聚类功能
在这个例子中，我们将看到如何使用 MiniSom 对 iris 数据集进行聚类。
首先，让我们加载数据并训练我们的 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
量化误差：0.864828807271489
现在我们将映射到特定神经元的所有样本视为一个簇。为了更容易地识别每个簇，我们将 SOM 上神经元的二维索引转换为单维索引：
# 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)
我们可以用不同的颜色绘制每个集群：
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();
