Alexey/IIS_2023_1
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Mamoru777 2023-12-07 11:46:31 +04:00
parent 8a51aacfb2
commit 269cb7af87
4 changed files with 87 additions and 11 deletions
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2
.idea/IIS_2023_1.iml
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@ -2,7 +2,7 @@
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5
.idea/misc.xml
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65
.idea/workspace.xml
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@ -5,7 +5,10 @@
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26
senkin_alexander_lab_4/senkin_alexander_lab_4.py
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@ -1,6 +1,9 @@
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from minisom import MiniSom
from sklearn.preprocessing import MinMaxScaler
import numpy as np
# Загрузка данных из csv-файла
data = pd.read_csv('us_tornado_dataset_1950_2021.csv')
@ -24,4 +27,27 @@ plt.scatter(data_subset['fat'], data_subset['mag'], c=data_subset['cluster'], cm
plt.xlabel('Фатальные исходы (fat)')
plt.ylabel('Магнитуда (mag)')
plt.title('Кластеризация данных о фаматльных исходах и магнитуде торнадо')
plt.show()
# Нормализуйте признаки
scaler = MinMaxScaler()
features_normalized = scaler.fit_transform(features)
# Задайте параметры SOM
grid_size = (10, 10) # Размер сетки SOM
som = MiniSom(grid_size[0], grid_size[1], features.shape[1], sigma=0.5, learning_rate=0.5)
# Инициализируйте веса SOM случайным образом
som.random_weights_init(features_normalized)
# Обучите SOM
som.train_random(features_normalized, 10000)
# Визуализация карты и кластеров
plt.figure(figsize=(10, 8))
for i, x in enumerate(features_normalized):
winner = som.winner(x)
plt.plot(winner[0]+0.5, winner[1]+0.5, 'o', markerfacecolor='None', markersize=10, markeredgecolor='r', markeredgewidth=2)
plt.title('SOM Clustering')
plt.show()