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I was bored at work and was playing with some math and python coding, when I noticed the following:
Recursively (or if using a for loop) you simply add integers together to get a given Fibonacci number. However there is also a direct equation for calculating Fibonacci numbers, and for large n this equation will give answers that are, frankly, quite wrong with respect to the recursively calculated Fibonacci number.
I imagine this is due to rounding and floating point arithmetic ( sqrt(5) is irrational after all), and if so can anyone point me into a direction on how I could modify the fibo_calc_direct function to return a more accurate result?
Thanks!
def fib_calc_recur(n, ii = 0, jj = 1):
#n is the index of the nth fibonacci number, F_n, where F_0 = 0, F_1 = 1, ...
if n == 0: #use recursion
return ii
if n == 1:
return jj
else:
return(fib_calc_recur(n -1, jj, ii + jj))
def fib_calc_direct(n):
a = (1 + np.sqrt(5))/2
b = (1 - np.sqrt(5))/2
f = (1/np.sqrt(5)) * (a**n - b**n)
return(f)
You could make use of Decimal numbers, and set its precision depending on the magninute of n
Not your question, but I'd use an iterative version of the addition method. Here is a script that makes both calculations (naive addition, direct with Decimal) for values of n up to 4000:
def fib_calc_iter(n):
a, b = 0, 1
if n < 2:
return n
for _ in range(1, n):
a, b = b, a + b
return b
from decimal import Decimal, getcontext
def fib_calc_decimal(n):
getcontext().prec = n // 4 + 3 # Choose a precision good enough for this n
sqrt5 = Decimal(5).sqrt()
da = (1 + sqrt5) / 2
db = (1 - sqrt5) / 2
f = (da**n - db**n) / sqrt5
return int(f + Decimal(0.5)) # Round to nearest int
# Test it...
for n in range(1, 4000):
x = fib_calc_iter(n)
y = fib_calc_decimal(n)
if x != y:
print(f"Difference found for n={n}.\nNaive method={x}.\nDecimal method={y}")
break
else:
print("No differences found")
Given :
I : a positive integer
n : a positive integer
nth Term of sequence for input = I :
F(I,1) = (I * (I+1)) / 2
F(I,2) = F(I,1) + F(I-1,1) + F(I-2,1) + .... F(2,1) + F(1,1)
F(I,3) = F(I,2) + F(I-1,2) + F(I-2,2) + .... F(2,2) + F(2,1)
..
..
F(I,n) = F(I,n-1) + F(I-1,n-1) + F(I-2,n-1) + .... F(2,n-1) + F(1,n-1)
nth term --> F(I,n)
Approach 1 : Used recursion to find the above :
def recursive_sum(I, n):
if n == 1:
return (I * (I + 1)) // 2
else:
return sum(recursive_sum(j, n - 1) for j in range(I, 0, -1))
Approach 2 : Iteration to store reusable values in a dictionary. Used this dictionary to get the nth term.:
def non_recursive_sum_using_data(I, n):
global data
if n == 1:
return (I * (I + 1)) // 2
else:
return sum(data[j][n - 1] for j in range(I, 0, -1))
def iterate(I,n):
global data
data = {}
i = 1
j = 1
for i in range(n+1):
for j in range(I+1):
if j not in data:
data[j] = {}
data[j][i] = recursive_sum(j,i)
return data[I][n]
The recursion approach is obviously not efficient due to maximum recursion depth. Also the next approach's time and space complexity will be poor.
Is there better way to recurse ? or a different approach than recursion ?
I am curious if we can find a formula for nth term.
You could just cache your recursive results:
from functools import lru_cache
#lru_cache(maxsize=None)
def recursive_sum(I, n):
if n == 1:
return (I * (I + 1)) // 2
return sum(recursive_sum(j, n - 1) for j in range(I, 0, -1))
That way you can get the readability and brevity of the recursive approach without most of the performance issues since the function is only called once for each argument combination (I, n).
Using the usual binomial(n,k) = n!/(k!*(n-k)!), you have
F(I,n) = binomial(I+n, n+1).
Then you can choose the method you like most to compute binomial coefficients.
Here an example:
def binomial(n, k):
numerator = denominator = 1
t = max(k, n-k)
for low,high in enumerate(range(t+1, n+1), 1):
numerator *= high
denominator *= low
return numerator // denominator
def F(I,n): return binomial(I+n, n+1)
The formula for the nth term of the sequence is the one you have already mentioned.
Also rightly so you have identified that it will lead to an inefficient algorithm and stack overflow.
You can look into dynamic programming approach where u calculate F(I,N) just once and just reuse the value.
For example this is how the fibonacci seq is calculated.
[just-example] https://www.geeksforgeeks.org/program-for-nth-fibonacci-number/
You need to find the same pattern and cache the value
I have an example for this here in this small code written in golang
https://play.golang.org/p/vRi-QMj7z2v
the standard DP
One can do a (tiny) bit of math to rewrite your function:
F(i,n) = sum_{k=0}^{i-1} F(i-k, n-1) = sum_{k=1}^{i} F(k, n-1)
Now notice, that if you consider a matrix F_{ixn}, to compute F(i,n) we just need to add the elements of the previous column.
x----+---
| + |
|----+ |
|----+-F(i,n)
We conclude that we can build the first layer (aka column). Then the second one. And so forth until we get to the n-th layer.
Finally we take the last element of our final layer which is F(i,n)
The computation time is about O(I*n)
More math based but faster
An other way is to consider our layer as a vector variable X.
We can write the recurrence relation as
X_n = MX_{n-1}
where M is a triangular matrix with 1 in the lower part.
We then want to compute the general term of X_n so we want to compute M^n.
By following Yves Daoust
(I just copy from the link above)
Coefficients should be indiced _{n+1} and _n, but here it is _1 and '' for readability
Moreover the matrix is upper triangular but we can just take the transpose afterwards...
a_1 b_1 c_1 d_1 1 1 1 1 a b c d
a_1 b_1 c_1 = 0 1 1 1 * 0 a b c
a_1 b_1 0 0 1 1 0 0 a b
a_1 0 0 0 1 0 0 0 a
by going from last row to first:
a = 1
from b_1 = a+b = 1 + b = n, b = n
from c_1 = a+b+c = 1+n+c, c = n(n+1)/2
from d_1 = a+b+c+d = 1+n+n(n+1)/2 +d, d = n(n+1)(n+2)/6
I have not proved it but I hint that e_1 = n(n+1)(n+2)(n+3)/24 (so basically C_n^k)
(I think the proof lies more in the fact that F(i,n) = F(i,n-1) + F(i-1,n) )
More generally instead of taking variables a,b,c... but X_n(0), X_n(1)...
X_n(0) = 1
X_n(i) = n*...*(n+i-1) / i!
And by applying recusion for computing X:
X_n(0) = 1
X_n(i) = X_n(i-1)*(n+i-1)/i
Finally we deduce F(i,n) as the scalar product Y_{n-1} * X_1 where Y_n is the reversed vector of X_n and X_1(n) = n*(n+1)/2
from functools import lru_cache
#this is copypasted from schwobaseggl
#lru_cache(maxsize=None)
def recursive_sum(I, n):
if n == 1:
return (I * (I + 1)) // 2
return sum(recursive_sum(j, n - 1) for j in range(I, 0, -1))
def iterative_sum(I,n):
layer = [ i*(i+1)//2 for i in range(1,I+1)]
x = 2
while x <= n:
next_layer = [layer[0]]
for i in range(1,I):
#we don't need to compute all the sum everytime
#take the previous sum and add it the new number
next_layer.append( next_layer[i-1] + layer[i] )
layer = next_layer
x += 1
return layer[-1]
def brutus(I,n):
if n == 1:
return I*(I+1)//2
X_1 = [ i*(i+1)//2 for i in range(1, I+1)]
X_n = [1]
for i in range(1, I):
X_n.append(X_n[-1] * (n-1 + i-1) / i )
X_n.reverse()
s = 0
for i in range(0, I):
s += X_1[i]*X_n[i]
return s
def do(k,n):
print('rec', recursive_sum(k,n))
print('it ', iterative_sum(k,n))
print('bru', brutus(k,n))
print('---')
do(1,4)
do(2,1)
do(3,2)
do(4,7)
do(7,4)
Trying to solve this problem with recursion but for input 7168 getting wrong answer.
Given a positive integer n, find the least number of perfect square numbers (for example, 1, 4, 9, 16, ...) which sum to n.
For example, given n = 12, return 3 because 12 = 4 + 4 + 4; given n = 13, return 2 because 13 = 4 + 9.
def recursive(self, n, result, dp):
if n in dp:
return dp[n]
#very large number
large_no = 1 << 31
if n < 1:
return 0
#checking if n is a square or not?
r = n**0.5
if int(r)*int(r) == n:
return result + 1
#iterate from square root till 1 checking all numbers
r = int(r)
while r >= 1:
large_no = min(large_no, self.recursive(n - int(r)*int(r), result + 1, dp))
r -= 1
#memoize the result
dp[n] = large_no
return large_no
I am calling above function as this: self.recursive(7168, 0, {})
Answer should be 4 but I am getting 5. Please don't suggest alternative ways to solve this problem as I have tried them already and it works. I am here to just know the problem with this logic.
First, you have a typo: m should be large_no.
But you're using dp incorrectly: you should be caching the smallest way to write i as the sum of squares, but you're actually caching the result of whatever path you happen to get there.
That means you may cache an accidentally larger value than necessary, and your algorithm is wrong. Although the algorithm is wrong, 7168 is the first value for which it produces the wrong result.
Drop the result argument, change return result+1 to return 1 and your recursive call to:
large_no = min(large_no, 1+self.recursive(n - int(r)*int(r), dp))
A cleaned-up, working version of your code:
def recursive(n, dp):
if n in dp:
return dp[n]
if n == 0: return 0
best = n
for r in xrange(int(n**0.5), 0, -1):
best = min(best, 1 + recursive(n - r*r, dp))
dp[n] = best
return dp[n]
I think the problem is that you're passing result down into your recursion but do not take it into account in memoizing.
recursive(X, Y, dp) and recursive(X, Z, dp) both return dp[X] if X in dp but return dp[X] + y and dp[X] + z, respectively, if dp[X] is not yet memoized (where y = R - Y and z = R - Z, with R the value of result when dp[X] got memoized).
I would get rid of result altogether:
def recursive(self, n, dp):
if n in dp:
return dp[n]
#very large number
large_no = 1 << 31
if n < 1:
return 0
#checking if n is a square or not?
r = n**0.5
if int(r)*int(r) == n:
return 1
#iterate from square root till 1 checking all numbers
r = int(r)
while r >= 1:
large_no = min(large_no, self.recursive(n - int(r)*int(r), dp))
r -= 1
#memoize the result
dp[n] = large_no + 1
return large_no + 1
I am trying to find an efficient way to compute Euler's totient function.
What is wrong with this code? It doesn't seem to be working.
def isPrime(a):
return not ( a < 2 or any(a % i == 0 for i in range(2, int(a ** 0.5) + 1)))
def phi(n):
y = 1
for i in range(2,n+1):
if isPrime(i) is True and n % i == 0 is True:
y = y * (1 - 1/i)
else:
continue
return int(y)
Here's a much faster, working way, based on this description on Wikipedia:
Thus if n is a positive integer, then φ(n) is the number of integers k in the range 1 ≤ k ≤ n for which gcd(n, k) = 1.
I'm not saying this is the fastest or cleanest, but it works.
from math import gcd
def phi(n):
amount = 0
for k in range(1, n + 1):
if gcd(n, k) == 1:
amount += 1
return amount
You have three different problems...
y needs to be equal to n as initial value, not 1
As some have mentioned in the comments, don't use integer division
n % i == 0 is True isn't doing what you think because of Python chaining the comparisons! Even if n % i equals 0 then 0 == 0 is True BUT 0 is True is False! Use parens or just get rid of comparing to True since that isn't necessary anyway.
Fixing those problems,
def phi(n):
y = n
for i in range(2,n+1):
if isPrime(i) and n % i == 0:
y *= 1 - 1.0/i
return int(y)
Calculating gcd for every pair in range is not efficient and does not scales. You don't need to iterate throught all the range, if n is not a prime you can check for prime factors up to its square root, refer to https://stackoverflow.com/a/5811176/3393095.
We must then update phi for every prime by phi = phi*(1 - 1/prime).
def totatives(n):
phi = int(n > 1 and n)
for p in range(2, int(n ** .5) + 1):
if not n % p:
phi -= phi // p
while not n % p:
n //= p
#if n is > 1 it means it is prime
if n > 1: phi -= phi // n
return phi
I'm working on a cryptographic library in python and this is what i'm using. gcd() is Euclid's method for calculating greatest common divisor, and phi() is the totient function.
def gcd(a, b):
while b:
a, b=b, a%b
return a
def phi(a):
b=a-1
c=0
while b:
if not gcd(a,b)-1:
c+=1
b-=1
return c
Most implementations mentioned by other users rely on calling a gcd() or isPrime() function. In the case you are going to use the phi() function many times, it pays of to calculated these values before hand. A way of doing this is by using a so called sieve algorithm.
https://stackoverflow.com/a/18997575/7217653 This answer on stackoverflow provides us with a fast way of finding all primes below a given number.
Oke, now we can replace isPrime() with a search in our array.
Now the actual phi function:
Wikipedia gives us a clear example: https://en.wikipedia.org/wiki/Euler%27s_totient_function#Example
phi(36) = phi(2^2 * 3^2) = 36 * (1- 1/2) * (1- 1/3) = 30 * 1/2 * 2/3 = 12
In words, this says that the distinct prime factors of 36 are 2 and 3; half of the thirty-six integers from 1 to 36 are divisible by 2, leaving eighteen; a third of those are divisible by 3, leaving twelve numbers that are coprime to 36. And indeed there are twelve positive integers that are coprime with 36 and lower than 36: 1, 5, 7, 11, 13, 17, 19, 23, 25, 29, 31, and 35.
TL;DR
With other words: We have to find all the prime factors of our number and then multiply these prime factors together using foreach prime_factor: n *= 1 - 1/prime_factor.
import math
MAX = 10**5
# CREDIT TO https://stackoverflow.com/a/18997575/7217653
def sieve_for_primes_to(n):
size = n//2
sieve = [1]*size
limit = int(n**0.5)
for i in range(1,limit):
if sieve[i]:
val = 2*i+1
tmp = ((size-1) - i)//val
sieve[i+val::val] = [0]*tmp
return [2] + [i*2+1 for i, v in enumerate(sieve) if v and i>0]
PRIMES = sieve_for_primes_to(MAX)
print("Primes generated")
def phi(n):
original_n = n
prime_factors = []
prime_index = 0
while n > 1: # As long as there are more factors to be found
p = PRIMES[prime_index]
if (n % p == 0): # is this prime a factor?
prime_factors.append(p)
while math.ceil(n / p) == math.floor(n / p): # as long as we can devide our current number by this factor and it gives back a integer remove it
n = n // p
prime_index += 1
for v in prime_factors: # Now we have the prime factors, we do the same calculation as wikipedia
original_n *= 1 - (1/v)
return int(original_n)
print(phi(36)) # = phi(2**2 * 3**2) = 36 * (1- 1/2) * (1- 1/3) = 36 * 1/2 * 2/3 = 12
It looks like you're trying to use Euler's product formula, but you're not calculating the number of primes which divide a. You're calculating the number of elements relatively prime to a.
In addition, since 1 and i are both integers, so is the division, in this case you always get 0.
With regards to efficiency, I haven't noticed anyone mention that gcd(k,n)=gcd(n-k,n). Using this fact can save roughly half the work needed for the methods involving the use of the gcd. Just start the count with 2 (because 1/n and (n-1)/k will always be irreducible) and add 2 each time the gcd is one.
Here is a shorter implementation of orlp's answer.
from math import gcd
def phi(n): return sum([gcd(n, k)==1 for k in range(1, n+1)])
As others have already mentioned it leaves room for performance optimization.
Actually to calculate phi(any number say n)
We use the Formula
where p are the prime factors of n.
So, you have few mistakes in your code:
1.y should be equal to n
2. For 1/i actually 1 and i both are integers so their evaluation will also be an integer,thus it will lead to wrong results.
Here is the code with required corrections.
def phi(n):
y = n
for i in range(2,n+1):
if isPrime(i) and n % i == 0 :
y -= y/i
else:
continue
return int(y)
This is my code:
def sum_even(a, b):
count = 0
for i in range(a, b, 1):
if(i % 2 == 0):
count += [i]
return count
An example I put was print(sum_even(3,7)) and the output is 0. I cannot figure out what is wrong.
Your indentation is off, it should be:
def sum_even(a, b):
count = 0
for i in range(a, b, 1):
if(i % 2 == 0):
count += i
return count
so that return count doesn't get scoped to your for loop (in which case it would return on the 1st iteration, causing it to return 0)
(And change [i] to i)
NOTE: another problem - you should be careful about using range:
>>> range(3,7)
[3, 4, 5, 6]
so if you were to do calls to:
sum_even(3,7)
sum_even(3,8)
right now, they would both output 10, which is incorrect for sum of even integers between 3 and 8, inclusive.
What you really want is probably this instead:
def sum_even(a, b):
return sum(i for i in range(a, b + 1) if i % 2 == 0)
Move the return statement out of the scope of the for loop (otherwise you will return on the first loop iteration).
Change count += [i] to count += i.
Also (not sure if you knew this), range(a, b, 1) will contain all the numbers from a to b - 1 (not b). Moreover, you don't need the 1 argument: range(a,b) will have the same effect. So to contain all the numbers from a to b you should use range(a, b+1).
Probably the quickest way to add all the even numbers from a to b is
sum(i for i in xrange(a, b + 1) if not i % 2)
You can make it far simpler than that, by properly using the step argument to the range function.
def sum_even(a, b):
return sum(range(a + a%2, b + 1, 2))
You don't need the loop; you can use simple algebra:
def sum_even(a, b):
if (a % 2 == 1):
a += 1
if (b % 2 == 1):
b -= 1
return a * (0.5 - 0.25 * a) + b * (0.25 * b + 0.5)
Edit:
As NPE pointed out, my original solution above uses floating-point maths. I wasn't too concerned, since the overhead of floating-point maths is negligible compared with the removal of the looping (e.g. if calling sum_even(10, 10000)). Furthermore, the calculations use (negative) powers of two, so shouldn't be subject by rounding errors.
Anyhow, with the simple trick of multiplying everything by 4 and then dividing again at the end we can use integers throughout, which is preferable.
def sum_even(a, b):
if (a % 2 == 1):
a += 1
if (b % 2 == 1):
b -= 1
return (a * (2 - a) + b * (2 + b)) // 4
I'd like you see how your loops work if b is close to 2^32 ;-)
As Matthew said there is no loop needed but he does not explain why.
The problem is just simple arithmetic sequence wiki. Sum of all items in such sequence is:
(a+b)
Sn = ------- * n
2
where 'a' is a first item, 'b' is last and 'n' is number if items.
If we make 'a' and b' even numbers we can easily solve given problem.
So making 'a' and 'b' even is just:
if ((a & 1)==1):
a = a + 1
if ((b & 1)==1):
b = b - 1
Now think how many items do we have between two even numbers - it is:
b-a
n = --- + 1
2
Put it into equation and you get:
a+b b-a
Sn = ----- * ( ------ + 1)
2 2
so your code looks like:
def sum_even(a,b):
if ((a & 1)==1):
a = a + 1
if ((b & 1)==1):
b = b - 1
return ((a+b)/2) * (1+((b-a)/2))
Of course you may add some code to prevent a be equal or bigger than b etc.
Indentation matters in Python. The code you write returns after the first item processed.
This might be a simple way of doing it using the range function.
the third number in range is a step number, i.e, 0, 2, 4, 6...100
sum = 0
for even_number in range(0,102,2):
sum += even_number
print (sum)
def sum_even(a,b):
count = 0
for i in range(a, b):
if(i % 2 == 0):
count += i
return count
Two mistakes here :
add i instead of [i]
you return the value directly at the first iteration. Move the return count out of the for loop
The sum of all the even numbers between the start and end number (inclusive).
def addEvenNumbers(start,end):
total = 0
if end%2==0:
for x in range(start,end):
if x%2==0:
total+=x
return total+end
else:
for x in range(start,end):
if x%2==0:
total+=x
return total
print addEvenNumbers(4,12)
little bit more fancy with advanced python feature.
def sum(a,b):
return a + b
def evensum(a,b):
a = reduce(sum,[x for x in range(a,b) if x %2 ==0])
return a
SUM of even numbers including min and max numbers:
def sum_evens(minimum, maximum):
sum=0
for i in range(minimum, maximum+1):
if i%2==0:
sum = sum +i
i= i+1
return sum
print(sum_evens(2, 6))
OUTPUT is : 12
sum_evens(2, 6) -> 12 (2 + 4 + 6 = 12)
List based approach,
Use b+1 if you want to include last value.
def sum_even(a, b):
even = [x for x in range (a, b) if x%2 ==0 ]
return sum(even)
print(sum_even(3,6))
4
[Program finished]
This will add up all your even values between 1 and 10 and output the answer which is stored in the variable x
x = 0
for i in range (1,10):
if i %2 == 0:
x = x+1
print(x)