I have an array of functions and I'm trying to produce one function which consists of the composition of the elements in my array.
My approach is:
def compose(list):
if len(list) == 1:
return lambda x:list[0](x)
list.reverse()
final=lambda x:x
for f in list:
final=lambda x:f(final(x))
return final
This method doesn't seems to be working, help will be appreciated.
(I'm reversing the list because this is the order of composition I want the functions to be)
The easiest approach would be first to write a composition of 2 functions:
def compose2(f, g):
return lambda *a, **kw: f(g(*a, **kw))
And then use reduce to compose more functions:
import functools
def compose(*fs):
return functools.reduce(compose2, fs)
Or you can use some library, which already contains compose function.
def compose (*functions):
def inner(arg):
for f in reversed(functions):
arg = f(arg)
return arg
return inner
Example:
>>> def square (x):
return x ** 2
>>> def increment (x):
return x + 1
>>> def half (x):
return x / 2
>>> composed = compose(square, increment, half) # square(increment(half(x)))
>>> composed(5) # square(increment(half(5))) = square(increment(2.5)) = square(3.5) = 12,25
12.25
It doesn't work because all the anonymous functions you create in the loop refer to the same loop variable and therefore share its final value.
As a quick fix, you can replace the assignment with:
final = lambda x, f=f, final=final: f(final(x))
Or, you can return the lambda from a function:
def wrap(accum, f):
return lambda x: f(accum(x))
...
final = wrap(final, f)
To understand what's going on, try this experiment:
>>> l = [lambda: n for n in xrange(10)]
>>> [f() for f in l]
[9, 9, 9, 9, 9, 9, 9, 9, 9, 9]
This result surprises many people, who expect the result to be [0, 1, 2, ...]. However, all the lambdas point to the same n variable, and all refer to its final value, which is 9. In your case, all the versions of final which are supposed to nest end up referring to the same f and, even worse, to the same final.
The topic of lambdas and for loops in Python has been already covered on SO.
One liner:
compose = lambda *F: reduce(lambda f, g: lambda x: f(g(x)), F)
Example usage:
f1 = lambda x: x+3
f2 = lambda x: x*2
f3 = lambda x: x-1
g = compose(f1, f2, f3)
assert(g(7) == 15)
Recursive implementation
Here's a fairly elegant recursive implementation, which uses features of Python 3 for clarity:
def strict_compose(*funcs):
*funcs, penultimate, last = funcs
if funcs:
penultimate = strict_compose(*funcs, penultimate)
return lambda *args, **kwargs: penultimate(last(*args, **kwargs))
Python 2 compatible version:
def strict_compose2(*funcs):
if len(funcs) > 2:
penultimate = strict_compose2(*funcs[:-1])
else:
penultimate = funcs[-2]
return lambda *args, **kwargs: penultimate(funcs[-1](*args, **kwargs))
This is an earlier version which uses lazy evaluation of the recursion:
def lazy_recursive_compose(*funcs):
def inner(*args, _funcs=funcs, **kwargs):
if len(_funcs) > 1:
return inner(_funcs[-1](*args, **kwargs), _funcs=_funcs[:-1])
else:
return _funcs[0](*args, **kwargs)
return inner
Both would seem to make a new tuple and dict of arguments each recursive call.
Comparison of all suggestions:
Let's test some of these implementations and determine which is most performant, first some single argument functions (Thank you poke):
def square(x):
return x ** 2
def increment(x):
return x + 1
def half(x):
return x / 2
Here's our implementations, I suspect my iterative version is the second most efficient (manual compose will naturally be fastest), but that may be in part due to it sidestepping the difficulty of passing any number of arguments or keyword arguments between functions - in most cases we'll only see the trivial one argument being passed.
from functools import reduce
def strict_recursive_compose(*funcs):
*funcs, penultimate, last = funcs
if funcs:
penultimate = strict_recursive_compose(*funcs, penultimate)
return lambda *args, **kwargs: penultimate(last(*args, **kwargs))
def strict_recursive_compose2(*funcs):
if len(funcs) > 2:
penultimate = strict_recursive_compose2(*funcs[:-1])
else:
penultimate = funcs[-2]
return lambda *args, **kwargs: penultimate(funcs[-1](*args, **kwargs))
def lazy_recursive_compose(*funcs):
def inner(*args, _funcs=funcs, **kwargs):
if len(_funcs) > 1:
return inner(_funcs[-1](*args, **kwargs), _funcs=_funcs[:-1])
else:
return _funcs[0](*args, **kwargs)
return inner
def iterative_compose(*functions):
"""my implementation, only accepts one argument."""
def inner(arg):
for f in reversed(functions):
arg = f(arg)
return arg
return inner
def _compose2(f, g):
return lambda *a, **kw: f(g(*a, **kw))
def reduce_compose1(*fs):
return reduce(_compose2, fs)
def reduce_compose2(*funcs):
"""bug fixed - added reversed()"""
return lambda x: reduce(lambda acc, f: f(acc), reversed(funcs), x)
And to test these:
import timeit
def manual_compose(n):
return square(increment(half(n)))
composes = (strict_recursive_compose, strict_recursive_compose2,
lazy_recursive_compose, iterative_compose,
reduce_compose1, reduce_compose2)
print('manual compose', min(timeit.repeat(lambda: manual_compose(5))), manual_compose(5))
for compose in composes:
fn = compose(square, increment, half)
result = min(timeit.repeat(lambda: fn(5)))
print(compose.__name__, result, fn(5))
Results
And we get the following output (same magnitude and proportion in Python 2 and 3):
manual compose 0.4963762479601428 12.25
strict_recursive_compose 0.6564744340721518 12.25
strict_recursive_compose2 0.7216697579715401 12.25
lazy_recursive_compose 1.260614730999805 12.25
iterative_compose 0.614982972969301 12.25
reduce_compose1 0.6768529079854488 12.25
reduce_compose2 0.9890829260693863 12.25
And my expectations were confirmed: the fastest is of course, manual function composition followed by the iterative implementation. The lazy recursive version is much slower - likely since a new stack frame is created by each function call and a new tuple of functions is created for each function.
For a better and perhaps more realistic comparison, if you remove **kwargs and change *args to arg in the functions, the ones that used them will be more performant, and we can better compare apples to apples - here, aside from manual composition, reduce_compose1 wins followed by the strict_recursive_compose:
manual compose 0.443808660027571 12.25
strict_recursive_compose 0.5409777010791004 12.25
strict_recursive_compose2 0.5698030130006373 12.25
lazy_recursive_compose 1.0381018499610946 12.25
iterative_compose 0.619289995986037 12.25
reduce_compose1 0.49532539502251893 12.25
reduce_compose2 0.9633988010464236 12.25
Functions with just one arg:
def strict_recursive_compose(*funcs):
*funcs, penultimate, last = funcs
if funcs:
penultimate = strict_recursive_compose(*funcs, penultimate)
return lambda arg: penultimate(last(arg))
def strict_recursive_compose2(*funcs):
if len(funcs) > 2:
penultimate = strict_recursive_compose2(*funcs[:-1])
else:
penultimate = funcs[-2]
return lambda arg: penultimate(funcs[-1](arg))
def lazy_recursive_compose(*funcs):
def inner(arg, _funcs=funcs):
if len(_funcs) > 1:
return inner(_funcs[-1](arg), _funcs=_funcs[:-1])
else:
return _funcs[0](arg)
return inner
def iterative_compose(*functions):
"""my implementation, only accepts one argument."""
def inner(arg):
for f in reversed(functions):
arg = f(arg)
return arg
return inner
def _compose2(f, g):
return lambda arg: f(g(arg))
def reduce_compose1(*fs):
return reduce(_compose2, fs)
def reduce_compose2(*funcs):
"""bug fixed - added reversed()"""
return lambda x: reduce(lambda acc, f: f(acc), reversed(funcs), x)
The most reliable implementation I have found is in the 3rd party library toolz. The compose function from this library also deals with docstring for the composition of functions.
The source code is freely available. Below is a simple example of usage.
from toolz import compose
def f(x):
return x+1
def g(x):
return x*2
def h(x):
return x+3
res = compose(f, g, h)(5) # 17
You can also create an array of functions and use reduce:
def f1(x): return x+1
def f2(x): return x+2
def f3(x): return x+3
x = 5
# Will print f3(f2(f1(x)))
print reduce(lambda acc, x: x(acc), [f1, f2, f3], x)
# As a function:
def compose(*funcs):
return lambda x: reduce(lambda acc, f: f(acc), funcs, x)
f = compose(f1, f2, f3)
pip install funcoperators is another library to implement it that allows infix notation:
from funcoperators import compose
# display = lambda x: hex(ord(list(x)))
display = hex *compose* ord *compose* list
# also works as a function
display = compose(hex, ord, list)
pip install funcoperators https://pypi.org/project/funcoperators/
Disclaimer: I'm the creator of the module
Suppose you have the following functions:
def square(x):
return x**2
def inc(x):
return x+1
def half(x):
return x/2
Define a compose function as follows:
import functools
def compose(*functions):
return functools.reduce(lambda f, g: lambda x: g(f(x)),
functions,
lambda x: x)
Usage:
composed = compose(square, inc, inc, half)
compose(10)
>>> 51.0
which executes the functions procedurally in the defined order:
square (= 100)
inc (= 101)
inc (= 102)
half (= 51)
Adapted from https://mathieularose.com/function-composition-in-python/.
I prefer this one due to readability/simplicity
from functools import reduce
def compose(*fs):
apply = lambda arg, f: f(arg)
composition = lambda x: reduce(apply, [x, *fs])
return composition
the pipe = compose(a, b, c) will first apply a, then b and then c.
With regard to maintainability (an debugging) I think actually this one is the easiest to use:
def compose(*fs):
def composition(x):
for f in fs:
x = f(x)
return x
return composition
You can use funcy.
Installation:
pip install funcy
Then you can use compose or rcompose as follows:
from funcy import compose, rcompose
def inc(x): return x + 1
def double(x): return x + x
def tripple(x): return x + x + x
print(compose(tripple, double, inc)(1)) # 12
print(rcompose(inc, double, tripple)(1)) # 12
I found this piece of code from GeeksforGeeks here for Python 3. Not sure of how efficient it is, but it is very simple to understand.
# importing reduce() from functools
from functools import reduce
# composite_function accepts N
# number of function as an
# argument and then compose them
def composite_function(*func):
def compose(f, g):
return lambda x : f(g(x))
return reduce(compose, func, lambda x : x)
# Function to add 2
def add(x):
return x + 2
# Function to multiply 2
def multiply(x):
return x * 2
# Function to subtract 2
def subtract(x):
return x - 1
# Here add_subtract_multiply will
# store lambda x : multiply(subtract(add(x)))
add_subtract_multiply = composite_function(multiply,
subtract,
add)
print("Adding 2 to 5, then subtracting 1 and multiplying the result with 2: ",
add_subtract_multiply(5))
You can keep adding more functions to composite_functions e.g.:
print(composite_function(multiply, add, subtract, multiply,subtract, add)(5))
More general solution of Imanol Luengo from my point of view (python notebook example):
from functools import reduce
from functools import partial
def f(*argv, **kwargs):
print('f: {} {}'.format(argv, kwargs))
return argv, kwargs
def g(*argv, **kwargs):
print('g: {} {}'.format(argv, kwargs))
return argv, kwargs
def compose(fs, *argv, **kwargs):
return reduce(lambda x, y: y(*x[0], **x[1]), fs, (argv, kwargs))
h = partial(compose, [f, g])
h('value', key='value')
output:
f: ('value',) {'key': 'value'}
g: ('value',) {'key': 'value'}
m = partial(compose, [h, f, g])
m('value', key='value')
output:
f: ('value',) {'key': 'value'}
g: ('value',) {'key': 'value'}
f: ('value',) {'key': 'value'}
g: ('value',) {'key': 'value'}
Perfectly good question, but the answers sure are unnecessarily complex. It's just:
def compose(*funs):
return (lambda x:
x if len(funs) == 0
else compose(*funs[:-1])(funs[-1](x)))
If you want no dependencies here is a one-liner recursive solution:
def compose(*f):
return f[0] if len(f) <= 1 else lambda *a,**kw: f[0](compose(*f[1:])(*a,**kw))
N.B. len(f) == 1 might seem more reasonable at first sight, but it allows to write compose() (i.e. no arguments) throwing an error only when you apply the empty compose function. On the contrary, with len(f) <= 1, compose() throws an error immediately, which is a more rational behavior.
This is my version
def compose(*fargs):
def inner(arg):
if not arg:
raise ValueError("Invalid argument")
if not all([callable(f) for f in fargs]):
raise TypeError("Function is not callable")
return reduce(lambda arg, func: func(arg), fargs, arg)
return inner
An example of how it's used
def calcMean(iterable):
return sum(iterable) / len(iterable)
def formatMean(mean):
return round(float(mean), 2)
def adder(val, value):
return val + value
def isEven(val):
return val % 2 == 0
if __name__ == '__main__':
# Ex1
rand_range = [random.randint(0, 10000) for x in range(0, 10000)]
isRandIntEven = compose(calcMean, formatMean,
partial(adder, value=0), math.floor.__call__, isEven)
print(isRandIntEven(rand_range))
Related
This question already has an answer here:
Compute a chain of functions in python
(1 answer)
Closed 7 months ago.
I am reading Joseph Howse OpenCV book. In the appendix, he's discussing creation of composite function from 2 other functions, as follows:
def createCompositeFunction(func1, func2):
return lambda x : func2(func1(x))
How could I write this for an arbitrary number of functions, like so
def createCompositeFunction(*funcs):
pass
I assume this should be done using recursion, but I can not wrap my head around it.
Any suggestions?
You don't need recursion; this is a simple iterative problem:
def createCompositeFunction(*funcs):
def apply(x):
for func in funcs:
x = func(x)
return x
return apply
def f1(x):
return x + 2
def f2(x):
return x * 3
def f3(x):
return x / 2
comp = createCompositeFunction(f1, f2, f3)
print("comp(1) =", comp(1))
print("comp(2) =", comp(2))
Running the above code will output:
comp(1) = 4.5
comp(2) = 6.0
What you're asking for in functional programming terms is a reducer higher-order function. Python provides functools.reduce to this end:
def reduce(function, iterable, initializer=None):
Where function should be an applicator, iterable is the chain of funcs you want to apply, and initializer is your argument.
Here's a simple example on one argument:
from functools import reduce
def sub1(a):
return a - 1
def mul2(a):
return a * 2
def apply(x, f):
return f(x)
def compose(*fns):
return lambda x: reduce(apply, fns, x)
print(compose(sub1, mul2)(4)) # => 6
You can partial or lambda in extra args as needed:
from functools import partial, reduce
from operator import mul, sub
def compose(*fns):
return lambda x: reduce(lambda x, f: f(x), fns, x)
print(compose(lambda x: sub(x, 2), partial(mul, 3))(4)) # => 6
There are a lot of ways to go with this sort of thing, so I'll leave it at this absent further information about your use case.
As it turns out, this is pretty much a more fleshed-out version of Compute a chain of functions in python.
#ggorlen offers an efficient solution using reduce. Here's a recursive form -
# right-to-left composition
def compose(f = lambda x: x, *funcs):
if not funcs:
return f
else:
return lambda x: f(compose(*funcs)(x))
# left-to-right composition
def compose(f = lambda x: x, *funcs):
if not funcs:
return f
else:
return lambda x: compose(*funcs)(f(x))
Using separate definitions for identity and comp2 may make it easier to see how things are working -
def identity(x):
return x
def comp2(f, g):
return lambda x: f(g(x))
# right-to-left composition
def compose(f = identity, *funcs):
if not funcs:
return f
else:
return comp2(f, compose(*funcs))
# left-to-right composition
def compose(f = identity, *funcs):
if not funcs:
return f
else:
return comp2(compose(*funcs), f)
Here's a complete demo using left-to-right compose and a curry helper. Because curry accepts function as input, we can conveniently use it as a decorator too -
def curry(arity):
def loop(f, n, args):
return f(*args) if n == 0 else lambda x: loop(f, n - 1, (*args, x))
return lambda f: loop(f, arity, ())
#curry(2)
def add(x, y):
return x + y
#curry(2)
def mul(x, y):
return x * y;
myfunc = compose(add(1), mul(2), mul(2), mul(2))
print(myfunc(0))
# (((0 + 1) * 2) * 2) * 2
# 8
You can use accumulate from functools (and keep intermediate results):
from itertools import accumulate
def f1(x): return x + 2
def f2(x): return x * 3
def f3(x): return x / 4
def createCompositeFunction(func1, func2):
return lambda x: func2(func1(x))
# For x=3
l = [f(3) for f in accumulate([f1, f2, f3], createCompositeFunction)]
Output:
>>> l
[5, 15, 3.75] # <- the last item l[-1] is the final value
#larsks has a pretty nice answer. If you're interested in recursion specifically, here's an option:
def createCompositeFunction(*funcs):
func = funcs[0]
funcs = funcs[1:]
if len(funcs) == 0:
return func
return lambda x: func(createCompositeFunction(*funcs)(x))
def square(x):
return x ** 2
square_thrice = createCompositeFunction(square, square, square)
print(square_thrice(2))
Output:
>>> 256
Recursive approached: Assumed that the range of each function is the same of the domain of the next one.
The freedom in the initial value infers a condition on the outputs of the function, it cannot be None (filter as is not None to avoid automatic casting, i.e. 1<-->True, ''<-->False, ...).
def direct_composition(funcs, init_value=None):
if funcs:
if init_value is not None:
return direct_composition(funcs[1:], funcs[0](init_value))
return direct_composition(funcs[1:], funcs[0]())
return init_value
# sample functions
def a0(): return 'a' # initial function with no args
def a(x): return 'a'+x
def b(x): return 'b' + x
def c(x): return 'c' + x
# test with initial function taking parameters
funcs = a, b, c
direct_composition(funcs, '>')
#cba>
# test with initial function taking no parameters
funcs = a0, a, b, c
direct_composition(funcs)
#cba
Double layer approach with no side-effects, no restriction on the output of the functions. A pushward is when you fix a function that will be the most internal one and the other functions will be applied in increasing order to it.
def pushforward(f, initial_value=None):
def apply(value, funcs):
if funcs:
return apply(funcs[0](value), funcs[1:])
return value
return (lambda funcs: apply(f(initial_value), funcs)) if initial_value else (lambda funcs: apply(f(), funcs))
# with no initial value
f_init = a0
funcs = a, b, c
res = pushforward(f_init)(funcs)
print(res)
# with initial value
f_init = a
funcs = b, c
res = pushforward(f_init, '>')(funcs)
print(res)
I would like to build the sum of functions in python. However, I don't know upfront the length of the sum. All functions are of similar type, i.e. having one input and same output type. For two something like this would work
In [180]: def H(f, g):
...: def _h(x):
...: return f(x) + g(x)
...: return _h
However, I would like to have something which is generic in the sense that I could write H(*args) and it returns me the sum of all function in args (also working for just one).
Am I correct that this is the only way to build sum of functions? I can't write something like h = f+g for two function ?
It is probably easier to write something that is extendable. And you should use the built-in function sum to do the actual summing. This returns a generator that applies each function to the input x:
def map_funcs(x, *funcs):
return (f(x) for f in funcs)
funcs = lambda x: x + 1, lambda x: x**2
x = 10
print(sum(map_funcs(x, *funcs)))
If you want to you can also make it a wrapper which returns something callable, similar to what you've already got:
def map_funcs(*funcs):
def wrapper(x):
return (f(x) for f in funcs)
return wrapper
funcs = lambda x: x + 1, lambda x: x**2
x = 10
print(sum(map_funcs(*funcs)(x)))
# 111
Yes, it's possible. You have to use the sum() builtin function that return the sum of all values in the given list. Before that, you of course have to compute the list of all the functions givent to H() run with the correct parameter:
def power_two(x):
return x**2
def plus_20(x):
return x + 20
def H(*args):
def _h(x):
_results = [f(x) for f in args]
return sum(_results)
return _h
if __name__ == '__main__':
the_func = H(power_two, plus_20)
final_result = the_func(2)
print("(2^2) + (2+20) = %s" % the_func(2))
print("(3^2) + (3+20) = %s" % the_func(3))
Returns:
(2^2) + (2+20) = 26
(3^2) + (3+20) = 32
Try this:-
def H(*args):
def _h(x):
for func in args:
z += func(x)
return z
return _h
Just loop around the functional arguments and then sum it. I guess simple?
I hope it helps!
Is there a function in Python that would do this:
val = f3(f2(f1(arg)))
by typing this (for example):
val = chainCalling(arg,f3,f2,f1)
I just figured, since python is (arguably) a functional language the function I'm looking for will make syntax brighter
Use the reduce() function to chain calls:
from functools import reduce
val = reduce(lambda r, f: f(r), (f1, f2, f3), arg)
I used the forward-compatible functools.reduce() function; in Python 3 reduce() is no longer in the built-in namespace.
This can also be made a separate function, of course:
from functools import reduce
def chain(*funcs):
def chained_call(arg):
return reduce(lambda r, f: f(r), funcs, arg)
return chained_call
You can use the reduce() functool — as Martijn briantly suggested, or you can write it yourself quite simply:
def chainCalling(arg, *funcs):
if len(funcs) > 0:
return chainCalling(funcs[0](arg), funcs[1:])
return arg
or, as an alternative not using recursion — so not bound to the call stack limitation, as suggested by Martijn:
def chainCalling(arg, *funcs):
result = arg
for f in funcs:
result = f(result)
return result
Obviously, you'll want to call it that way, to avoid an useless reversal of the arguments:
chainCalling(arg, f1, f2, f3)
In case you want to apply the chain of functions to multiple arguments, you can create an aggregated function.
g = lambda x: f3(f2(f1(x)))
or more flexible (when there is an arbitrary list of functions):
from functools import reduce, partial
f3 = lambda x: -x
f2 = lambda x: x ** 2
f1 = lambda x: x + 1
function_list = (f1, f2, f3)
g = partial(reduce, lambda r, f: f(r), function_list)
print(g(3)) # results in -16
It might be a tad late, but try my solution below. chain_func essentially creates a new wrapper function that calls all underlying functions in chain when invoked in runtime.
def chain_func(*funcs):
def _chain(*args, **kwargs):
cur_args, cur_kwargs = args, kwargs
ret = None
for f in reversed(funcs):
cur_args, cur_kwargs = (f(*cur_args, **cur_kwargs), ), {}
ret = cur_args[0]
return ret
return _chain
I wrote a function "rep" that takes a function f and takes n compositions of f.
So rep(square,3) behaves like this: square(square(square(x))).
And when I pass 3 into it, rep(square,3)(3)=6561.
There is no problem with my code, but I was wondering if there was a way to make it "prettier" (or shorter) without having to call another function or import anything. Thanks!
def compose1(f, g):
"""Return a function h, such that h(x) = f(g(x))."""
def h(x):
return f(g(x))
return h
def rep(f,n):
newfunc = f
count=1
while count < n:
newfunc = compose1(f,newfunc)
count+=1
return newfunc
If you're looking for speed, the for loop is clearly the way to go. But if you're looking for theoretical academic acceptance ;-), stick to terse functional idioms. Like:
def rep(f, n):
return f if n == 1 else lambda x: f(rep(f, n-1)(x))
def rep(f, n):
def repeated(x):
for i in xrange(n):
x = f(x)
return x
return repeated
Using a for loop instead of while is shorter and more readable, and compose1 doesn't really need to be a separate function.
While I agree that repeated composition of the same function is best done with a loop, you could use *args to compose an arbitrary number of functions:
def identity(x):
return x
def compose(*funcs):
if funcs:
rest = compose(*funcs[1:])
return lambda x: funcs[0](rest(x))
else:
return identity
And in this case you would have:
def rep(f,n):
funcs = (f,)*n # tuple with f repeated n times
return compose(*funcs)
And as DSM kindly pointed out in the comments, you could remove the recursion like so:
def compose(*funcs):
if not funcs:
return identity
else:
def composed(x):
for f in reversed(funcs):
x = f(x)
return x
return composed
(also note that you can replace x with *args if you also want to support arbitrary arguments to the functions you're composing, but I left it at one argument since that's how you have it in the original problem)
Maybe someone will find this solution useful
Compose number of functions
from functools import reduce
def compose(*functions):
return reduce(lambda x, y: (lambda arg: x(y(arg))), functions)
Use list comprehensions to generate list of functions
def multi(how_many, func):
return compose(*[func for num in range(how_many)])
Usage
def square(x):
return x * x
multi(3, square)(3) == 6561
I'm trying to write a function in python that is like:
def repeated(f, n):
...
where f is a function that takes one argument and n is a positive integer.
For example if I defined square as:
def square(x):
return x * x
and I called
repeated(square, 2)(3)
this would square 3, 2 times.
That should do it:
def repeated(f, n):
def rfun(p):
return reduce(lambda x, _: f(x), xrange(n), p)
return rfun
def square(x):
print "square(%d)" % x
return x * x
print repeated(square, 5)(3)
output:
square(3)
square(9)
square(81)
square(6561)
square(43046721)
1853020188851841
or lambda-less?
def repeated(f, n):
def rfun(p):
acc = p
for _ in xrange(n):
acc = f(acc)
return acc
return rfun
Using reduce and lamba.
Build a tuple starting with your parameter, followed by all functions you want to call:
>>> path = "/a/b/c/d/e/f"
>>> reduce(lambda val,func: func(val), (path,) + (os.path.dirname,) * 3)
"/a/b/c"
Something like this?
def repeat(f, n):
if n==0:
return (lambda x: x)
return (lambda x: f (repeat(f, n-1)(x)))
Use an itertools recipe called repeatfunc that performs this operation.
Given
def square(x):
"""Return the square of a value."""
return x * x
Code
From itertools recipes:
def repeatfunc(func, times=None, *args):
"""Repeat calls to func with specified arguments.
Example: repeatfunc(random.random)
"""
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times))
Demo
Optional: You can use a third-party library, more_itertools, that conveniently implements these recipes:
import more_itertools as mit
list(mit.repeatfunc(square, 2, 3))
# [9, 9]
Install via > pip install more_itertools
Using reduce and itertools.repeat (as Marcin suggested):
from itertools import repeat
from functools import reduce # necessary for python3
def repeated(func, n):
def apply(x, f):
return f(x)
def ret(x):
return reduce(apply, repeat(func, n), x)
return ret
You can use it as follows:
>>> repeated(os.path.dirname, 3)('/a/b/c/d/e/f')
'/a/b/c'
>>> repeated(square, 5)(3)
1853020188851841
(after importing os or defining square respectively)
I think you want function composition:
def compose(f, x, n):
if n == 0:
return x
return compose(f, f(x), n - 1)
def square(x):
return pow(x, 2)
y = compose(square, 3, 2)
print y
Here's a recipe using reduce:
def power(f, p, myapply = lambda init, g:g(init)):
ff = (f,)*p # tuple of length p containing only f in each slot
return lambda x:reduce(myapply, ff, x)
def square(x):
return x * x
power(square, 2)(3)
#=> 81
I call this power, because this is literally what the power function does, with composition replacing multiplication.
(f,)*p creates a tuple of length p filled with f in every index. If you wanted to get fancy, you would use a generator to generate such a sequence (see itertools) - but note it would have to be created inside the lambda.
myapply is defined in the parameter list so that it is only created once.