o_18ufhmfmq19t513t3lgmn5l1qa8a.pdf

CHAPTER 7 ■ MORE ABSTRACTION 151
>>> f = FoodExpert()
>>> f.init()
>>> map(f.addGoodFood, ['SPAM', 'Eggs', 'Bacon', 'Rat', 'Spring Surprise'])
[None, None, None, None, None]
The first two lines instantiate FoodExpert and initialize the instance, which is assigned to f.
The map call simply uses the method addGoodFood with its self parameter bound to f. Because
this method doesn’t return anything, the result is a list filled with None. However, a side effect is
that f has been updated:
>>> f.goodFood
['SPAM', 'Eggs', 'Bacon', 'Rat', 'Spring Surprise']
Let’s use this expert to give us a list of recommendations:
>>> menu = ['Filet Mignon', 'Pasta', 'Pizza', 'Eggs', 'Bacon', 'Tomato', 'SPAM']
>>> rec = filter(f.likes, menu)
>>> rec
['Eggs', 'Bacon', 'SPAM']
What I did here was simply apply f.likes as a filter to a menu; the dishes the expert didn’t
like were simply discarded. But what if you want to find out which of these dishes the expert
would prefer? I once again turn to the trusty (if rarely used) reduce:
>>> reduce(f.prefers, rec)
'SPAM'
This basically works just like the example using reduce with max in Chapter 6 (in the section
“reduce”).
If I had used a different expert, initialized with different preferences, of course, I’d get
completely different results, even though the method definitions would be exactly the same. This
is the primary difference between standard functional programming and this quasi-functional
programming using bound methods; the methods have access to a state that can be used to
“customize” them.
■Note You can pass state along with a function like this by using nested scopes as well, as discussed in
the previous chapter.
The Class Namespace
The following two statements are (more or less) equivalent:
def foo(x): return x*x
foo = lambda x: x*x