This question already has answers here:
Python function as a function argument?
(10 answers)
Python Argument Binders
(7 answers)
Closed 5 months ago.
Is it possible to pass functions with arguments to another function in Python?
Say for something like:
def perform(function):
return function()
But the functions to be passed will have arguments like:
action1()
action2(p)
action3(p,r)
Do you mean this?
def perform(fun, *args):
fun(*args)
def action1(args):
# something
def action2(args):
# something
perform(action1)
perform(action2, p)
perform(action3, p, r)
This is what lambda is for:
def perform(f):
f()
perform(lambda: action1())
perform(lambda: action2(p))
perform(lambda: action3(p, r))
You can use the partial function from functools like so.
from functools import partial
def perform(f):
f()
perform(Action1)
perform(partial(Action2, p))
perform(partial(Action3, p, r))
Also works with keywords
perform(partial(Action4, param1=p))
Use functools.partial, not lambdas! And ofc Perform is a useless function, you can pass around functions directly.
for func in [Action1, partial(Action2, p), partial(Action3, p, r)]:
func()
This is called partial functions and there are at least 3 ways to do this. My favorite way is using lambda because it avoids dependency on extra package and is the least verbose. Assume you have a function add(x, y) and you want to pass add(3, y) to some other function as parameter such that the other function decides the value for y.
Use lambda
# generic function takes op and its argument
def runOp(op, val):
return op(val)
# declare full function
def add(x, y):
return x+y
# run example
def main():
f = lambda y: add(3, y)
result = runOp(f, 1) # is 4
Create Your Own Wrapper
Here you need to create a function that returns the partial function. This is obviously lot more verbose.
# generic function takes op and its argument
def runOp(op, val):
return op(val)
# declare full function
def add(x, y):
return x+y
# declare partial function
def addPartial(x):
def _wrapper(y):
return add(x, y)
return _wrapper
# run example
def main():
f = addPartial(3)
result = runOp(f, 1) # is 4
Use partial from functools
This is almost identical to lambda shown above. Then why do we need this? There are few reasons. In short, partial might be bit faster in some cases (see its implementation) and that you can use it for early binding vs lambda's late binding.
from functools import partial
# generic function takes op and its argument
def runOp(op, val):
return op(val)
# declare full function
def add(x, y):
return x+y
# run example
def main():
f = partial(add, 3)
result = runOp(f, 1) # is 4
(months later) a tiny real example where lambda is useful, partial not:
say you want various 1-dimensional cross-sections through a 2-dimensional function,
like slices through a row of hills.
quadf( x, f ) takes a 1-d f and calls it for various x.
To call it for vertical cuts at y = -1 0 1 and horizontal cuts at x = -1 0 1,
fx1 = quadf( x, lambda x: f( x, 1 ))
fx0 = quadf( x, lambda x: f( x, 0 ))
fx_1 = quadf( x, lambda x: f( x, -1 ))
fxy = parabola( y, fx_1, fx0, fx1 )
f_1y = quadf( y, lambda y: f( -1, y ))
f0y = quadf( y, lambda y: f( 0, y ))
f1y = quadf( y, lambda y: f( 1, y ))
fyx = parabola( x, f_1y, f0y, f1y )
As far as I know, partial can't do this --
quadf( y, partial( f, x=1 ))
TypeError: f() got multiple values for keyword argument 'x'
(How to add tags numpy, partial, lambda to this ?)
Although all the responses are very accurate and well explained.
I want to make a clarification that you also can pass anonymous functions.
def perform(fun, *arg):
return fun(*arg)
# Pass anonymous function
print(perform(lambda x: x + 1, 3)) # output: 4
print(perform(lambda x, y: x + y + 1, 3, 2)) # output: 6
# Pass defined function
perform(lambda: action1())
perform(lambda: action2(p))
perform(lambda: action3(p, r))
Here is a way to do it with a closure:
def generate_add_mult_func(func):
def function_generator(x):
return reduce(func,range(1,x))
return function_generator
def add(x,y):
return x+y
def mult(x,y):
return x*y
adding=generate_add_mult_func(add)
multiplying=generate_add_mult_func(mult)
print adding(10)
print multiplying(10)
I think this is what you're looking for...
def action1(action):
print(f'doing {action} here!')
def perform(function):
return function()
perform(lambda : action1('business action'))
lambda packages up func and args in closure and passes to perform()
Thanks to David Beasley.
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'm looking for a nice functional way to do the following:
def add(x, y):
return x + y
def neg(x):
return -x
def c(x, y):
# Apply neg to inputs for add
_x = neg(x)
_y = neg(y)
return add(_x, _y)
neg_sum = c(2, 2) # -4
It seems related to currying, but all of the examples I can find use functions that only have one input variable. I would like something that looks like this:
def add(x, y):
return x + y
def neg(x):
return -x
c = apply(neg, add)
neg_sum = c(2, 2) # -4
This is a fairly direct way to do it:
def add(x, y):
return x + y
def neg(x):
return -x
def apply(g, f):
# h is a function that returns
# f(g(arg1), g(arg2), ...)
def h(*args):
return f(*map(g, args))
return h
# or this:
# def apply(g, f):
# return lambda *args: f(*map(g, args))
c = apply(neg, add)
neg_sum = c(2, 2) # -4
Note that when you use *myvar as an argument in a function definition, myvar becomes a list of all non-keyword arguments that are received. And if you call a function with *expression as an argument, then all the items in expression are unpacked and sent as separate arguments to the function. I use these two behaviors to make h accept an unknown list of arguments, then apply function g to each one (with map), then pass all of them as arguments to f.
A different approach, depending on how extensible you need this to be, is to create an object which implements your operator methods, which each return the same object, allowing you to chain operators together in arbitrary orders.
If you can cope with it always returning a list, you might be able to make it work.
class mathifier:
def __init__(self,values):
self.values = values
def neg(self):
self.values = [-value for value in self.values]
return self
def add(self):
self.values = [sum(self.values)]
return self
print (mathifier([2,3]).neg().add().values)
And you can still get your named function for any set of chained functions:
neg_add = lambda x : mathifier(x).neg().add()
print(neg_add([2,3]).values)
From Matthias Fripp's answer, I asked myself : I'd like to compose add and neg both ways : add_neg(*args) and neg_add(*args). This requires hacking Matthias suggestion a bit. The idea is to get some hint on the arity (number of args) of the functions to compose. This information is obtained with a bit of introspection, thanks to inspect module. With this in mind, we adapt the way args are passed through the chain of funcs. The main assumption here is that we deal with real functions, in the mathematical sense, i.e. functions returning ONE float, and taking at least one argument.
from functools import reduce
from inspect import getfullargspec
def arity_one(func):
spec = getfullargspec(func)
return len(spec[0])==1 and spec[1] is None
def add(*args):
return reduce(lambda x,y:x+y, args, 0)
def neg(x):
return -x
def compose(fun1,fun2):
def comp(*args):
if arity_one(fun2): return fun1(*(map( fun2, args)))
else: return fun1(fun2(*args))
return comp
neg_add = compose(neg, add)
add_neg = compose(add, neg)
print(f"-2+(-3) = {add_neg(2, 3)}")
print(f"-(2+3) = {neg_add(2, 3)}")
The solution is still very adhoc...
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!
This question already has answers here:
Python Argument Binders
(7 answers)
Closed 6 months ago.
How should I make function with lesser dimensionality than the original one by fixing an
argument of it:
For example I want to make successor function out of sum function as follows:
def add(x, y):
return x + y
Now I am looking for something like this:
g = f(~, 1) which would be the successor function, i.e. g(x) = x+1.
You can write your own function:
def g(y):
return f(2, y)
Or more concisely:
g = lambda y: f(2, y)
There's also functools.partial:
import functools
def f(x, y):
return x + y
g = functools.partial(f, 2)
You can then call it as before:
>>> g(3)
5
If you do more than a little of this then you could use something like a decorator.
def with_x(x, fn, name=None):
def foo(*args, **kwargs):
return fn(x, *args, **kwargs)
if name:
foo.__name__ = name
return foo
def example(x,y):
return x**y
g = with_x(2, example)
g(3) #8
Use name = parameter if you care about the __name__ of the resulting function. You could get fancier with other hacks into the enclosed function innards possibly using the __inspect__ module if need be. But then you have re-written the previously mentioned functools partial stuff just to avoid having to give keyword arguments.
I am studying the properties of functions in Python and I came across an exercise that asks to:
Write a function which returns de power of a number. Conditions: The function may only take 1 argument and must use another function to return the value of the power of a given number.
The code that solves this exercise is:
def power(x):
return lambda y: y**x
For example, if we would like to know the value of the power: 2^3, we would call the function like this: power(3)(2)
Here is what I would like to know:
Is there any way to write a function that, when called, has a similar structure: function()()().
In other words, is it possible to write a function, that requires three or more parentheses ()()() when called?
If it is possible, could you please give me an example code of that function and briefly explain it?
Also:
def power(x):
def power_extra(y):
return y
def power_another(z):
return z
return power_extra and power_another
Possible?
Sure you can:
def power_times(k):
"""use as power_times(k)(x)(y) => k * y^x"""
return lambda x: lambda y: k * y**x
print power_times(2)(3)(4) # returns 2 * 4^3 = 128
When you call this function with argument 2 (power_times(2)), it returns a lambda function that works like lambda x: lambda y: 2 * y ** x (that is, like your original function, only with an extra "times 2").
You can stack as many lambdas on top of each other as you like:
def many_lambdas(x):
"""many_lambdas(x)(y)(z)(q) => x + y * z^q"""
return lambda y: lambda z: lambda q: x + y * z ** q
print many_lambdas(1)(2)(3)(4) # prints 163
Indeed, it might be even clearer if you skipped using def at all, and just wrote:
many_lambdas = lambda x: lambda y: lambda z: lambda q: x + y * z ** q
Or, alternatively, you could skip using lambda ever and just use them as nested functions:
def many_funcs(x):
def many_funcs_y(y):
def many_funcs_z(z):
def many_funcs_q(q):
return x + y * z ** q
return many_funcs_q
return many_funcs_z
return many_funcs_y
print many_funcs(1)(2)(3)(4) # prints 163
#David's answer would aptly answer you question for fixed nested function calls. For undefined nesting, you may want to define a class and overload the __call__ method along with __repr__ and __int__ to serve your Purpose.
>>> class Power(object):
def __init__(self, value):
self.value = value
def __call__(self, value):
self.value **= value
return self
def __int__(self):
return self.value
def __repr__(self):
return str(self.value)
>>> print Power(2)(2)(2)(2)(2)
65536
>>> int(Power(2)(2)(2)(2)(2)) / 2
32768