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I have seen this question asked a lot but never seen a true concrete answer to it. So I am going to post one here which will hopefully help people understand why exactly there is "modulo bias" when using a random number generator, like rand() in C++.
So rand() is a pseudo-random number generator which chooses a natural number between 0 and RAND_MAX, which is a constant defined in cstdlib (see this article for a general overview on rand()).
Now what happens if you want to generate a random number between say 0 and 2? For the sake of explanation, let's say RAND_MAX is 10 and I decide to generate a random number between 0 and 2 by calling rand()%3. However, rand()%3 does not produce the numbers between 0 and 2 with equal probability!
When rand() returns 0, 3, 6, or 9, rand()%3 == 0. Therefore, P(0) = 4/11
When rand() returns 1, 4, 7, or 10, rand()%3 == 1. Therefore, P(1) = 4/11
When rand() returns 2, 5, or 8, rand()%3 == 2. Therefore, P(2) = 3/11
This does not generate the numbers between 0 and 2 with equal probability. Of course for small ranges this might not be the biggest issue but for a larger range this could skew the distribution, biasing the smaller numbers.
So when does rand()%n return a range of numbers from 0 to n-1 with equal probability? When RAND_MAX%n == n - 1. In this case, along with our earlier assumption rand() does return a number between 0 and RAND_MAX with equal probability, the modulo classes of n would also be equally distributed.
So how do we solve this problem? A crude way is to keep generating random numbers until you get a number in your desired range:
int x;
do {
x = rand();
} while (x >= n);
but that's inefficient for low values of n, since you only have a n/RAND_MAX chance of getting a value in your range, and so you'll need to perform RAND_MAX/n calls to rand() on average.
A more efficient formula approach would be to take some large range with a length divisible by n, like RAND_MAX - RAND_MAX % n, keep generating random numbers until you get one that lies in the range, and then take the modulus:
int x;
do {
x = rand();
} while (x >= (RAND_MAX - RAND_MAX % n));
x %= n;
For small values of n, this will rarely require more than one call to rand().
Works cited and further reading:
CPlusPlus Reference
Eternally Confuzzled
Keep selecting a random is a good way to remove the bias.
Update
We could make the code fast if we search for an x in range divisible by n.
// Assumptions
// rand() in [0, RAND_MAX]
// n in (0, RAND_MAX]
int x;
// Keep searching for an x in a range divisible by n
do {
x = rand();
} while (x >= RAND_MAX - (RAND_MAX % n))
x %= n;
The above loop should be very fast, say 1 iteration on average.
#user1413793 is correct about the problem. I'm not going to discuss that further, except to make one point: yes, for small values of n and large values of RAND_MAX, the modulo bias can be very small. But using a bias-inducing pattern means that you must consider the bias every time you calculate a random number and choose different patterns for different cases. And if you make the wrong choice, the bugs it introduces are subtle and almost impossible to unit test. Compared to just using the proper tool (such as arc4random_uniform), that's extra work, not less work. Doing more work and getting a worse solution is terrible engineering, especially when doing it right every time is easy on most platforms.
Unfortunately, the implementations of the solution are all incorrect or less efficient than they should be. (Each solution has various comments explaining the problems, but none of the solutions have been fixed to address them.) This is likely to confuse the casual answer-seeker, so I'm providing a known-good implementation here.
Again, the best solution is just to use arc4random_uniform on platforms that provide it, or a similar ranged solution for your platform (such as Random.nextInt on Java). It will do the right thing at no code cost to you. This is almost always the correct call to make.
If you don't have arc4random_uniform, then you can use the power of opensource to see exactly how it is implemented on top of a wider-range RNG (ar4random in this case, but a similar approach could also work on top of other RNGs).
Here is the OpenBSD implementation:
/*
* Calculate a uniformly distributed random number less than upper_bound
* avoiding "modulo bias".
*
* Uniformity is achieved by generating new random numbers until the one
* returned is outside the range [0, 2**32 % upper_bound). This
* guarantees the selected random number will be inside
* [2**32 % upper_bound, 2**32) which maps back to [0, upper_bound)
* after reduction modulo upper_bound.
*/
u_int32_t
arc4random_uniform(u_int32_t upper_bound)
{
u_int32_t r, min;
if (upper_bound < 2)
return 0;
/* 2**32 % x == (2**32 - x) % x */
min = -upper_bound % upper_bound;
/*
* This could theoretically loop forever but each retry has
* p > 0.5 (worst case, usually far better) of selecting a
* number inside the range we need, so it should rarely need
* to re-roll.
*/
for (;;) {
r = arc4random();
if (r >= min)
break;
}
return r % upper_bound;
}
It is worth noting the latest commit comment on this code for those who need to implement similar things:
Change arc4random_uniform() to calculate 2**32 % upper_bound as
-upper_bound % upper_bound. Simplifies the code and makes it the
same on both ILP32 and LP64 architectures, and also slightly faster on
LP64 architectures by using a 32-bit remainder instead of a 64-bit
remainder.
Pointed out by Jorden Verwer on tech#
ok deraadt; no objections from djm or otto
The Java implementation is also easily findable (see previous link):
public int nextInt(int n) {
if (n <= 0)
throw new IllegalArgumentException("n must be positive");
if ((n & -n) == n) // i.e., n is a power of 2
return (int)((n * (long)next(31)) >> 31);
int bits, val;
do {
bits = next(31);
val = bits % n;
} while (bits - val + (n-1) < 0);
return val;
}
Definition
Modulo Bias is the inherent bias in using modulo arithmetic to reduce an output set to a subset of the input set. In general, a bias exists whenever the mapping between the input and output set is not equally distributed, as in the case of using modulo arithmetic when the size of the output set is not a divisor of the size of the input set.
This bias is particularly hard to avoid in computing, where numbers are represented as strings of bits: 0s and 1s. Finding truly random sources of randomness is also extremely difficult, but is beyond the scope of this discussion. For the remainder of this answer, assume that there exists an unlimited source of truly random bits.
Problem Example
Let's consider simulating a die roll (0 to 5) using these random bits. There are 6 possibilities, so we need enough bits to represent the number 6, which is 3 bits. Unfortunately, 3 random bits yields 8 possible outcomes:
000 = 0, 001 = 1, 010 = 2, 011 = 3
100 = 4, 101 = 5, 110 = 6, 111 = 7
We can reduce the size of the outcome set to exactly 6 by taking the value modulo 6, however this presents the modulo bias problem: 110 yields a 0, and 111 yields a 1. This die is loaded.
Potential Solutions
Approach 0:
Rather than rely on random bits, in theory one could hire a small army to roll dice all day and record the results in a database, and then use each result only once. This is about as practical as it sounds, and more than likely would not yield truly random results anyway (pun intended).
Approach 1:
Instead of using the modulus, a naive but mathematically correct solution is to discard results that yield 110 and 111 and simply try again with 3 new bits. Unfortunately, this means that there is a 25% chance on each roll that a re-roll will be required, including each of the re-rolls themselves. This is clearly impractical for all but the most trivial of uses.
Approach 2:
Use more bits: instead of 3 bits, use 4. This yield 16 possible outcomes. Of course, re-rolling anytime the result is greater than 5 makes things worse (10/16 = 62.5%) so that alone won't help.
Notice that 2 * 6 = 12 < 16, so we can safely take any outcome less than 12 and reduce that modulo 6 to evenly distribute the outcomes. The other 4 outcomes must be discarded, and then re-rolled as in the previous approach.
Sounds good at first, but let's check the math:
4 discarded results / 16 possibilities = 25%
In this case, 1 extra bit didn't help at all!
That result is unfortunate, but let's try again with 5 bits:
32 % 6 = 2 discarded results; and
2 discarded results / 32 possibilities = 6.25%
A definite improvement, but not good enough in many practical cases. The good news is, adding more bits will never increase the chances of needing to discard and re-roll. This holds not just for dice, but in all cases.
As demonstrated however, adding an 1 extra bit may not change anything. In fact if we increase our roll to 6 bits, the probability remains 6.25%.
This begs 2 additional questions:
If we add enough bits, is there a guarantee that the probability of a discard will diminish?
How many bits are enough in the general case?
General Solution
Thankfully the answer to the first question is yes. The problem with 6 is that 2^x mod 6 flips between 2 and 4 which coincidentally are a multiple of 2 from each other, so that for an even x > 1,
[2^x mod 6] / 2^x == [2^(x+1) mod 6] / 2^(x+1)
Thus 6 is an exception rather than the rule. It is possible to find larger moduli that yield consecutive powers of 2 in the same way, but eventually this must wrap around, and the probability of a discard will be reduced.
Without offering further proof, in general using double the number
of bits required will provide a smaller, usually insignificant,
chance of a discard.
Proof of Concept
Here is an example program that uses OpenSSL's libcrypo to supply random bytes. When compiling, be sure to link to the library with -lcrypto which most everyone should have available.
#include <iostream>
#include <assert.h>
#include <limits>
#include <openssl/rand.h>
volatile uint32_t dummy;
uint64_t discardCount;
uint32_t uniformRandomUint32(uint32_t upperBound)
{
assert(RAND_status() == 1);
uint64_t discard = (std::numeric_limits<uint64_t>::max() - upperBound) % upperBound;
RAND_bytes((uint8_t*)(&randomPool), sizeof(randomPool));
while(randomPool > (std::numeric_limits<uint64_t>::max() - discard)) {
RAND_bytes((uint8_t*)(&randomPool), sizeof(randomPool));
++discardCount;
}
return randomPool % upperBound;
}
int main() {
discardCount = 0;
const uint32_t MODULUS = (1ul << 31)-1;
const uint32_t ROLLS = 10000000;
for(uint32_t i = 0; i < ROLLS; ++i) {
dummy = uniformRandomUint32(MODULUS);
}
std::cout << "Discard count = " << discardCount << std::endl;
}
I encourage playing with the MODULUS and ROLLS values to see how many re-rolls actually happen under most conditions. A sceptical person may also wish to save the computed values to file and verify the distribution appears normal.
Mark's Solution (The accepted solution) is Nearly Perfect.
int x;
do {
x = rand();
} while (x >= (RAND_MAX - RAND_MAX % n));
x %= n;
edited Mar 25 '16 at 23:16
Mark Amery 39k21170211
However, it has a caveat which discards 1 valid set of outcomes in any scenario where RAND_MAX (RM) is 1 less than a multiple of N (Where N = the Number of possible valid outcomes).
ie, When the 'count of values discarded' (D) is equal to N, then they are actually a valid set (V), not an invalid set (I).
What causes this is at some point Mark loses sight of the difference between N and Rand_Max.
N is a set who's valid members are comprised only of Positive Integers, as it contains a count of responses that would be valid. (eg: Set N = {1, 2, 3, ... n } )
Rand_max However is a set which ( as defined for our purposes ) includes any number of non-negative integers.
In it's most generic form, what is defined here as Rand Max is the Set of all valid outcomes, which could theoretically include negative numbers or non-numeric values.
Therefore Rand_Max is better defined as the set of "Possible Responses".
However N operates against the count of the values within the set of valid responses, so even as defined in our specific case, Rand_Max will be a value one less than the total number it contains.
Using Mark's Solution, Values are Discarded when: X => RM - RM % N
EG:
Ran Max Value (RM) = 255
Valid Outcome (N) = 4
When X => 252, Discarded values for X are: 252, 253, 254, 255
So, if Random Value Selected (X) = {252, 253, 254, 255}
Number of discarded Values (I) = RM % N + 1 == N
IE:
I = RM % N + 1
I = 255 % 4 + 1
I = 3 + 1
I = 4
X => ( RM - RM % N )
255 => (255 - 255 % 4)
255 => (255 - 3)
255 => (252)
Discard Returns $True
As you can see in the example above, when the value of X (the random number we get from the initial function) is 252, 253, 254, or 255 we would discard it even though these four values comprise a valid set of returned values.
IE: When the count of the values Discarded (I) = N (The number of valid outcomes) then a Valid set of return values will be discarded by the original function.
If we describe the difference between the values N and RM as D, ie:
D = (RM - N)
Then as the value of D becomes smaller, the Percentage of unneeded re-rolls due to this method increases at each natural multiplicative. (When RAND_MAX is NOT equal to a Prime Number this is of valid concern)
EG:
RM=255 , N=2 Then: D = 253, Lost percentage = 0.78125%
RM=255 , N=4 Then: D = 251, Lost percentage = 1.5625%
RM=255 , N=8 Then: D = 247, Lost percentage = 3.125%
RM=255 , N=16 Then: D = 239, Lost percentage = 6.25%
RM=255 , N=32 Then: D = 223, Lost percentage = 12.5%
RM=255 , N=64 Then: D = 191, Lost percentage = 25%
RM=255 , N= 128 Then D = 127, Lost percentage = 50%
Since the percentage of Rerolls needed increases the closer N comes to RM, this can be of valid concern at many different values depending on the constraints of the system running he code and the values being looked for.
To negate this we can make a simple amendment As shown here:
int x;
do {
x = rand();
} while (x > (RAND_MAX - ( ( ( RAND_MAX % n ) + 1 ) % n) );
x %= n;
This provides a more general version of the formula which accounts for the additional peculiarities of using modulus to define your max values.
Examples of using a small value for RAND_MAX which is a multiplicative of N.
Mark'original Version:
RAND_MAX = 3, n = 2, Values in RAND_MAX = 0,1,2,3, Valid Sets = 0,1 and 2,3.
When X >= (RAND_MAX - ( RAND_MAX % n ) )
When X >= 2 the value will be discarded, even though the set is valid.
Generalized Version 1:
RAND_MAX = 3, n = 2, Values in RAND_MAX = 0,1,2,3, Valid Sets = 0,1 and 2,3.
When X > (RAND_MAX - ( ( RAND_MAX % n ) + 1 ) % n )
When X > 3 the value would be discarded, but this is not a vlue in the set RAND_MAX so there will be no discard.
Additionally, in the case where N should be the number of values in RAND_MAX; in this case, you could set N = RAND_MAX +1, unless RAND_MAX = INT_MAX.
Loop-wise you could just use N = 1, and any value of X will be accepted, however, and put an IF statement in for your final multiplier. But perhaps you have code that may have a valid reason to return a 1 when the function is called with n = 1...
So it may be better to use 0, which would normally provide a Div 0 Error, when you wish to have n = RAND_MAX+1
Generalized Version 2:
int x;
if n != 0 {
do {
x = rand();
} while (x > (RAND_MAX - ( ( ( RAND_MAX % n ) + 1 ) % n) );
x %= n;
} else {
x = rand();
}
Both of these solutions resolve the issue with needlessly discarded valid results which will occur when RM+1 is a product of n.
The second version also covers the edge case scenario when you need n to equal the total possible set of values contained in RAND_MAX.
The modified approach in both is the same and allows for a more general solution to the need of providing valid random numbers and minimizing discarded values.
To reiterate:
The Basic General Solution which extends mark's example:
// Assumes:
// RAND_MAX is a globally defined constant, returned from the environment.
// int n; // User input, or externally defined, number of valid choices.
int x;
do {
x = rand();
} while (x > (RAND_MAX - ( ( ( RAND_MAX % n ) + 1 ) % n) ) );
x %= n;
The Extended General Solution which Allows one additional scenario of RAND_MAX+1 = n:
// Assumes:
// RAND_MAX is a globally defined constant, returned from the environment.
// int n; // User input, or externally defined, number of valid choices.
int x;
if n != 0 {
do {
x = rand();
} while (x > (RAND_MAX - ( ( ( RAND_MAX % n ) + 1 ) % n) ) );
x %= n;
} else {
x = rand();
}
In some languages ( particularly interpreted languages ) doing the calculations of the compare-operation outside of the while condition may lead to faster results as this is a one-time calculation no matter how many re-tries are required. YMMV!
// Assumes:
// RAND_MAX is a globally defined constant, returned from the environment.
// int n; // User input, or externally defined, number of valid choices.
int x; // Resulting random number
int y; // One-time calculation of the compare value for x
y = RAND_MAX - ( ( ( RAND_MAX % n ) + 1 ) % n)
if n != 0 {
do {
x = rand();
} while (x > y);
x %= n;
} else {
x = rand();
}
There are two usual complaints with the use of modulo.
one is valid for all generators. It is easier to see in a limit case. If your generator has a RAND_MAX which is 2 (that isn't compliant with the C standard) and you want only 0 or 1 as value, using modulo will generate 0 twice as often (when the generator generates 0 and 2) as it will generate 1 (when the generator generates 1). Note that this is true as soon as you don't drop values, whatever the mapping you are using from the generator values to the wanted one, one will occurs twice as often as the other.
some kind of generator have their less significant bits less random than the other, at least for some of their parameters, but sadly those parameter have other interesting characteristic (such has being able to have RAND_MAX one less than a power of 2). The problem is well known and for a long time library implementation probably avoid the problem (for instance the sample rand() implementation in the C standard use this kind of generator, but drop the 16 less significant bits), but some like to complain about that and you may have bad luck
Using something like
int alea(int n){
assert (0 < n && n <= RAND_MAX);
int partSize =
n == RAND_MAX ? 1 : 1 + (RAND_MAX-n)/(n+1);
int maxUsefull = partSize * n + (partSize-1);
int draw;
do {
draw = rand();
} while (draw > maxUsefull);
return draw/partSize;
}
to generate a random number between 0 and n will avoid both problems (and it avoids overflow with RAND_MAX == INT_MAX)
BTW, C++11 introduced standard ways to the the reduction and other generator than rand().
With a RAND_MAX value of 3 (in reality it should be much higher than that but the bias would still exist) it makes sense from these calculations that there is a bias:
1 % 2 = 1
2 % 2 = 0
3 % 2 = 1
random_between(1, 3) % 2 = more likely a 1
In this case, the % 2 is what you shouldn't do when you want a random number between 0 and 1. You could get a random number between 0 and 2 by doing % 3 though, because in this case: RAND_MAX is a multiple of 3.
Another method
There is much simpler but to add to other answers, here is my solution to get a random number between 0 and n - 1, so n different possibilities, without bias.
the number of bits (not bytes) needed to encode the number of possibilities is the number of bits of random data you'll need
encode the number from random bits
if this number is >= n, restart (no modulo).
Really random data is not easy to obtain, so why use more bits than needed.
Below is an example in Smalltalk, using a cache of bits from a pseudo-random number generator. I'm no security expert so use at your own risk.
next: n
| bitSize r from to |
n < 0 ifTrue: [^0 - (self next: 0 - n)].
n = 0 ifTrue: [^nil].
n = 1 ifTrue: [^0].
cache isNil ifTrue: [cache := OrderedCollection new].
cache size < (self randmax highBit) ifTrue: [
Security.DSSRandom default next asByteArray do: [ :byte |
(1 to: 8) do: [ :i | cache add: (byte bitAt: i)]
]
].
r := 0.
bitSize := n highBit.
to := cache size.
from := to - bitSize + 1.
(from to: to) do: [ :i |
r := r bitAt: i - from + 1 put: (cache at: i)
].
cache removeFrom: from to: to.
r >= n ifTrue: [^self next: n].
^r
Modulo reduction is a commonly seen way to make a random integer generator avoid the worst case of running forever.
When the range of possible integers is unknown, however, there is no way in general to "fix" this worst case of running forever without introducing bias. It's not just modulo reduction (rand() % n, discussed in the accepted answer) that will introduce bias this way, but also the "multiply-and-shift" reduction of Daniel Lemire, or if you stop rejecting an outcome after a set number of iterations. (To be clear, this doesn't mean there is no way to fix the bias issues present in pseudorandom generators. For example, even though modulo and other reductions are biased in general, they will have no issues with bias if the range of possible integers is a power of 2 and if the random generator produces unbiased random bits or blocks of them.)
The following answer of mine discusses the relationship between running time and bias in random generators, assuming we have a "true" random generator that can produce unbiased and independent random bits. The answer doesn't even involve the rand() function in C because it has many issues. Perhaps the most serious here is the fact that the C standard does not explicitly specify a particular distribution for the numbers returned by rand(), not even a uniform distribution.
How to generate a random integer in the range [0,n] from a stream of random bits without wasting bits?
As the accepted answer indicates, "modulo bias" has its roots in the low value of RAND_MAX. He uses an extremely small value of RAND_MAX (10) to show that if RAND_MAX were 10, then you tried to generate a number between 0 and 2 using %, the following outcomes would result:
rand() % 3 // if RAND_MAX were only 10, gives
output of rand() | rand()%3
0 | 0
1 | 1
2 | 2
3 | 0
4 | 1
5 | 2
6 | 0
7 | 1
8 | 2
9 | 0
So there are 4 outputs of 0's (4/10 chance) and only 3 outputs of 1 and 2 (3/10 chances each).
So it's biased. The lower numbers have a better chance of coming out.
But that only shows up so obviously when RAND_MAX is small. Or more specifically, when the number your are modding by is large compared to RAND_MAX.
A much better solution than looping (which is insanely inefficient and shouldn't even be suggested) is to use a PRNG with a much larger output range. The Mersenne Twister algorithm has a maximum output of 4,294,967,295. As such doing MersenneTwister::genrand_int32() % 10 for all intents and purposes, will be equally distributed and the modulo bias effect will all but disappear.
I just wrote a code for Von Neumann's Unbiased Coin Flip Method, that should theoretically eliminate any bias in the random number generation process. More info can be found at (http://en.wikipedia.org/wiki/Fair_coin)
int unbiased_random_bit() {
int x1, x2, prev;
prev = 2;
x1 = rand() % 2;
x2 = rand() % 2;
for (;; x1 = rand() % 2, x2 = rand() % 2)
{
if (x1 ^ x2) // 01 -> 1, or 10 -> 0.
{
return x2;
}
else if (x1 & x2)
{
if (!prev) // 0011
return 1;
else
prev = 1; // 1111 -> continue, bias unresolved
}
else
{
if (prev == 1)// 1100
return 0;
else // 0000 -> continue, bias unresolved
prev = 0;
}
}
}
I actually tried to search this, I'm sure this basic algorithm is everywhere on internet, CS textbooks etc, but I cannot find the right words to search it.
What I want from this algorithm to do is write "A" and "B" with the limit always increasing by 2. Like I want it to write A 3 times, then B 5 times, then A 7 times, then B 9 times and so on. And I plan to have 100 elements in total.
Like: AAABBBBBAAAAAAABBBBBBBBB...
I only want to use a single "for loop" for the entire 100 elements starting from 1 to 100. And just direct/sort "A" and "B" through "if/else if/ else".
I'm just asking for the basic mathematical algorithm behind it, showing it through any programming language would be better or redirecting me to such topic would also be fine.
You can do something like this:
There might be shorter answers, but I find this one easy to understand.
Basically, you keep a bool variable that will tell you if it's A's turn or Bs. Then we keep a variable switch that will tell us when we should switch between them. times is being updated with the repeated times we need to print the next character.
A_B = true
times = 3 // 3,5,7,9,...
switch = 3 // 3,8,15,24,...
for (i from 1 to 100)
if (A_B)
print 'A'
else
print 'B'
if (i == switch)
times += 2
switch += times
A_B = !A_B
Python:
for n in range(1, 101):
print "BA"[(int(sqrt(n)) % 2)],
The parity of the square roots of the integers follows that pattern. (Think that (n+1)²-n² = 2n+1.)
If you prefer to avoid the square root, it suffices to use an extra variable that represents the integer square root and keep it updated
r= 1
for n in range(1, 101):
if r * r <= n:
r+= 1
print "AB"[r % 2],
Here is the snippet you can test on this page. It is an example for about 500 letters totally, sure you can modify it for 100 letters. It is quite flexible that you can change the constants to produce lot of different strings in the same manner.
var toRepeat = ['A', 'B'];
var result='', j, i=3;
var sum=i;
var counter = 0;
while (sum < 500) {
j = counter % 2;
result = result + toRepeat[j].repeat(i);
sum = sum + i;
i = i + 2;
counter++;
}
document.getElementById('hLetters').innerHTML=result;
console.log(result);
<div id="hLetters"></div>
If you want it to be exactly 500 / 100 letters, just use a substring function to trim off the extra letters from the end.
To get 100 groups of A and B with increasing length of 3, 5, 7 and so on, you can run this Python code:
''.join(('B' if i % 2 else 'A') * (2 * i + 3) for i in range(100))
The output is a string of 10200 characters.
If you want the output to have only 100 characters, you can use:
import math
''.join(('B' if math.ceil(math.sqrt(i)) % 2 else 'A') for i in range(2, 102))
In js you can start with somethink like this :
$res ="";
count2 = 0;
for (i=2;i<100; i = i+2) {
count = 0;
alert(i);
while (count < i ) {
$res = $res.concat(String.fromCharCode(65+count2));
count++;
}
count2++;
}
alert ($res);
Actually, this question can be generalized as below:
Find all possible combinations from a given set of elements, which meets
a certain criteria.
So, any good algorithms?
There are only 16 possibilities (and one of those is to add together "none of them", which ain't gonna give you 24), so the old-fashioned "brute force" algorithm looks pretty good to me:
for (unsigned int choice = 1; choice < 16; ++choice) {
int sum = 0;
if (choice & 1) sum += elements[0];
if (choice & 2) sum += elements[1];
if (choice & 4) sum += elements[2];
if (choice & 8) sum += elements[3];
if (sum == 24) {
// we have a winner
}
}
In the completely general form of your problem, the only way to tell whether a combination meets "certain criteria" is to evaluate those criteria for every single combination. Given more information about the criteria, maybe you could work out some ways to avoid testing every combination and build an algorithm accordingly, but not without those details. So again, brute force is king.
There are two interesting explanations about the sum problem, both in Wikipedia and MathWorld.
In the case of the first question you asked, the first answer is good for a limited number of elements. You should realize that the reason Mr. Jessop used 16 as the boundary for his loop is because this is 2^4, where 4 is the number of elements in your set. If you had 100 elements, the loop limit would become 2^100 and your algorithm would literally take forever to finish.
In the case of a bounded sum, you should consider a depth first search, because when the sum of elements exceeds the sum you are looking for, you can prune your branch and backtrack.
In the case of the generic question, finding the subset of elements that satisfy certain criteria, this is known as the Knapsack problem, which is known to be NP-Complete. Given that, there is no algorithm that will solve it in less than exponential time.
Nevertheless, there are several heuristics that bring good results to the table, including (but not limited to) genetic algorithms (one I personally like, for I wrote a book on them) and dynamic programming. A simple search in Google will show many scientific papers that describe different solutions for this problem.
Find all possible combinations from a given set of elements, which
meets a certain criteria
If i understood you right, this code will helpful for you:
>>> from itertools import combinations as combi
>>> combi.__doc__
'combinations(iterable, r) --> combinations object\n\nReturn successive r-length
combinations of elements in the iterable.\n\ncombinations(range(4), 3) --> (0,1
,2), (0,1,3), (0,2,3), (1,2,3)'
>>> set = range(4)
>>> set
[0, 1, 2, 3]
>>> criteria = range(3)
>>> criteria
[0, 1, 2]
>>> for tuple in list(combi(set, len(criteria))):
... if cmp(list(tuple), criteria) == 0:
... print 'criteria exists in tuple: ', tuple
...
criteria exists in tuple: (0, 1, 2)
>>> list(combi(set, len(criteria)))
[(0, 1, 2), (0, 1, 3), (0, 2, 3), (1, 2, 3)]
Generally for a problem as this you have to try all posebilities, the thing you should do have the code abort the building of combiantion if you know it will not satesfie the criteria (if you criteria is that you do not have more then two blue balls, then you have to abort calculation that has more then two). Backtracing
def perm(set,permutation):
if lenght(set) == lenght(permutation):
print permutation
else:
for element in set:
if permutation.add(element) == criteria:
perm(sett,permutation)
else:
permutation.pop() //remove the element added in the if
The set of input numbers matters, as you can tell as soon as you allow e.g. negative numbers, imaginary numbers, rational numbers etc in your start set. You could also restrict to e.g. all even numbers, all odd number inputs etc.
That means that it's hard to build something deductive. You need brute force, a.k.a. try every combination etc.
In this particular problem you could build an algoritm that recurses - e.g. find every combination of 3 Int ( 1,22) that add up to 23, then add 1, every combination that add to 22 and add 2 etc. Which can again be broken into every combination of 2 that add up to 21 etc. You need to decide if you can count same number twice.
Once you have that you have a recursive function to call -
combinations( 24 , 4 ) = combinations( 23, 3 ) + combinations( 22, 3 ) + ... combinations( 4, 3 );
combinations( 23 , 3 ) = combinations( 22, 2 ) + ... combinations( 3, 2 );
etc
This works well except you have to be careful around repeating numbers in the recursion.
private int[][] work()
{
const int target = 24;
List<int[]> combos = new List<int[]>();
for(int i = 0; i < 9; i++)
for(int x = 0; x < 9; x++)
for(int y = 0; y < 9; y++)
for (int z = 0; z < 9; z++)
{
int res = x + y + z + i;
if (res == target)
{
combos.Add(new int[] { x, y, z, i });
}
}
return combos.ToArray();
}
It works instantly, but there probably are better methods rather than 'guess and check'. All I am doing is looping through every possibility, adding them all together, and seeing if it comes out to the target value.
If i understand your question correctly, what you are asking for is called "Permutations" or the number (N) of possible ways to arrange (X) numbers taken from a set of (Y) numbers.
N = Y! / (Y - X)!
I don't know if this will help, but this is a solution I came up with for an assignment on permutations.
You have an input of : 123 (string) using the substr functions
1) put each number of the input into an array
array[N1,N2,N3,...]
2)Create a swap function
function swap(Number A, Number B)
{
temp = Number B
Number B = Number A
Number A = temp
}
3)This algorithm uses the swap function to move the numbers around until all permutations are done.
original_string= '123'
temp_string=''
While( temp_string != original_string)
{
swap(array element[i], array element[i+1])
if (i == 1)
i == 0
temp_string = array.toString
i++
}
Hopefully you can follow my pseudo code, but this works at least for 3 digit permutations
(n X n )
built up a square matrix of nxn
and print all together its corresponding crossed values
e.g.
1 2 3 4
1 11 12 13 14
2 .. .. .. ..
3 ..
4 .. ..
Suppose I have an int x = 54897, old digit index (0 based), and the new value for that digit. What's the fastest way to get the new value?
Example
x = 54897
index = 3
value = 2
y = f(x, index, value) // => 54827
Edit: by fastest, I definitely mean faster performance. No string processing.
In simplest case (considering the digits are numbered from LSB to MSB, the first one being 0) AND knowing the old digit, we could do as simple as that:
num += (new_digit - old_digit) * 10**pos;
For the real problem we would need:
1) the MSB-first version of the pos, that could cost you a log() or at most log10(MAX_INT) divisions by ten (could be improved using binary search).
2) the digit from that pos that would need at most 2 divisions (or zero, using results from step 1).
You could also use the special fpu instruction from x86 that is able to save a float in BCD (I have no idea how slow it is).
UPDATE: the first step could be done even faster, without any divisions, with a binary search like this:
int my_log10(unsigned short n){
// short: 0.. 64k -> 1.. 5 digits
if (n < 1000){ // 1..3
if (n < 10) return 1;
if (n < 100) return 2;
return 3;
} else { // 4..5
if (n < 10000) return 4;
return 5;
}
}
If your index started at the least significant digit, you could do something like
p = pow(10,index);
x = (x / (p*10) * (p*10) + value * p + x % p).
But since your index is backwards, a string is probably the way to go. It would also be more readable and maintainable.
Calculate the "mask" M: 10 raised to the power of index, where index is a zero-based index from the right. If you need to index from the left, recalculate index accordingly.
Calculate the "prefix" PRE = x / (M * 10) * (M * 10)
Calculate the "suffix" SUF = x % M
Calculate the new "middle part" MID = value * M
Generate the new number new_x = PRE + MID + POST.
P.S. ruslik's answer does it more elegantly :)
You need to start by figuring out how many digits are in your input. I can think of two ways of doing that, one with a loop and one with logarithms. Here's the loop version. This will fail for negative and zero inputs and when the index is out of bounds, probably other conditions too, but it's a starting point.
def f(x, index, value):
place = 1
residual = x
while residual > 0:
if index < 0:
place *= 10
index -= 1
residual /= 10
digit = (x / place) % 10
return x - (place * digit) + (place * value)
P.S. This is working Python code. The principle of something simple like this is easy to work out, but the details are so tricky that you really need to iterate it a bit. In this case I started with the principle that I wanted to subtract out the old digit and add the new one; from there it was a matter of getting the correct multiplier.
You gotta get specific with your compute platform if you're talking about performance.
I would approach this by converting the number into pairs of decimal digits, 4 bit each.
Then I would find and process the pair that needs modification as a byte.
Then I would put the number back together.
There are assemblers that do this very well.
I've been learning Ruby, so I thought I'd try my hand at some of the project Euler puzzles. Embarrassingly, I only made it to problem 4...
Problem 4 goes as follows:
A palindromic number reads the same
both ways. The largest palindrome made
from the product of two 2-digit
numbers is 9009 = 91 × 99.
Find the largest palindrome made from
the product of two 3-digit numbers.
So I figured I would loop down from 999 to 100 in a nested for loop and do a test for the palindrome and then break out of the loops when I found the first one (which should be the largest one):
final=nil
range = 100...1000
for a in range.to_a.reverse do
for b in range.to_a.reverse do
c=a*b
final=c if c.to_s == c.to_s.reverse
break if !final.nil?
end
break if !final.nil?
end
puts final
This does output a palindrome 580085, but apparently this isn't the highest product of two three-digit numbers within the range. Strangely, the same code succeeds to return 9009, like in the example, if I change the range to 10...100.
Can someone tell me where I am going
wrong?
Also, is there a nicer way to
break out of the internal loop?
Thanks
You are testing 999* (999...100), then 998 * (999...100)
Hence you will be testing 999 * 500 before you test 997 * 996.
So, how you we find that right number?
First note the multiplication is reflective, a * b == b * a, so b need not go from 999...0 every time, just a ...0.
When you find a palindrone, add the two factors together and save the sum (save the two factors also)
Inside the loop, if (a+b) is ever less than the saved sum, abandon the inner loop and move to the next a. When a falls below sum/2, no future value you could find would be higher than the one you've already found, so you're done.
The problem is that you might find a palindrome for an a of 999 and a b of 200, but you break too soon, so you never see that there is one for 998*997 (just example numbers).
You need to either look for all palindromes or once you find the first one, set that b as your minimum bound and continue looking through the a loop.
Regarding the second question, my advice is to approach the problem in more functional, than procedural manner. So, rather than looping, you may try to "describe" your problem functionally, and let Ruby does the work:
From all the pairs of 3-digit numbers,
select only those whose product is a palindrome,
and find the one with the largest product
Although this approach may not yield the most efficient of the solutions, it may teach you couple of Ruby idioms.
Consider the digits of P – let them be x, y and z. P must be at least 6 digits long since the palindrome 111111 = 143×777 – the product of two 3-digit integers. Since P is palindromic:
P=100000x + 10000y + 1000z + 100z + 10y + x
P=100001x + 10010y + 1100z
P=11(9091x + 910y + 100z)
Since 11 is prime, at least one of the integers a or b must have a factor of 11. So if a is not divisible by 11 then we know b must be. Using this information we can determine what values of b we check depending on a.
C# Implementation :
using System;
namespace HighestPalindrome
{
class Program
{
static void Main(string[] args)
{
int i, j;
int m = 1;
bool flag = false;
while (true)
{
if (flag) j = m + 1;
else j = m;
for (i = m; i > 0; i--)
{
Console.WriteLine("{0} * {1} = {2}", 1000 - i, 1000 - j, (1000 - i) * (1000 - j));
j++;
//--- Palindrome Check ------------------------------
int number, temp, remainder, sum = 0;
number = temp = (1000 - i) * (1000 - j);
while (number > 0)
{
remainder = number % 10;
number /= 10;
sum = sum * 10 + remainder;
}
if (sum == temp)
{
Console.WriteLine("Highest Palindrome Number is - {0} * {1} = {2}", 1000 - i, 1000 - j, temp);
Console.ReadKey();
return;
}
//---------------------------------------------------
}
if (flag)
m++;
flag = !flag;
}
}
}
}
The mistake is you assume that if you find palindrom with greatest a value it will give the greatest product it isn't true. Solution is to keep max_product value and update it against solution you find.
I can answer your first question: You need to find the highest product, not the product containing the highest factor. In other words a * b could be greater than c * d even if c > a > b.
You're breaking on the first palindrome you come to, not necessarily the biggest.
Say you have A,B,C,D,E. You test E * A before you test D * C.
The main thing is to go through all the possible values. Don't try to break when you find the first answer just start with a best answer of zero then try all combinations and keep updating best. The secondary thing is to try to reduce the set of "all combinations".
One thing you can do is limit your inner loop to values less than or equal to a (since ab == ba). This puts the larger value of your equation always in a and substantially reduces the number of values you have to test.
for a in range.to_a.reverse do
for b in (100..a).to_a.reverse do
The next thing you can do is break out of the inner loop whenever the product is less than the current best value.
c = a*b
next if c < best
Next, if you're going to go through them all anyway there's no benefit to going through them in reverse. By starting at the top of the range it takes a while before you find a palindromic number and as a result it takes a while to reduce your search set. If you start at the bottom you begin to increase the lower bound quickly.
for a in range.to_a do
for b in (100..a).to_a do
My tests show that either way you try some 405K pairs however. So how about thinking of the problem a different way. What is the largest possible product of two 3 digit numbers? 999 * 999 = 998001 and the smallest is 100*100 = 10000. How about we take the idea you had of breaking on the first answer but apply it to a different range, that being 998001 to 10000 (or 999*999 to 100*100).
for c in (10000...998001).to_a.reverse do
We get to a palindrome after only 202 tests... the problem is it isn't a product of two 3-digit numbers. So now we have to check whether the palindrome we've found is a product of 2 3-digit numbers. As soon as we find a value in the range that is a palindrome and a product of two 3-digit numbers we're done. My tests show we find the highest palindrome that meets the requirement after less than 93K tests. But since we have the overhead of checking that all palindromes to that point were products of two 3-digit numbers it may not be more efficient than the previous solution.
So lets go back to the original improvement.
for a in range.to_a.reverse do
for b in (100..a).to_a.reverse do
We're looping rows then columns and trying to be efficient by detecting a point where we can go to the next row because any additional trys on the current row could not possibly be better than our current best. What if, instead of going down the rows, we go across the diagonals?
Since the products get smaller diagonal by diagonal you can stop as soon as you find a palindome number. This is a really efficient solution but with a more complex implementation. It turns out this method finds the highest palindrome after slightly more than 2200 trys.
ar=[]
limit = 100..999
for a in limit.to_a.reverse do
for b in (100..a).to_a.reverse do
c=a*b
if c.to_s == c.to_s.reverse
palndrm=c
ar << palndrm
end
end
end
print ar
print"\n"
puts ar.max
puts ar.min
an implementation:
max = 100.upto(999).inject([-1,0,0]) do |m, a|
a.upto(999) do |b|
prod = a * b
m = [prod, a, b] if prod.to_s == prod.to_s.reverse and prod > m[0]
end
m
end
puts "%d = %d * %d" % max
prints 906609 = 913 * 993
Here's what I came up with in Ruby:
def largest_palindrome_product(digits)
largest, upper, lower = 0, 10**digits - 1, 10**(digits - 1)
for i in upper.downto(lower) do
for j in i.downto(lower) do
product = i * j
largest = product if product > largest && palindrome?(product)
end
end
largest
end
And here's the function to check if the number is a palindrome:
def palindrome?(input)
chars = input.to_s.chars
for i in 0..(chars.size - 1) do
return false if chars[i] != chars[chars.size - i - 1]
end
true
end
I guess there's probably a more efficient solution out there, though.
For this problem, as we are looking for the highest palindrom, i assumed it would start with a 9. Thus ending with a 9 (palindrom).
if you pay attention, to get a number finishing by 9, you can only get it with numbers finishing by 9 and 1, 3 and 3, 7 and 7.
Then it is useless to check the other values (for instance 999*998 as it will not end with a 9).
Starting from 999 and 991, you can then substract 10 to 991, trying 999 and 981 etc...
You do the same with 993 and 993 ... 993 * 983
same with 997 * 997 then 997 * 987 etc
You don't need to go further than 900 or 10^4 - 10^3 as you can be sure the highest will be before.
int PB4_firstTry(int size)
{
int nb1 = (int)pow(10.0,size+1.0) - 1, nb2 = (int)pow(10.0,size+1.0) - 1;
int pal91 = getFirstPalindrome(size,9,1);
int pal33 = getFirstPalindrome(size,3,3);
int pal77 = getFirstPalindrome(size,7,7);
int bigger1 = (pal91 > pal33) ? pal91 : pal33;
return (bigger1 > pal77) ? bigger1 : pal77;
}
int getFirstPalindrome(int size,int ending1,int ending2)
{
int st1 = (int)pow(10.0,size+1.0) - 10 + ending1;
int comp = st1 - pow(10.0,size);
int st2 = (int)pow(10.0,size+1.0) - 10 + ending2;
int answer = -1;
while (st1 > comp)
{
for (int i = st2; i > comp && st1*i > answer; i-=10)
{
if (PB4_isPalindrome(st1*i))
answer = st1*i;
}
st1 -= 10;
}
return answer;
}
bool PB4_isPalindrome(int number)
{
std::string str = intToString(number);
for (int i = 0; i < (int)(str.length() / 2); i++)
{
if (str[i] != str[str.length() - 1 - i])
return false;
}
return true;
}
std::string intToString(int number)
{
std::ostringstream convert;
convert << number;
return convert.str();
}
Of course, this works for 4 size digits factors etc.