41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Wed Dec 8 16:07:28 2021
|
|
|
|
@author: pfaure
|
|
"""
|
|
|
|
from sklearn.neighbors import NearestNeighbors
|
|
import numpy as np
|
|
|
|
from myplotlib import print_1d_data, print_2d_data
|
|
from mydatalib import extract_data_txt, scale_data
|
|
|
|
path = './new-data/'
|
|
dataset_name = "w2"
|
|
save = False
|
|
|
|
print("-----------------------------------------------------------")
|
|
print(" Chargement du dataset : " + dataset_name)
|
|
data = extract_data_txt(path + dataset_name)
|
|
print(data)
|
|
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)
|
|
|
|
print("-----------------------------------------------------------")
|
|
print(" Calcul du voisinage")
|
|
n = 50
|
|
neighbors = NearestNeighbors(n_neighbors=n)
|
|
neighbors.fit(data_scaled)
|
|
distances, indices = neighbors.kneighbors(data_scaled)
|
|
distances = list(map(lambda x: sum(x[1:n-1])/(len(x)-1), distances))
|
|
distances = np.sort(distances, axis=0)
|
|
print_1d_data(distances, range(1, len(distances)+1), x_name="distance_moyenne",
|
|
y_name="nombre_de_points", stop=False, save=save)
|