We Keep Coding

Find b that (a+b) divisible to K

I have integer input: 0 < a, K, N < 10^9
I need to find all b numbers that satisfy:
a + b <= N
(a + b) % K = 0
For example: 10 6 40 -> [2, 8, 14, 20, 26]
I tried a simple brute force and failed (Time Limit Exceeded). Can anyone suggest answer? Thanks
a, K, N = [int(x) for x in input().split()]
count = 0
b = 1
while (a + b <= N):
if ((a + b) % K) == 0:
count+=1
print(b, end=" ")
b+=1
if (count == 0):
print(-1)

The first condition is trivial in the sense that it just poses an upper limit on b. The second condition can be rephrased using the definition of % as
a + b = P * K
For some arbitrary integer P. From this, is simple to compute the smallest b by finding the smallest P that gives you a positive result for P * K - a. In other words
P * K - a >= 0
P * K >= a
P >= a / K
P = ceil(a / K)
So you have
b0 = ceil(a / K) * K - a
b = range(b0, N + 1, K)
range is a generator, so it won't compute the values up front. You can force that by doing list(b).
At the same time, if you only need the count of elements, range objects will do the math on the limits and step size for you conveniently, all without computing the actual values, so you can just do len(b).

To find the list of bs, you can use some maths. First, we note that (a + b) % K is equivalent to a % K + b % K. Also when n % K is 0, that means that n is a multiple of K. So the smallest value of b is n * K - a for the smallest value of n where this calculation is still positive. Once you find that value, you can simply add K repeatedly to find all other values of b.

b = k - a%k
Example: a=19, k=11, b = 11-19%11 = 11-8 =3

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

Codewars. Some tests are passed, but i need to get tests which outputs the following mistake: 3263 should equal -1

Can you explain it what problems are here? To my mind, this code is like a heap of crap but with the right solving. I beg your pardon for my english.
the task of this kata:
Some numbers have funny properties. For example:
89 --> 8¹ + 9² = 89 * 1
695 --> 6² + 9³ + 5⁴= 1390 = 695 * 2
46288 --> 4³ + 6⁴+ 2⁵ + 8⁶ + 8⁷ = 2360688 = 46288 * 51
Given a positive integer n written as abcd... (a, b, c, d... being digits) and a positive integer p we want to find a positive integer k, if it exists, such as the sum of the digits of n taken to the successive powers of p is equal to k * n. In other words:
Is there an integer k such as : (a ^ p + b ^ (p+1) + c ^(p+2) + d ^ (p+3) + ...) = n * k
If it is the case we will return k, if not return -1.
Note: n, p will always be given as strictly positive integers.
dig_pow(89, 1) should return 1 since 8¹ + 9² = 89 = 89 * 1
dig_pow(92, 1) should return -1 since there is no k such as 9¹ + 2² equals 92 * k
dig_pow(695, 2) should return 2 since 6² + 9³ + 5⁴= 1390 = 695 * 2
dig_pow(46288, 3) should return 51 since 4³ + 6⁴+ 2⁵ + 8⁶ + 8⁷ = 2360688 = 46288 * 51
def dig_pow(n, p):
if n > 0 and p > 0:
b = []
a = str(n)
result = []
for i in a:
b.append(int(i))
for x in b:
if p != 1:
result.append(x ** p)
p += 1
else:
result.append(x ** (p + 1))
if int((sum(result)) / n) < 1:
return -1
elif int((sum(result)) / n) < 2:
return 1
else:
return int((sum(result)) / n)
test results:
Test Passed
Test Passed
Test Passed
Test Passed
3263 should equal -1

I don't know what exact version of Python you're using. This following code are in Python 3. And if I get you correctly, the code can be as simple as
def dig_pow(n, p):
assert n > 0 and p > 0
digits = (int(i) for i in str(n)) # replaces your a,b part with generator
result = 0 # you don't use result as a list, so an int suffice
for x in digits: # why do you need if in the loop? (am I missing something?)
result += x ** p
p += 1
if result % n: # you just test for divisibility
return -1
else:
return result // n
The major problem is that, in your objective, you have only two option of returning, but you wrote if elif else, which is definitely unnecessary and leads to problems and bugs. The % is modulus operator.
Also, having an if and not returning anything in the other branch is often not a good idea (see the assert part). Of course, if you don't like it, just fall back to if.

I believe this could work as well and I find it a little easier to read, however it can definitely be improved:
def dig_pow(n, p):
value = 0
for digit in str(n):
value += int(digit)**p
p += 1
for k in range(1,value):
if value/k == n:
return k
return -1

this is some example simple example than using:
digits = (int(i) for i in str(n))
I'm opting to use this version since I am still a beginner which can be done with this alt way:
result = 0
for digits in str(n):
#iterate through each digit from n
# single of digits turn to int & power to p
for number in digits:
result += int(number) ** p
p += 1
as for the full solution, it goes like this:
def dig_pow(n, p):
# example n = 123 , change it to string = 1, 2, 3
# each string[] **p, and p iterate by 1
# if n % p not equal to p return - 1
result = 0
for digits in str(n):
#iterate through each digit from n
# single digit turn to int & power to p
for number in digits:
result += int(number) ** p
p += 1
if result % n:
return -1
else:
return result // n

Homework: Implementing the Z algorithm in python, it's really slow, slower than naive string search

I have to implement the Z algorithm and use it to search a target text for a specific pattern. I've implemented what I thought was the correct algorithm and search function using it but it's really slow. For the naive implementation of string search I consistently got times lower than 1.5 seconds and for the z string search I consistently got times over 3 seconds (for my biggest test case) so I have to be doing something wrong. The results seem to be correct, or were at least for the few test cases we were given. The code for the functions mentioned in my rant is below:
import sys
import time
# z algorithm a.k.a. the fundemental preprocessing algorithm
def z(P, start=1, max_box_size=sys.maxsize):
n = len(P)
boxes = [0] * n
l = -1
r = -1
for k in range(start, n):
if k > r:
i = 0
while k + i < n and P[i] == P[k + i] and i < max_box_size:
i += 1
boxes[k] = i
if i:
l = k
r = k + i - 1
else:
kp = k - l
Z_kp = boxes[kp]
if Z_kp < r - k + 1:
boxes[k] = Z_kp
else:
i = r + 1
while i < n and P[i] == P[i - k] and i - k < max_box_size:
i += 1
boxes[k] = i - k
l = k
r = i - 1
return boxes
# a simple string search
def naive_string_search(P, T):
m = len(T)
n = len(P)
indices = []
for i in range(m - n + 1):
if P == T[i: i + n]:
indices.append(i)
return indices
# string search using the z algorithm.
# The pattern you're searching for is simply prepended to the target text
# and than the z algorithm is run on that concatenation
def z_string_search(P, T):
PT = P + T
n = len(P)
boxes = z(PT, start=n, max_box_size=n)
return list(map(lambda x: x[0]-n, filter(lambda x: x[1] >= n, enumerate(boxes))))

Your's implementation of z-function def z(..) is algorithmically ok and asymptotically ok.
It has O(m + n) time complexity in worst case while implementation of naive string search has O(m*n) time complexity in worst case, so I think that the problem is in your test cases.
For example if we take this test case:
T = ['a'] * 1000000
P = ['a'] * 1000
we will get for z-function:
real 0m0.650s
user 0m0.606s
sys 0m0.036s
and for naive string matching:
real 0m8.235s
user 0m8.071s
sys 0m0.085s
PS: You should understand that there are a lot of test cases where naive string matching works in linear time too, for example:
T = ['a'] * 1000000
P = ['a'] * 1000000
Thus the worst case for a naive string matching is where function should apply pattern and check again and again. But in this case it will do only one check because of the lengths of the input (it cannot apply pattern from index 1 so it won't continue).

Optimise the solution to Project Euler 12 (Python)

I have the following code for Project Euler Problem 12. However, it takes a very long time to execute. Does anyone have any suggestions for speeding it up?
n = input("Enter number: ")
def genfact(n):
t = []
for i in xrange(1, n+1):
if n%i == 0:
t.append(i)
return t
print "Numbers of divisors: ", len(genfact(n))
print
m = input("Enter the number of triangle numbers to check: ")
print
for i in xrange (2, m+2):
a = sum(xrange(i))
b = len(genfact(a))
if b > 500:
print a
For n, I enter an arbitrary number such as 6 just to check whether it indeed returns the length of the list of the number of factors.
For m, I enter entered 80 000 000
It works relatively quickly for small numbers. If I enter b > 50 ; it returns 28 for a, which is correct.

My answer here isn't pretty or elegant, it is still brute force. But, it simplifies the problem space a little and terminates successfully in less than 10 seconds.
Getting factors of n:
Like #usethedeathstar mentioned, it is possible to test for factors only up to n/2. However, we can do better by testing only up to the square root of n:
let n = 36
=> factors(n) : (1x36, 2x18, 3x12, 4x9, 6x6, 9x4, 12x3, 18x2, 36x1)
As you can see, it loops around after 6 (the square root of 36). We also don't need to explicitly return the factors, just find out how many there are... so just count them off with a generator inside of sum():
import math
def get_factors(n):
return sum(2 for i in range(1, round(math.sqrt(n)+1)) if not n % i)
Testing the triangular numbers
I have used a generator function to yield the triangular numbers:
def generate_triangles(limit):
l = 1
while l <= limit:
yield sum(range(l + 1))
l += 1
And finally, start testing:
def test_triangles():
triangles = generate_triangles(100000)
for i in triangles:
if get_factors(i) > 499:
return i
Running this with the profiler, it completes in less than 10 seconds:
$ python3 -m cProfile euler12.py
361986 function calls in 8.006 seconds
The BIGGEST time saving here is get_factors(n) testing only up to the square root of n - this makes it heeeaps quicker and you save heaps of memory overhead by not generating a list of factors.
As I said, it still isn't pretty - I am sure there are more elegant solutions. But, it fits the bill of being faster :)

I got my answer to run in 1.8 seconds with Python.
import time
from math import sqrt
def count_divisors(n):
d = {}
count = 1
while n % 2 == 0:
n = n / 2
try:
d[2] += 1
except KeyError:
d[2] = 1
for i in range(3, int(sqrt(n+1)), 2):
while n % i == 0 and i != n:
n = n / i
try:
d[i] += 1
except KeyError:
d[i] = 1
d[n] = 1
for _,v in d.items():
count = count * (v + 1)
return count
def tri_number(num):
next = 1 + int(sqrt(1+(8 * num)))
return num + (next/2)
def main():
i = 1
while count_divisors(i) < 500:
i = tri_number(i)
return i
start = time.time()
answer = main()
elapsed = (time.time() - start)
print("result %s returned in %s seconds." % (answer, elapsed))
Here is the output showing the timedelta and correct answer:
$ python ./project012.py
result 76576500 returned in 1.82238006592 seconds.
Factoring
For counting the divisors, I start by initializing an empty dictionary and a counter. For each factor found, I create key of d[factor] with value of 1 if it does not exist, otherwise, I increment the value d[factor].
For example, if we counted the factors 100, we would see d = {25: 1, 2: 2}
The first while loop, I factor out all 2's, dividing n by 2 each time. Next, I begin factoring at 3, skipping two each time (since we factored all even numbers already), and stopping once I get to the square root of n+1.
We stop at the square_root of n because if there's a pair of factors with one of the numbers bigger than square_root of n, the other of the pair has to be less than 10. If the smaller one doesn't exist, there is no matching larger factor.
https://math.stackexchange.com/questions/1343171/why-only-square-root-approach-to-check-number-is-prime
while n % 2 == 0:
n = n / 2
try:
d[2] += 1
except KeyError:
d[2] = 1
for i in range(3, int(sqrt(n+1)), 2):
while n % i == 0 and i != n:
n = n / i
try:
d[i] += 1
except KeyError:
d[i] = 1
d[n] = 1
Now that I have gotten each factor, and added it to the dictionary, we have to add the last factor (which is just n).
Counting Divisors
Now that the dictionary is complete, we loop through each of the items, and apply the following formula: d(n)=(a+1)(b+1)(c+1)...
https://www.wikihow.com/Determine-the-Number-of-Divisors-of-an-Integer
All this formula means is taking all of the counts of each factor, adding 1, then multiplying them together. Take 100 for example, which has factors 25, 2, and 2. We would calculate d(n)=(a+1)(b+1) = (1+1)(2+1) = (2)(3) = 6 total divisors
for _,v in d.items():
count = count * (v + 1)
return count
Calculate Triangle Numbers
Now, taking a look at tri_number(), you can see that I opted to calculate the next triangle number in a sequence without manually adding each whole number together (saving me millions of operations). Instead I used T(n) = n (n+1) / 2
http://www.maths.surrey.ac.uk/hosted-sites/R.Knott/runsums/triNbProof.html
We are providing a whole number to the function as an argument, so we need to solve for n, which is going to be the whole number to add next. Once we have the next number (n), we simply add that single number to num and return
S=n(n+1)2
S=n2+n2
2S=n2+n
n2+n−2S=0
At this point, we use the quadratic formula for : ax2+bx+c=0.
n=−b±√b2−4ac / 2a
n=−1±√1−4(1)(−2S) / 2
n=−1±√1+8S / 2
https://socratic.org/questions/how-do-you-solve-for-n-in-s-n-n-1-2
So all tri_number() does is evaluate n=1+√1+8S / 2 (we ignore the negative equation here). The answer that is returned is the next triangle number in the sequence.
def tri_number(num):
next = 1 + int(sqrt(1+(8 * num)))
return num + (next/2)
Main Loop
Finally, we can look at main(). We start at whole number 1. We count the divisor of 1. If it is less than 500, we get the next triangle number, then try again and again until we get a number with > 500 divisors.
def main():
i = 1
while count_divisors(i) < 500:
i = tri_number(i)
return i
I am sure there are additional ways to optimize but I am not smart enough to understand those ways. If you find any better ways to optimize python, let me know! I originally solved project 12 in Golang, and that run in 25 milliseconds!
$ go run project012.go
76576500
2018/07/12 01:56:31 TIME: main() took 23.581558ms

one of the hints i can give is
def genfact(n):
t = []
for i in xrange(1, n+1):
if n%i == 0:
t.append(i)
return t
change that to
def genfact(n):
t=[]
for i in xrange(1,numpy.sqrt(n)+1):
if(n%i==0):
t.append(i)
t.apend(n/i)
since if a is a divisor than so is b=n/a, since a*b=a*n/b=n, That should help a part already (not sure if in your case a square is possible, but if so, add another case to exclude adding the same number twice)
You could devise a recursive thing too, (like if it is something like for 28, you get 1,28,2,14 and at the moment you are at knowing 14, you put in something to actually remember the divisors of 14 (memoize), than check if they are alraedy in the list, and if not, add them to the list, together with 28/d for each of the divisors of 14, and at the end just take out the duplicates
If you think my first answer is still not fast enough, ask for more, and i will check how it would be done to solve it faster with some more tricks (could probably make use of erastothenes sieve or so too, and some other tricks could be thought up as well if you would wish to really blow up the problem to huge proportions, like to check the first one with over 10k divisors or so)

while True:
c=0
n=1
m=1
for i in range(1,n+1):
if n%i==0:
c=c+1
m=m+1
n=m*(m+1)/2
if c>500:
break
print n

this is not my code but it is so optimized.
source: http://code.jasonbhill.com/sage/project-euler-problem-12/
import time
def num_divisors(n):
if n % 2 == 0: n = n / 2
divisors = 1
count = 0
while n % 2 == 0:
count += 1
n = n / 2
divisors = divisors * (count + 1)
p = 3
while n != 1:
count = 0
while n % p == 0:
count += 1
n = n / p
divisors = divisors * (count + 1)
p += 2
return divisors
def find_triangular_index(factor_limit):
n = 1
lnum, rnum = num_divisors(n), num_divisors(n + 1)
while lnum * rnum < 500:
n += 1
lnum, rnum = rnum, num_divisors(n + 1)
return n
start = time.time()
index = find_triangular_index(500)
triangle = (index * (index + 1)) / 2
elapsed = (time.time() - start)
print("result %s returned in %s seconds." % (triangle, elapsed))