A number is called a stepping number if all adjacent digits in the number have an absolute difference of 1.
Examples of stepping numbers :- 0,1,2,3,4,5,6,7,8,9,10,12,21,23,...
I have to generate stepping numbers upto a given number N. The numbers generated should be in order.
I used the simple method of moving over all the numbers upto N and checking if it is stepping number or not. My teacher told me it is brute force and will take more time. Now, I have to optimize my approach.
Any suggestions.
Stepping numbers can be generated using Breadth First Search like approach.
Example to find all the stepping numbers from 0 to N
-> 0 is a stepping Number and it is in the range
so display it.
-> 1 is a Stepping Number, find neighbors of 1 i.e.,
10 and 12 and push them into the queue
How to get 10 and 12?
Here U is 1 and last Digit is also 1
V = 10 + 0 = 10 ( Adding lastDigit - 1 )
V = 10 + 2 = 12 ( Adding lastDigit + 1 )
Then do the same for 10 and 12 this will result into
101, 123, 121 but these Numbers are out of range.
Now any number transformed from 10 and 12 will result
into a number greater than 21 so no need to explore
their neighbors.
-> 2 is a Stepping Number, find neighbors of 2 i.e.
21, 23.
-> generate stepping numbers till N.
The other stepping numbers will be 3, 4, 5, 6, 7, 8, 9.
C++ code to do generate stepping numbers in a given range:
#include<bits/stdc++.h>
using namespace std;
// Prints all stepping numbers reachable from num
// and in range [n, m]
void bfs(int n, int m)
{
// Queue will contain all the stepping Numbers
queue<int> q;
for (int i = 0 ; i <= 9 ; i++)
q.push(i);
while (!q.empty())
{
// Get the front element and pop from the queue
int stepNum = q.front();
q.pop();
// If the Stepping Number is in the range
// [n, m] then display
if (stepNum <= m && stepNum >= n)
cout << stepNum << " ";
// If Stepping Number is 0 or greater than m,
// need to explore the neighbors
if (stepNum == 0 || stepNum > m)
continue;
// Get the last digit of the currently visited
// Stepping Number
int lastDigit = stepNum % 10;
// There can be 2 cases either digit to be
// appended is lastDigit + 1 or lastDigit - 1
int stepNumA = stepNum * 10 + (lastDigit- 1);
int stepNumB = stepNum * 10 + (lastDigit + 1);
// If lastDigit is 0 then only possible digit
// after 0 can be 1 for a Stepping Number
if (lastDigit == 0)
q.push(stepNumB);
//If lastDigit is 9 then only possible
//digit after 9 can be 8 for a Stepping
//Number
else if (lastDigit == 9)
q.push(stepNumA);
else
{
q.push(stepNumA);
q.push(stepNumB);
}
}
}
//Driver program to test above function
int main()
{
int n = 0, m = 99;
// Display Stepping Numbers in the
// range [n,m]
bfs(n,m);
return 0;
}
Visit this link.
The mentioned link has both BFS and DFS approach.
It will provide you with explaination and code in different languages for the above problem.
We also can use simple rules to move to the next stepping number and generate them in order to avoid storing "parents".
C.f. OEIS sequence
#include <iostream>
int next_stepping(int n) {
int left = n / 10;
if (left == 0)
return (n + 1); // 6=>7
int last = n % 10;
int leftlast = left % 10;
if (leftlast - last == 1 & last < 8)
return (n + 2); // 32=>34
int nxt = next_stepping(left);
int nxtlast = nxt % 10;
if (nxtlast == 0)
return (nxt * 10 + 1); // to get 101
return (nxt * 10 + nxtlast - 1); //to get 121
}
int main()
{
int t = 0;
for (int i = 1; i < 126; i++, t = next_stepping(t)) {
std::cout << t << "\t";
if (i % 10 == 0)
std::cout << "\n";
}
}
0 1 2 3 4 5 6 7 8 9
10 12 21 23 32 34 43 45 54 56
65 67 76 78 87 89 98 101 121 123
210 212 232 234 321 323 343 345 432 434
454 456 543 545 565 567 654 656 676 678
765 767 787 789 876 878 898 987 989 1010
1012 1210 1212 1232 1234 2101 2121 2123 2321 2323
2343 2345 3210 3212 3232 3234 3432 3434 3454 3456
4321 4323 4343 4345 4543 4545 4565 4567 5432 5434
5454 5456 5654 5656 5676 5678 6543 6545 6565 6567
6765 6767 6787 6789 7654 7656 7676 7678 7876 7878
7898 8765 8767 8787 8789 8987 8989 9876 9878 9898
10101 10121 10123 12101 12121
def steppingNumbers(self, n, m):
def _solve(v):
if v>m: return 0
ans = 1 if n<=v<=m else 0
last = v%10
if last > 0: ans += _solve(v*10 + last-1)
if last < 9: ans += _solve(v*10 + last+1)
return ans
ans = 0 if n>0 else 1
for i in range(1, 10):
ans += _solve(i)
return ans
Related
Here is the link to the question. Essentially, it asks to find the kth number having digit sum as 10. I have tried multiple solutions and also looked upon solutions online. Specifically this one (also shared below). The one with constant time talks about outliers in Arithmetic Progression and uses it to find the nth number having sum as 10. Obviously, the code is incorrect as it fails for test cases when k=1000 etc.
#include <bits/stdc++.h>
using namespace std;
int findNth(int n)
{
int nthElement = 19 + (n - 1) * 9;
int outliersCount = (int)log10(nthElement) - 1;
// find the nth perfect number
nthElement += 9 * outliersCount;
return nthElement;
}
int main()
{
cout << findNth(5) << endl;
return 0;
}
Eventually, I ended up writing combination of Arithmetic Progression + brute force as below
#include <bits/stdc++.h>
using namespace std;
#define ll unsigned long long
int main() {
int n;
cin >> n;
int count = 0;
ll i = 19;
for (; ; i += 9) {
int curr = i;
int localSum = 0;
while (curr) {
localSum += curr%10;
curr /= 10;
}
if (localSum == 10) {
count += 1;
}
if (count == n) {
break;
}
}
cout << i << endl;
return 0;
}
I am wondering, if there is no constant time or better algorithm that does not require me to calculate the sum, but my algorithm always hops in a way that I have number whose digit sum is 10?
Here is a Python solution that you can translate into C++.
cached_count_ds_l = {}
def count_digit_sum_length (s, l):
k = (s, l)
if k not in cached_count_ds_l:
if l < 2:
if s == 0:
return 1
elif l == 1 and s < 10:
return 1
else:
return 0
else:
ans = 0
for i in range(min(10, s+1)):
ans += count_digit_sum_length(s-i, l-1)
cached_count_ds_l[k] = ans
return cached_count_ds_l[k]
def nth_of_sum (s, n):
l = 0
while count_digit_sum_length(s, l) < n:
l += 1
digits = []
while 0 < l:
for i in range(10):
if count_digit_sum_length(s-i, l-1) < n:
n -= count_digit_sum_length(s-i, l-1)
else:
digits.append(str(i))
s -= i
l -= 1
break
return int("".join(digits))
print(nth_of_sum(10, 1000))
The idea is to use dynamic programming to find how many numbers there are of a given maximum length with a given digit sum. And then to use that to cross off whole blocks of numbers on the way to finding the right one.
The main logic goes like this:
0 numbers of length 0 sum to 10
- need longer
0 numbers of length 1 sum to 10
- need longer
9 numbers of length 2 sum to 10
- need longer
63 numbers of length 3 sum to 10
- need longer
282 numbers of length 4 sum to 10
- need longer
996 numbers of length 5 sum to 10
- need longer
2997 numbers of length 6 sum to 10
- answer has length 6
Looking for 1000th number of length 6 that sums to 10
- 996 with a leading 0 sum to 10
- Need the 4th past 99999
- 715 with a leading 1 sum to 10
- Have a leading 1
Looking for 4th number of length 5 that sums to 9
- 495 with a leading 0 sum to 9
- Have a leading 10
Looking for 4th number of length 4 that sums to 9
- 220 with a leading 0 sum to 9
- Have a leading 100
Looking for 4th number of length 3 that sums to 9
- 55 with a leading 0 sum to 9
- Have a leading 1000
Looking for 4th number of length 2 that sums to 9
- 1 with a leading 0 sum to 9
- Need the 3rd past 9
- 1 with a leading 1 sum to 9
- Need the 2nd past 19
- 1 with a leading 2 sum to 9
- Need the 1st past 29
- 1 with a leading 3 sum to 9
- Have a leading 10003
Looking for 1st number of length 1 that sums to 6
- 0 with a leading 0 sum to 6
- Need the 1st past 0
- 0 with a leading 1 sum to 6
- Need the 1st past 1
- 0 with a leading 2 sum to 6
- Need the 1st past 2
- 0 with a leading 3 sum to 6
- Need the 1st past 3
- 0 with a leading 4 sum to 6
- Need the 1st past 4
- 0 with a leading 5 sum to 6
- Need the 1st past 5
- 1 with a leading 6 sum to 6
- Have a leading 100036
And it finishes in a fraction of a second.
Incidentally the million'th is 20111220000010, the billionth is 10111000000002000000010000002100, and the trillionth is 10000000100000100000100000000000001000000000000100000000010110001000.
The problem: I need to print the Pascal triangle for any (unsigned int) input passed as a command line argument. All the values must be stored in a LINEAR array and elements must only be manipulated as dereferenced pointers. Following this, the array elements must printed as a lower triangular matrix and subsequently deleted. My implementation functions perfectly for input ranging from 0 to 12 but produces spurious results for higher values.
I tried two different implementations.
Declare a pointer to an array of size (n+1)*(n+2)/2 (which is the number of elements in the triangle for input 'n'). Assign/print variables within a nested loop. Delete the pointer once both loops have been executed.
Run a nested loop, 0 <= i <= n, and 0 <= j <= i. Declare a pointer to an array of size (i+1) in the outer loop. Assign/print elements in the inner loop. Delete the pointer once the inner loop has been executed.
// VERSION 1
unsigned N = (n+1)*(n+2)/2;
unsigned* elements = new unsigned[N];
for(i = 0; i <= n; i++) {
for(j = 0; j <= i; j++) {
*(elements + j+(i*i+i)/2) = fact(i) / (fact(j) * fact(i-j));
// print statement
}
cout << endl;
}
delete [] elements;
// VERSION 2
for(i = 0; i <= n; i++) {
unsigned* elements = new unsigned[i+1];
for(j = 0; j <= i; j++) {
*(elements + j) = fact(i) / (fact(j) * fact(i-j));
// print statement
}
delete [] elements;
cout << endl;
}
Both these versions were tried separately on Xcode. In both cases, the triangle printed correctly until the 12th layer, i.e. n=12, but generated incorrect results for higher values.
0 | 1
1 | 1 1
2 | 1 2 1
3 | 1 3 3 1
4 | 1 4 6 4 1
5 | 1 5 10 10 5 1
6 | 1 6 15 20 15 6 1
7 | 1 7 21 35 35 21 7 1
8 | 1 8 28 56 70 56 28 8 1
9 | 1 9 36 84 126 126 84 36 9 1
10 | 1 10 45 120 210 252 210 120 45 10 1
11 | 1 11 55 165 330 462 462 330 165 55 11 1
12 | 1 12 66 220 495 792 924 792 495 220 66 12 1
13 | 1 4 24 88 221 399 532 532 399 221 88 24 4 1
14 | 1 0 1 5 14 29 44 50 44 29 14 5 1 0 1
15 | 1 1 0 0 2 4 7 9 9 7 4 2 0 0 1 1
16 | 1 0 0 0 0 4 0 1 1 1 0 4 0 0 0 0 1
The debugger, to the extent that I can use it, produced no error messages.
What is happening and how do I fix it?
fact(i) overflows really fast. I haven't checked the numbers, but I'm pretty sure that's what's happening.
Instead, use the fact that a number in Pascal's triangle is the sum of the two numbers above it.
Wikipedia has a nice animation for this.
When i is 13, fact(i) is 6227020800, which is too big to fit in a 32-bit unsigned integer, so integer overflow occurs.
This question already has answers here:
Get `n` random values between 2 numbers having average `x`
(5 answers)
Closed 6 years ago.
Problem: Getting a set of random numbers between two values that will have a certain mean value.
Let say we getting n number of random number where the number will be between 1 and 100. We have a mean of 25.
My first approach is to have 2 modes where we have aboveMean and belowMean where the first random number is the initial range 1 and 100. Every subsequent number will check the total sum. If the total sum is above the mean, we go to case aboveMean which is then get a random number between 1 and 25. If the total sum is below the mean, we do case belowMean then get a random number between 26 and 100.
I need some idea on how to approach this problem beside the crude get a random number to add it to the total then get the average. If it above the mean in question, we get a random number below the mean and so forth. While it does work, doesn't seem to be the best method.
I'm guessing I should brush up on probability to approach this random number generator.
Let us divide the range into left and right portions. Use a value from the portion at a frequency corresponding to the relative width of the other half.
int Leruce_rand(int min, int mean, int max) {
int r = rand()%(max - min + 1);
if (r < mean) {
// find number in right half
return rand()%(max - mean + 1) + mean;
} else {
// find number in left half
return rand()%(mean - min) + min;
}
Assumes mean is part of the right half. This quick solution likely has small bias.
Given OP's values, roughly, the average of the left half is 12.5 and called 75% of the time. Average of the right is 62.5 called 25% of the time: average 25.
This approach differs from OP's which "Every subsequent number will check the total sum. If the total sum is above the mean, we go to case aboveMean which is then get a random number between 1 and 25." As that absolutely prevents a set of occurrences above or below the mean. With RNG, the value generated should not be biased on the history of previous generated values.
There's literally an infinite number of ways to achieve this. For instance, generate 3 random numbers between 1 and 100 (std::uniform_int_distribution) and take the minimum of those (std::min(a,b,c)).
Obviously, for a mean of 75 you'll need to pick the maximum of 3 numbers.
The benefit of this method is that each outcome is independent of the previous ones. It's completely random.
Take some good distribution and use it. Say, Binomial distribution. Use B(99,24/99),
so sampled values are in the range 0...99, with parameter p equal to 24/99.
So if you have routine which sample from B, then all you need is to add 1
to be in he range 1...100
Mean value for binomial would be p*n, in this case equal to 24. Because you're adding 1, your mean value would be 25 as required. C++11 has binomial RNG in the
standard library
Some code (not tested)
#include <iostream>
#include <random>
int main() {
std::default_random_engine generator;
std::binomial_distribution<int> distribution(99, double(24)/double(99));
for (int i=0; i != 1000; ++i) {
int number = distribution(generator) + 1;
std::cout << number << std::endl;
}
return 0;
}
Assume a fair random(a,b) function (this question should not be about which random function is better) then simply just restrcting ithe ranges that is piced from should be a good start, like;
const int desiredCount = 16;
const int deiredMean = 25;
int sumValues = random(a,b);
int count = 1;
while (count < desriredCount - 1) {
int mean = sumValue/count;
int nextValue = 0;
if (mean < desiredMean) // Too small, reduce probablity of smaller numbers
nextValue = random(a+(desiredMean-mean)/(desriredCount-count),b);
else //too large, reduce probability of larger numbers
nextValue = random(a,b-(mean-desiredMean)/(desriredCount-count));
sumValue += nextValue;
count += 1;
}
int lastValue = desiredMean*desriredCount - sumValue/count;
sumValue += lastValue;
count += 1;
Note: The above is not tested, and my thinking is that the trimming of the upper and lower bound may not be sufficently aggressive to do the trick, but I hope that i will get you going.
Some boundary conditions, such as if you only want 2 numbers and a means of 25 from numbers between 0 and 100, the initial random number cannot be larger than 50, since that makes it impossible to pick the second (last) number -- so if you want the algo to give you exact mean values under all circumstances, then a bit more tweaking is needed.
OP's wants a set of numbers meeting certain criteria.
Consider generating all possible sets of n numbers in the range [min max] and then eliminating all sets but those with the desired mean. Now randomly select one of those sets. This would meet OP's goal and IMO would pass fair randomness tests. Yet this direct approach is potentially a huge task.
Alternatively, randomly generate lots of sets until one is found that meets the mean test.
The below meets OP's requirement of a specified mean without directly biasing the random numbers selected. Certainly not an efficient method when the desired mean is far from the min/max average.
#include <stdio.h>
#include <stdlib.h>
void L_set(int *set, size_t n, int min, int mean, int max) {
assert(n > 0);
assert(min >= 0);
assert(mean >= min);
assert(max >= mean);
size_t i;
long long diff;
long long sum_target = n;
unsigned long long loop = 0;
sum_target *= mean;
int range = max - min + 1;
do {
loop++;
long long sum = 0;
for (i = 1; i < n; i++) {
set[i] = rand() % range + min;
sum += set[i];
}
diff = sum_target - sum; // What does the final number need to be?
} while (diff < min || diff > max);
set[0] = (int) diff;
printf("n:%zu min:%d mean:%2d max:%3d loop:%6llu {", n, min, mean, max, loop);
for (i = 0; i < n; i++) {
printf("%3d,", set[i]);
}
printf("}\n");
fflush(stdout);
}
int main(void) {
int set[1000];
L_set(set, 10, 1, 2, 4);
L_set(set, 16, 1, 50, 100);
L_set(set, 16, 1, 25, 100);
L_set(set, 16, 1, 20, 100);
return 0;
}
Output
n:10 min:1 mean: 2 max: 4 loop: 1 { 4, 2, 4, 3, 2, 1, 1, 1, 1, 1,}
n:16 min:1 mean:50 max:100 loop: 2 { 45, 81, 24, 50, 93, 65, 70, 52, 28, 91, 25, 36, 21, 45, 11, 63,}
n:16 min:1 mean:25 max:100 loop: 3257 { 52, 1, 15, 70, 66, 30, 1, 4, 26, 1, 16, 4, 48, 42, 19, 5,}
n:16 min:1 mean:20 max:100 loop:192974 { 24, 10, 13, 3, 3, 53, 22, 12, 29, 1, 7, 6, 90, 11, 20, 16,}
you have to go into some probabilities theory. there are a lot of methods to judge on random sequence. for example if you lower the deviation you will get triangle-looking-on-a-graph sequence, which can in the end be proven not trully random. so there is not really much choice than getting random generator and discarding the sequences you don't like.
EDIT: this generates numbers in the range 1..100 with a theoretical mean of 25.25. It does this by using a random modulus in the range 1..100. Note that the required mean is 25, which is not exactly a quarter of the range 1..100.
OP wanted a way of varying the next number chosen according to whether the mean is less than or more than 25, but that lends some predictabilty - if the mean is more than 25 then you know the next "random" number will be less than 25.
The random calculation in the code is a very simple one line.
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define RUNS 10000000
#define MAXN 100
int main() {
int n, i, sum = 0, min = MAXN, max = 0;
int freq[MAXN+1] = {0};
srand((unsigned)time(NULL));
for(i = 0; i < RUNS; i++) {
n = 1 + rand() % (1 + rand() % 100); // average modulus is (1 + MAX) / 2
if(max < n) {
max = n; // check that whole range is picked
}
if(min > n) {
min = n;
}
freq[n]++; // keep a tally
sum += n;
}
// show statistis
printf("Mean = %f, min = %d, max = %d\n", (double)sum / RUNS, min, max);
for(n = MAXN; n > 0; n--) {
printf("%3d ", n);
for(i = (freq[n] + 5000) / 10000; i > 0; i--) {
printf("|");
}
printf("\n");
}
return 0;
}
Program output showing distribution / 10000:
Mean = 25.728128, min = 1, max = 100
100
99
98
97
96 |
95 |
94 |
93 |
92 |
91 |
90 |
89 |
88 |
87 |
86 ||
85 ||
84 ||
83 ||
82 ||
81 ||
80 ||
79 ||
78 |||
77 |||
76 |||
75 |||
74 |||
73 |||
72 |||
71 ||||
70 ||||
69 ||||
68 ||||
67 ||||
66 ||||
65 ||||
64 |||||
63 |||||
62 |||||
61 |||||
60 |||||
59 |||||
58 ||||||
57 ||||||
56 ||||||
55 ||||||
54 ||||||
53 ||||||
52 |||||||
51 |||||||
50 |||||||
49 |||||||
48 |||||||
47 ||||||||
46 ||||||||
45 ||||||||
44 ||||||||
43 |||||||||
42 |||||||||
41 |||||||||
40 |||||||||
39 ||||||||||
38 ||||||||||
37 ||||||||||
36 ||||||||||
35 |||||||||||
34 |||||||||||
33 |||||||||||
32 ||||||||||||
31 ||||||||||||
30 ||||||||||||
29 |||||||||||||
28 |||||||||||||
27 |||||||||||||
26 ||||||||||||||
25 ||||||||||||||
24 ||||||||||||||
23 |||||||||||||||
22 |||||||||||||||
21 ||||||||||||||||
20 ||||||||||||||||
19 |||||||||||||||||
18 |||||||||||||||||
17 ||||||||||||||||||
16 |||||||||||||||||||
15 |||||||||||||||||||
14 ||||||||||||||||||||
13 |||||||||||||||||||||
12 ||||||||||||||||||||||
11 |||||||||||||||||||||||
10 ||||||||||||||||||||||||
9 |||||||||||||||||||||||||
8 ||||||||||||||||||||||||||
7 |||||||||||||||||||||||||||
6 |||||||||||||||||||||||||||||
5 |||||||||||||||||||||||||||||||
4 |||||||||||||||||||||||||||||||||
3 |||||||||||||||||||||||||||||||||||||
2 ||||||||||||||||||||||||||||||||||||||||||
1 ||||||||||||||||||||||||||||||||||||||||||||||||||||
OP did not state what kind of distribution was wanted, for example two straight lines pivoting at 25, or perhaps equal distribution each side of 25. However this solution is very simple to implement.
I'm making a program where I have a list of 34 cities and I am wanting to give each of these cities a probability of being chosen.
So I have:
vector<float> vec;
int s;
cin >> s;
srand(s);
for (int k=0; k < 34; k++)
{
float p1= (float)rand()/(float)((unsigned)RAND_MAX+1);
vec.push_back(p1);
}
So that gives each city a probability. The problem I am now having is I want to then do a random number generator that will choose two of these cities. So, for example city1 will have a 5%, city2 a 2%, city3, a 3%, etc. How can I randomly choose two of these cities based off the probabilities given?
I did this in genetic algorithm.
for your cities consider a line of 10 units.
now from 0-5 units on line are city1 6-7 for city2 and 8-9 for city3.
now choose a number at random from 0-9.
and found out in which cities range it comes in.
At first glance my solution will be :
Create a number equals to all city's probability
Create a random number, with max random number is equal to the previous number
Take the random number, and go throught your city vector and take the one who is corresponding.
Example :
City 1 : 5%
City 2 : 8%
City 3 : 2%
City 4 : 5%
City 5 : 12%
Create a number -> Number a = 32 (5+8+2+5+12)
Generate a number with : 1 <= number
Assume that the number is equal to 12
City 1 is choose if : 1 <= number <= 5 (So not)
City 2 is choose if : 6 <= number <= 13 (So yes)
City 2 is choose.
If you have any questions about that, you are welcome :)
Edit :
Well i will give you some more explaination.
Take this code :
for (int k=0; k < 10; k++)
{
float p1= (float)rand()/(float)((unsigned)RAND_MAX+1);
vec.push_back(p1);
}
Assume now that vec contain the following informations :
5
3
8
5
12
14
8
5
6
18
With each number correspond to the probability to choose a city.
5 -> 5% probability to choose (City1)
3 -> 3% probability to choose (City2)
8 -> 8% probability to choose (City3)
... etc
Now i will give you some code and i will explain it :
int nbReference = 0;
for (auto it = vec.begin() ; it != vec.end() ; ++it)
{
nbReference += *it;
}
nbReference = rand(nbReference);
int i = 0;
int cityProbability = 0;
for (auto it = vec.begin() ; it != vec.end() ; ++it)
{
cityProbability = *it;
if ((i + cityProbability) > nbReference)
{
return (i + 1);
}
i += cityProbability;
}
First i create a number equals to the addition of all city's probability
int nbReference = 0;
for (auto it = vec.begin() ; it != vec.end() ; ++it)
{
nbReference += *it;
}
Second, i generate a number that is respect the following range -> 0 < nbReference
Third, i create a loop that take all city one by one and quit when we got right city.
How does we know when a city is good?
Let's take an example!
With our previous probability
5 3 8 5 12 14 8 5 6 18
NbReference is equals to (5+3+8+5+12+14+8+5+6+18) so 84
To each city we are going to put a range equals to his probability plus all of previous city's probability. Let me show you :
5 -> Range 0 to 4 (0 to 4 = 5 ---> 5%)
3 -> Range 5 to 8 (5 to 8 = 3 ---> 3%)
8 -> Range 9 to 17
5 -> Range 18 to 22
... etc
If the number that we create here
nbReference = rand(nbReference);
Is in a city range, so that city is choosed.
Example : If the number is 16, city3 is choosed!
5 -> Range 0 to 4 Number is 16 so NOPE
3 -> Range 5 to 8 Number is 16 so NOPE
8 -> Range 9 to 17 Number is 16 so YES!
5 -> Range 18 to 22
... etc
Does is this helpfull? :)
Any questions? You are welcome
Maybe this code can help you (follows partially WhozCraig advice)
#include <iostream>
#include <random>
#include <algorithm>
int main(int argc, const char * argv[])
{
using namespace std::chrono;
system_clock::time_point tp = system_clock::now();
system_clock::duration dtn = tp.time_since_epoch();
std::default_random_engine generator(static_cast<int>(dtn.count()));
//Generate 34 cities
std::uniform_real_distribution<double> gen_distribution(0,1);
auto getProb = std::bind ( gen_distribution, generator );
std::vector<double> citiesProb;
double probSum(0.0);
double cityProb(0.0);
for (int k=0; k < 34; k++)
{
cityProb = getProb();
probSum += cityProb;
citiesProb.push_back(cityProb);
}
//Pick 7 cities
std::uniform_real_distribution<double> pick_distribution(0,probSum);
auto pickCity = std::bind ( pick_distribution, generator );
double chooseCity;
double probBasket;
for (int k=0; k < 7; ++k)
{
probBasket = 0.0;
chooseCity = pickCity();
for (int i = 0; i < citiesProb.size(); ++i)
{
if (chooseCity >= probBasket && chooseCity < probBasket + citiesProb[i])
{
std::cout << "City with index " << i << " picked" << std::endl;
}
probBasket += citiesProb[i];
}
}
return 0;
}
How it works:
city1 5%(0.05), city2 25%(0.25), city3 8%(0.08), city4 10%(0.1)
then
probSum = 0.05 + 0.25 + 0.08 + 0.1 = 0.48
then choose a number between 0 and 0.48 (named pickProb) and
if pickProb is between 0 and 0.05 pick city1 (prob = 0.05/0.48 = 10%)
if pickProb is between 0.05 and 0.30 pick city2 (prob = 0.25/0.48 = 52%)
if pickProb is between 0.30 and 0.38 pick city3 (prob = 0.08/0.48 = 16%)
if pickProb is between 0.38 and 0.48 pick city4 (prob = 0.1/0.48 = 20%)
if probSum = 1.0 then city1 is picked with probability 5%, city2 is picked with probability 25% and so on.
I have to XOR numbers from 1 to N, does there exist a direct formula for it ?
For example if N = 6 then 1^2^3^4^5^6 = 7 I want to do it without using any loop so I need an O(1) formula (if any)
Your formula is N & (N % 2 ? 0 : ~0) | ( ((N & 2)>>1) ^ (N & 1) ):
int main()
{
int S = 0;
for (int N = 0; N < 50; ++N) {
S = (S^N);
int check = N & (N % 2 ? 0 : ~0) | ( ((N & 2)>>1) ^ (N & 1) );
std::cout << "N = " << N << ": " << S << ", " << check << std::endl;
if (check != S) throw;
}
return 0;
}
Output:
N = 0: 0, 0 N = 1: 1, 1 N = 2: 3, 3
N = 3: 0, 0 N = 4: 4, 4 N = 5: 1, 1
N = 6: 7, 7 N = 7: 0, 0 N = 8: 8, 8
N = 9: 1, 1 N = 10: 11, 11 N = 11: 0, 0
N = 12: 12, 12 N = 13: 1, 1 N = 14: 15, 15
N = 15: 0, 0 N = 16: 16, 16 N = 17: 1, 1
N = 18: 19, 19 N = 19: 0, 0 N = 20: 20, 20
N = 21: 1, 1 N = 22: 23, 23 N = 23: 0, 0
N = 24: 24, 24 N = 25: 1, 1 N = 26: 27, 27
N = 27: 0, 0 N = 28: 28, 28 N = 29: 1, 1
N = 30: 31, 31 N = 31: 0, 0 N = 32: 32, 32
N = 33: 1, 1 N = 34: 35, 35 N = 35: 0, 0
N = 36: 36, 36 N = 37: 1, 1 N = 38: 39, 39
N = 39: 0, 0 N = 40: 40, 40 N = 41: 1, 1
N = 42: 43, 43 N = 43: 0, 0 N = 44: 44, 44
N = 45: 1, 1 N = 46: 47, 47 N = 47: 0, 0
N = 48: 48, 48 N = 49: 1, 1 N = 50: 51, 51
Explanation:
Low bit is XOR between low bit and next bit.
For each bit except low bit the following holds:
if N is odd then that bit is 0.
if N is even then that bit is equal to corresponded bit of N.
Thus for the case of odd N the result is always 0 or 1.
edit
GSerg Has posted a formula without loops, but deleted it for some reason (undeleted now). The formula is perfectly valid (apart from a little mistake). Here's the C++-like version.
if n % 2 == 1 {
result = (n % 4 == 1) ? 1 : 0;
} else {
result = (n % 4 == 0) ? n : n + 1;
}
One can prove it by induction, checking all reminders of division by 4. Although, no idea how you can come up with it without generating output and seeing regularity.
Please explain your approach a bit more.
Since each bit is independent in xor operation, you can calculate them separately.
Also, if you look at k-th bit of number 0..n, it'll form a pattern. E.g., numbers from 0 to 7 in binary form.
000
001
010
011
100
101
110
111
You see that for k-th bit (k starts from 0), there're 2^k zeroes, 2^k ones, then 2^k zeroes again, etc.
Therefore, you can for each bit calculate how many ones there are without actually going through all numbers from 1 to n.
E.g., for k = 2, there're repeated blocks of 2^2 == 4 zeroes and ones. Then,
int ones = (n / 8) * 4; // full blocks
if (n % 8 >= 4) { // consider incomplete blocks in the end
ones += n % 8 - 3;
}
For odd N, the result is either 1 or 0 (cyclic, 0 for N=3, 1 for N=5, 0 for N=7 etc.)
For even N, the result is either N or N+1 (cyclic, N+1 for N=2, N for N=4, N+1 for N=6, N for N=8 etc).
Pseudocode:
if (N mod 2) = 0
if (N mod 4) = 0 then r = N else r = N+1
else
if (N mod 4) = 1 then r = 1 else r = 0
Lets say the function that XORs all the values from 1 to N be XOR(N), then
XOR(1) = 000 1 = 0 1 ( The 0 is the dec of bin 000)
XOR(2) = 001 1 = 1 1
XOR(3) = 000 0 = 0 0
XOR(4) = 010 0 = 2 0
XOR(5) = 000 1 = 0 1
XOR(6) = 011 1 = 3 1
XOR(7) = 000 0 = 0 0
XOR(8) = 100 0 = 4 0
XOR(9) = 000 1 = 0 1
XOR(10)= 101 1 = 5 1
XOR(11)= 000 0 = 0 0
XOR(12)= 110 0 = 6 0
I hope you can see the pattern. It should be similar for other numbers too.
Try this:
the LSB gets toggled each time the N is odd, so we can say that
rez & 1 == (N & 1) ^ ((N >> 1) & 1)
The same pattern can be observed for the rest of the bits.
Each time the bits B and B+1 (starting from LSB) in N will be different, bit B in the result should be set.
So, the final result will be (including N): rez = N ^ (N >> 1)
EDIT: sorry, it was wrong. the correct answer:
for odd N: rez = (N ^ (N >> 1)) & 1
for even N: rez = (N & ~1) | ((N ^ (N >> 1)) & 1)
Great answer by Alexey Malistov! A variation of his formula: n & 1 ? (n & 2) >> 1 ^ 1 : n | (n & 2) >> 1 or equivalently n & 1 ? !(n & 2) : n | (n & 2) >> 1.
this method avoids using conditionals F(N)=(N&((N&1)-1))|((N&1)^((N&3)>>1)
F(N)= (N&(b0-1)) | (b0^b1)
If you look at the XOR of the first few numbers you get:
N | F(N)
------+------
0001 | 0001
0010 | 0011
0011 | 0000
0100 | 0100
0101 | 0001
0110 | 0111
0111 | 0000
1000 | 1000
1001 | 0001
Hopefully you notice the pattern:
if N mod 4 = 1 than F(N)=1
if N mod 4 = 3 than F(N)=0
if N mod 4 = 0 than F(N)=N
if N mod 4 = 2 than F(N)=N but with the first bit as 1 so N|1
the tricky part is getting this in one statement without conditionals ill explain the logic I used to do this.
take the first 2 significant bits of N call them:
b0 and b1 and these are obtained with:
b0 = N&1
b1 = N&3>>1
Notice that if b0 == 1 we have to 0 all of the bits, but if it isn't all of the bits except for the first bit stay the same. We can do this behavior by:
N & (b0-1) : this works because of 2's complement, -1 is equal to a number with all bits set to 1 and 1-1=0 so when b0=1 this results in F(N)=0.. so that is the first part of the function:
F(N)= (N&(b0-1))...
now this will work for for N mod 4 == 3 and 0, for the other 2 cases lets look solely at b1, b0 and F(N)0:
b0|b1|F(N)0
--+--+-----
1| 1| 0
0| 0| 0
1| 0| 1
0| 1| 1
Ok hopefully this truth table looks familiar! it is b0 XOR b1 (b1^b0). so now that we know how to get the last bit let put that on our function:
F(N)=(N&(b0-1))|b0^b1
and there you go, a function without using conditionals. also this is useful if you want to compute the XOR from positive numbers a to b. you can do:
F(a) XOR F(b).
With minimum change to the original logic:
int xor = 0;
for (int i = 1; i <= N; i++) {
xor ^= i;
}
We can have:
int xor = 0;
for (int i = N - (N % 4); i <= N; i++) {
xor ^= i;
}
It does have a loop but it would take a constant time to execute. The number of times we iterate through the for-loop would vary between 1 and 4.
How about this?
!(n&1)*n+(n%4&n%4<3)
This works fine without any issues for any n;
unsigned int xorn(unsigned int n)
{
if (n % 4 == 0)
return n;
else if(n % 4 == 1)
return 1;
else if(n % 4 == 2)
return n+1;
else
return 0;
}
Take a look at this. This will solve your problem.
https://stackoverflow.com/a/10670524/4973570
To calculate the XOR sum from 1 to N:
int ans,mod=N%4;
if(mod==0) ans=N;
else if(mod==1) ans=1;
else if(mod==2) ans=N+1;
else if(mod==3) ans=0;
If still someone needs it here simple python solution:
def XorSum(L):
res = 0
if (L-1)%4 == 0:
res = L-1
elif (L-1)%4 == 1:
res = 1
elif (L-1)%4 == 2:
res = (L-1)^1
else: #3
res= 0
return res