I want to get all combination of numbers to sum.
My input is X that is variable and X is the number of numbers,
For example:
X=4 means we have 1,2,3,4
x=100 means 1,2,3,4,5,,98,99,100
Now I want to get {(1,2)(1,3)(1,4)(2,3)(2,4)(3,4),(1,2,3)(1,3,4)…}
We can’t have repetitive sequence like (1,2)(2,1)(1,2,3)(1,3,2)
I want to get all combinations that these numbers can be sum without repetitive sequence.
Can anyone help me to find it’s algorithm? I must code it in VBA of Excel using for loops
the "algorithm" is solved from Maths and given by the combinations of X by 2:
X!/((X-2)!*2!)=X!/((X-2)!*2)
(note: Just in case... "!" is the factorial...)
now if you want to use for-loop to calculate factorial (written in c):
int main()
{
int num,factorial=1;
cout<<" Enter Number To Find Its Factorial: ";
cin>>num;
for(int a=1;a<=num;a++)
{
factorial=factorial*a;
}
Related
I have recently sat a computing exam in university in which we were never taught beforehand about the modulus function or any other check for odd/even function and we have no access to external documentation except our previous lecture notes. Is it possible to do this without these and how?
Bitwise AND (&)
Extract the last bit of the number using the bitwise AND operator. If the last bit is 1, then it's odd, else it's even. This is the simplest and most efficient way of testing it. Examples in some languages:
C / C++ / C#
bool is_even(int value) {
return (value & 1) == 0;
}
Java
public static boolean is_even(int value) {
return (value & 1) == 0;
}
Python
def is_even(value):
return (value & 1) == 0
I assume this is only for integer numbers as the concept of odd/even eludes me for floating point values.
For these integer numbers, the check of the Least Significant Bit (LSB) as proposed by Rotem is the most straightforward method, but there are many other ways to accomplish that.
For example, you could use the integer division operation as a test. This is one of the most basic operation which is implemented in virtually every platform. The result of an integer division is always another integer. For example:
>> x = int64( 13 ) ;
>> x / 2
ans =
7
Here I cast the value 13 as a int64 to make sure MATLAB treats the number as an integer instead of double data type.
Also here the result is actually rounded towards infinity to the next integral value. This is MATLAB specific implementation, other platform might round down but it does not matter for us as the only behavior we look for is the rounding, whichever way it goes. The rounding allow us to define the following behavior:
If a number is even: Dividing it by 2 will produce an exact result, such that if we multiply this result by 2, we obtain the original number.
If a number is odd: Dividing it by 2 will result in a rounded result, such that multiplying it by 2 will yield a different number than the original input.
Now you have the logic worked out, the code is pretty straightforward:
%% sample input
x = int64(42) ;
y = int64(43) ;
%% define the checking function
% uses only multiplication and division operator, no high level function
is_even = #(x) int64(x) == (int64(x)/2)*2 ;
And obvisouly, this will yield:
>> is_even(x)
ans =
1
>> is_even(y)
ans =
0
I found out from a fellow student how to solve this simplistically with maths instead of functions.
Using (-1)^n :
If n is odd then the outcome is -1
If n is even then the outcome is 1
This is some pretty out-of-the-box thinking, but it would be the only way to solve this without previous knowledge of complex functions including mod.
Given an integer n such that (1<=n<=10^18)
We need to calculate f(1)+f(2)+f(3)+f(4)+....+f(n).
f(x) is given as :-
Say, x = 1112222333,
then f(x)=1002000300.
Whenever we see a contiguous subsequence of same numbers, we replace it with the first number and zeroes all behind it.
Formally, f(x) = Sum over all (first element of the contiguous subsequence * 10^i ), where i is the index of first element from left of a particular contiguous subsequence.
f(x)=1*10^9 + 2*10^6 + 3*10^2 = 1002000300.
In, x=1112222333,
Element at index '9':-1
and so on...
We follow zero based indexing :-)
For, x=1234.
Element at index-'0':-4,element at index -'1':3,element at index '2':-2,element at index 3:-1
How to calculate f(1)+f(2)+f(3)+....+f(n)?
I want to generate an algorithm which calculates this sum efficiently.
There is nothing to calculate.
Multiplying each position in the array od numbers will yeild thebsame number.
So all you want to do is end up with 0s on a repeated number
IE lets populate some static values in an array in psuedo code
$As[1]='0'
$As[2]='00'
$As[3]='000'
...etc
$As[18]='000000000000000000'```
these are the "results" of 10^index
Given a value n of `1234`
```1&000 + 2&00 +3 & 0 + 4```
Results in `1234`
So, if you are putting this on a chip, then probably your most efficient method is to do a bitwise XOR between each register and the next up the line as a single operation
Then you will have 0s in all the spots you care about, and just retrive the values in the registers with a 1
In code, I think it would be most efficient to do the following
```$n = arbitrary value 11223334
$x=$n*10
$zeros=($x-$n)/10```
Okay yeah we can just do bit shifting to get a value like 100200300400 etc.
To approach this problem, it could help to begin with one digit numbers and see what sum you get.
I mean like this:
Let's say, we define , then we have:
F(1)= 45 # =10*9/2 by Euler's sum formula
F(2)= F(1)*9 + F(1)*100 # F(1)*9 is the part that comes from the last digit
# because for each of the 10 possible digits in the
# first position, we have 9 digits in the last
# because both can't be equal and so one out of ten
# becomse zero. F(1)*100 comes from the leading digit
# which is multiplied by 100 (10 because we add the
# second digit and another factor of 10 because we
# get the digit ten times in that position)
If you now continue with this scheme, for k>=1 in general you get
F(k+1)= F(k)*100+10^(k-1)*45*9
The rest is probably straightforward.
Can you tell me, which Hackerrank task this is? I guess one of the Project Euler tasks right?
I am trying to rank these functions — 2n, n100, (n + 1)2, n·lg(n), 100n, n!, lg(n), and n99 + n98 — so that each function is the big-O of the next function, but I do not know a method of determining if one function is the big-O of another. I'd really appreciate if someone could explain how I would go about doing this.
Assuming you have some programming background. Say you have below code:
void SomeMethod(int x)
{
for(int i = 0; i< x; i++)
{
// Do Some Work
}
}
Notice that the loop runs for x iterations. Generalizing, we say that you will get the solution after N iterations (where N will be the value of x ex: number of items in array/input etc).
so This type of implementation/algorithm is said to have Time Complexity of Order of N written as O(n)
Similarly, a Nested For (2 Loops) is O(n-squared) => O(n^2)
If you have Binary decisions made and you reduce possibilities into halves and pick only one half for solution. Then complexity is O(log n)
Found this link to be interesting.
For: Himanshu
While the Link explains how log(base2)N complexity comes into picture very well, Lets me put the same in my words.
Suppose you have a Pre-Sorted List like:
1,2,3,4,5,6,7,8,9,10
Now, you have been asked to Find whether 10 exists in the list. The first solution that comes to mind is Loop through the list and Find it. Which means O(n). Can it be made better?
Approach 1:
As we know that List of already sorted in ascending order So:
Break list at center (say at 5).
Compare the value of Center (5) with the Search Value (10).
If Center Value == Search Value => Item Found
If Center < Search Value => Do above steps for Right Half of the List
If Center > Search Value => Do above steps for Left Half of the List
For this simple example we will find 10 after doing 3 or 4 breaks (at: 5 then 8 then 9) (depending on how you implement)
That means For N = 10 Items - Search time was 3 (or 4). Putting some mathematics over here;
2^3 + 2 = 10 for simplicity sake lets say
2^3 = 10 (nearly equals --- this is just to do simple Logarithms base 2)
This can be re-written as:
Log-Base-2 10 = 3 (again nearly)
We know 10 was number of items & 3 was the number of breaks/lookup we had to do to find item. It Becomes
log N = K
That is the Complexity of the alogorithm above. O(log N)
Generally when a loop is nested we multiply the values as O(outerloop max value * innerloop max value) n so on. egfor (i to n){ for(j to k){}} here meaning if youll say for i=1 j=1 to k i.e. 1 * k next i=2,j=1 to k so i.e. the O(max(i)*max(j)) implies O(n*k).. Further, if you want to find order you need to recall basic operations with logarithmic usage like O(n+n(addition)) <O(n*n(multiplication)) for log it minimizes the value in it saying O(log n) <O(n) <O(n+n(addition)) <O(n*n(multiplication)) and so on. By this way you can acheive with other functions as well.
Approach should be better first generalised the equation for calculating time complexity. liken! =n*(n-1)*(n-2)*..n-(n-1)so somewhere O(nk) would be generalised formated worst case complexity like this way you can compare if k=2 then O(nk) =O(n*n)
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Closed 10 years ago.
Possible Duplicate:
Finding three elements in an array whose sum is closest to an given number
How can I write an Objective C code to check if the sum of any three numbers in an array/list matches a given number?
step 1: sort, O(nlgn)
step 2: iterate every number, say A,(this costs O(n)), then check whether the sum of any two numbers equals to the given number minus A(this is a classic problem which costs O(n))
total complexity: O(n^2)
Here is another way
X,Y,Z are indices of array and P is given Number .
If conditions is X+Y=P
then we sort the array
and then We pick each element and then search P-Y in remaining array .If searching is successful then fine are else return False .
So searching takes log(n) time(binary search) so for n elements it takes O(nlog(n)) time .
Now Our condition is X+Y+Z=P
We deduce it to X+Y=P-Z
Now Pick a number Z and calculate P-Z and let it be R .
Now the problem is deduce to X+Y=R .So time complexity is O(nlog(n))
Since R varies n times for n picks in array so complexity is O((N^2)log(n))) .
Here's a brute-force solution in Python, valuable only for its succinctness, not at all for its efficiency:
import itertools
def anyThreeEqualTo(list, value):
return any([sum(c) == value for c in itertools.combinations(list, 3)])
Another idea:
import itertools
def anyThreeEqualTo(list, value):
for c in itertools.combinations(list, 3)])
if sum(c) == value:
return True
return False
These solutions try each of the triplets in turn until one is found with the desired sum.
I need a simple way to randomly select a letter from the alphabet, weighted on the percentage I want it to come up. For example, I want the letter 'E' to come up in the random function 5.9% of the time, but I only want 'Z' to come up 0.3% of the time (and so on, based on the average occurrence of each letter in the alphabet). Any suggestions? The only way I see is to populate an array with, say, 10000 letters (590 'E's, 3 'Z's, and so on) and then randomly select an letter from that array, but it seems memory intensive and clumsy.
Not sure if this would work, but it seems like it might do the trick:
Take your list of letters and frequencies and sort them from
smallest frequency to largest.
Create a 26 element array where each element n contains the sum of all previous weights and the element n from the list of frequencies. Make note of the sum in the
last element of the array
Generate a random number between 0 and the sum you made note of above
Do a binary search of the array of sums until you reach the element where that number would fall
That's a little hard to follow, so it would be something like this:
if you have a 5 letter alphabet with these frequencies, a = 5%, b = 20%, c = 10%, d = 40%, e = 25%, sort them by frequency: a,c,b,e,d
Keep a running sum of the elements: 5, 15, 35, 60, 100
Generate a random number between 0 and 100. Say it came out 22.
Do a binary search for the element where 22 would fall. In this case it would be between element 2 and 3, which would be the letter "b" (rounding up is what you want here, I think)
You've already acknowledged the tradeoff between space and speed, so I won't get into that.
If you can calculate the frequency of each letter a priori, then you can pre-generate an array (or dynamically create and fill an array once) to scale up with your desired level of precision.
Since you used percentages with a single digit of precision after the decimal point, then consider an array of 1000 entries. Each index represents one tenth of one percent of frequency. So you'd have letter[0] to letter[82] equal to 'a', letter[83] to letter[97] equal to 'b', and so on up until letter[999] equal to 'z'. (Values according to Relative frequencies of letters in the English language)
Now generate a random number between 0 and 1 (using whatever favourite PRNG you have, assuming uniform distribution) and multiply the result by 1000. That gives you the index into your array, and your weighted-random letter.
Use the method explained here. Alas this is for Python but could be rewritten for C etc.
https://stackoverflow.com/a/4113400/129202
First you need to make a NSDicationary of the letters and their frequencies;
I'll explain it with an example:
let's say your dictionary is something like this:
{#"a": #0.2, #"b", #0.5, #"c": #0.3};
So the frequency of you letters covers the interval of [0, 1] this way:
a->[0, 0.2] + b->[0.2, 0.7] + c->[0.7, 1]
You generate a random number between 0 and 1. Then easily by checking that this random belongs to which interval and returning the corresponding letter you get what you want.
you seed the random function at the beginning of you program: srand48(time(0));
-(NSSting *)weightedRandomForDicLetters:(NSDictionary *)letterFreq
{
double randomNumber = drand48();
double endOfInterval = 0;
for (NSString *letter in dic){
endOfInterval += [[letterFreq objectForKey:letter] doubleValue];
if (randomNumber < endOfInterval) {
return letter;
}
}
}