自組織神經網絡（SOM）的Python第三方庫minisom聚類功能實作

聚類功能

在這個例子中，我們将看到如何使用 MiniSom 對 iris 資料集進行聚類。

首先，讓我們加載資料并訓練我們的 SOM：

from minisom import MiniSom
import numpy as np
import pandas as pd

data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt', 
                    names=['area', 'perimeter', 'compactness', 'length_kernel', 'width_kernel',
                   'asymmetry_coefficient', 'length_kernel_groove', 'target'], usecols=[0, 5], 
                   sep='\t+', engine='python')
# data normalization
data = (data - np.mean(data, axis=0)) / np.std(data, axis=0)
data = data.values

# Initialization and training
som_shape = (1, 3)
som = MiniSom(som_shape[0], som_shape[1], data.shape[1], sigma=.5, learning_rate=.5,
              neighborhood_function='gaussian', random_seed=10)

som.train_batch(data, 500, verbose=True)      

[ 500 / 500 ] 100% - 0:00:00 左

量化誤差：0.864828807271489

現在我們将映射到特定神經元的所有樣本視為一個簇。為了更容易地識别每個簇，我們将 SOM 上神經元的二維索引轉換為單維索引：

# each neuron represents a cluster
winner_coordinates = np.array([som.winner(x) for x in data]).T
# with np.ravel_multi_index we convert the bidimensional
# coordinates to a monodimensional index
cluster_index = np.ravel_multi_index(winner_coordinates, som_shape)      

我們可以用不同的顔色繪制每個叢集：

import matplotlib.pyplot as plt
%matplotlib inline

# plotting the clusters using the first 2 dimentions of the data
for c in np.unique(cluster_index):
    plt.scatter(data[cluster_index == c, 0],
                data[cluster_index == c, 1], label='cluster='+str(c), alpha=.7)

# plotting centroids
for centroid in som.get_weights():
    plt.scatter(centroid[:, 0], centroid[:, 1], marker='x', 
                s=80, linewidths=35, color='k', label='centroid')
plt.legend();