factors

I am attempting project euler question number 3, and I don't get the desired result. My logic: List all the factors of the number 13195 and save them in an array. Check if each number in the array is a prime. If the number is found to be prime save it in an other array. display the contents of the second array. Hope it contains only prime factors. RESULT: The first array contains all the factors as expected, The second I think duplicates the first array or slips in some non-primes, Please help! :) My code: #include <iostream> using namespace std; long int x,y=2; long int number=13195; long int

One of the requirements for Telegram Authentication is decomposing a given number into 2 prime co-factors. In particular P*Q = N, where N < 2^63 How can we find the smaller prime co-factor, such that P < square_root(N) My Suggestions: 1) pre-compute primes from 3 to 2^31.5 , then test if N mod P = 0 2) Find an algorithm to test for primes (but we still have to test N mod P =0 ) Is there an algorithm for primes that is well suited to this case? Ugh! I just put this program in and then realized you had tagged your question C#. This is C++, a version of Pollard Rho I wrote a couple years ago and

I am still relatively new to R, so apologies in advance if my question seems too basic. My problem is as follows: I have a data set containing several factor variables, which have the same categories. I need to find the category, which occurs most frequently for each observation across the factor variables. In case of ties an arbitrary value can be chosen, although it would be great if I can have more control over it. My data set contains over a hundred factors. However, the structure is something like that: id <- 1:3 var1 <- c("red","yellow","green") var2 <- c("red","yellow","green") var3 <-

I'm not sure if I completely understand how factors work. So please correct me in an easy to understand way if I'm wrong. I always assumed that when doing regressions and what not, R behind the scenes concerts categorical variables into integers, but this part was outside of my train of thought. It would use the categorical values in a training set and after building a model, check for the same categorical value in the test dataset. Whatever the underlying 'levels' were - didnt matter to me. However, I've been thinking more... and need clarification - especially if I'm doing this wrong on how

If memory servies me, in R there is a data type called factor which when used within a DataFrame can be automatically unpacked into the necessary columns of a regression design matrix. For example, a factor containing True/False/Maybe values would be transformed into: 1 0 0 0 1 0 or 0 0 1 for the purpose of using lower level regression code. Is there a way to achieve something similar using the pandas library? I see that there is some regression support within Pandas, but since I have my own customised regression routines I am really interested in the construction of the design matrix (a 2d

I'm trying to refactor this algorithm to make it faster. What would be the first refactoring here for speed? public int GetHowManyFactors(int numberToCheck) { // we know 1 is a factor and the numberToCheck int factorCount = 2; // start from 2 as we know 1 is a factor, and less than as numberToCheck is a factor for (int i = 2; i < numberToCheck; i++) { if (numberToCheck % i == 0) factorCount++; } return factorCount; } The first optimization you could make is that you only need to check up to the square root of the number. This is because factors come in pairs where one is less than the square