Fredrik Lundh schrieb:
> Danny Colligan wrote:
>
>> Carsten mentioned that generators are more memory-efficient to use when
>> dealing with large numbers of objects. Is this the main advantage of
>> using generators? Also, in what other novel ways are generators used
>> that are clearly superior to alternatives?
>
> the main advantage is that it lets you decouple the generation of data
> from the use of data; instead of inlining calculations, or using a pre-
> defined operation on their result (e.g printing them or adding them to a
> container), you can simply yield them, and let the user use whatever
> mechanism she wants to process them. i.e. instead of
>
> for item in range:
> calculate result
> print result
>
> you'll write
>
> def generate():
> for item in range:
> yield result
>
> and can then use
>
> for item in generate():
> print item
>
> or
>
> list(process(s) for s in generate())
>
> or
>
> sys.stdout.writelines(generate())
>
> or
>
> sum(generate())
>
> etc, without having to change the generator.
>
> you can also do various tricks with "endless" generators, such as the
> following pi digit generator, based on an algorithm by Python's grand-
> father Lambert Meertens:
>
> def pi():
> k, a, b, a1, b1 = 2, 4, 1, 12, 4
> while 1:
> # Next approximation
> p, q, k = k*k, 2*k+1, k+1
> a, b, a1, b1 = a1, b1, p*a+q*a1, p*b+q*b1
> # Yield common digits
> d, d1 = a/b, a1/b1
> while d == d1:
> yield str(d)
> a, a1 = 10*(a%b), 10*(a1%b1)
> d, d1 = a/b, a1/b1
>
> import sys
> sys.stdout.writelines(pi())
>
> </F>
>
or fibonacci:
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a+b
now get the first 10 ones:
>>> from itertools import *
>>> list(islice(fib(),10))
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
or primes:
def primes():
yield 1
yield 2
p = 3
psofar = []
smallerPrimes = lambda: takewhile(lambda x: x+x <= p,psofar)
while True:
if all(p % n != 0 for n in smallerPrimes()):
psofar.append(p)
yield p
p += 2
etc.
--
http://mail.python.org/mailman/listinfo/python-list
