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So I am supposed to find the sum of this series :
f(n) = 1 + (2*3) + (4*5*6) + .....n terms
I did this using recursion as follows:
def f(n):
if n == 1:
return 1
else:
product = 1
add = 0
s = (n * (n+1))/2
for i in range (0,n):
product = product * s
s = s - 1
add = product + f(n-1)
return add
Now please bear with me
I thought I could do this faster if I could use special series in linear algebra:
Here is what I attempted:
I found the nth term(through some vigorous calculations) : Tn =
Now is there a method I can use this formula to find sum of Tn and hence the series using python.
I also want to know whether we can do such things in python or not?
You can translate that product to Python using a for loop, analog to how you kept track of the product in your recursive function. So T(n) would be:
def T(n):
product = 1
for r in range(1, n+1):
product *= (n * (n - 1)) / 2 + r
return product
Now as you said, you need to find the sum of T(x) for x from 1 to n. In Python:
def f(n):
sum = 0
for i in range(1, n+1):
sum += T(i)
return sum
FYI:
a += x is the same as a = a + x,
analog a *= x is equal to a = a * x
This question was asked in a challenge in HackerEarth:
Mark is solving an interesting question. He needs to find out number
of distinct ways such that
(i + 2*j+ k) % (x + y + 2*z) = 0, where 1 <= i,j,k,x,y,z <= N
Help him find it.
Constraints:
1<= T<= 10
1<=N<= 1000
Input Format:
First line contains T, the number of test cases. Each of the test case
contains a single integer,N in a separate line.
Output Format:
For each test case , output in a separate line, the number of distinct
ways.
Sample Input
2
1
2
Sample Output
1
15
Explanation
In the first case, the only possible way is i = j = k = x =y = z = 1
I am not getting any way how to solve this problem, I have tried one and I know it's not even close to the question.
import random
def CountWays (N):
# Write your code here
i = random.uniform(1,N)
j = random.uniform(1,N)
k = random.uniform(1,N)
x = random.uniform(1,N)
y = random.uniform(1,N)
z = random.uniform(1,N)
d = 0
for i in range(N):
if (i+2*j+k)%(x+y+2*z)==0:
d += 1
return d
T = int(input())
for _ in range(T):
N = int(input())
out_ = CountWays(N)
print (out_)
My Output
0
0
Instead it should give the output
1
15
The value of the numerator (num) can range from 4 to 4N. The value of the denominator (dom) can range from 4 to num. You can split your problem into two smaller problems: 1) How many values of the denominator is a given value of the numerator divisible by? 2) How many ways can a given denominator and numerator be constructed?
To answer 1) we can simply loop through all the possible values of the numerator, then loop over all the values of the denominator where numerator % denominator == 0. To answer 2) we can find all the partitions of the numerator and denominator that satisfies the equality and constraints. The number of ways to construct a given numerator and denominator will be the product of the number of partitions of each.
import itertools
def divisible_numbers(n):
"""
Get all numbers with which n is divisible.
"""
for i in range(1,n+1):
if n % i == 0:
yield i
if i >= n:
break
def get_partitions(n):
"""
Generate ALL ways n can be partitioned into 3 integers.
Modified from http://code.activestate.com/recipes/218332-generator-for-integer-partitions/#c9
"""
a = [1]*n
y = -1
v = n
while v > 0:
v -= 1
x = a[v] + 1
while y >= 2 * x:
a[v] = x
y -= x
v += 1
w = v + 1
while x <= y:
a[v] = x
a[w] = y
if w == 2:
yield a[:w + 1]
x += 1
y -= 1
a[v] = x + y
y = a[v] - 1
if w == 3:
yield a[:w]
def get_number_of_valid_partitions(num, N):
"""
Get number of valid partitions of num, given that
num = i + j + 2k, and that 1<=i,j,k<=N
"""
n = 0
for partition in get_partitions(num):
# This can be done a bit more cleverly, but makes
# the code extremely complicated to read, so
# instead we just brute force the 6 combinations,
# ignoring non-unique permutations using a set
for i,j,k in set(itertools.permutations(partition)):
if i <= N and j <= N and k <= 2*N and k % 2 == 0:
n += 1
return n
def get_number_of_combinations(N):
"""
Get number of ways the equality can be solved under the given constraints
"""
out = 0
# Create a dictionary of number of valid partitions
# for all numerator values we can encounter
n_valid_partitions = {i: get_number_of_valid_partitions(i, N) for i in range(1,4*N+1)}
for numerator in range(4,4*N+1):
numerator_permutations = n_valid_partitions[numerator]
for denominator in divisible_numbers(numerator):
denominator_permutations = n_valid_partitions[denominator]
if denominator < 4:
continue
out += numerator_permutations * denominator_permutations
return out
N = 2
out = get_number_of_combinations(N)
print(out)
The scaling of the code right now is very poor due to the way the get_partitions and the get_number_of_valid_partitions functions interact.
EDIT
The following code is much faster. There's a small improvement to divisible_numbers, but the main speedup lies in get_number_of_valid_partitions not creating a needless amount of temporary lists as it has now been joined with get_partitions in a single function. Other big speedups comes from using numba. The code of get_number_of_valid_partitions is all but unreadable now, so I've added a much simpler but slightly slower version named get_number_of_valid_partitions_simple so you can understand what is going on in the complicated function.
import numba
#numba.njit
def divisible_numbers(n):
"""
Get all numbers with which n is divisible.
Modified from·
"""
# We can save some time by only looking at
# values up to n/2
for i in range(4,n//2+1):
if n % i == 0:
yield i
yield n
def get_number_of_combinations(N):
"""
Get number of ways the equality can be solved under the given constraints
"""
out = 0
# Create a dictionary of number of valid partitions
# for all numerator values we can encounter
n_valid_partitions = {i: get_number_of_valid_partitions(i, N) for i in range(4,4*N+1)}
for numerator in range(4,4*N+1):
numerator_permutations = n_valid_partitions[numerator]
for denominator in divisible_numbers(numerator):
if denominator < 4:
continue
denominator_permutations = n_valid_partitions[denominator]
out += numerator_permutations * denominator_permutations
return out
#numba.njit
def get_number_of_valid_partitions(num, N):
"""
Get number of valid partitions of num, given that
num = i + j + 2l, and that 1<=i,j,l<=N.
"""
count = 0
# In the following, k = 2*l
#There's different cases for i,j,k that we can treat separately
# to give some speedup due to symmetry.
#i,j can be even or odd. k <= N or N < k <= 2N.
# Some combinations only possible if num is even/odd
# num is even
if num % 2 == 0:
# i,j odd, k <= 2N
k_min = max(2, num - 2 * (N - (N + 1) % 2))
k_max = min(2 * N, num - 2)
for k in range(k_min, k_max + 1, 2):
# only look at i<=j
i_min = max(1, num - k - N + (N + 1) % 2)
i_max = min(N, (num - k)//2)
for i in range(i_min, i_max + 1, 2):
j = num - i - k
# if i == j, only one permutations
# otherwise two due to symmetry
if i == j:
count += 1
else:
count += 2
# i,j even, k <= N
# only look at k<=i<=j
k_min = max(2, num - 2 * (N - N % 2))
k_max = min(N, num // 3)
for k in range(k_min, k_max + 1, 2):
i_min = max(k, num - k - N + N % 2)
i_max = min(N, (num - k) // 2)
for i in range(i_min, i_max + 1, 2):
j = num - i - k
if i == j == k:
# if i == j == k, only one permutation
count += 1
elif i == j or i == k or j == k:
# if only two of i,j,k are the same there are 3 permutations
count += 3
else:
# if all differ, there are six permutations
count += 6
# i,j even, N < k <= 2N
k_min = max(N + 1 + (N + 1) % 2, num - 2 * N)
k_max = min(2 * N, num - 4)
for k in range(k_min, k_max + 1, 2):
# only look for i<=j
i_min = max(2, num - k - N + 1 - (N + 1) % 2)
i_max = min(N, (num - k) // 2)
for i in range(i_min, i_max + 1, 2):
j = num - i - k
if i == j:
# if i == j, only one permutation
count += 1
else:
# if all differ, there are two permutations
count += 2
# num is odd
else:
# one of i,j is even, the other is odd. k <= N
# We assume that j is odd, k<=i and correct the symmetry in the counts
k_min = max(2, num - 2 * N + 1)
k_max = min(N, (num - 1) // 2)
for k in range(k_min, k_max + 1, 2):
i_min = max(k, num - k - N + 1 - N % 2)
i_max = min(N, num - k - 1)
for i in range(i_min, i_max + 1, 2):
j = num - i - k
if i == k:
# if i == j, two permutations
count += 2
else:
# if i and k differ, there are four permutations
count += 4
# one of i,j is even, the other is odd. N < k <= 2N
# We assume that j is odd and correct the symmetry in the counts
k_min = max(N + 1 + (N + 1) % 2, num - 2 * N + 1)
k_max = min(2 * N, num - 3)
for k in range(k_min, k_max + 1, 2):
i_min = max(2, num - k - N + (N + 1) % 2)
i_max = min(N, num - k - 1)
for i in range(i_min, i_max + 1, 2):
j = num - i - k
count += 2
return count
#numba.njit
def get_number_of_valid_partitions_simple(num, N):
"""
Simpler but slower version of 'get_number_of_valid_partitions'
"""
count = 0
for k in range(2, 2 * N + 1, 2):
for i in range(1, N + 1):
j = num - i - k
if 1 <= j <= N:
count += 1
return count
if __name__ == "__main__":
N = int(sys.argv[1])
out = get_number_of_combinations(N)
print(out)
The current issue with your code is that you've picked random numbers once, then calculate the same equation N times.
I assume you wanted to generate 1..N for each individual variable, which would require 6 nested loops from 1..N, for each variable
Now, that's the brute force solution, which probably fails on large N values, so as I commented, there's some trick to find the multiples of the right side of the modulo, then check if the left side portion is contained in that list. That would only require two triple nested lists, I think
(i + 2*j+ k) % (x + y + 2*z) = 0, where 1 <= i,j,k,x,y,z <= N
(2*j + i + k) is a multiple of (2*z + x + y)
N = 2
min(2*j + i + k) = 4
max(2*j + i + k) = 8
ways to make 4: 1 * 1 = 1
ways to make 5: 2 * 2 = 4
ways to make 6: 2 * 2 = 4
ways to make 7: 2 * 2 = 4
ways to make 8: 1 * 1 = 1
Total = 14
But 8 is a multiple of 4 so we add one more instance for a total of 15.
I have been working on recursion and tried to solve the Knapsack problem [https://en.wikipedia.org/wiki/Knapsack_problem]. I came up with the algorithm below which works just fine:
cost_array = [2,3,4,5,9]
value_array = [3,4,8,8,10]
def KP(Weight, C, V):
if Weight < 2:
return 0
q = 0
for i in range(len(C)):
q = max(q, (KP(Weight-C[i], [x for j,x in enumerate(C) if j!=i], \
[x for j,x in enumerate(V) if j!=i]) + V[i]*(Weight-C[i] >= 0)))
return q
print(KP(25,cost_array,value_array))
However when I change the value of q to q < 0 and call print(KP(25,cost_array,value_array)) the result I get is 33 - q. With 33 being the max value the knapsack can have.
What is weird here is that I only get this behavior if I call the initial function with a Weight > 23 and here 23=2+3+4+5+9.
I can't figure out at what point the negative q gets added to my result for me this line never performs such an operation, can you guys enlighten me ?
q = max(q, (KP(W-C[i], [x for j,x in enumerate(C) if j!=i], [x for j,x in enumerate(V) if j!=i]) + V[i]*(W-C[i] >= 0)))
Thanks,
d_darric
Suppose q=-2 (a negative value)
Therefore you are filling your base cases with -2 . That is -2 is returned for base cases of your function which is then getting added to the answer on each step in recursion. Try a bottom up approach with a 2D array. You can look at that here https://www.youtube.com/watch?v=8LusJS5-AGo . In your case you are filling matrix base cases with -2.
def knapSack(W, wt, val, n):
K = [[0 for x in range(W+1)] for x in range(n+1)]
q=-2 #Make it zero for correct answer
# Build table K[][] in bottom up manner
for i in range(n+1):
for w in range(W+1):
if i==0 or w==0:
K[i][w] = q #Here you are assigning negative value
elif wt[i-1] <= w:
K[i][w] = max(val[i-1] + K[i-1][w-wt[i-1]], K[i-1][w])
else:
K[i][w] = K[i-1][w]
return K[n][W]
# Driver program to test above function
value_array = [3,4,8,8,10]
cost_array = [2,3,4,5,9]
Weight = 25
n = len(val)
print(knapSack(Weight, cost_array, value_array, n))
I'm trying to create a list (b) that is list (a) rotating a's members k times to the left. I came up with this on Python 3:
n = 5
k = 4
a = [1,2,3,4,5]
b = []
for i in a:
if (i + k) <= (n - 1):
b.append(a[i+k])
elif (i+k-n) < (n-1):
b.append(a[i+k-n])
print(b)
But for some reason, it doesn't work since when I tell print(b) it returns a list that is exactly like list a
What am I missing here?
A simple solution:
k = k % len(a) #we don't care about shifting x*len(a) times since it does not have any effect
b = a[k:] + a[:k]
Thanks to #RemcoGerlich for helping me see my mistake! Here's how I quickly fixed my code:
n = 5
k = 4
a = [1,2,3,4,5]
b = []
i = 0
while (i < n):
if (i + k) <= (n - 1):
b.append(a[i+k])
elif (i+k-n) < (n-1):
b.append(a[i+k-n])
i = i+1
print(b)
# Uses python3
# Given two integers n and m, output Fn mod m (that is, the remainder of Fn when divided by m
def Huge_Fib(n,m):
if n == 0 : return 0
elif n == 1: return 1
else:
a,b = 0,1
for i in range(1,n):
a, b = b, (a+b) % m
print(b);
n,m = map(int, input().split());
Huge_Fib(n,m);
The code works very well. However, when I run a case as n = 99999999999999999, m = 2, it takes me much time. Do you have any better solutions?
Here is my solution, you don't have to go through 99999999999999999 iterations if you find the pisano period.
I also recommend that you watch this video: https://www.youtube.com/watch?v=Nu-lW-Ifyec
# Uses python3
import sys
def get_fibonacci_huge(n, m):
if n <= 1:
return n
arr = [0, 1]
previousMod = 0
currentMod = 1
for i in range(n - 1):
tempMod = previousMod
previousMod = currentMod % m
currentMod = (tempMod + currentMod) % m
arr.append(currentMod)
if currentMod == 1 and previousMod == 0:
index = (n % (i + 1))
return arr[index]
return currentMod
if __name__ == '__main__':
input = sys.stdin.read();
n, m = map(int, input.split())
print(get_fibonacci_huge(n,m))
# Uses python3
# Given two integers n and m, output Fn mod m (that is, the remainder of Fn when divided by m
def Huge_Fib(n,m):
# Initialize a matrix [[1,1],[1,0]]
v1, v2, v3 = 1, 1, 0
# Perform fast exponentiation of the matrix (quickly raise it to the nth power)
for rec in bin(n)[3:]:
calc = (v2*v2) % m
v1, v2, v3 = (v1*v1+calc) % m, ((v1+v3)*v2) % m, (calc+v3*v3) % m
if rec == '1': v1, v2, v3 = (v1+v2) % m, v1, v2
print(v2);
n,m = map(int, input().split());
Huge_Fib(n,m);
This is a superfast solution refer to https://stackoverflow.com/a/23462371/3700852
I solved it in Python 3. This the fastest algorithm to compute a huge Fibonacci number modulo m.For example for n =2816213588, m = 239, it took Max time used: 0.01/5.00, max memory used: 9424896/536870912.)
def pisanoPeriod(m):
previous, current = 0, 1
for i in range(0, m * m):
previous, current = current, (previous + current) % m
# A Pisano Period starts with 01
if (previous == 0 and current == 1):
return i + 1
def calc_fib(n,m):
p = pisanoPeriod(m)
n = n % p
if (n <= 1):
return n
else:
previous,current = 0,1
for i in range(2,n+1):
previous,current = current,(previous+current)
return current%m
n,m =map(int,input().split(" "))
print(calc_fib(n,m))
In the below code we are using two concepts of Fibonacci series:
Pisano periods follows a Fibonacci sequence and hence each repetition(pattern) begins with 0 and 1 appearing consecutively one after the other.
fib(n) divides fib(m) only when n divides m which means if fib(4)%3==0,then fib(4+4)%3==0,fib(4+4+4)%3==0 and so on.This helps us in finding the Pisano period.
To know about Pisano periods,I recommend that you watch this video: https://www.youtube.com/watch?v=Nu-lW-Ifyec
#python3
def pisano_length(m):
i=2
while(fib(i)%m!=0):
i+=1
if(fib(i+1)%m!=1):
while(fib(i+1)%m!=1):
i+=i
print("The pisano length for mod {} is: {}".format(m,i))
return(i)
def fib(n):
a,b=0,1
if(n==0 or n==1):
return n
else:
for i in range(2,n+1):
b,a=a+b,b
return(b)
#we want to calculate fib(n)%m for big numbers
n,m=map(int,input().split())
remainder=n%pisano_length(m)
print(fib(remainder)%m)
You should look up Pisano periods.
https://en.wikipedia.org/wiki/Pisano_period and
http://webspace.ship.edu/msrenault/fibonacci/fibfactory.htm should give you a good understanding of what they are.
edit: Just googling "fibonacci modulo" gives you those two as the top two results.
For any integer m>=2, the sequence fn modulo m is periodic - Pisano Period.
So no need to store and find fn. Instead, find a repeating pattern for given m.
This is how i have done by calculating the pisano period.(Java)
public static long get_pisano_period(long m) {
long a = 0, b = 1;
long c;
for (int i = 0; i < m * m; i++) {
c = (a + b) % m;
a = b;
b = c;
if (a == 0 && b == 1)
return i + 1;
}
return 0;
}
public static BigInteger get_fibonacci_huge(long n,long m) {
long remainder = n % get_pisano_period(m);
BigInteger first = BigInteger.valueOf(0);
BigInteger second=BigInteger.valueOf(1);
BigInteger m1=BigInteger.valueOf(m);
BigInteger res = BigInteger.valueOf(remainder);
for (long i = 1; i < remainder; i++) {
res = (first.add(second)).mod(m1);
first = second;
second = res;
}
return res.mod(m1);
}