We Keep Coding

Counting number of ways I can have unique numbers in array

I am trying to find the number of ways to construct an array such that consecutive positions contain different values.
Specifically, I need to construct an array with elements such that each element 1 between and k , all inclusive. I also want the first and last elements of the array to be 1 and x.
Complete problem statement:
Here is what I tried:
def countArray(n, k, x):
# Return the number of ways to fill in the array.
if x > k:
return 0
if x == 1:
return 0
def fact(n):
if n == 0:
return 1
fact_range = n+1
T = [1 for i in range(fact_range)]
for i in range(1,fact_range):
T[i] = i * T[i-1]
return T[fact_range-1]
ways = fact(k) / (fact(n-2)*fact(k-(n-2)))
return int(ways)
In short, I did K(C)N-2 to find the ways. How could I solve this?
It passes one of the base case with inputs as countArray(4,3,2) but fails for 16 other cases.

Let X(n) be the number of ways of constructing an array of length n, starting with 1 and ending in x (and not repeating any numbers). Let Y(n) be the number of ways of constructing an array of length n, starting with 1 and NOT ending in x (and not repeating any numbers).
Then there's these recurrence relations (for n>1)
X(n+1) = Y(n)
Y(n+1) = X(n)*(k-1) + Y(n)*(k-2)
In words: If you want an array of length n+1 ending in x, then you need an array of length n not ending in x. And if you want an array of length n+1 not ending in x, then you can either add any of the k-1 symbols to an array of length n ending in x, or you can take an array of length n not ending in x, and add any of the k-2 symbols that aren't x and don't repeat the last value.
For the base case, n=1, if x is 1 then X(1)=1, Y(1)=0 otherwise, X(1)=0, Y(1)=1
This gives you an O(n)-time method of computing the result.
def ways(n, k, x):
M = 10**9 + 7
wx = (x == 1)
wnx = (x != 1)
for _ in range(n-1):
wx, wnx = wnx, wx * (k-1) + wnx*(k-2)
wnx = wnx % M
return wx
print(ways(100, 5, 2))
In principle you can reduce this to O(log n) by expressing the recurrence relations as a matrix and computing the matrix power (mod M), but it's probably not necessary for the question.
[Additional working]
We have the recurrence relations:
X(n+1) = Y(n)
Y(n+1) = X(n)*(k-1) + Y(n)*(k-2)
Using the first, we can replace the Y(_) in the second with X(_+1) to reduce it down to a single variable. Then:
X(n+2) = X(n)*(k-1) + X(n+1)*(k-2)
Using standard techniques, we can solve this linear recurrence relation exactly.
In the case x!=1, we have:
X(n) = ((k-1)^(n-1) - (-1)^n) / k
And in the case x=1, we have:
X(n) = ((k-1)^(n-1) - (1-k)(-1)^n)/k
We can compute these mod M using Fermat's little theorem because M is prime. So 1/k = k^(M-2) mod M.
Thus we have (with a little bit of optimization) this short program that solves the problem and runs in O(log n) time:
def ways2(n, k, x):
S = -1 if n%2 else 1
return ((pow(k-1, n-1, M) + S) * pow(k, M-2, M) - S*(x==1)) % M

could you try this DP version: (it's passed all tests) (it's inspired by #PaulHankin and take DP approach - will run performance later to see what's diff for big matrix)
def countArray(n, k, x):
# Return the number of ways to fill in the array.
big_mod = 10 ** 9 + 7
dp = [[1], [1]]
if x == 1:
dp = [[1], [0]]
else:
dp = [[1], [1]]
for _ in range(n-2):
dp[0].append(dp[0][-1] * (k - 1) % big_mod)
dp[1].append((dp[0][-1] - dp[1][-1]) % big_mod)
return dp[1][-1]

Trying to define one of Euler's approximations to pi, getting unsupported operand type(s) for 'list and 'int'

I am trying to define a function which will approximate pi in python using one of Euler's methods. His formula is as follows:
My code so far is this:
def pi_euler1(n):
numerator = list(range(2 , n))
for i in numerator:
j = 2
while i * j <= numerator[-1]:
if i * j in numerator:
numerator.remove(i * j)
j += 1
for k in numerator:
if (k + 1) % 4 == 0:
denominator = k + 1
else:
denominator = k - 1
#Because all primes are odd, both numbers inbetween them are divisible by 2,
#and by extension 1 of the 2 numbers is divisible by 4
term = numerator / denominator
I know this is wrong, and also incomplete. I'm just not quite sure what the TypeError that I mentioned earlier actually means. I'm just quite stuck with it, I want to create a list of the terms and then find their products. Am I on the right lines?
Update:
I have worked ways around this, fixing the clearly obvious errors that were prevalent thanks to msconi and Johanc, now with the following code:
import math
def pi_euler1(n):
numerator = list(range(2 , 13 + math.ceil(n*(math.log(n)+math.log(math.log(n))))))
denominator=[]
for i in numerator:
j = 2
while i * j <= numerator[-1]:
if (i * j) in numerator:
numerator.remove(i * j)
j += 1
numerator.remove(2)
for k in numerator:
if (k + 1) % 4 == 0:
denominator.append(k+1)
else:
denominator.append(k-1)
a=1
for i in range(n):
a *= numerator[i] / denominator[i]
return 4*a
This seems to work, when I tried to plot a graph of the errors from pi in a semilogy axes scale, I was getting a domain error, but i needed to change the upper bound of the range to n+1 because log(0) is undefined. Thank you guys

Here is the code with some small modifications to get it working:
import math
def pi_euler1(n):
lim = n * n + 4
numerator = list(range(3, lim, 2))
for i in numerator:
j = 3
while i * j <= numerator[-1]:
if i * j in numerator:
numerator.remove(i * j)
j += 2
euler_product = 1
for k in numerator[:n]:
if (k + 1) % 4 == 0:
denominator = k + 1
else:
denominator = k - 1
factor = k / denominator
euler_product *= factor
return euler_product * 4
print(pi_euler1(3))
print(pi_euler1(10000))
print(math.pi)
Output:
3.28125
3.148427801913721
3.141592653589793
Remarks:
You only want the odd primes, so you can start with a list of odd numbers.
j can start with 3 and increment in steps of 2. In fact, j can start at i because all the multiples of i smaller than i*i are already removed earlier.
In general it is very bad practise to remove elements from the list over which you are iterating. See e.g. this post. Internally, Python uses an index into the list over which it iterates. Coincidently, this is not a problem in this specific case, because only numbers larger than the current are removed.
Also, removing elements from a very long list is very slow, as each time the complete list needs to be moved to fill the gap. Therefore, it is better to work with two separate lists.
You didn't calculate the resulting product, nor did you return it.
As you notice, this formula converges very slowly.
As mentioned in the comments, the previous version interpreted n as the limit for highest prime, while in fact n should be the number of primes. I adapted the code to rectify that. In the above version with a crude limit; the version below tries a tighter approximation for the limit.
Here is a reworked version, without removing from the list you're iterating. Instead of removing elements, it just marks them. This is much faster, so a larger n can be used in a reasonable time:
import math
def pi_euler_v3(n):
if n < 3:
lim = 6
else:
lim = n*n
while lim / math.log(lim) / 2 > n:
lim //= 2
print(n, lim)
numerator = list(range(3, lim, 2))
odd_primes = []
for i in numerator:
if i is not None:
odd_primes.append(i)
if len(odd_primes) >= n:
break
j = i
while i * j < lim:
numerator[(i*j-3) // 2] = None
j += 2
if len(odd_primes) != n:
print(f"Wrong limit calculation, only {len(odd_primes)} primes instead of {n}")
euler_product = 1
for k in odd_primes:
denominator = k + 1 if k % 4 == 3 else k - 1
euler_product *= k / denominator
return euler_product * 4
print(pi_euler_v2(100000))
print(math.pi)
Output:
3.141752253548891
3.141592653589793

In term = numerator / denominator you are dividing a list by a number, which doesn't make sense. Divide k by the denominator in the loop in order to use the numerator element for each of the equation's factors one by one. Then you could multiply them repeatedly to the term term *= i / denominator, which you initialize in the beginning as term = 1.
Another issue is the first loop, which won't give you the first n prime numbers. For example, for n=3, list(range(2 , n)) = [2]. Therefore, the only prime you will get is 2.

How to find sum of cubes of the divisors for every number from 1 to input number x in python where x can be very large

Examples,
1.Input=4
Output=111
Explanation,
1 = 1³(divisors of 1)
2 = 1³ + 2³(divisors of 2)
3 = 1³ + 3³(divisors of 3)
4 = 1³ + 2³ + 4³(divisors of 4)
------------------------
sum = 111(output)
1.Input=5
Output=237
Explanation,
1 = 1³(divisors of 1)
2 = 1³ + 2³(divisors of 2)
3 = 1³ + 3³(divisors of 3)
4 = 1³ + 2³ + 4³(divisors of 4)
5 = 1³ + 5³(divisors of 5)
-----------------------------
sum = 237 (output)
x=int(raw_input().strip())
tot=0
for i in range(1,x+1):
for j in range(1,i+1):
if(i%j==0):
tot+=j**3
print tot
Using this code I can find the answer for small number less than one million.
But I want to find the answer for very large numbers. Is there any algorithm
for how to solve it easily for large numbers?

Offhand I don't see a slick way to make this truly efficient, but it's easy to make it a whole lot faster. If you view your examples as matrices, you're summing them a row at a time. This requires, for each i, finding all the divisors of i and summing their cubes. In all, this requires a number of operations proportional to x**2.
You can easily cut that to a number of operations proportional to x, by summing the matrix by columns instead. Given an integer j, how many integers in 1..x are divisible by j? That's easy: there are x//j multiples of j in the range, so divisor j contributes j**3 * (x // j) to the grand total.
def better(x):
return sum(j**3 * (x // j) for j in range(1, x+1))
That runs much faster, but still takes time proportional to x.
There are lower-level tricks you can play to speed that in turn by constant factors, but they still take O(x) time overall. For example, note that x // j == 1 for all j such that x // 2 < j <= x. So about half the terms in the sum can be skipped, replaced by closed-form expressions for a sum of consecutive cubes:
def sum3(x):
"""Return sum(i**3 for i in range(1, x+1))"""
return (x * (x+1) // 2)**2
def better2(x):
result = sum(j**3 * (x // j) for j in range(1, x//2 + 1))
result += sum3(x) - sum3(x//2)
return result
better2() is about twice as fast as better(), but to get faster than O(x) would require deeper insight.
Quicker
Thinking about this in spare moments, I still don't have a truly clever idea. But the last idea I gave can be carried to a logical conclusion: don't just group together divisors with only one multiple in range, but also those with two multiples in range, and three, and four, and ... That leads to better3() below, which does a number of operations roughly proportional to the square root of x:
def better3(x):
result = 0
for i in range(1, x+1):
q1 = x // i
# value i has q1 multiples in range
result += i**3 * q1
# which values have i multiples?
q2 = x // (i+1) + 1
assert x // q1 == i == x // q2
if i < q2:
result += i * (sum3(q1) - sum3(q2 - 1))
if i+1 >= q2: # this becomes true when i reaches roughly sqrt(x)
break
return result
Of course O(sqrt(x)) is an enormous improvement over the original O(x**2), but for very large arguments it's still impractical. For example better3(10**6) appears to complete instantly, but better3(10**12) takes a few seconds, and better3(10**16) is time for a coffee break ;-)
Note: I'm using Python 3. If you're using Python 2, use xrange() instead of range().
One more
better4() has the same O(sqrt(x)) time behavior as better3(), but does the summations in a different order that allows for simpler code and fewer calls to sum3(). For "large" arguments, it's about 50% faster than better3() on my box.
def better4(x):
result = 0
for i in range(1, x+1):
d = x // i
if d >= i:
# d is the largest divisor that appears `i` times, and
# all divisors less than `d` also appear at least that
# often. Account for one occurence of each.
result += sum3(d)
else:
i -= 1
lastd = x // i
# We already accounted for i occurrences of all divisors
# < lastd, and all occurrences of divisors >= lastd.
# Account for the rest.
result += sum(j**3 * (x // j - i)
for j in range(1, lastd))
break
return result
It may be possible to do better by extending the algorithm in "A Successive Approximation Algorithm for Computing the Divisor Summatory Function". That takes O(cube_root(x)) time for the possibly simpler problem of summing the number of divisors. But it's much more involved, and I don't care enough about this problem to pursue it myself ;-)
Subtlety
There's a subtlety in the math that's easy to miss, so I'll spell it out, but only as it pertains to better4().
After d = x // i, the comment claims that d is the largest divisor that appears i times. But is that true? The actual number of times d appears is x // d, which we did not compute. How do we know that x // d in fact equals i?
That's the purpose of the if d >= i: guarding that comment. After d = x // i we know that
x == d*i + r
for some integer r satisfying 0 <= r < i. That's essentially what floor division means. But since d >= i is also known (that's what the if test ensures), it must also be the case that 0 <= r < d. And that's how we know x // d is i.
This can break down when d >= i is not true, which is why a different method needs to be used then. For example, if x == 500 and i == 51, d (x // i) is 9, but it's certainly not the case that 9 is the largest divisor that appears 51 times. In fact, 9 appears 500 // 9 == 55 times. While for positive real numbers
d == x/i
if and only if
i == x/d
that's not always so for floor division. But, as above, the first does imply the second if we also know that d >= i.
Just for Fun
better5() rewrites better4() for about another 10% speed gain. The real pedagogical point is to show that it's easy to compute all the loop limits in advance. Part of the point of the odd code structure above is that it magically returns 0 for a 0 input without needing to test for that. better5() gives up on that:
def isqrt(n):
"Return floor(sqrt(n)) for int n > 0."
g = 1 << ((n.bit_length() + 1) >> 1)
d = n // g
while d < g:
g = (d + g) >> 1
d = n // g
return g
def better5(x):
assert x > 0
u = isqrt(x)
v = x // u
return (sum(map(sum3, (x // d for d in range(1, u+1)))) +
sum(x // i * i**3 for i in range(1, v)) -
u * sum3(v-1))

def sum_divisors(n):
sum = 0
i = 0
for i in range (1, n) :
if n % i == 0 and n != 0 :
sum = sum + i
# Return the sum of all divisors of n, not including n
return sum
print(sum_divisors(0))
# 0
print(sum_divisors(3)) # Should sum of 1
# 1
print(sum_divisors(36)) # Should sum of 1+2+3+4+6+9+12+18
# 55
print(sum_divisors(102)) # Should be sum of 2+3+6+17+34+51
# 114

An Explanation for the totient finder in this program

I need an explanation for the program suggested in the edit in the first answer over here. It is a program to find the totients of a range of numbers. Can somebody provide a simple explanation? (Ignore the summation part for now, I need to find out how the init method finds the totients.) I know there is an explanation in the answer, but that is an explanation for different programs, I need an explanation for this particular one.
class Totient:
def __init__(self, n):
self.totients = [1 for i in range(n)]
for i in range(2, n):
if self.totients[i] == 1:
for j in range(i, n, i):
self.totients[j] *= i - 1
k = j / i
while k % i == 0:
self.totients[j] *= i
k /= i
def __call__(self, i):
return self.totients[i]
if __name__ == '__main__':
from itertools import imap
totient = Totient(10000)
print sum(imap(totient, range(10000)))

It's a variant of the Sieve of Eratosthenes for finding prime numbers.
If you want to know the totient of a single number n, the best way to find it is to factor n and take the product of 1 less than each factor; for instance, 30 = 2 * 3 * 5, and subtracting 1 from each factor, then multiplying, gives the totient 1 * 2 * 4 = 8. But if you want to find the totients of all the numbers less than a given n, a better approach than factoring each of them is sieving. The idea is simple: Set up an array X from 0 to n, store i in each Xi, then run through the array starting from 0 and whenever Xi = i loop over the multiples of i, multiplying each by i − 1.
Further discussion and code at my blog.

I'm not completely sure what the code is doing -- but frankly it looks pretty bad. It clearly is trying to use that Euler's totient function is multiplicative, meaning that a,b are relatively prime then t(a,b) = t(a)*t(b), together with the fact that if p is a prime then t(p) = p-1. But -- it seems to be using crude trial division to determine such things. If you really want to calculate the totient of all numbers in a given range then you should use an algorithm that sieves the numbers as you go along.
Here is a version which sieves as it goes and exploits the multiplicative nature to the hilt. At each pass through the main loop it starts with a prime, p which hasn't yet been processed. It determines all powers of p <= n and then uses a direct formula for these powers (see https://en.wikipedia.org/wiki/Euler%27s_totient_function ). Once these totients have been added, it forms all possible products <= n of these powers and the numbers for which the totients have been previously computed. This gives a whole slew of numbers to add to the list of previously determined numbers. At most sqrt(n) passes need to be made through the main loop. It runs almost instantly for n = 10000. It returns a list where the ith value is the totient of i (with t(0) = 0 for convenience):
def allTotients(n):
totients = [None]*(n+1) #totients[i] will contain the t(i)
totients[0] = 0
totients[1] = 1
knownTotients = [] #known in range 2 to n
p = 2
while len(knownTotients) < n - 1:
powers = [p]
k = 2
while p ** k <= n:
powers.append(p ** k)
k +=1
totients[p] = p - 1
for i in range(1,len(powers)):
totients[powers[i]] = powers[i] - powers[i-1]
#at this stage powers represent newly discovered totients
#combine with previously discovered totients to get still more
newTotients = powers[:]
for m in knownTotients:
for pk in powers:
if m*pk > n: break
totients[m*pk] = totients[m]*totients[pk]
newTotients.append(m*pk)
knownTotients.extend(newTotients)
#if there are any unkown totients -- the smallest such will be prime
if len(knownTotients) < n-1:
p = totients.index(None)
return totients
For completeness sake, here is a Python implementation of the algorithm to compute the totient of a single number which user448810 described in their answer:
from math import sqrt
#crude factoring algorithm:
small_primes = [2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,
53,59,61,67,71,73,79,83,89,97]
def factor(n):
#returns a list of prime factors
factors = []
num = n
#first pull out small prime factors
for p in small_primes:
while num % p == 0:
factors.append(p)
num = num // p
if num == 1: return factors
#now do trial division, starting at 101
k = 101
while k <= sqrt(num):
while num % k == 0:
factors.append(k)
num = num // k
k += 2
if num == 1:
return factors
else:
factors.append(num)
return factors
def totient(n):
factors = factor(n)
unique_factors = set()
t = 1
for p in factors:
if p in unique_factors:
t *= p
else:
unique_factors.add(p)
t *= (p-1)
return t

Computing Eulers Totient Function

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)