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test_generators.py
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2211 lines (1861 loc) · 58.5 KB
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import copy
import gc
import pickle
import sys
import unittest
import warnings
import weakref
import inspect
from test import support
try:
import _testcapi
except ImportError:
_testcapi = None
# This tests to make sure that if a SIGINT arrives just before we send into a
# yield from chain, the KeyboardInterrupt is raised in the innermost
# generator (see bpo-30039).
@unittest.skipUnless(_testcapi is not None and
hasattr(_testcapi, "raise_SIGINT_then_send_None"),
"needs _testcapi.raise_SIGINT_then_send_None")
class SignalAndYieldFromTest(unittest.TestCase):
def generator1(self):
return (yield from self.generator2())
def generator2(self):
try:
yield
except KeyboardInterrupt:
return "PASSED"
else:
return "FAILED"
def test_raise_and_yield_from(self):
gen = self.generator1()
gen.send(None)
try:
_testcapi.raise_SIGINT_then_send_None(gen)
except BaseException as _exc:
exc = _exc
self.assertIs(type(exc), StopIteration)
self.assertEqual(exc.value, "PASSED")
class FinalizationTest(unittest.TestCase):
def test_frame_resurrect(self):
# A generator frame can be resurrected by a generator's finalization.
def gen():
nonlocal frame
try:
yield
finally:
frame = sys._getframe()
g = gen()
wr = weakref.ref(g)
next(g)
del g
support.gc_collect()
self.assertIs(wr(), None)
self.assertTrue(frame)
del frame
support.gc_collect()
def test_refcycle(self):
# A generator caught in a refcycle gets finalized anyway.
old_garbage = gc.garbage[:]
finalized = False
def gen():
nonlocal finalized
try:
g = yield
yield 1
finally:
finalized = True
g = gen()
next(g)
g.send(g)
self.assertGreater(sys.getrefcount(g), 2)
self.assertFalse(finalized)
del g
support.gc_collect()
self.assertTrue(finalized)
self.assertEqual(gc.garbage, old_garbage)
def test_lambda_generator(self):
# Issue #23192: Test that a lambda returning a generator behaves
# like the equivalent function
f = lambda: (yield 1)
def g(): return (yield 1)
# test 'yield from'
f2 = lambda: (yield from g())
def g2(): return (yield from g())
f3 = lambda: (yield from f())
def g3(): return (yield from f())
for gen_fun in (f, g, f2, g2, f3, g3):
gen = gen_fun()
self.assertEqual(next(gen), 1)
with self.assertRaises(StopIteration) as cm:
gen.send(2)
self.assertEqual(cm.exception.value, 2)
class GeneratorTest(unittest.TestCase):
def test_name(self):
def func():
yield 1
# check generator names
gen = func()
self.assertEqual(gen.__name__, "func")
self.assertEqual(gen.__qualname__,
"GeneratorTest.test_name.<locals>.func")
# modify generator names
gen.__name__ = "name"
gen.__qualname__ = "qualname"
self.assertEqual(gen.__name__, "name")
self.assertEqual(gen.__qualname__, "qualname")
# generator names must be a string and cannot be deleted
self.assertRaises(TypeError, setattr, gen, '__name__', 123)
self.assertRaises(TypeError, setattr, gen, '__qualname__', 123)
self.assertRaises(TypeError, delattr, gen, '__name__')
self.assertRaises(TypeError, delattr, gen, '__qualname__')
# modify names of the function creating the generator
func.__qualname__ = "func_qualname"
func.__name__ = "func_name"
gen = func()
self.assertEqual(gen.__name__, "func_name")
self.assertEqual(gen.__qualname__, "func_qualname")
# unnamed generator
gen = (x for x in range(10))
self.assertEqual(gen.__name__,
"<genexpr>")
self.assertEqual(gen.__qualname__,
"GeneratorTest.test_name.<locals>.<genexpr>")
def test_copy(self):
def f():
yield 1
g = f()
with self.assertRaises(TypeError):
copy.copy(g)
def test_pickle(self):
def f():
yield 1
g = f()
for proto in range(pickle.HIGHEST_PROTOCOL + 1):
with self.assertRaises((TypeError, pickle.PicklingError)):
pickle.dumps(g, proto)
class ExceptionTest(unittest.TestCase):
# Tests for the issue #23353: check that the currently handled exception
# is correctly saved/restored in PyEval_EvalFrameEx().
def test_except_throw(self):
def store_raise_exc_generator():
try:
self.assertEqual(sys.exc_info()[0], None)
yield
except Exception as exc:
# exception raised by gen.throw(exc)
self.assertEqual(sys.exc_info()[0], ValueError)
self.assertIsNone(exc.__context__)
yield
# ensure that the exception is not lost
self.assertEqual(sys.exc_info()[0], ValueError)
yield
# we should be able to raise back the ValueError
raise
make = store_raise_exc_generator()
next(make)
try:
raise ValueError()
except Exception as exc:
try:
make.throw(exc)
except Exception:
pass
next(make)
with self.assertRaises(ValueError) as cm:
next(make)
self.assertIsNone(cm.exception.__context__)
self.assertEqual(sys.exc_info(), (None, None, None))
def test_except_next(self):
def gen():
self.assertEqual(sys.exc_info()[0], ValueError)
yield "done"
g = gen()
try:
raise ValueError
except Exception:
self.assertEqual(next(g), "done")
self.assertEqual(sys.exc_info(), (None, None, None))
def test_except_gen_except(self):
def gen():
try:
self.assertEqual(sys.exc_info()[0], None)
yield
# we are called from "except ValueError:", TypeError must
# inherit ValueError in its context
raise TypeError()
except TypeError as exc:
self.assertEqual(sys.exc_info()[0], TypeError)
self.assertEqual(type(exc.__context__), ValueError)
# here we are still called from the "except ValueError:"
self.assertEqual(sys.exc_info()[0], ValueError)
yield
self.assertIsNone(sys.exc_info()[0])
yield "done"
g = gen()
next(g)
try:
raise ValueError
except Exception:
next(g)
self.assertEqual(next(g), "done")
self.assertEqual(sys.exc_info(), (None, None, None))
def test_except_throw_exception_context(self):
def gen():
try:
try:
self.assertEqual(sys.exc_info()[0], None)
yield
except ValueError:
# we are called from "except ValueError:"
self.assertEqual(sys.exc_info()[0], ValueError)
raise TypeError()
except Exception as exc:
self.assertEqual(sys.exc_info()[0], TypeError)
self.assertEqual(type(exc.__context__), ValueError)
# we are still called from "except ValueError:"
self.assertEqual(sys.exc_info()[0], ValueError)
yield
self.assertIsNone(sys.exc_info()[0])
yield "done"
g = gen()
next(g)
try:
raise ValueError
except Exception as exc:
g.throw(exc)
self.assertEqual(next(g), "done")
self.assertEqual(sys.exc_info(), (None, None, None))
def test_stopiteration_error(self):
# See also PEP 479.
def gen():
raise StopIteration
yield
with self.assertRaisesRegex(RuntimeError, 'raised StopIteration'):
next(gen())
def test_tutorial_stopiteration(self):
# Raise StopIteration" stops the generator too:
def f():
yield 1
raise StopIteration
yield 2 # never reached
g = f()
self.assertEqual(next(g), 1)
with self.assertRaisesRegex(RuntimeError, 'raised StopIteration'):
next(g)
def test_return_tuple(self):
def g():
return (yield 1)
gen = g()
self.assertEqual(next(gen), 1)
with self.assertRaises(StopIteration) as cm:
gen.send((2,))
self.assertEqual(cm.exception.value, (2,))
def test_return_stopiteration(self):
def g():
return (yield 1)
gen = g()
self.assertEqual(next(gen), 1)
with self.assertRaises(StopIteration) as cm:
gen.send(StopIteration(2))
self.assertIsInstance(cm.exception.value, StopIteration)
self.assertEqual(cm.exception.value.value, 2)
class YieldFromTests(unittest.TestCase):
def test_generator_gi_yieldfrom(self):
def a():
self.assertEqual(inspect.getgeneratorstate(gen_b), inspect.GEN_RUNNING)
self.assertIsNone(gen_b.gi_yieldfrom)
yield
self.assertEqual(inspect.getgeneratorstate(gen_b), inspect.GEN_RUNNING)
self.assertIsNone(gen_b.gi_yieldfrom)
def b():
self.assertIsNone(gen_b.gi_yieldfrom)
yield from a()
self.assertIsNone(gen_b.gi_yieldfrom)
yield
self.assertIsNone(gen_b.gi_yieldfrom)
gen_b = b()
self.assertEqual(inspect.getgeneratorstate(gen_b), inspect.GEN_CREATED)
self.assertIsNone(gen_b.gi_yieldfrom)
gen_b.send(None)
self.assertEqual(inspect.getgeneratorstate(gen_b), inspect.GEN_SUSPENDED)
self.assertEqual(gen_b.gi_yieldfrom.gi_code.co_name, 'a')
gen_b.send(None)
self.assertEqual(inspect.getgeneratorstate(gen_b), inspect.GEN_SUSPENDED)
self.assertIsNone(gen_b.gi_yieldfrom)
[] = gen_b # Exhaust generator
self.assertEqual(inspect.getgeneratorstate(gen_b), inspect.GEN_CLOSED)
self.assertIsNone(gen_b.gi_yieldfrom)
tutorial_tests = """
Let's try a simple generator:
>>> def f():
... yield 1
... yield 2
>>> for i in f():
... print(i)
1
2
>>> g = f()
>>> next(g)
1
>>> next(g)
2
"Falling off the end" stops the generator:
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 2, in g
StopIteration
"return" also stops the generator:
>>> def f():
... yield 1
... return
... yield 2 # never reached
...
>>> g = f()
>>> next(g)
1
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 3, in f
StopIteration
>>> next(g) # once stopped, can't be resumed
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
However, "return" and StopIteration are not exactly equivalent:
>>> def g1():
... try:
... return
... except:
... yield 1
...
>>> list(g1())
[]
>>> def g2():
... try:
... raise StopIteration
... except:
... yield 42
>>> print(list(g2()))
[42]
This may be surprising at first:
>>> def g3():
... try:
... return
... finally:
... yield 1
...
>>> list(g3())
[1]
Let's create an alternate range() function implemented as a generator:
>>> def yrange(n):
... for i in range(n):
... yield i
...
>>> list(yrange(5))
[0, 1, 2, 3, 4]
Generators always return to the most recent caller:
>>> def creator():
... r = yrange(5)
... print("creator", next(r))
... return r
...
>>> def caller():
... r = creator()
... for i in r:
... print("caller", i)
...
>>> caller()
creator 0
caller 1
caller 2
caller 3
caller 4
Generators can call other generators:
>>> def zrange(n):
... for i in yrange(n):
... yield i
...
>>> list(zrange(5))
[0, 1, 2, 3, 4]
"""
# The examples from PEP 255.
pep_tests = """
Specification: Yield
Restriction: A generator cannot be resumed while it is actively
running:
>>> def g():
... i = next(me)
... yield i
>>> me = g()
>>> next(me)
Traceback (most recent call last):
...
File "<string>", line 2, in g
ValueError: generator already executing
Specification: Return
Note that return isn't always equivalent to raising StopIteration: the
difference lies in how enclosing try/except constructs are treated.
For example,
>>> def f1():
... try:
... return
... except:
... yield 1
>>> print(list(f1()))
[]
because, as in any function, return simply exits, but
>>> def f2():
... try:
... raise StopIteration
... except:
... yield 42
>>> print(list(f2()))
[42]
because StopIteration is captured by a bare "except", as is any
exception.
Specification: Generators and Exception Propagation
>>> def f():
... return 1//0
>>> def g():
... yield f() # the zero division exception propagates
... yield 42 # and we'll never get here
>>> k = g()
>>> next(k)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "<stdin>", line 2, in g
File "<stdin>", line 2, in f
ZeroDivisionError: integer division or modulo by zero
>>> next(k) # and the generator cannot be resumed
Traceback (most recent call last):
File "<stdin>", line 1, in ?
StopIteration
>>>
Specification: Try/Except/Finally
>>> def f():
... try:
... yield 1
... try:
... yield 2
... 1//0
... yield 3 # never get here
... except ZeroDivisionError:
... yield 4
... yield 5
... raise
... except:
... yield 6
... yield 7 # the "raise" above stops this
... except:
... yield 8
... yield 9
... try:
... x = 12
... finally:
... yield 10
... yield 11
>>> print(list(f()))
[1, 2, 4, 5, 8, 9, 10, 11]
>>>
Guido's binary tree example.
>>> # A binary tree class.
>>> class Tree:
...
... def __init__(self, label, left=None, right=None):
... self.label = label
... self.left = left
... self.right = right
...
... def __repr__(self, level=0, indent=" "):
... s = level*indent + repr(self.label)
... if self.left:
... s = s + "\\n" + self.left.__repr__(level+1, indent)
... if self.right:
... s = s + "\\n" + self.right.__repr__(level+1, indent)
... return s
...
... def __iter__(self):
... return inorder(self)
>>> # Create a Tree from a list.
>>> def tree(list):
... n = len(list)
... if n == 0:
... return []
... i = n // 2
... return Tree(list[i], tree(list[:i]), tree(list[i+1:]))
>>> # Show it off: create a tree.
>>> t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
>>> # A recursive generator that generates Tree labels in in-order.
>>> def inorder(t):
... if t:
... for x in inorder(t.left):
... yield x
... yield t.label
... for x in inorder(t.right):
... yield x
>>> # Show it off: create a tree.
>>> t = tree("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
>>> # Print the nodes of the tree in in-order.
>>> for x in t:
... print(' '+x, end='')
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
>>> # A non-recursive generator.
>>> def inorder(node):
... stack = []
... while node:
... while node.left:
... stack.append(node)
... node = node.left
... yield node.label
... while not node.right:
... try:
... node = stack.pop()
... except IndexError:
... return
... yield node.label
... node = node.right
>>> # Exercise the non-recursive generator.
>>> for x in t:
... print(' '+x, end='')
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
"""
# Examples from Iterator-List and Python-Dev and c.l.py.
email_tests = """
The difference between yielding None and returning it.
>>> def g():
... for i in range(3):
... yield None
... yield None
... return
>>> list(g())
[None, None, None, None]
Ensure that explicitly raising StopIteration acts like any other exception
in try/except, not like a return.
>>> def g():
... yield 1
... try:
... raise StopIteration
... except:
... yield 2
... yield 3
>>> list(g())
[1, 2, 3]
Next one was posted to c.l.py.
>>> def gcomb(x, k):
... "Generate all combinations of k elements from list x."
...
... if k > len(x):
... return
... if k == 0:
... yield []
... else:
... first, rest = x[0], x[1:]
... # A combination does or doesn't contain first.
... # If it does, the remainder is a k-1 comb of rest.
... for c in gcomb(rest, k-1):
... c.insert(0, first)
... yield c
... # If it doesn't contain first, it's a k comb of rest.
... for c in gcomb(rest, k):
... yield c
>>> seq = list(range(1, 5))
>>> for k in range(len(seq) + 2):
... print("%d-combs of %s:" % (k, seq))
... for c in gcomb(seq, k):
... print(" ", c)
0-combs of [1, 2, 3, 4]:
[]
1-combs of [1, 2, 3, 4]:
[1]
[2]
[3]
[4]
2-combs of [1, 2, 3, 4]:
[1, 2]
[1, 3]
[1, 4]
[2, 3]
[2, 4]
[3, 4]
3-combs of [1, 2, 3, 4]:
[1, 2, 3]
[1, 2, 4]
[1, 3, 4]
[2, 3, 4]
4-combs of [1, 2, 3, 4]:
[1, 2, 3, 4]
5-combs of [1, 2, 3, 4]:
From the Iterators list, about the types of these things.
>>> def g():
... yield 1
...
>>> type(g)
<class 'function'>
>>> i = g()
>>> type(i)
<class 'generator'>
>>> [s for s in dir(i) if not s.startswith('_')]
['close', 'gi_code', 'gi_frame', 'gi_running', 'gi_yieldfrom', 'send', 'throw']
>>> from test.support import HAVE_DOCSTRINGS
>>> print(i.__next__.__doc__ if HAVE_DOCSTRINGS else 'Implement next(self).')
Implement next(self).
>>> iter(i) is i
True
>>> import types
>>> isinstance(i, types.GeneratorType)
True
And more, added later.
>>> i.gi_running
0
>>> type(i.gi_frame)
<class 'frame'>
>>> i.gi_running = 42
Traceback (most recent call last):
...
AttributeError: readonly attribute
>>> def g():
... yield me.gi_running
>>> me = g()
>>> me.gi_running
0
>>> next(me)
1
>>> me.gi_running
0
A clever union-find implementation from c.l.py, due to David Eppstein.
Sent: Friday, June 29, 2001 12:16 PM
To: python-list@python.org
Subject: Re: PEP 255: Simple Generators
>>> class disjointSet:
... def __init__(self, name):
... self.name = name
... self.parent = None
... self.generator = self.generate()
...
... def generate(self):
... while not self.parent:
... yield self
... for x in self.parent.generator:
... yield x
...
... def find(self):
... return next(self.generator)
...
... def union(self, parent):
... if self.parent:
... raise ValueError("Sorry, I'm not a root!")
... self.parent = parent
...
... def __str__(self):
... return self.name
>>> names = "ABCDEFGHIJKLM"
>>> sets = [disjointSet(name) for name in names]
>>> roots = sets[:]
>>> import random
>>> gen = random.Random(42)
>>> while 1:
... for s in sets:
... print(" %s->%s" % (s, s.find()), end='')
... print()
... if len(roots) > 1:
... s1 = gen.choice(roots)
... roots.remove(s1)
... s2 = gen.choice(roots)
... s1.union(s2)
... print("merged", s1, "into", s2)
... else:
... break
A->A B->B C->C D->D E->E F->F G->G H->H I->I J->J K->K L->L M->M
merged K into B
A->A B->B C->C D->D E->E F->F G->G H->H I->I J->J K->B L->L M->M
merged A into F
A->F B->B C->C D->D E->E F->F G->G H->H I->I J->J K->B L->L M->M
merged E into F
A->F B->B C->C D->D E->F F->F G->G H->H I->I J->J K->B L->L M->M
merged D into C
A->F B->B C->C D->C E->F F->F G->G H->H I->I J->J K->B L->L M->M
merged M into C
A->F B->B C->C D->C E->F F->F G->G H->H I->I J->J K->B L->L M->C
merged J into B
A->F B->B C->C D->C E->F F->F G->G H->H I->I J->B K->B L->L M->C
merged B into C
A->F B->C C->C D->C E->F F->F G->G H->H I->I J->C K->C L->L M->C
merged F into G
A->G B->C C->C D->C E->G F->G G->G H->H I->I J->C K->C L->L M->C
merged L into C
A->G B->C C->C D->C E->G F->G G->G H->H I->I J->C K->C L->C M->C
merged G into I
A->I B->C C->C D->C E->I F->I G->I H->H I->I J->C K->C L->C M->C
merged I into H
A->H B->C C->C D->C E->H F->H G->H H->H I->H J->C K->C L->C M->C
merged C into H
A->H B->H C->H D->H E->H F->H G->H H->H I->H J->H K->H L->H M->H
"""
# Emacs turd '
# Fun tests (for sufficiently warped notions of "fun").
fun_tests = """
Build up to a recursive Sieve of Eratosthenes generator.
>>> def firstn(g, n):
... return [next(g) for i in range(n)]
>>> def intsfrom(i):
... while 1:
... yield i
... i += 1
>>> firstn(intsfrom(5), 7)
[5, 6, 7, 8, 9, 10, 11]
>>> def exclude_multiples(n, ints):
... for i in ints:
... if i % n:
... yield i
>>> firstn(exclude_multiples(3, intsfrom(1)), 6)
[1, 2, 4, 5, 7, 8]
>>> def sieve(ints):
... prime = next(ints)
... yield prime
... not_divisible_by_prime = exclude_multiples(prime, ints)
... for p in sieve(not_divisible_by_prime):
... yield p
>>> primes = sieve(intsfrom(2))
>>> firstn(primes, 20)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71]
Another famous problem: generate all integers of the form
2**i * 3**j * 5**k
in increasing order, where i,j,k >= 0. Trickier than it may look at first!
Try writing it without generators, and correctly, and without generating
3 internal results for each result output.
>>> def times(n, g):
... for i in g:
... yield n * i
>>> firstn(times(10, intsfrom(1)), 10)
[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
>>> def merge(g, h):
... ng = next(g)
... nh = next(h)
... while 1:
... if ng < nh:
... yield ng
... ng = next(g)
... elif ng > nh:
... yield nh
... nh = next(h)
... else:
... yield ng
... ng = next(g)
... nh = next(h)
The following works, but is doing a whale of a lot of redundant work --
it's not clear how to get the internal uses of m235 to share a single
generator. Note that me_times2 (etc) each need to see every element in the
result sequence. So this is an example where lazy lists are more natural
(you can look at the head of a lazy list any number of times).
>>> def m235():
... yield 1
... me_times2 = times(2, m235())
... me_times3 = times(3, m235())
... me_times5 = times(5, m235())
... for i in merge(merge(me_times2,
... me_times3),
... me_times5):
... yield i
Don't print "too many" of these -- the implementation above is extremely
inefficient: each call of m235() leads to 3 recursive calls, and in
turn each of those 3 more, and so on, and so on, until we've descended
enough levels to satisfy the print stmts. Very odd: when I printed 5
lines of results below, this managed to screw up Win98's malloc in "the
usual" way, i.e. the heap grew over 4Mb so Win98 started fragmenting
address space, and it *looked* like a very slow leak.
>>> result = m235()
>>> for i in range(3):
... print(firstn(result, 15))
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
[25, 27, 30, 32, 36, 40, 45, 48, 50, 54, 60, 64, 72, 75, 80]
[81, 90, 96, 100, 108, 120, 125, 128, 135, 144, 150, 160, 162, 180, 192]
Heh. Here's one way to get a shared list, complete with an excruciating
namespace renaming trick. The *pretty* part is that the times() and merge()
functions can be reused as-is, because they only assume their stream
arguments are iterable -- a LazyList is the same as a generator to times().
>>> class LazyList:
... def __init__(self, g):
... self.sofar = []
... self.fetch = g.__next__
...
... def __getitem__(self, i):
... sofar, fetch = self.sofar, self.fetch
... while i >= len(sofar):
... sofar.append(fetch())
... return sofar[i]
>>> def m235():
... yield 1
... # Gack: m235 below actually refers to a LazyList.
... me_times2 = times(2, m235)
... me_times3 = times(3, m235)
... me_times5 = times(5, m235)
... for i in merge(merge(me_times2,
... me_times3),
... me_times5):
... yield i
Print as many of these as you like -- *this* implementation is memory-
efficient.
>>> m235 = LazyList(m235())
>>> for i in range(5):
... print([m235[j] for j in range(15*i, 15*(i+1))])
[1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24]
[25, 27, 30, 32, 36, 40, 45, 48, 50, 54, 60, 64, 72, 75, 80]
[81, 90, 96, 100, 108, 120, 125, 128, 135, 144, 150, 160, 162, 180, 192]
[200, 216, 225, 240, 243, 250, 256, 270, 288, 300, 320, 324, 360, 375, 384]
[400, 405, 432, 450, 480, 486, 500, 512, 540, 576, 600, 625, 640, 648, 675]
Ye olde Fibonacci generator, LazyList style.
>>> def fibgen(a, b):
...
... def sum(g, h):
... while 1:
... yield next(g) + next(h)
...
... def tail(g):
... next(g) # throw first away
... for x in g:
... yield x
...
... yield a
... yield b
... for s in sum(iter(fib),
... tail(iter(fib))):
... yield s
>>> fib = LazyList(fibgen(1, 2))
>>> firstn(iter(fib), 17)
[1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584]
Running after your tail with itertools.tee (new in version 2.4)
The algorithms "m235" (Hamming) and Fibonacci presented above are both
examples of a whole family of FP (functional programming) algorithms
where a function produces and returns a list while the production algorithm
suppose the list as already produced by recursively calling itself.
For these algorithms to work, they must:
- produce at least a first element without presupposing the existence of
the rest of the list
- produce their elements in a lazy manner
To work efficiently, the beginning of the list must not be recomputed over
and over again. This is ensured in most FP languages as a built-in feature.
In python, we have to explicitly maintain a list of already computed results
and abandon genuine recursivity.
This is what had been attempted above with the LazyList class. One problem
with that class is that it keeps a list of all of the generated results and
therefore continually grows. This partially defeats the goal of the generator