Solving our initial challenge

We will now put everything together and demonstrate our system for the following new post that we assign to the new_post variable:

new_post = '''Disk drive problems. Hi, I have a problem with my hard disk.After 1 year it is working only sporadically now.I tried to format it, but now it doesn't boot any more.Any ideas? Thanks. '''

As you learned earlier, you will first have to vectorize this post before you predict its label:

>>> new_post_vec = vectorizer.transform([new_post])>>> new_post_label = km.predict(new_post_vec)[0]

Now that we have the clustering, we do not need to compare new_post_vec to all post vectors. Instead, we can focus only on the posts of the same cluster. Let's fetch their indices in the original data set:

>>> similar_indices = (km.labels_ == new_post_label).nonzero()[0]

The comparison in the bracket results in a Boolean array, and nonzero converts that array into a smaller array containing the indices of the True elements...