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typesetting/csp/aima/utils.txt

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>>> d = DefaultDict(0)
>>> d['x'] += 1
>>> d['x']
1
>>> d = DefaultDict([])
>>> d['x'] += [1]
>>> d['y'] += [2]
>>> d['x']
[1]
>>> s = Struct(a=1, b=2)
>>> s.a
1
>>> s.a = 3
>>> s
Struct(a=3, b=2)
>>> def is_even(x):
... return x % 2 == 0
>>> sorted([1, 2, -3])
[-3, 1, 2]
>>> sorted(range(10), key=is_even)
[1, 3, 5, 7, 9, 0, 2, 4, 6, 8]
>>> sorted(range(10), lambda x,y: y-x)
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
>>> removeall(4, [])
[]
>>> removeall('s', 'This is a test. Was a test.')
'Thi i a tet. Wa a tet.'
>>> removeall('s', 'Something')
'Something'
>>> removeall('s', '')
''
>>> list(reversed([]))
[]
>>> count_if(is_even, [1, 2, 3, 4])
2
>>> count_if(is_even, [])
0
>>> argmax([1], lambda x: x*x)
1
>>> argmin([1], lambda x: x*x)
1
# Test of memoize with slots in structures
>>> countries = [Struct(name='united states'), Struct(name='canada')]
# Pretend that 'gnp' was some big hairy operation:
>>> def gnp(country):
... print 'calculating gnp ...'
... return len(country.name) * 1e10
>>> gnp = memoize(gnp, '_gnp')
>>> map(gnp, countries)
calculating gnp ...
calculating gnp ...
[130000000000.0, 60000000000.0]
>>> countries
[Struct(_gnp=130000000000.0, name='united states'), Struct(_gnp=60000000000.0, name='canada')]
# This time we avoid re-doing the calculation
>>> map(gnp, countries)
[130000000000.0, 60000000000.0]
# Test Queues:
>>> nums = [1, 8, 2, 7, 5, 6, -99, 99, 4, 3, 0]
>>> def qtest(q):
... return [q.extend(nums), [q.pop() for i in range(len(q))]][1]
>>> qtest(Stack())
[0, 3, 4, 99, -99, 6, 5, 7, 2, 8, 1]
>>> qtest(FIFOQueue())
[1, 8, 2, 7, 5, 6, -99, 99, 4, 3, 0]
>>> qtest(PriorityQueue(min))
[-99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 99]
>>> qtest(PriorityQueue(max))
[99, 8, 7, 6, 5, 4, 3, 2, 1, 0, -99]
>>> qtest(PriorityQueue(min, abs))
[0, 1, 2, 3, 4, 5, 6, 7, 8, -99, 99]
>>> qtest(PriorityQueue(max, abs))
[99, -99, 8, 7, 6, 5, 4, 3, 2, 1, 0]
>>> vals = [100, 110, 160, 200, 160, 110, 200, 200, 220]
>>> histogram(vals)
[(100, 1), (110, 2), (160, 2), (200, 3), (220, 1)]
>>> histogram(vals, 1)
[(200, 3), (110, 2), (160, 2), (220, 1), (100, 1)]
>>> histogram(vals, 1, lambda v: round(v, -2))
[(200.0, 6), (100.0, 3)]
>>> log2(1.0)
0.0
>>> def fib(n):
... return (n<=1 and 1) or (fib(n-1) + fib(n-2))
>>> fib(9)
55
# Now we make it faster:
>>> fib = memoize(fib)
>>> fib(9)
55
>>> q = Stack()
>>> q.append(1)
>>> q.append(2)
>>> q.pop(), q.pop()
(2, 1)
>>> q = FIFOQueue()
>>> q.append(1)
>>> q.append(2)
>>> q.pop(), q.pop()
(1, 2)
>>> abc = set('abc')
>>> bcd = set('bcd')
>>> 'a' in abc
True
>>> 'a' in bcd
False
>>> list(abc.intersection(bcd))
['c', 'b']
>>> list(abc.union(bcd))
['a', 'c', 'b', 'd']
## From "What's new in Python 2.4", but I added calls to sl
>>> def sl(x):
... return sorted(list(x))
>>> a = set('abracadabra') # form a set from a string
>>> 'z' in a # fast membership testing
False
>>> sl(a) # unique letters in a
['a', 'b', 'c', 'd', 'r']
>>> b = set('alacazam') # form a second set
>>> sl(a - b) # letters in a but not in b
['b', 'd', 'r']
>>> sl(a | b) # letters in either a or b
['a', 'b', 'c', 'd', 'l', 'm', 'r', 'z']
>>> sl(a & b) # letters in both a and b
['a', 'c']
>>> sl(a ^ b) # letters in a or b but not both
['b', 'd', 'l', 'm', 'r', 'z']
>>> a.add('z') # add a new element
>>> a.update('wxy') # add multiple new elements
>>> sl(a)
['a', 'b', 'c', 'd', 'r', 'w', 'x', 'y', 'z']
>>> a.remove('x') # take one element out
>>> sl(a)
['a', 'b', 'c', 'd', 'r', 'w', 'y', 'z']