Does Python 3 implement short-circuiting in built-in functions whenever possible, just like it does for boolean statements?
A specific example, take the below code snippet:
min((20,11), key = lambda x : x % 10) # 20
Does Python evaluate beforehand that the minimum value possible of the function passed as the key argument is 0, and therefore stops right after evaluating the first integer in the iterable passed (20) as 20 % 10 is equal to 0?
Or does it have to evaluate all the elements in the iterable before returning the answer?
I guess short-circuiting isn't even always possible especially for more complex functions, but what about for well-known, built-in functions or operators like %?
I couldn't find the answer in the official docs.
Thanks,
python has to evaluate all values inside the iterable because the languaje evaluate element by element, if you have in your tuple something that is not a number it will trigger an exception when try to perform the % operation. Python can not guess what is inside your list. You can test this by defining a function instead of a lambda and set debug point inside.
def my_mod(x):
import ipdb; ipdb.set_trace()
return x % 20
then call the function
min((20,11), key = my_mod)
you can do a quick error test case with
min((20,11, "s"), key = my_mod)
It will trigger an exception but first had to evaluate all the previous element in the list.
Related
Let´s say I have 2 functions like these ones:
def list(n):
l=[x for x in range(n)]
return l
def square(l):
l=list(map(lambda x:x**2,l))
print(l)
The first one makes a list from all numbers in a given range which is "n" and the second one receives a list as a parameter and returns the squared values of this list.
However when I write:
square(list(20))
it raises the error "map object cannot be interpreted as an integer" and whenever I erase one of the functions above and run the other one it runs perfectly and I have no idea what mistake I made.
You redefined the standard function list()! Rename it to my_list() and clean the code accordingly.
As a side note, your function list() is doing exactly what list(range(n)) would do. Why do you need it at all? In fact, for most purposes (including your example), range(n) alone is sufficient.
Finally, you do not pass a function as a parameter. You pass the value generated by another function. It is not the same.
I'm kind of newbie as programmer, but I wish to master Python and I'm developing open source application. This application has function to gather some information. This function takes 1 parameter. This parameter can be 0, 1 or 2. 0 = False, 1 = True, 2 = Multi. Also I have an if statement that does 2 actions. 1st - (when False) gathers single type value, 2nd - (when True) gathers multiple type values and when parameter is 2 (multi) then it will gather single type (1st) and multiple types (2nd). My if statement looks like this:
if False:
get_single_type = code.of.action
generators.generate_data(False, get_single_type)
elif True:
get_multiple_type = code.of.action
generators.generate_data(True, get_multiple_type)
else:
get_single_type = code.of.action
generators.generate_data(False, get_single_type)
get_multiple_type = code.of.action
generators.generate_data(True, get_multiple_type)
Is there maybe better way of avoiding this kind of coding, like in last else statement when I call both single and multiple.
Thank you in advance.
One thing I learned from Python is that although it lacks the Switch operator, you can use dictionary in a similar fashion to get things done since everything is an object:
def get_single():
# define your single function
get_single_type = code.of.action
generators.generate_data(False, get_single_type)
def get_multi():
# define your multi function
get_multiple_type = code.of.action
generators.generate_data(True, get_multiple_type)
actions = {
0: [get_single],
1: [get_multi],
2: [get_single, get_multi]
}
parameter = 0 # replace this line with however you are capturing the parameter
for action in actions[parameter]:
action()
This way you avoid c+p your code everywhere and have it referenced from the function, and your "actions" dictionary define the function to be used based on the parameter given.
In this case since you have multiple functions you want to call, I kept all dictionary items as a list so the structure is consistent and it can be iterated through to perform any number of actions.
Ensure you use leave out the () in the dictionary so that the functions aren't instantiated when the dictionary is defined. And remember to add () when you are actually calling the function from the dictionary to instantiate it.
This is something you will often encounter and it is pretty much always bad practice to be repeating code. Anyway, the way to do this is use two if-statements. This way, even if the first case passes, the second case can still pass. Oh, and assuming your variable that can be 0, 1 or 2 is called x, then we could either use or and two checks:
if x == 0 or x == 2:
but, personally, I prefer using in on a tuple:
if x in (0, 2):
get_single_type = code.of.action
generators.generate_data(False, get_single_type)
if x in (1, 2):
get_multiple_type = code.of.action
generators.generate_data(True, get_multiple_type)
I have written a below small python program
def abc(x):
print(x)
and then called
map(abc, [1,2,3])
but the above map function has just displayed
<map object at 0x0000000001F0BC88>
instead of printing x value.
I know map is an iterator in python 3, but still it should have printed the 'x' value right. Does it mean that abc(x) method is not called when we use "map"?
The map iterator lazily computes the values, so you will not see the output until you iterate through them. Here's an explicit way you could make the values print out:
def abc(x):
print(x)
it = map(abc, [1,2,3])
next(it)
next(it)
next(it)
The next function calls it.__next__ to step to the next value. This is what is used under the hood when you use for i in it: # do something or when you construct a list from an iterator, list(it), so doing either of these things would also print out of the values.
So, why laziness? It comes in handy when working with very large or infinite sequences. Imagine if instead of passing [1,2,3] to map, you passed itertools.count() to it. The laziness allows you to still iterate over the resulting map without trying to generate all (and there are infinitely many) values up front.
lazy-evaluation
map(or range, etc.) in Python3 is lazy-evaluation: it will evaluate when you need it.
If you want a result of map, you can use:
list(map(abc, [1,2,3]))
I am interested in creating a list / array of functions "G" consisting of many small functions "g". This essentially should correspond to a series of functions 'evolving' in time.
Each "g" takes-in two variables and returns the product of these variables with an outside global variable indexed at the same time-step.
Assume obs_mat (T x 1) is a pre-defined global array, and t corresponds to the time-steps
G = []
for t in range(T):
# tried declaring obs here too.
def g(current_state, observation_noise):
obs = obs_mat[t]
return current_state * observation_noise * obs
G.append(g)
Unfortunately when I test the resultant functions, they do not seem to pick up on the difference in the obs time-varying constant i.e. (Got G[0](100,100) same as G[5](100,100)). I tried playing around with the scope of obs but without much luck. Would anyone be able to help guide me in the right direction?
This is a common "gotcha" to referencing variables from an outer scope when in an inner function. The outer variable is looked up when the inner function is run, not when the inner function is defined (so all versions of the function see the variable's last value). For each function to see a different value, you either need to make sure they're looking in separate namespaces, or you need to bind the value to a default parameter of the inner function.
Here's an approach that uses an extra namespace:
def make_func(x):
def func(a, b):
return a*b*x
return func
list_of_funcs = [make_func(i) for i in range(10)]
Each inner function func has access to the x parameter in the enclosing make_func function. Since they're all created by separate calls to make_func, they each see separate namespaces with different x values.
Here's the other approach that uses a default argument (with functions created by a lambda expression):
list_of_funcs = [lambda a, b, x=i: a*b*x for i in range(10)]
In this version, the i variable from the list comprehension is bound to the default value of the x parameter in the lambda expression. This binding means that the functions wont care about the value of i changing later on. The downside to this solution is that any code that accidentally calls one of the functions with three arguments instead of two may work without an exception (perhaps with odd results).
The problem you are running into is one of scoping. Function bodies aren't evaluated until the fuction is actually called, so the functions you have there will use whatever is the current value of the variable within their scope at time of evaluation (which means they'll have the same t if you call them all after the for-loop has ended)
In order to see the value that you would like, you'd need to immediately call the function and save the result.
I'm not really sure why you're using an array of functions. Perhaps what you're trying to do is map a partial function across the time series, something like the following?
from functools import partial
def g(current_state, observation_noise, t):
obs = obs_mat[t]
return current_state * observation_noise * obs
g_maker = partial(g, current, observation)
results = list(map(g_maker, range(T)))
What's happening here is that partial creates a partially-applied function, which is merely waiting for its final value to be evaluated. That final value is dynamic (but the first two are fixed in this example), so mapping that partially-applied function over a range of values gets you answers for each value.
Honestly, this is a guess because it's hard to see what else you are trying to do with this data and it's hard to see what you're trying to achieve with the array of functions (and there are certainly other ways to do this).
The issue (assuming that your G.append call is mis-indented) is simply that the name t is mutated when you loop over the iterator returned by range(T). Since every function g you create stores returns the same name t, they wind up all returning the same value, T - 1. The fix is to de-reference the name (the simplest way to do this is by sending t into your function as a default value for an argument in g's argument list):
G = []
for t in range(T):
def g(current_state, observation_noise, t_kw=t):
obs = obs_mat[t_kw]
return current_state * observation_noise * obs
G.append(g)
This works because it creates another name that points at the value that t references during that iteration of the loop (you could still use t rather than t_kw and it would still just work because tg is bound to the value that tf is bound to - the value never changes, but tf is bound to another value on the next iteration, while tg still points at the "original" value.
Is there a way that I can write a predicate function that will compare two strings and see which one is greater? Right now I have
def helper1(x, y):
return x > y
However, I'm trying to use the function in this way,
new_tuple = divide((helper1(some_value, l[0]),l[1:])
Please note that the above function call is probably wrong because my helper1 is incomplete. But the gist is I'm trying to compare two items to see if one's greater than the other, and the items are l[1:] to l[0]
Divide is a function that, given a predicate and a list, divides that list into a tuple that has two lists, based on what the predicate comes out as. Divide is very long, so I don't think I should post it on here.
So given that a predicate should only take one parameter, how should I write it so that it will take one parameter?
You should write a closure.
def helper(x):
def cmp(y):
return x > y
return cmp
...
new_tuple = divide(helper1(l[0]), l[1:])
...