Here, I written some codes to find components numbers of a graph, the input style is like this:
5
4
0 1
0 2
1 2
3 4
First line is the number of vertices, second line is the number of edges, the rest shows each edges.
In my codes, I used a global variable in the recursive function, so is there some good ways to replace it? In other way, how to program the function in a better way?
Code:
def dfs(graph, root_vertex):
global visited
visited.add(root_vertex)
for pairs in graph:
if (root_vertex in pairs) and pairs[1 -pairs.index(root_vertex)] not in visited :
visited.add(pairs[1 - pairs.index(root_vertex)])
dfs(graph, pairs[1 - pairs.index(root_vertex)])
vertex_num = int(input())
edge_num = int(input())
graph = []
for i in range(edge_num):
graph.append(tuple(sorted(map(int,input().split()))))
tot_vertex = set([i for i in range(vertex_num)])
visited = set()
count = 0
while list(tot_vertex - visited):
dfs(graph, list(set(tot_vertex) - set(visited))[0])
count += 1
print(count)
When you need variables accessible across different function invocations - whether different functions or recursive invocations of the same function - the pattern is to make the function(s) methods of a class, then use a member variable for the "global". This is a fundamental use case of classes.
Here the visited set can be a member variable as long as you'll never search from more than one thread at a time. That's okay as a restriction because the Search should be the class, not the thing being searched. If you need to search with more than one thread simultaneously, then create one Search object per thread.
Related
I started coding in Python 4 days ago, so I'm a complete newbie. I have a dataset that comprises an undefined number of dictionaries. Each dictionary is the x and y of a point in the coordinates.
I'm trying to compute the summatory of xy by nesting the loop that multiplies xy within the loop that sums the products.
However I haven't been able to figure out how to multiply the values for the two keys in each dictionary (so far I only got to multiply all the x*y)
So far I've got this:
If my data set were to be d= [{'x':0, 'y':0}, {'x':1, 'y':1}, {'x':2, 'y':3}]
I've got the code for the function that calculates the product of each pair of x and y:
def product_xy (product_x_per_y):
prod_xy =[]
n = 0
for i in range (len(d)):
result = d[n]['x']*d[n]['y']
prod_xy.append(result)
n+1
return prod_xy
I also have the function to add up the elements of a list (like prod_xy):
def total_xy_prod (sum_prod):
all = 0
for s in sum_prod:
all+= s
return all
I've been trying to find a way to nest this two functions so that I can iterate through the multiplication of each x*y and then add up all the products.
Make sure your code works as expected
First, your functions have a few mistakes. For example, in product_xy, you assign n=0, and later do n + 1; you probably meant to do n += 1 instead of n + 1. But n is also completely unnecessary; you can simply use the i from the range iteration to replace n like so: result = d[i]['x']*d[i]['y']
Nesting these two functions: part 1
To answer your question, it's fairly straightforward to get the sum of the products of the elements from your current code:
coord_sum = total_xy_prod(product_xy(d))
Nesting these two functions: part 2
However, there is a much shorter and more efficient way to tackle this problem. For one, Python provides the built-in function sum() to sum the elements of a list (and other iterables), so there's no need create total_xy_prod. Our code could at this point read as follows:
coord_sum = sum(product_xy(d))
But product_xy is also unnecessarily long and inefficient, and we could also replace it entirely with a shorter expression. In this case, the shortening comes from generator expressions, which are basically compact for-loops. The Python docs give some of the basic details of how the syntax works at list comprehensions, which are distinct, but closely related to generator expressions. For the purposes of answering this question, I will simply present the final, most simplified form of your desired result:
coord_sum = sum(e['x'] * e['y'] for e in d)
Here, the generator expression iterates through every element in d (using for e in d), multiplies the numbers stored in the dictionary keys 'x' and 'y' of each element (using e['x'] * e['y']), and then sums each of those products from the entire sequence.
There is also some documentation on generator expressions, but it's a bit technical, so it's probably not approachable for the Python beginner.
This question has somehow to do with an earlier post from me. See here overlap-of-nested-lists-creates-unwanted-gap
I think that I have found a solution but i can't figure out how to implement it.
First the relevant code since I think it is easier to explain my problem that way. I have prepared a fiddle to show the code:
PYFiddle here
Each iteration fills a nested list in ag depending on the axis. The next iteration is supposed to fill the next nested list in ag but depending on the length of the list filled before.
The generell idea to realise this is as follows:
First I would assign each nested list within the top for-loop to a variable like that:
x = ag[0]
y = ag[1]
z = ag[2]
In order to identify that first list I need to access data_j like that. I think the access would work that way.
data_j[i-1]['axis']
data_j[i-1]['axis'] returns either x,y or z as string
Now I need to get the length of the list which corresponds to the axis returned from data_j[i-1]['axis'].
The problem is how do I connect the "value" of data_j[i-1]['axis'] with its corresponding x = ag[0], y = ag[1] or z = ag[2]
Since eval() and globals() are bad practice I would need a push into the right direction. I couldn't find a solution
EDIT:
I think I figured out a way. Instead of taking the detour of using the actual axis name I will try to use the iterator i of the parent loop (See the fiddle) since it increases for each element from data_j it kinda creates an id which I think I can use to create a method to use it for the index of the nest to address the correct list.
I managed to solve it using the iterator i. See the fiddle from my original post in order to comprehend what I did with the following piece of code:
if i < 0:
cond = 0
else:
cond = i
pred_axis = data_j[cond]['axis']
if pred_axis == 'x':
g = 0
elif pred_axis == 'y':
g = 1
elif pred_axis == 'z':
g = 2
calc_size = len(ag[g])
n_offset = calc_size+offset
I haven't figured yet why cond must be i and not i-1 but it works. As soon as I figure out the logic behind it I will post it.
EDIT: It doesn't work for i it works for i-1. My indices for the relevant list start at 1. ag[0] is reserved for a constant which can be added if necessary for further calculations. So since the relevant indices are moved up by the value of 1 from the beginning already i don't need to decrease the iterator in each run.
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)
Write a function multisplit that consumes two positive integers total and split and produces the number of times total is repeatedly divided into split even pieces before each piece is of size at most 1. For example, the value returned by multisplit(8, 2) will be 3, since 8 can be split into 2 pieces of size 4, which are then each split into 2 pieces of size 2, which are then each split into 2 pieces of size 1 (at which point no further splitting takes place since the pieces are of size at most 1).
total= int(input("Total:"))
split= int(input("Split:"))
def multisplit(total,split):
x=o
while value>=1:
value= total//split
x= x+1
return x
print(x)
It's telling me that the name 'x' is not defined
There are several issues with the code you posted:
In python, the contents of a function must be indented.
def myfunction():
# code inside the function goes here
# code after you've unindented is not in the function
You didn't define your value variable before using it.
Assuming that your final line gets appropriately unindented, so that it won't be completely ignored because of being inside the function, but after the return statement:
You're trying to print the value of a variable that was defined in a different scope. Specifically, you defined x inside the function, and now you're trying to look at it outside the function.
You never called your function...
If I understand what you're trying to do, you want to call the function inside print. i.e.: print(multisplit(total, split))
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.