solution
-
- k_max = 2
-
- k = []
- durations = []
- silouettes = []
- daviess = []
- calinskis = []
- for bandwidth in arange(0.1, k_max, 0.1):
- # Application du clustering
- (model, duration) = apply_mean_shift(
- data_scaled, bandwidth=bandwidth)
- # Affichage des clusters
- print_2d_data(data_scaled, dataset_name=dataset_name,
- method_name=method_name, k=bandwidth,
- stop=False, save=save, c=model.labels_)
- # Evaluation de la solution de clustering
- (silouette, davies, calinski) = evaluate(data_scaled, model)
- # Enregistrement des valeurs
- k += [bandwidth]
- durations += [duration]
- silouettes += [silouette]
- daviess += [davies]
- calinskis += [calinski]
-
- # Affichage des résultats
- print_1d_data(k, k, x_name="k", y_name="k", dataset_name=dataset_name,
- method_name=method_name, stop=False, save=save)
- print_1d_data(k, durations, x_name="k", y_name="temps_de_calcul", y_unit="ms",
- dataset_name=dataset_name,
- method_name=method_name, stop=False, save=save)
- print_1d_data(k, silouettes, x_name="k", y_name="coeficient_de_silhouette",
- dataset_name=dataset_name,
- method_name=method_name, stop=False, save=save)
- print_1d_data(k, daviess, x_name="k", y_name="coeficient_de_Davies",
- dataset_name=dataset_name,
- method_name=method_name, stop=False, save=save)
- print_1d_data(k, calinskis, x_name="k", y_name="coeficient_de_Calinski",
- dataset_name=dataset_name,
- method_name=method_name, stop=False, save=save)
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