I am trying to write a function that will return a list of the first n catalan numbers. This is what I have come up with so far.
def catalan_numbers(n):
if n == 0:
return 1
else:
n -= 1
return int((((2*n+2) * (2*n+1))/((n+1)*(n+2))) * catalan_numbers(n))
So far this provide me with a correct solution for a single index. So if I were to call catalan_numbers(4), 14 would be returned which is correct but exactly what I am seeking. I tried to fix this issue doing the following:
def catalan_numbers(n):
catalan = [1]
for x in range(0, n):
catalan.append(int((((2*n+2) * (2*n+1))/((n+1)*(n+2))) * catalan_numbers(n))
return catalan
But this returns:
RuntimeError: maximum recursion depth exceeded in comparison
the error is because you don't have a base case also check the following code instead of returning a one number it returns a list and concatenate the current n catalan number with the list for n-1
def catalan_numbers(n):
if n == 0:
return [1]
else:
n -= 1
t = catalan_numbers(n)
return t + [int((((2*n+2) * (2*n+1))/((n+1)*(n+2))) * t[-1])]
I would suggest iteration over recursion:
def catalan_numbers(N):
C = [1]
for n in range(1, N):
C.append((4*n - 2) * C[-1] // (n + 1))
return C
Related
I am trying to solve a problem where recursion is a must. The tasks is: Write a function that takes in an integer n and returns the highest integer in the corresponding Collatz sequence.
My solution is this:
collatz = []
def max_collatz(num):
collatz.append(num)
if num == 1:
return max(collatz)
else:
return max_collatz(num / 2) if num%2 == 0 else max_collatz((3 * num) + 1)
However, I need to find a way to solve it without using a list outside of the function. I really couldn't find a solution, is there any?
It's either the current number or the largest in the rest of the sequence.
def max_collatz(n):
if n == 1:
return 1
elif n % 2:
return max_collatz(3 * n + 1)
else:
return max(n, max_collatz(n // 2))
So basically if i have an iteration like this in python
Ive editted the question to include my full code
class Solution:
def myPow(self, x: float, n: int) -> float:
temp = [];
span = range(1,abs(n))
if n ==0:
return 1
if abs(n)==1:
temp.append(x)
else:
for y in span:
if y == 1:
temp = []
temp.append(x*x)
else:
temp.append(temp[-1] * x)
if(n < 0):
return 1/temp[-1]
else:
return temp[-1]
The problem link is : Pow(x,n)-leetcode
How can I modify this to conserve memory and time. Is there another data structure i can use. Im just learning python....
------------EDIT------------
ive modified the code to use a variable instead of a list for the temp data
class Solution:
def myPow(self, x: float, n: int) -> float:
span = range(1,abs(n))
if n ==0:
return 1
if abs(n)==1:
temp = x
else:
for y in span:
if y == 1:
temp = x*x
else:
temp = temp * x
if(n < 0):
return 1/temp
else:
return temp
I still have a problem with my time complexity.
Its working for many testcases, however when it trys to run with x = 0.00001 and n = 2147483647. The time limit issue arises
To reduce the time complexity you can divide the work each time by taking x to the power of 2 and dividing the exponent by two. This makes a logarithmic time algorithm since the exponent is halved at each step.
Consider the following examples:
10^8 = 10^(2*4) = (10^2)^4 = (10*10)^4
Now, there is one edge case. When the exponent is an odd number you can't integer divide it by 2. So in that case you need to multiply the results by the base one additional time.
The following is a direct recursive implementation of the above idea:
class Solution:
def myPow(self, x: float, n: int) -> float:
sign = -1 if n < 0 else 1
n = abs(n)
def helper(x, n):
if n == 1: return x
if n == 0: return 1
if n % 2 == 1:
return helper(x*x, n // 2) * x
else:
return helper(x*x, n // 2)
res = helper(x, n)
if sign == -1:
return 1/res
else:
return res
Note that we have taken abs of the exponent and stored the sign and deal with it at the end.
Instead of iterating from 1 to n, use divide-and-conquer: divide the exponent by 2 and use recursion to get that power, and then square that result. If n was odd, multiply one time more with x:
class Solution:
def myPow(self, x: float, n: int) -> float:
if n == 0:
return 1
if n == 1:
return x
if n < 0:
return self.myPow(1/x, -n)
temp = self.myPow(x, n // 2)
temp *= temp
if n % 2:
temp *= x
return temp
A simple naive solution might be:
def myPow(x: float, n: int) -> float:
## -----------------------
## if we have a negative n then invert x and take the absolute value of n
## -----------------------
if n < 0:
x = 1/x
n = -n
## -----------------------
retval = 1
for _ in range(n):
retval *= x
return retval
While this technically works, you will wait until the cows come home to get a result for:
x = 0.00001 and n = 2147483647
So we need to find a shortcut. Lets' consider 2^5. Our naïve method would calculate that as:
(((2 * 2) * 2) * 2) * 2 == 32
However, what might we observe about the problem if we group some stuff together in a different way:
(2 * 2) * (2 * 2) * 2 == 32
similarly:
((2 * 2) * (2 * 2) * 2) * ((2 * 2) * (2 * 2) * 2) == 32 * 32 = 1024
We might observe that we only technically need to calculate
(2 * 2) * (2 * 2) * 2 == 32
once and use it twice to get 2^10.
Similarly we only need to calcuate:
2 * 2 = 4
once and use it twice to get 2^5....
This suggests a recursion to me.
Let's modify our first attempt to use this divide and concur method.
def myPow2(x: float, n: int) -> float:
## -----------------------
## if we have a negative n then invert x and take the absolute value of n
## -----------------------
if n < 0:
x = 1/x
n = -n
## -----------------------
## -----------------------
## We only need to calculate approximately half the work and use it twice
## at any step.
## -----------------------
def _recurse(x, n):
if n == 0:
return 1
res = _recurse(x, n//2) # calculate it once
res = res * res # use it twice
return res * x if n % 2 else res # if n is odd, multiple by x one more time (see 2^5 above)
## -----------------------
return _recurse(x, n)
Now let's try:
print(myPow2(2.0, 0))
print(myPow2(2.0, 1))
print(myPow2(2.0, 5))
print(myPow2(2.1, 3))
print(myPow2(2.0, -2))
print(myPow2(0.00001, 2147483647))
That gives me:
1
2.0
32.0
9.261000000000001
0.25
0.0
If you have to loop, you have to lope and there is nothing that can be done. Loops in python are slow. That said you may not have to loop and if you do have to loop, it may be possible to push this loop to a highly optimised internal function. Tell us what you are trying to do (not how you think you have to do it, appending elements to a lis may or may not be needed). Always recall the two rules of program optimisation General Rule: Don't do it. Rule for experts: Don't do it yet. Make it work before you make it fast, who knows, it may be fast enough.
I've got a question- how do I write a function sum_even_factorials that finds the sum of the factorials of the even numbers that are less than or equal to n.
Eg:
sum_even_factorials(1)=
1
sum_even_factorials (3)=
3
sum_even_factorials (6)=
747
This is my current code:
Is there a logical error in the current code?
Is there a logical error in the current code?
To begin with, function sum_even_factorial doesn't return a value in every execution flow:
if cond1:
return val1
elif cond2:
return val2
else: # this part is missing in your code
return val3
In addition, note that when you call this function, you are not doing anything with the value that it returns:
sum_even_factorial(6)
Finally, although parts of your code are not visible in your question, I tend to guess that you cannot recursively compute the factorials of even numbers the way you did it, because the factorial of an even number n depends on the factorial of the odd number n - 1.
I think the code needs a loop. I'd write it like so:
def factorial(n):
if n == 0 or n == 1:
return 1;
else:
return n * factorial(n-1)
def sum_even_factorial(n):
current_sum = 0
while n >= 0:
if n % 2 == 0:
current_sum += factorial(n)
n -= 1
return current_sum
print(sum_even_factorial(6))
If you return tuples from the function, you can do it with one single function. See the comments in the code on how it works ...
def factorial_with_sum(n):
if n < 2:
return 1, 0 # first item of the tuple is the factorial, second item is the sum
else:
f, s = factorial_with_sum(n - 1) # calc factorial and sum for n - 1
f = f * n # factorial = n * factorial (n - 1)
if n % 2 == 0:
s = s + f # if n is even, add the current factorial to the sum
return f, s
fact, sum = factorial_with_sum(6)
print(fact)
print(sum)
You can also do it iteratively with a simple for loop as follows
def factorial_with_sum_iterative(n):
s = 0 # initialize sum
f = 1 # and factorial
for i in range(2, n + 1): # iterate from 2 to n
f = f * i # calculate factorial for current i
if i % 2 == 0:
s = s + f # if current i is even, add it to sum
return f, s
I know how to check if the number can be represented as the sum of two squares with a brute-force approach.
def sumSquare( n) :
i = 1
while i * i <= n :
j = 1
while(j * j <= n) :
if (i * i + j * j == n) :
print(i, "^2 + ", j , "^2" )
return True
j = j + 1
i = i + 1
return False
But how to do it for n distinct positive integers. So the question would be:
Function which checks if the number can be written as sum of 'n' different squares
I have some examples.
For e.g.
is_sum_of_squares(18, 2) would be false because 18 can be written as the sum of two squares (3^2 + 3^2) but they are not distinct.
(38,3) would be true because 5^2+3^2+2^2 = 38 and 5!=3!=2.
I can't extend the if condition for more values. I think it could be done with recursion, but I have problems with it.
I found this function very useful since it finds the number of squares the number can be split into.
def findMinSquares(n):
T = [0] * (n + 1)
for i in range(n + 1):
T[i] = i
j = 1
while j * j <= i:
T[i] = min(T[i], 1 + T[i - j * j])
j += 1
return T[n]
But again I can't do it with recursion. Sadly I can't wrap my head around it. We started learning it a few weeks ago (I am in high school) and it is so different from the iterative approach.
Recursive approach:
def is_sum_of_squares(x, n, used=None):
x_sqrt = int(x**0.5)
if n == 1:
if x_sqrt**2 == x:
return used.union([x_sqrt])
return None
used = used or set()
for i in set(range(max(used, default=0)+1, int((x/n)**0.5))):
squares = is_sum_of_squares(x-i**2, n-1, used.union([i]))
if squares:
return squares
return None
Quite a compelling exercise. I have attempted solving it using recursion in a form of backtracking. Start with an empty list, run a for loop to add numbers to it from 1 to max feasible (square root of target number) and for each added number continue with recursion. Once the list reaches the required size n, validate the result. If the result is incorrect, backtrack by removing the last number.
Not sure if it is 100% correct though. In terms of speed, I tried it on the (1000,13) input and the process finished reasonably fast (3-4s).
def is_sum_of_squares(num, count):
max_num = int(num ** 0.5)
return backtrack([], num, max_num, count)
def backtrack(candidates, target, max_num, count):
"""
candidates = list of ints of max length <count>
target = sum of squares of <count> nonidentical numbers
max_num = square root of target, rounded
count = desired size of candidates list
"""
result_num = sum([x * x for x in candidates]) # calculate sum of squares
if result_num > target: # if sum exceeded target number stop recursion
return False
if len(candidates) == count: # if candidates reach desired length, check if result is valid and return result
result = result_num == target
if result: # print for result sense check, can be removed
print("Found: ", candidates)
return result
for i in range(1, max_num + 1): # cycle from 1 to max feasible number
if candidates and i <= candidates[-1]:
# for non empty list, skip numbers smaller than the last number.
# allow only ascending order to eliminate duplicates
continue
candidates.append(i) # add number to list
if backtrack(candidates, target, max_num, count): # next recursion
return True
candidates.pop() # if combination was not valid then backtrack and remove the last number
return False
assert(is_sum_of_squares(38, 3))
assert(is_sum_of_squares(30, 3))
assert(is_sum_of_squares(30, 4))
assert(is_sum_of_squares(36, 1))
assert not(is_sum_of_squares(35, 1))
assert not(is_sum_of_squares(18, 2))
assert not(is_sum_of_squares(1000, 13))
I need a function that takes n and returns 2n - 1 . It sounds simple enough, but the function has to be recursive. So far I have just 2n:
def required_steps(n):
if n == 0:
return 1
return 2 * req_steps(n-1)
The exercise states: "You can assume that the parameter n is always a positive integer and greater than 0"
2**n -1 is also 1+2+4+...+2n-1 which can made into a single recursive function (without the second one to subtract 1 from the power of 2).
Hint: 1+2*(1+2*(...))
Solution below, don't look if you want to try the hint first.
This works if n is guaranteed to be greater than zero (as was actually promised in the problem statement):
def required_steps(n):
if n == 1: # changed because we need one less going down
return 1
return 1 + 2 * required_steps(n-1)
A more robust version would handle zero and negative values too:
def required_steps(n):
if n < 0:
raise ValueError("n must be non-negative")
if n == 0:
return 0
return 1 + 2 * required_steps(n-1)
(Adding a check for non-integers is left as an exercise.)
To solve a problem with a recursive approach you would have to find out how you can define the function with a given input in terms of the same function with a different input. In this case, since f(n) = 2 * f(n - 1) + 1, you can do:
def required_steps(n):
return n and 2 * required_steps(n - 1) + 1
so that:
for i in range(5):
print(required_steps(i))
outputs:
0
1
3
7
15
You can extract the really recursive part to another function
def f(n):
return required_steps(n) - 1
Or you can set a flag and define just when to subtract
def required_steps(n, sub=True):
if n == 0: return 1
return 2 * required_steps(n-1, False) - sub
>>> print(required_steps(10))
1023
Using an additional parameter for the result, r -
def required_steps (n = 0, r = 1):
if n == 0:
return r - 1
else:
return required_steps(n - 1, r * 2)
for x in range(6):
print(f"f({x}) = {required_steps(x)}")
# f(0) = 0
# f(1) = 1
# f(2) = 3
# f(3) = 7
# f(4) = 15
# f(5) = 31
You can also write it using bitwise left shift, << -
def required_steps (n = 0, r = 1):
if n == 0:
return r - 1
else:
return required_steps(n - 1, r << 1)
The output is the same
Have a placeholder to remember original value of n and then for the very first step i.e. n == N, return 2^n-1
n = 10
# constant to hold initial value of n
N = n
def required_steps(n, N):
if n == 0:
return 1
elif n == N:
return 2 * required_steps(n-1, N) - 1
return 2 * required_steps(n-1, N)
required_steps(n, N)
One way to get the offset of "-1" is to apply it in the return from the first function call using an argument with a default value, then explicitly set the offset argument to zero during the recursive calls.
def required_steps(n, offset = -1):
if n == 0:
return 1
return offset + 2 * required_steps(n-1,0)
On top of all the awesome answers given earlier, below will show its implementation with inner functions.
def outer(n):
k=n
def p(n):
if n==1:
return 2
if n==k:
return 2*p(n-1)-1
return 2*p(n-1)
return p(n)
n=5
print(outer(n))
Basically, it is assigning a global value of n to k and recursing through it with appropriate comparisons.