Now you can train the filter with some text.

SPAM> (train "Make money fast" 'spam)

And then see what the classifier thinks.

(defun classification (score)
     ((<= score *max-ham-score*) 'ham)
     ((>= score *min-spam-score*) 'spam)
     (t 'unsure))

You can make this change and then recompile just this one function. Because classify returns whatever returns, it’ll also now return two values. But since the primary return value is the same, callers of either function who expect only one value won’t be affected. Now when you test classify, you can see exactly what score went into the classification.

And now you can see what happens if you train the filter with some more ham text.

SPAM> (train "Do you have any money for the movies?" 'ham)
1
SPAM> (classify "Make money fast")
0.7685351219857626D0

And now this is clearly recognizable ham thanks to the presence of the word movies, now a hammy feature.

However, you don’t really want to train the filter by hand. What you’d really like is an easy way to point it at a bunch of files and train it on them. And if you want to test how well the filter actually works, you’d like to then use it to classify another set of files of known types and see how it does. So the last bit of code you’ll write in this chapter will be a test harness that tests the filter on a corpus of messages of known types, using a certain fraction for training and then measuring how accurate the filter is when classifying the remainder.