我正在使用来自sklearn.mixture
的Gaussian Mixture Model (GMM)来执行我的数据集的聚类。
我可以使用函数score()
来计算模型下的对数概率。
但是,我正在寻找在this article中定义的名为“purity”的指标。
我如何在Python中实现它?我当前的实现如下所示:
from sklearn.mixture import GMM
# X is a 1000 x 2 array (1000 samples of 2 coordinates).
# It is actually a 2 dimensional PCA projection of data
# extracted from the MNIST dataset, but this random array
# is equivalent as far as the code is concerned.
X = np.random.rand(1000, 2)
clusterer = GMM(3, 'diag')
clusterer.fit(X)
cluster_labels = clusterer.predict(X)
# Now I can count the labels for each cluster..
count0 = list(cluster_labels).count(0)
count1 = list(cluster_labels).count(1)
count2 = list(cluster_labels).count(2)
但是我不能遍历每个集群来计算混淆矩阵(根据这个question)
https://stackoverflow.com/questions/34047540
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