"\u001b[0;32m<ipython-input-153-0d3135aace65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get prior distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgmvaep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'prior'"
groups = hard_groups[np.max(groupings, axis=1) > minimum_confidence].flatten()
groups = np.concatenate([groups, np.arange(25)])
sns.countplot(groups)
plt.xlabel("Cluster")
plt.title("Training instances per cluster")
plt.show()
```
%% Output
%% Cell type:markdown id: tags:
The slider in the figure above lets you set the minimum confidence the model may yield when assigning a training instance to a cluster in order to be visualized.