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I would like to understand how to solve the Codility ArrayRecovery challenge, but I don't even know what branch of knowledge to consult. Is it combinatorics, optimization, computer science, set theory, or something else?
Edit:
The branch of knowledge to consult is constraint programming, particularly constraint propagation. You also need some combinatorics to know that if you take k numbers at a time from the range [1..n], with the restriction that no number can be bigger than the one before it, that works out to be
(n+k-1)!/k!(n-1)! possible combinations
which is the same as the number of combinations with replacements of n things taken k at a time, which has the mathematical notation . You can read about why it works out like that here.
Peter Norvig provides an excellent example of how to solve this kind of problem with his Sudoku solver.
You can read the full description of the ArrayRecovery problem via the link above. The short story is that there is an encoder that takes a sequence of integers in the range 1 up to some given limit (say 100 for our purposes) and for each element of the input sequence outputs the most recently seen integer that is smaller than the current input, or 0 if none exists.
input 1, 2, 3, 4 => output 0, 1, 2, 3
input 2, 4, 3 => output 0, 2, 2
The full task is, given the output (and the range of allowable input), figure out how many possible inputs could have generated it. But before I even get to that calculation, I'm not confident about how to even approach formulating the equation. That is what I am asking for help with. (Of course a full solution would be welcome, too, if it is explained.)
I just look at some possible outputs and wonder. Here are some sample encoder outputs and the inputs I can come up with, with * meaning any valid input and something like > 4 meaning any valid input greater than 4. If needed, inputs are referred to as A1, A2, A3, ... (1-based indexing)
Edit #2
Part of the problem I was having with this challenge is that I did not manually generate the exactly correct sets of possible inputs for an output. I believe the set below is correct now. Look at this answer's edit history if you want to see my earlier mistakes.
output #1: 0, 0, 0, 4
possible inputs: [>= 4, A1 >= * >= 4, 4, > 4]
output #2: 0, 0, 0, 2, 3, 4 # A5 ↴ See more in discussion below
possible inputs: [>= 2, A1 >= * >=2, 2, 3, 4, > 4]
output #3: 0, 0, 0, 4, 3, 1
possible inputs: none # [4, 3, 1, 1 >= * > 4, 4, > 1] but there is no number 1 >= * > 4
The second input sequence is very tightly constrained compared to the first just by adding 2 more outputs. The third sequence is so constrained as to be impossible.
But the set of constraints on A5 in example #2 is a bit harder to articulate. Of course A5 > O5, that is the basic constraint on all the inputs. But any output > A4 and after O5 has to appear in the input after A4, so A5 has to be an element of the set of numbers that comes after A5 that is also > A4. Since there is only 1 such number (A6 == 4), A5 has to be it, but it gets more complicated if there is a longer string of numbers that follow. (Editor's note: actually it doesn't.)
As the output set gets longer, I worry these constraints just get more complicated and harder to get right. I cannot think of any data structures for efficiently representing these in a way that leads to efficiently calculating the number of possible combinations. I also don't quite see how to algorithmically add constraint sets together.
Here are the constraints I see so far for any given An
An > On
An <= min(Set of other possible numbers from O1 to n-1 > On). How to define the set of possible numbers greater than On?
Numbers greater than On that came after the most recent occurrence of On in the input
An >= max(Set of other possible numbers from O1 to n-1 < On). How to define the set of possible numbers less than On?
Actually this set is empty because On is, by definition, the largest possible number from the previous input sequence. (Which it not to say it is strictly the largest number from the previous input sequence.)
Any number smaller than On that came before the last occurrence of it in the input would be ineligible because of the "nearest" rule. No numbers smaller that On could have occurred after the most recent occurrence because of the "nearest" rule and because of the transitive property: if Ai < On and Aj < Ai then Aj < On
Then there is the set theory:
An must be an element of the set of unaccounted-for elements of the set of On+1 to Om, where m is the smallest m > n such that Om < On. Any output after such Om and larger than Om (which An is) would have to appear as or after Am.
An element is unaccounted-for if it is seen in the output but does not appear in the input in a position that is consistent with the rest of the output. Obviously I need a better definition than this in order to code and algorithm to calculate it.
It seems like perhaps some kind of set theory and/or combinatorics or maybe linear algebra would help with figuring out the number of possible sequences that would account for all of the unaccounted-for outputs and fit the other constraints. (Editor's note: actually, things never get that complicated.)
The code below passes all of Codility's tests. The OP added a main function to use it on the command line.
The constraints are not as complex as the OP thinks. In particular, there is never a situation where you need to add a restriction that an input be an element of some set of specific integers seen elsewhere in the output. Every input position has a well-defined minimum and maximum.
The only complication to that rule is that sometimes the maximum is "the value of the previous input" and that input itself has a range. But even then, all the values like that are consecutive and have the same range, so the number of possibilities can be calculated with basic combinatorics, and those inputs as a group are independent of the other inputs (which only serve to set the range), so the possibilities of that group can be combined with the possibilities of other input positions by simple multiplication.
Algorithm overview
The algorithm makes a single pass through the output array updating the possible numbers of input arrays after every span, which is what I am calling repetitions of numbers in the output. (You might say maximal subsequences of the output where every element is identical.) For example, for output 0,1,1,2 we have three spans: 0, 1,1 and 2. When a new span begins, the number of possibilities for the previous span is calculated.
This decision was based on a few observations:
For spans longer than 1 in length, the minimum value of the input
allowed in the first position is whatever the value is of the input
in the second position. Calculating the number of possibilities of a
span is straightforward combinatorics, but the standard formula
requires knowing the range of the numbers and the length of the span.
Every time the value of the
output changes (and a new span beings), that strongly constrains the value of the previous span:
When the output goes up, the only possible reason is that the previous input was the value of the new, higher output and the input corresponding to the position of the new, higher output, was even higher.
When an output goes down, new constraints are established, but those are a bit harder to articulate. The algorithm stores stairs (see below) in order to quantify the constraints imposed when the output goes down
The aim here was to confine the range of possible values for every span. Once we do that accurately, calculating the number of combinations is straightforward.
Because the encoder backtracks looking to output a number that relates to the input in 2 ways, both smaller and closer, we know we can throw out numbers that are larger and farther away. After a small number appears in the output, no larger number from before that position can have any influence on what follows.
So to confine these ranges of input when the output sequence decreased, we need to store stairs - a list of increasingly larger possible values for the position in the original array. E.g for 0,2,5,7,2,4 stairs build up like this: 0, 0,2, 0,2,5, 0,2,5,7, 0,2, 0,2,4.
Using these bounds we can tell for sure that the number in the position of the second 2 (next to last position in the example) must be in (2,5], because 5 is the next stair. If the input were greater than 5, a 5 would have been output in that space instead of a 2. Observe, that if the last number in the encoded array was not 4, but 6, we would exit early returning 0, because we know that the previous number couldn't be bigger than 5.
The complexity is O(n*lg(min(n,m))).
Functions
CombinationsWithReplacement - counts number of combinations with replacements of size k from n numbers. E.g. for (3, 2) it counts 3,3, 3,2, 3,1, 2,2, 2,1, 1,1, so returns 6 It is the same as choose(n - 1 + k, n - 1).
nextBigger - finds next bigger element in a range. E.g. for 4 in sub-array 1,2,3,4,5 it returns 5, and in sub-array 1,3 it returns its parameter Max.
countSpan (lambda) - counts how many different combinations a span we have just passed can have. Consider span 2,2 for 0,2,5,7,2,2,7.
When curr gets to the final position, curr is 7 and prev is the final 2 of the 2,2 span.
It computes maximum and minimum possible values of the prev span. At this point stairs consist of 2,5,7 then maximum possible value is 5 (nextBigger after 2 in the stair 2,5,7). A value of greater than 5 in this span would have output a 5, not a 2.
It computes a minimum value for the span (which is the minimum value for every element in the span), which is prev at this point, (remember curr at this moment equals to 7 and prev to 2). We know for sure that in place of the final 2 output, the original input has to have 7, so the minimum is 7. (This is a consequence of the "output goes up" rule. If we had 7,7,2 and curr would be 2 then the minimum for the previous span (the 7,7) would be 8 which is prev + 1.
It adjusts the number of combinations. For a span of length L with a range of n possibilities (1+max-min), there are possibilities, with k being either L or L-1 depending on what follows the span.
For a span followed by a larger number, like 2,2,7, k = L - 1 because the last position of the 2,2 span has to be 7 (the value of the first number after the span).
For a span followed by a smaller number, like 7,7,2, k = L because
the last element of 7,7 has no special constraints.
Finally, it calls CombinationsWithReplacement to find out the number of branches (or possibilities), computes new res partial results value (remainder values in the modulo arithmetic we are doing), and returns new res value and max for further handling.
solution - iterates over the given Encoder Output array. In the main loop, while in a span it counts the span length, and at span boundaries it updates res by calling countSpan and possibly updates the stairs.
If the current span consists of a bigger number than the previous one, then:
Check validity of the next number. E.g 0,2,5,2,7 is invalid input, becuase there is can't be 7 in the next-to-last position, only 3, or 4, or 5.
It updates the stairs. When we have seen only 0,2, the stairs are 0,2, but after the next 5, the stairs become 0,2,5.
If the current span consists of a smaller number then the previous one, then:
It updates stairs. When we have seen only 0,2,5, our stairs are 0,2,5, but after we have seen 0,2,5,2 the stairs become 0,2.
After the main loop it accounts for the last span by calling countSpan with -1 which triggers the "output goes down" branch of calculations.
normalizeMod, extendedEuclidInternal, extendedEuclid, invMod - these auxiliary functions help to deal with modulo arithmetic.
For stairs I use storage for the encoded array, as the number of stairs never exceeds current position.
#include <algorithm>
#include <cassert>
#include <vector>
#include <tuple>
const int Modulus = 1'000'000'007;
int CombinationsWithReplacement(int n, int k);
template <class It>
auto nextBigger(It begin, It end, int value, int Max) {
auto maxIt = std::upper_bound(begin, end, value);
auto max = Max;
if (maxIt != end) {
max = *maxIt;
}
return max;
}
auto solution(std::vector<int> &B, const int Max) {
auto res = 1;
const auto size = (int)B.size();
auto spanLength = 1;
auto prev = 0;
// Stairs is the list of numbers which could be smaller than number in the next position
const auto stairsBegin = B.begin();
// This includes first entry (zero) into stairs
// We need to include 0 because we can meet another zero later in encoded array
// and we need to be able to find in stairs
auto stairsEnd = stairsBegin + 1;
auto countSpan = [&](int curr) {
const auto max = nextBigger(stairsBegin, stairsEnd, prev, Max);
// At the moment when we switch from the current span to the next span
// prev is the number from previous span and curr from current.
// E.g. 1,1,7, when we move to the third position cur = 7 and prev = 1.
// Observe that, in this case minimum value possible in place of any of 1's can be at least 2=1+1=prev+1.
// But if we consider 7, then we have even more stringent condition for numbers in place of 1, it is 7
const auto min = std::max(prev + 1, curr);
const bool countLast = prev > curr;
const auto branchesCount = CombinationsWithReplacement(max - min + 1, spanLength - (countLast ? 0 : 1));
return std::make_pair(res * (long long)branchesCount % Modulus, max);
};
for (int i = 1; i < size; ++i) {
const auto curr = B[i];
if (curr == prev) {
++spanLength;
}
else {
int max;
std::tie(res, max) = countSpan(curr);
if (prev < curr) {
if (curr > max) {
// 0,1,5,1,7 - invalid because number in the fourth position lies in [2,5]
// and so in the fifth encoded position we can't something bigger than 5
return 0;
}
// It is time to possibly shrink stairs.
// E.g if we had stairs 0,2,4,9,17 and current value is 5,
// then we no more interested in 9 and 17, and we change stairs to 0,2,4,5.
// That's because any number bigger than 9 or 17 also bigger than 5.
const auto s = std::lower_bound(stairsBegin, stairsEnd, curr);
stairsEnd = s;
*stairsEnd++ = curr;
}
else {
assert(curr < prev);
auto it = std::lower_bound(stairsBegin, stairsEnd, curr);
if (it == stairsEnd || *it != curr) {
// 0,5,1 is invalid sequence because original sequence lloks like this 5,>5,>1
// and there is no 1 in any of the two first positions, so
// it can't appear in the third position of the encoded array
return 0;
}
}
spanLength = 1;
}
prev = curr;
}
res = countSpan(-1).first;
return res;
}
template <class T> T normalizeMod(T a, T m) {
if (a < 0) return a + m;
return a;
}
template <class T> std::pair<T, std::pair<T, T>> extendedEuclidInternal(T a, T b) {
T old_x = 1;
T old_y = 0;
T x = 0;
T y = 1;
while (true) {
T q = a / b;
T t = a - b * q;
if (t == 0) {
break;
}
a = b;
b = t;
t = x; x = old_x - x * q; old_x = t;
t = y; y = old_y - y * q; old_y = t;
}
return std::make_pair(b, std::make_pair(x, y));
}
// Returns gcd and Bezout's coefficients
template <class T> std::pair<T, std::pair<T, T>> extendedEuclid(T a, T b) {
if (a > b) {
if (b == 0) return std::make_pair(a, std::make_pair(1, 0));
return extendedEuclidInternal(a, b);
}
else {
if (a == 0) return std::make_pair(b, std::make_pair(0, 1));
auto p = extendedEuclidInternal(b, a);
std::swap(p.second.first, p.second.second);
return p;
}
}
template <class T> T invMod(T a, T m) {
auto p = extendedEuclid(a, m);
assert(p.first == 1);
return normalizeMod(p.second.first, m);
}
int CombinationsWithReplacement(int n, int k) {
int res = 1;
for (long long i = n; i < n + k; ++i) {
res = res * i % Modulus;
}
int denom = 1;
for (long long i = k; i > 0; --i) {
denom = denom * i % Modulus;
}
res = res * (long long)invMod(denom, Modulus) % Modulus;
return res;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//
// Only the above is needed for the Codility challenge. Below is to run on the command line.
//
// Compile with: gcc -std=gnu++14 -lc++ -lstdc++ array_recovery.cpp
//
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
#include <string.h>
// Usage: 0 1 2,3, 4 M
// Last arg is M, the max value for an input.
// Remaining args are B (the output of the encoder) separated by commas and/or spaces
// Parentheses and brackets are ignored, so you can use the same input form as Codility's tests: ([1,2,3], M)
int main(int argc, char* argv[]) {
int Max;
std::vector<int> B;
const char* delim = " ,[]()";
if (argc < 2 ) {
printf("Usage: %s M 0 1 2,3, 4... \n", argv[0]);
return 1;
}
for (int i = 1; i < argc; i++) {
char* parse;
parse = strtok(argv[i], delim);
while (parse != NULL)
{
B.push_back(atoi(parse));
parse = strtok (NULL, delim);
}
}
Max = B.back();
B.pop_back();
printf("%d\n", solution(B, Max));
return 0;
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//
// Only the above is needed for the Codility challenge. Below is to run on the command line.
//
// Compile with: gcc -std=gnu++14 -lc++ -lstdc++ array_recovery.cpp
//
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
#include <string.h>
// Usage: M 0 1 2,3, 4
// first arg is M, the max value for an input.
// remaining args are B (the output of the encoder) separated by commas and/or spaces
int main(int argc, char* argv[]) {
int Max;
std::vector<int> B;
const char* delim = " ,";
if (argc < 3 ) {
printf("Usage: %s M 0 1 2,3, 4... \n", argv[0]);
return 1;
}
Max = atoi(argv[1]);
for (int i = 2; i < argc; i++) {
char* parse;
parse = strtok(argv[i], delim);
while (parse != NULL)
{
B.push_back(atoi(parse));
parse = strtok (NULL, delim);
}
}
printf("%d\n", solution(B, Max));
return 0;
}
Let's see an example:
Max = 5
Array is
0 1 3 0 1 1 3
1
1 2..5
1 3 4..5
1 3 4..5 1
1 3 4..5 1 2..5
1 3 4..5 1 2..5 >=..2 (sorry, for a cumbersome way of writing)
1 3 4..5 1 3..5 >=..3 4..5
Now count:
1 1 2 1 3 2 which amounts to 12 total.
Here's an idea. One known method to construct the output is to use a stack. We pop it while the element is greater or equal, then output the smaller element if it exists, then push the greater element onto the stack. Now what if we attempted to do this backwards from the output?
First we'll demonstrate the stack method using the c∅dility example.
[2, 5, 3, 7, 9, 6]
2: output 0, stack [2]
5: output 2, stack [2,5]
3: pop 5, output, 2, stack [2,3]
7: output 3, stack [2,3,7]
... etc.
Final output: [0, 2, 2, 3, 7, 3]
Now let's try reconstruction! We'll use stack both as the imaginary stack and as the reconstituted input:
(Input: [2, 5, 3, 7, 9, 6])
Output: [0, 2, 2, 3, 7, 3]
* Something >3 that reached 3 in the stack
stack = [3, 3 < *]
* Something >7 that reached 7 in the stack
but both of those would've popped before 3
stack = [3, 7, 7 < x, 3 < * <= x]
* Something >3, 7 qualifies
stack = [3, 7, 7 < x, 3 < * <= x]
* Something >2, 3 qualifies
stack = [2, 3, 7, 7 < x, 3 < * <= x]
* Something >2 and >=3 since 3 reached 2
stack = [2, 2 < *, 3, 7, 7 < x, 3 < * <= x]
Let's attempt your examples:
Example 1:
[0, 0, 0, 2, 3, 4]
* Something >4
stack = [4, 4 < *]
* Something >3, 4 qualifies
stack = [3, 4, 4 < *]
* Something >2, 3 qualifies
stack = [2, 3, 4, 4 < *]
* The rest is non-increasing with lowerbound 2
stack = [y >= x, x >= 2, 2, 3, 4, >4]
Example 2:
[0, 0, 0, 4]
* Something >4
stack [4, 4 < *]
* Non-increasing
stack = [z >= y, y >= 4, 4, 4 < *]
Calculating the number of combinations is achieved by multiplying together the possibilities for all the sections. A section is either a bounded single cell; or a bound, non-increasing subarray of one or more cells. To calculate the latter we use the multi-choose binomial, (n + k - 1) choose (k - 1). Consider that we can express the differences between the cells of a bound, non-increasing sequence of 3 cells as:
(ub - cell_3) + (cell_3 - cell_2) + (cell_2 - cell_1) + (cell_1 - lb) = ub - lb
Then the number of ways to distribute ub - lb into (x + 1) cells is
(n + k - 1) choose (k - 1)
or
(ub - lb + x) choose x
For example, the number of non-increasing sequences between
(3,4) in two cells is (4 - 3 + 2) choose 2 = 3: [3,3] [4,3] [4,4]
And the number of non-increasing sequences between
(3,4) in three cells is (4 - 3 + 3) choose 3 = 4: [3,3,3] [4,3,3] [4,4,3] [4,4,4]
(Explanation attributed to Brian M. Scott.)
Rough JavaScript sketch (the code is unreliable; it's only meant to illustrate the encoding. The encoder lists [lower_bound, upper_bound], or a non-increasing sequence as [non_inc, length, lower_bound, upper_bound]):
function f(A, M){
console.log(JSON.stringify(A), M);
let i = A.length - 1;
let last = A[i];
let s = [[last,last]];
if (A[i-1] == last){
let d = 1;
s.splice(1,0,['non_inc',d++,last,M]);
while (i > 0 && A[i-1] == last){
s.splice(1,0,['non_inc',d++,last,M]);
i--
}
} else {
s.push([last+1,M]);
i--;
}
if (i == 0)
s.splice(0,1);
for (; i>0; i--){
let x = A[i];
if (x < s[0][0])
s = [[x,x]].concat(s);
if (x > s[0][0]){
let [l, _l] = s[0];
let [lb, ub] = s[1];
s[0] = [x+1, M];
s[1] = [lb, x];
s = [[l,_l], [x,x]].concat(s);
}
if (x == s[0][0]){
let [l,_l] = s[0];
let [lb, ub] = s[1];
let d = 1;
s.splice(0,1);
while (i > 0 && A[i-1] == x){
s =
[['non_inc', d++, lb, M]].concat(s);
i--;
}
if (i > 0)
s = [[l,_l]].concat(s);
}
}
// dirty fix
if (s[0][0] == 0)
s.splice(0,1);
return s;
}
var a = [2, 5, 3, 7, 9, 6]
var b = [0, 2, 2, 3, 7, 3]
console.log(JSON.stringify(a));
console.log(JSON.stringify(f(b,10)));
b = [0,0,0,4]
console.log(JSON.stringify(f(b,10)));
b = [0,2,0,0,0,4]
console.log(JSON.stringify(f(b,10)));
b = [0,0,0,2,3,4]
console.log(JSON.stringify(f(b,10)));
b = [0,2,2]
console.log(JSON.stringify(f(b,4)));
b = [0,3,5,6]
console.log(JSON.stringify(f(b,10)));
b = [0,0,3,0]
console.log(JSON.stringify(f(b,10)));
I need to sort a set of n Numbers into "bins" that have a certain range.
Here is an example of what I want to do:
Array = [1, 5, 6, 4, 2, 10]
binA has a range from 1 to 3 -> 1,2 gets sorted in
binB has a range from 4 to 6 -> 4,5,6 gets sorted in
binC has a range from 7 to 10 -> 10 gets sorted in
binD has a range from 11 to 12 -> nothing gets sorted in
The bin ranges are defined previously by me, the order of the numbers being put into certain bins do not matter.
If your bins are always of same size (here 3), you can adress the bin by function (Pseudocode):
bin = new Array (binA, binB, binC, binD)
def bin (i: Int) : Int = bin((i+1)/3).add (i)
You can create a new class for your Bins (unrelated to above code!):
case class Bin (val lo: Int, val hi: Int) {
override def toString : String = s"from $lo to $hi ${lb} ${if (next == None) "-|" else "\n" + next.get.toString} "
val lb = collection.mutable.ListBuffer[Int] ()
var next : Option[Bin] = None
def add (i: Int) {
if (i < hi)
lb += i
else {
if (next == None) {
val bin = Bin (hi+1, hi + 4)
bin.add (i)
next = Some (bin)
}
else next.map (bin => bin.add (i))
}
}
}
val sample = List (1, 5, 6, 4, 2, 10)
val binA = Bin (1, 3)
sample.map (binA.add (_))
println (binA)
Result:
from 1 to 3 ListBuffer(1, 2)
from 4 to 7 ListBuffer(5, 6, 4)
from 8 to 11 ListBuffer(10) -|
For different Sizes of Bins, like characters in big encyclopedias (A-B, C-D, E, F-H, ...) you would, of course, need a different way of initialization, but each bin could pass values to the next bin, if it exceeds his bounds.
I wonder what would be the correct way to replace (overwriting) a part of a given std::vector "input" by another, smaller std::vector?
I do neet to keep the rest of the original vector unchanged.
Also I do not need to bother what has been in the original vector and
I don't need to keep the smaller vector afterwards anymore.
Say I have this:
std::vector<int> input = { 0, 0, 1, 1, 2, 22, 3, 33, 99 };
std::vector<int> a = { 1, 2, 3 };
std::vector<int> b = { 4, 5, 6, 7, 8 };
And I want to achieve that:
input = { 1, 2, 3, 4, 5, 6, 7, 8, 99}
What is the right way to do it? I thought of something like
input.replace(input.beginn(), input.beginn()+a.size(), a);
// intermediate input would look like that: input = { 1, 2, 3, 1, 2, 22, 3, 33, 99 };
input.replace(input.beginn()+a.size(), input.beginn()+a.size()+b.size(), b);
There should be a standard way to do it, shouldn't it?
My thoughts on this so far are the following:
I can not use std::vector::assign for it destroys all elements of input
std::vector::push_back would not replace but enlarge the input --> not what I want
std::vector::insert also creates new elements and enlages the input vector but I know for sure that the vectors a.size() + b.size() <= input.size()
std::vector::swap would not work since there is some content of input that needs to remain there ( in the example the last element) also it would not work to add b that way
std::vector::emplace also increases the input.size -> seems wrong as well
Also I would prefer if the solution would not waste performance by unnecessary clears or writing back values into the vectors a or b. My vectors will be very large for real and this is about performance in the end.
Any competent help would be appreciated very much.
You seem to be after std::copy(). This is how you would use it in your example (live demo on Coliru):
#include <algorithm> // Necessary for `std::copy`...
// ...
std::vector<int> input = { 0, 0, 1, 1, 2, 22, 3, 33, 99 };
std::vector<int> a = { 1, 2, 3 };
std::vector<int> b = { 4, 5, 6, 7, 8 };
std::copy(std::begin(a), std::end(a), std::begin(input));
std::copy(std::begin(b), std::end(b), std::begin(input) + a.size());
As Zyx2000 notes in the comments, in this case you can also use the iterator returned by the first call to std::copy() as the insertion point for the next copy:
auto last = std::copy(std::begin(a), std::end(a), std::begin(input));
std::copy(std::begin(b), std::end(b), last);
This way, random-access iterators are no longer required - that was the case when we had the expression std::begin(input) + a.size().
The first two arguments to std::copy() denote the source range of elements you want to copy. The third argument is an iterator to the first element you want to overwrite in the destination container.
When using std::copy(), make sure that the destination container is large enough to accommodate the number of elements you intend to copy.
Also, the source and the target range should not interleave.
Try this:
#include <iostream>
#include <vector>
#include <algorithm>
int main() {
std::vector<int> input = { 0, 0, 1, 1, 2, 22, 3, 33, 99 };
std::vector<int> a = { 1, 2, 3 };
std::vector<int> b = { 4, 5, 6, 7, 8 };
std::set_union( a.begin(), a.end(), b.begin(), b.end(), input.begin() );
for ( std::vector<int>::const_iterator iter = input.begin();
iter != input.end();
++iter )
{
std::cout << *iter << " ";
}
return 0;
}
It outputs:
1 2 3 4 5 6 7 8 99
This is a part of a program that analyzes the odds of poker, specifically Texas Hold'em. I have a program I'm happy with, but it needs some small optimizations to be perfect.
I use this type (among others, of course):
type
T7Cards = array[0..6] of integer;
There are two things about this array that may be important when deciding how to sort it:
Every item is a value from 0 to 51. No other values are possible.
There are no duplicates. Never.
With this information, what is the absolutely fastest way to sort this array? I use Delphi, so pascal code would be the best, but I can read C and pseudo, albeit a bit more slowly :-)
At the moment I use quicksort, but the funny thing is that this is almost no faster than bubblesort! Possible because of the small number of items. The sorting counts for almost 50% of the total running time of the method.
EDIT:
Mason Wheeler asked why it's necessary to optimize. One reason is that the method will be called 2118760 times.
Basic poker information: All players are dealt two cards (the pocket) and then five cards are dealt to the table (the 3 first are called the flop, the next is the turn and the last is the river. Each player picks the five best cards to make up their hand)
If I have two cards in the pocket, P1 and P2, I will use the following loops to generate all possible combinations:
for C1 := 0 to 51-4 do
if (C1<>P1) and (C1<>P2) then
for C2 := C1+1 to 51-3 do
if (C2<>P1) and (C2<>P2) then
for C3 := C2+1 to 51-2 do
if (C3<>P1) and (C3<>P2) then
for C4 := C3+1 to 51-1 do
if (C4<>P1) and (C4<>P2) then
for C5 := C4+1 to 51 do
if (C5<>P1) and (C5<>P2) then
begin
//This code will be executed 2 118 760 times
inc(ComboCounter[GetComboFromCards([P1,P2,C1,C2,C3,C4,C5])]);
end;
As I write this I notice one thing more: The last five elements of the array will always be sorted, so it's just a question of putting the first two elements in the right position in the array. That should simplify matters a bit.
So, the new question is: What is the fastest possible way to sort an array of 7 integers when the last 5 elements are already sorted. I believe this could be solved with a couple (?) of if's and swaps :-)
For a very small set, insertion sort can usually beat quicksort because it has very low overhead.
WRT your edit, if you're already mostly in sort order (last 5 elements are already sorted), insertion sort is definitely the way to go. In an almost-sorted set of data, it'll beat quicksort every time, even for large sets. (Especially for large sets! This is insertion sort's best-case scenario and quicksort's worst case.)
Don't know how you are implementing this, but what you could do is have an array of 52 instead of 7, and just insert the card in its slot directly when you get it since there can never be duplicates, that way you never have to sort the array. This might be faster depending on how its used.
I don't know that much about Texas Hold'em: Does it matter what suit P1 and P2 are, or does it only matter if they are of the same suit or not? If only suit(P1)==suit(P2) matters, then you could separate the two cases, you have only 13x12/2 different possibilities for P1/P2, and you can easily precalculate a table for the two cases.
Otherwise, I would suggest something like this:
(* C1 < C2 < P1 *)
for C1:=0 to P1-2 do
for C2:=C1+1 to P1-1 do
Cards[0] = C1;
Cards[1] = C2;
Cards[2] = P1;
(* generate C3...C7 *)
(* C1 < P1 < C2 *)
for C1:=0 to P1-1 do
for C2:=P1+1 to 51 do
Cards[0] = C1;
Cards[1] = P1;
Cards[2] = C2;
(* generate C3...C7 *)
(* P1 < C1 < C2 *)
for C1:=P1+1 to 51 do
for C2:=C1+1 to 51 do
Cards[0] = P1;
Cards[1] = C1;
Cards[2] = C2;
(* generate C3...C7 *)
(this is just a demonstration for one card P1, you would have to expand that for P2, but I think that's straightforward. Although it'll be a lot of typing...)
That way, the sorting doesn't take any time at all. The generated permutations are already ordered.
There are only 5040 permutations of 7 elements. You can programmaticaly generate a program that finds the one represented by your input in a minimal number of comparisons. It will be a big tree of if-then-else instructions, each comparing a fixed pair of nodes, for example if (a[3]<=a[6]).
The tricky part is deciding which 2 elements to compare in a particular internal node. For this, you have to take into account the results of comparisons in the ancestor nodes from root to the particular node (for example a[0]<=a[1], not a[2]<=a[7], a[2]<=a[5]) and the set of possible permutations that satisfy the comparisons. Compare the pair of elements that splits the set into as equal parts as possible (minimize the size of the larger part).
Once you have the permutation, it is trivial to sort it in a minimal set of swaps.
Since the last 5 items are already sorted, the code can be written just to reposition the first 2 items. Since you're using Pascal, I've written and tested a sorting algorithm that can execute 2,118,760 times in about 62 milliseconds.
procedure SortT7Cards(var Cards: T7Cards);
const
CardsLength = Length(Cards);
var
I, J, V: Integer;
V1, V2: Integer;
begin
// Last 5 items will always be sorted, so we want to place the first two into
// the right location.
V1 := Cards[0];
V2 := Cards[1];
if V2 < V1 then
begin
I := V1;
V1 := V2;
V2 := I;
end;
J := 0;
I := 2;
while I < CardsLength do
begin
V := Cards[I];
if V1 < V then
begin
Cards[J] := V1;
Inc(J);
Break;
end;
Cards[J] := V;
Inc(J);
Inc(I);
end;
while I < CardsLength do
begin
V := Cards[I];
if V2 < V then
begin
Cards[J] := V2;
Break;
end;
Cards[J] := V;
Inc(J);
Inc(I);
end;
if J = (CardsLength - 2) then
begin
Cards[J] := V1;
Cards[J + 1] := V2;
end
else if J = (CardsLength - 1) then
begin
Cards[J] := V2;
end;
end;
Use min-sort. Search for minimal and maximal element at once and place them into resultant array. Repeat three times. (EDIT: No, I won't try to measure the speed theoretically :_))
var
cards,result: array[0..6] of integer;
i,min,max: integer;
begin
n=0;
while (n<3) do begin
min:=-1;
max:=52;
for i from 0 to 6 do begin
if cards[i]<min then min:=cards[i]
else if cards[i]>max then max:=cards[i]
end
result[n]:=min;
result[6-n]:=max;
inc(n);
end
for i from 0 to 6 do
if (cards[i]<52) and (cards[i]>=0) then begin
result[3] := cards[i];
break;
end
{ Result is sorted here! }
end
This is the fastest method: since the 5-card list is already sorted, sort the two-card list (a compare & swap), and then merge the two lists, which is O(k * (5+2). In this case (k) will normally be 5: the loop test(1), the compare(2), the copy(3), the input-list increment(4) and the output list increment(5). That's 35 + 2.5. Throw in loop initialization and you get 41.5 statements, total.
You could also unroll the loops which would save you maybe 8 statements or execution, but make the whole routine about 4-5 times longer which may mess with your instruction cache hit ratio.
Given P(0 to 2), C(0 to 5) and copying to H(0 to 6)
with C() already sorted (ascending):
If P(0) > P(1) Then
// Swap:
T = P(0)
P(0) = P(1)
P(1) = T
// 1stmt + (3stmt * 50%) = 2.5stmt
End
P(2), C(5) = 53 \\ Note these are end-of-list flags
k = 0 \\ P() index
J = 0 \\ H() index
i = 0 \\ C() index
// 4 stmt
Do While (j) < 7
If P(k) < C(I) then
H(j) = P(k)
k = k+1
Else
H(j) = C(i)
j = j+1
End if
j = j+1
// 5stmt * 7loops = 35stmt
Loop
And note that this is faster than the other algorithm that would be "fastest" if you had to truly sort all 7 cards: use a bit-mask(52) to map & bit-set all 7 cards into that range of all possible 52 cards (the bit-mask), and then scan the bit-mask in order looking for the 7 bits that are set. That takes 60-120 statements at best (but is still faster than any other sorting approach).
For seven numbers, the most efficient algorithm that exists with regards to the number of comparisons is Ford-Johnson's. In fact, wikipedia references a paper, easily found on google, that claims Ford-Johnson's the best for up to 47 numbers. Unfortunately, references to Ford-Johnson's aren't all that easy to found, and the algorithm uses some complex data structures.
It appears on The Art Of Computer Programming, Volume 3, by Donald Knuth, if you have access to that book.
There's a paper which describes FJ and a more memory efficient version here.
At any rate, because of the memory overhead of that algorithm, I doubt it would be worth your while for integers, as the cost of comparing two integers is rather cheap compared to the cost of allocating memory and manipulating pointers.
Now, you mentioned that 5 cards are already sorted, and you just need to insert two. You can do this with insertion sort most efficiently like this:
Order the two cards so that P1 > P2
Insert P1 going from the high end to the low end
(list) Insert P2 going from after P1 to the low end
(array) Insert P2 going from the low end to the high end
How you do that will depend on the data structure. With an array you'll be swapping each element, so place P1 at 1st, P2 and 7th (ordered high to low), and then swap P1 up, and then P2 down. With a list, you just need to fix the pointers as appropriate.
However once more, because of the particularity of your code, it really is best if you follow nikie suggestion and just generate the for loops apropriately for every variation in which P1 and P2 can appear in the list.
For example, sort P1 and P2 so that P1 < P2. Let's make Po1 and Po2 the position from 0 to 6, of P1 and P2 on the list. Then do this:
Loop Po1 from 0 to 5
Loop Po2 from Po1 + 1 to 6
If (Po2 == 1) C1start := P2 + 1; C1end := 51 - 4
If (Po1 == 0 && Po2 == 2) C1start := P1+1; C1end := P2 - 1
If (Po1 == 0 && Po2 > 2) C1start := P1+1; C1end := 51 - 5
If (Po1 > 0) C1start := 0; C1end := 51 - 6
for C1 := C1start to C1end
// Repeat logic to compute C2start and C2end
// C2 can begin at C1+1, P1+1 or P2+1
// C2 can finish at P1-1, P2-1, 51 - 3, 51 - 4 or 51 -5
etc
You then call a function passing Po1, Po2, P1, P2, C1, C2, C3, C4, C5, and have this function return all possible permutations based on Po1 and Po2 (that's 36 combinations).
Personally, I think that's the fastest you can get. You completely avoid having to order anything, because the data will be pre-ordered. You incur in some comparisons anyway to compute the starts and ends, but their cost is minimized as most of them will be on the outermost loops, so they won't be repeated much. And they can even be more optimized at the cost of more code duplication.
For 7 elements, there are only few options. You can easily write a generator that produces method to sort all possible combinations of 7 elements. Something like this method for 3 elements:
if a[1] < a[2] {
if a[2] < a[3] {
// nothing to do, a[1] < a[2] < a[3]
} else {
if a[1] < a[3] {
// correct order should be a[1], a[3], a[2]
swap a[2], a[3]
} else {
// correct order should be a[3], a[1], a[2]
swap a[2], a[3]
swap a[1], a[3]
}
}
} else {
// here we know that a[1] >= a[2]
...
}
Of course method for 7 elements will be bigger, but it's not that hard to generate.
The code below is close to optimal. It could be made better by composing a list to be traversed while making the tree, but I'm out of time right now. Cheers!
object Sort7 {
def left(i: Int) = i * 4
def right(i: Int) = i * 4 + 1
def up(i: Int) = i * 4 + 2
def value(i: Int) = i * 4 + 3
val a = new Array[Int](7 * 4)
def reset = {
0 until 7 foreach {
i => {
a(left(i)) = -1
a(right(i)) = -1
a(up(i)) = -1
a(value(i)) = scala.util.Random.nextInt(52)
}
}
}
def sortN(i : Int) {
var index = 0
def getNext = if (a(value(i)) < a(value(index))) left(index) else right(index)
var next = getNext
while(a(next) != -1) {
index = a(next)
next = getNext
}
a(next) = i
a(up(i)) = index
}
def sort = 1 until 7 foreach (sortN(_))
def print {
traverse(0)
def traverse(i: Int): Unit = {
if (i != -1) {
traverse(a(left(i)))
println(a(value(i)))
traverse(a(right(i)))
}
}
}
}
In pseudo code:
int64 temp = 0;
int index, bit_position;
for index := 0 to 6 do
temp |= 1 << cards[index];
for index := 0 to 6 do
begin
bit_position = find_first_set(temp);
temp &= ~(1 << bit_position);
cards[index] = bit_position;
end;
It's an application of bucket sort, which should generally be faster than any of the comparison sorts that were suggested.
Note: The second part could also be implemented by iterating over bits in linear time, but in practice it may not be faster:
index = 0;
for bit_position := 0 to 51 do
begin
if (temp & (1 << bit_position)) > 0 then
begin
cards[index] = bit_position;
index++;
end;
end;
Assuming that you need an array of cards at the end of it.
Map the original cards to bits in a 64 bit integer ( or any integer with >= 52 bits ).
If during the initial mapping the array is sorted, don't change it.
Partition the integer into nibbles - each will correspond to values 0x0 to 0xf.
Use the nibbles as indices to corresponding sorted sub-arrays. You'll need 13 sets of 16 sub-arrays ( or just 16 sub-arrays and use a second indirection, or do the bit ops rather than looking the answer up; what is faster will vary by platform ).
Concatenate the non-empty sub-arrays into the final array.
You could use larger than nibbles if you want; bytes would give 7 sets of 256 arrays and make it more likely that the non-empty arrays require concatenating.
This assumes that branches are expensive and cached array accesses cheap.
#include <stdio.h>
#include <stdbool.h>
#include <stdint.h>
// for general case of 7 from 52, rather than assuming last 5 sorted
uint32_t card_masks[16][5] = {
{ 0, 0, 0, 0, 0 },
{ 1, 0, 0, 0, 0 },
{ 2, 0, 0, 0, 0 },
{ 1, 2, 0, 0, 0 },
{ 3, 0, 0, 0, 0 },
{ 1, 3, 0, 0, 0 },
{ 2, 3, 0, 0, 0 },
{ 1, 2, 3, 0, 0 },
{ 4, 0, 0, 0, 0 },
{ 1, 4, 0, 0, 0 },
{ 2, 4, 0, 0, 0 },
{ 1, 2, 4, 0, 0 },
{ 3, 4, 0, 0, 0 },
{ 1, 3, 4, 0, 0 },
{ 2, 3, 4, 0, 0 },
{ 1, 2, 3, 4, 0 },
};
void sort7 ( uint32_t* cards) {
uint64_t bitset = ( ( 1LL << cards[ 0 ] ) | ( 1LL << cards[ 1LL ] ) | ( 1LL << cards[ 2 ] ) | ( 1LL << cards[ 3 ] ) | ( 1LL << cards[ 4 ] ) | ( 1LL << cards[ 5 ] ) | ( 1LL << cards[ 6 ] ) ) >> 1;
uint32_t* p = cards;
uint32_t base = 0;
do {
uint32_t* card_mask = card_masks[ bitset & 0xf ];
// you might remove this test somehow, as well as unrolling the outer loop
// having separate arrays for each nibble would save 7 additions and the increment of base
while ( *card_mask )
*(p++) = base + *(card_mask++);
bitset >>= 4;
base += 4;
} while ( bitset );
}
void print_cards ( uint32_t* cards ) {
printf ( "[ %d %d %d %d %d %d %d ]\n", cards[0], cards[1], cards[2], cards[3], cards[4], cards[5], cards[6] );
}
int main ( void ) {
uint32_t cards[7] = { 3, 9, 23, 17, 2, 42, 52 };
print_cards ( cards );
sort7 ( cards );
print_cards ( cards );
return 0;
}
Use a sorting network, like in this C++ code:
template<class T>
inline void sort7(T data) {
#define SORT2(x,y) {if(data##x>data##y)std::swap(data##x,data##y);}
//DD = Define Data, create a local copy of the data to aid the optimizer.
#define DD1(a) register auto data##a=*(data+a);
#define DD2(a,b) register auto data##a=*(data+a);register auto data##b=*(data+b);
//CB = Copy Back
#define CB1(a) *(data+a)=data##a;
#define CB2(a,b) *(data+a)=data##a;*(data+b)=data##b;
DD2(1,2) SORT2(1,2)
DD2(3,4) SORT2(3,4)
DD2(5,6) SORT2(5,6)
DD1(0) SORT2(0,2)
SORT2(3,5)
SORT2(4,6)
SORT2(0,1)
SORT2(4,5)
SORT2(2,6) CB1(6)
SORT2(0,4)
SORT2(1,5)
SORT2(0,3) CB1(0)
SORT2(2,5) CB1(5)
SORT2(1,3) CB1(1)
SORT2(2,4) CB1(4)
SORT2(2,3) CB2(2,3)
#undef CB1
#undef CB2
#undef DD1
#undef DD2
#undef SORT2
}
Use the function above if you want to pass it an iterator or a pointer and use the function below if you want to pass it the seven arguments one by one. BTW, using templates allows compilers to generate really optimized code so don't get ride of the template<> unless you want C code (or some other language's code).
template<class T>
inline void sort7(T& e0, T& e1, T& e2, T& e3, T& e4, T& e5, T& e6) {
#define SORT2(x,y) {if(data##x>data##y)std::swap(data##x,data##y);}
#define DD1(a) register auto data##a=e##a;
#define DD2(a,b) register auto data##a=e##a;register auto data##b=e##b;
#define CB1(a) e##a=data##a;
#define CB2(a,b) e##a=data##a;e##b=data##b;
DD2(1,2) SORT2(1,2)
DD2(3,4) SORT2(3,4)
DD2(5,6) SORT2(5,6)
DD1(0) SORT2(0,2)
SORT2(3,5)
SORT2(4,6)
SORT2(0,1)
SORT2(4,5)
SORT2(2,6) CB1(6)
SORT2(0,4)
SORT2(1,5)
SORT2(0,3) CB1(0)
SORT2(2,5) CB1(5)
SORT2(1,3) CB1(1)
SORT2(2,4) CB1(4)
SORT2(2,3) CB2(2,3)
#undef CB1
#undef CB2
#undef DD1
#undef DD2
#undef SORT2
}
Take a look at this:
http://en.wikipedia.org/wiki/Sorting_algorithm
You would need to pick one that will have a stable worst case cost...
Another option could be to keep the array sorted the whole time, so an addition of a card would keep the array sorted automatically, that way you could skip to sorting...
What JRL is referring to is a bucket sort. Since you have a finite discrete set of possible values, you can declare 52 buckets and just drop each element in a bucket in O(1) time. Hence bucket sort is O(n). Without the guarantee of a finite number of different elements, the fastest theoretical sort is O(n log n) which things like merge sort an quick sort are. It's just a balance of best and worst case scenarios then.
But long answer short, use bucket sort.
If you like the above mentioned suggestion to keep a 52 element array which always keeps your array sorted, then may be you could keep another list of 7 elements which would reference the 7 valid elements in the 52 element array. This way we can even avoid parsing the 52 element array.
I guess for this to be really efficient, we would need to have a linked list type of structure which be supports operations: InsertAtPosition() and DeleteAtPosition() and be efficient at that.
There are a lot of loops in the answers. Given his speed requirement and the tiny size of the data set I would not do ANY loops.
I have not tried it but I suspect the best answer is a fully unrolled bubble sort. It would also probably gain a fair amount of advantage from being done in assembly.
I wonder if this is the right approach, though. How are you going to analyze a 7 card hand?? I think you're going to end up converting it to some other representation for analysis anyway. Would not a 4x13 array be a more useful representation? (And it would render the sorting issue moot, anyway.)
Considering that last 5 elements are always sorted:
for i := 0 to 1 do begin
j := i;
x := array[j];
while (j+1 <= 6) and (array[j+1] < x) do begin
array[j] := array[j+1];
inc(j);
end;
array[j] := X;
end;
bubble sort is your friend. Other sorts have too many overhead codes and not suitable for small number of elements
Cheers
Here is your basic O(n) sort. I'm not sure how it compares to the others. It uses unrolled loops.
char card[7]; // the original table of 7 numbers in range 0..51
char table[52]; // workspace
// clear the workspace
memset(table, 0, sizeof(table));
// set the 7 bits corresponding to the 7 cards
table[card[0]] = 1;
table[card[1]] = 1;
...
table[card[6]] = 1;
// read the cards back out
int j = 0;
if (table[0]) card[j++] = 0;
if (table[1]) card[j++] = 1;
...
if (table[51]) card[j++] = 51;
If you are looking for a very low overhead, optimal sort, you should create a sorting network. You can generate the code for a 7 integer network using the Bose-Nelson algorithm.
This would guarentee a fixed number of compares and an equal number of swaps in the worst case.
The generated code is ugly, but it is optimal.
Your data is in a sorted array and I'll assume you swap the new two if needed so also sorted, so
a. if you want to keep it in place then use a form of insertion sort;
b. if you want to have it the result in another array do a merging by copying.
With the small numbers, binary chop is overkill, and ternary chop is appropriate anyway:
One new card will mostly like split into two and three, viz. 2+3 or 3+2,
two cards into singles and pairs, e.g. 2+1+2.
So the most time-space efficient approach to placing the smaller new card is to compare with a[1] (viz. skip a[0]) and then search left or right to find the card it should displace, then swap and move right (shifting rather than bubbling), comparing with the larger new card till you find where it goes. After this you'll be shifting forward by twos (two cards have been inserted).
The variables holding the new cards (and swaps) should be registers.
The look up approach would be faster but use more memory.
Does anyone know a simple algorithm to check if a Sudoku-Configuration is valid? The simplest algorithm I came up with is (for a board of size n) in Pseudocode
for each row
for each number k in 1..n
if k is not in the row (using another for-loop)
return not-a-solution
..do the same for each column
But I'm quite sure there must be a better (in the sense of more elegant) solution. Efficiency is quite unimportant.
You need to check for all the constraints of Sudoku :
check the sum on each row
check the sum on each column
check for sum on each box
check for duplicate numbers on each row
check for duplicate numbers on each column
check for duplicate numbers on each box
that's 6 checks altogether.. using a brute force approach.
Some sort of mathematical optimization can be used if you know the size of the board (ie 3x3 or 9x9)
Edit: explanation for the sum constraint: Checking for the sum first (and stoping if the sum is not 45) is much faster (and simpler) than checking for duplicates. It provides an easy way of discarding a wrong solution.
Peter Norvig has a great article on solving sudoku puzzles (with python),
https://norvig.com/sudoku.html
Maybe it's too much for what you want to do, but it's a great read anyway
Check each row, column and box such that it contains the numbers 1-9 each, with no duplicates. Most answers here already discuss this.
But how to do that efficiently? Answer: Use a loop like
result=0;
for each entry:
result |= 1<<(value-1)
return (result==511);
Each number will set one bit of the result. If all 9 numbers are unique, the lowest 9
bits will be set.
So the "check for duplicates" test is just a check that all 9 bits are set, which is the same as testing result==511.
You need to do 27 of these checks.. one for each row, column, and box.
Just a thought: don't you need to also check the numbers in each 3x3 square?
I'm trying to figure out if it is possible to have the rows and columns conditions satisfied without having a correct sudoku
This is my solution in Python, I'm glad to see it's the shortest one yet :)
The code:
def check(sud):
zippedsud = zip(*sud)
boxedsud=[]
for li,line in enumerate(sud):
for box in range(3):
if not li % 3: boxedsud.append([]) # build a new box every 3 lines
boxedsud[box + li/3*3].extend(line[box*3:box*3+3])
for li in range(9):
if [x for x in [set(sud[li]), set(zippedsud[li]), set(boxedsud[li])] if x != set(range(1,10))]:
return False
return True
And the execution:
sudoku=[
[7, 5, 1, 8, 4, 3, 9, 2, 6],
[8, 9, 3, 6, 2, 5, 1, 7, 4],
[6, 4, 2, 1, 7, 9, 5, 8, 3],
[4, 2, 5, 3, 1, 6, 7, 9, 8],
[1, 7, 6, 9, 8, 2, 3, 4, 5],
[9, 3, 8, 7, 5, 4, 6, 1, 2],
[3, 6, 4, 2, 9, 7, 8, 5, 1],
[2, 8, 9, 5, 3, 1, 4, 6, 7],
[5, 1, 7, 4, 6, 8, 2, 3, 9]]
print check(sudoku)
Create an array of booleans for every row, column, and square. The array's index represents the value that got placed into that row, column, or square. In other words, if you add a 5 to the second row, first column, you would set rows[2][5] to true, along with columns[1][5] and squares[4][5], to indicate that the row, column, and square now have a 5 value.
Regardless of how your original board is being represented, this can be a simple and very fast way to check it for completeness and correctness. Simply take the numbers in the order that they appear on the board, and begin building this data structure. As you place numbers in the board, it becomes a O(1) operation to determine whether any values are being duplicated in a given row, column, or square. (You'll also want to check that each value is a legitimate number: if they give you a blank or a too-high number, you know that the board is not complete.) When you get to the end of the board, you'll know that all the values are correct, and there is no more checking required.
Someone also pointed out that you can use any form of Set to do this. Arrays arranged in this manner are just a particularly lightweight and performant form of a Set that works well for a small, consecutive, fixed set of numbers. If you know the size of your board, you could also choose to do bit-masking, but that's probably a little overly tedious considering that efficiency isn't that big a deal to you.
Create cell sets, where each set contains 9 cells, and create sets for vertical columns, horizontal rows, and 3x3 squares.
Then for each cell, simply identify the sets it's part of and analyze those.
You could extract all values in a set (row, column, box) into a list, sort it, then compare to '(1, 2, 3, 4, 5, 6, 7, 8, 9)
I did this once for a class project. I used a total of 27 sets to represent each row, column and box. I'd check the numbers as I added them to each set (each placement of a number causes the number to be added to 3 sets, a row, a column, and a box) to make sure the user only entered the digits 1-9. The only way a set could get filled is if it was properly filled with unique digits. If all 27 sets got filled, the puzzle was solved. Setting up the mappings from the user interface to the 27 sets was a bit tedious, but made the rest of the logic a breeze to implement.
It would be very interesting to check if:
when the sum of each row/column/box equals n*(n+1)/2
and the product equals n!
with n = number of rows or columns
this suffices the rules of a sudoku. Because that would allow for an algorithm of O(n^2), summing and multiplying the correct cells.
Looking at n = 9, the sums should be 45, the products 362880.
You would do something like:
for i = 0 to n-1 do
boxsum[i] := 0;
colsum[i] := 0;
rowsum[i] := 0;
boxprod[i] := 1;
colprod[i] := 1;
rowprod[i] := 1;
end;
for i = 0 to n-1 do
for j = 0 to n-1 do
box := (i div n^1/2) + (j div n^1/2)*n^1/2;
boxsum[box] := boxsum[box] + cell[i,j];
boxprod[box] := boxprod[box] * cell[i,j];
colsum[i] := colsum[i] + cell[i,j];
colprod[i] := colprod[i] * cell[i,j];
rowsum[j] := colsum[j] + cell[i,j];
rowprod[j] := colprod[j] * cell[i,j];
end;
end;
for i = 0 to n-1 do
if boxsum[i] <> 45
or colsum[i] <> 45
or rowsum[i] <> 45
or boxprod[i] <> 362880
or colprod[i] <> 362880
or rowprod[i] <> 362880
return false;
Some time ago, I wrote a sudoku checker that checks for duplicate number on each row, duplicate number on each column & duplicate number on each box. I would love it if someone could come up one with like a few lines of Linq code though.
char VerifySudoku(char grid[81])
{
for (char r = 0; r < 9; ++r)
{
unsigned int bigFlags = 0;
for (char c = 0; c < 9; ++c)
{
unsigned short buffer = r/3*3+c/3;
// check horizontally
bitFlags |= 1 << (27-grid[(r<<3)+r+c])
// check vertically
| 1 << (18-grid[(c<<3)+c+r])
// check subgrids
| 1 << (9-grid[(buffer<<3)+buffer+r%3*3+c%3]);
}
if (bitFlags != 0x7ffffff)
return 0; // invalid
}
return 1; // valid
}
if the sum and the multiplication of a row/col equals to the right number 45/362880
First, you would need to make a boolean, "correct". Then, make a for loop, as previously stated. The code for the loop and everything afterwards (in java) is as stated, where field is a 2D array with equal sides, col is another one with the same dimensions, and l is a 1D one:
for(int i=0; i<field.length(); i++){
for(int j=0; j<field[i].length; j++){
if(field[i][j]>9||field[i][j]<1){
checking=false;
break;
}
else{
col[field[i].length()-j][i]=field[i][j];
}
}
}
I don't know the exact algorithim to check the 3x3 boxes, but you should check all the rows in field and col with "/*array name goes here*/[i].contains(1)&&/*array name goes here*/[i].contains(2)" (continues until you reach the length of a row) inside another for loop.
def solution(board):
for i in board:
if sum(i) != 45:
return "Incorrect"
for i in range(9):
temp2 = []
for x in range(9):
temp2.append(board[i][x])
if sum(temp2) != 45:
return "Incorrect"
return "Correct"
board = []
for i in range(9):
inp = raw_input()
temp = [int(i) for i in inp]
board.append(temp)
print solution(board)
Here's a nice readable approach in Python:
from itertools import chain
def valid(puzzle):
def get_block(x,y):
return chain(*[puzzle[i][3*x:3*x+3] for i in range(3*y, 3*y+3)])
rows = [set(row) for row in puzzle]
columns = [set(column) for column in zip(*puzzle)]
blocks = [set(get_block(x,y)) for x in range(0,3) for y in range(0,3)]
return all(map(lambda s: s == set([1,2,3,4,5,6,7,8,9]), rows + columns + blocks))
Each 3x3 square is referred to as a block, and there are 9 of them in a 3x3 grid. It is assumed as the puzzle is input as a list of list, with each inner list being a row.
Let's say int sudoku[0..8,0..8] is the sudoku field.
bool CheckSudoku(int[,] sudoku)
{
int flag = 0;
// Check rows
for(int row = 0; row < 9; row++)
{
flag = 0;
for (int col = 0; col < 9; col++)
{
// edited : check range step (see comments)
if ((sudoku[row, col] < 1)||(sudoku[row, col] > 9))
{
return false;
}
// if n-th bit is set.. but you can use a bool array for readability
if ((flag & (1 << sudoku[row, col])) != 0)
{
return false;
}
// set the n-th bit
flag |= (1 << sudoku[row, col]);
}
}
// Check columns
for(int col= 0; col < 9; col++)
{
flag = 0;
for (int row = 0; row < 9; row++)
{
if ((flag & (1 << sudoku[row, col])) != 0)
{
return false;
}
flag |= (1 << sudoku[row, col]);
}
}
// Check 3x3 boxes
for(int box= 0; box < 9; box++)
{
flag = 0;
for (int ofs = 0; ofs < 9; ofs++)
{
int col = (box % 3) * 3;
int row = ((int)(box / 3)) * 3;
if ((flag & (1 << sudoku[row, col])) != 0)
{
return false;
}
flag |= (1 << sudoku[row, col]);
}
}
return true;
}
Let's assume that your board goes from 1 - n.
We'll create a verification array, fill it and then verify it.
grid [0-(n-1)][0-(n-1)]; //this is the input grid
//each verification takes n^2 bits, so three verifications gives us 3n^2
boolean VArray (3*n*n) //make sure this is initialized to false
for i = 0 to n
for j = 0 to n
/*
each coordinate consists of three parts
row/col/box start pos, index offset, val offset
*/
//to validate rows
VArray( (0) + (j*n) + (grid[i][j]-1) ) = 1
//to validate cols
VArray( (n*n) + (i*n) + (grid[i][j]-1) ) = 1
//to validate boxes
VArray( (2*n*n) + (3*(floor (i/3)*n)+ floor(j/3)*n) + (grid[i][j]-1) ) = 1
next
next
if every array value is true then the solution is correct.
I think that will do the trick, although i'm sure i made a couple of stupid mistakes in there. I might even have missed the boat entirely.
array = [1,2,3,4,5,6,7,8,9]
sudoku = int [][]
puzzle = 9 #9x9
columns = map []
units = map [] # box
unit_l = 3 # box width/height
check_puzzle()
def strike_numbers(line, line_num, columns, units, unit_l):
count = 0
for n in line:
# check which unit we're in
unit = ceil(n / unit_l) + ceil(line_num / unit_l) # this line is wrong - rushed
if units[unit].contains(n): #is n in unit already?
return columns, units, 1
units[unit].add(n)
if columns[count].contains(n): #is n in column already?
return columns, units, 1
columns[count].add(n)
line.remove(n) #remove num from temp row
return columns, units, line.length # was a number not eliminated?
def check_puzzle(columns, sudoku, puzzle, array, units):
for (i=0;i< puzzle;i++):
columns, units, left_over = strike_numbers(sudoku[i], i, columns, units) # iterate through rows
if (left_over > 0): return false
Without thoroughly checking, off the top of my head, this should work (with a bit of debugging) while only looping twice. O(n^2) instead of O(3(n^2))
Here is paper by math professor J.F. Crook: A Pencil-and-Paper Algorithm for Solving Sudoku Puzzles
This paper was published in April 2009 and it got lots of publicity as definite Sudoku solution (check google for "J.F.Crook Sudoku" ).
Besides algorithm, there is also a mathematical proof that algorithm works (professor admitted that he does not find Sudoku very interesting, so he threw some math in paper to make it more fun).
I'd write an interface that has functions that receive the sudoku field and returns true/false if it's a solution.
Then implement the constraints as single validation classes per constraint.
To verify just iterate through all constraint classes and when all pass the sudoku is correct. To speedup put the ones that most likely fail to the front and stop in the first result that points to invalid field.
Pretty generic pattern. ;-)
You can of course enhance this to provide hints which field is presumably wrong and so on.
First constraint, just check if all fields are filled out. (Simple loop)
Second check if all numbers are in each block (nested loops)
Third check for complete rows and columns (almost same procedure as above but different access scheme)
Here is mine in C. Only pass each square once.
int checkSudoku(int board[]) {
int i;
int check[13] = { 0 };
for (i = 0; i < 81; i++) {
if (i % 9 == 0) {
check[9] = 0;
if (i % 27 == 0) {
check[10] = 0;
check[11] = 0;
check[12] = 0;
}
}
if (check[i % 9] & (1 << board[i])) {
return 0;
}
check[i % 9] |= (1 << board[i]);
if (check[9] & (1 << board[i])) {
return 0;
}
check[9] |= (1 << board[i]);
if (i % 9 < 3) {
if (check[10] & (1 << board[i])) {
return 0;
}
check[10] |= (1 << board[i]);
} else if (i % 9 < 6) {
if (check[11] & (1 << board[i])) {
return 0;
}
check[11] |= (1 << board[i]);
} else {
if (check[12] & (1 << board[i])) {
return 0;
}
check[12] |= (1 << board[i]);
}
}
}
Here is what I just did for this:
boolean checkers=true;
String checking="";
if(a.length/3==1){}
else{
for(int l=1; l<a.length/3; l++){
for(int n=0;n<3*l;n++){
for(int lm=1; lm<a[n].length/3; lm++){
for(int m=0;m<3*l;m++){
System.out.print(" "+a[n][m]);
if(a[n][m]<=0){
System.out.print(" (Values must be positive!) ");
}
if(n==0){
if(m!=0){
checking+=", "+a[n][m];
}
else{
checking+=a[n][m];
}
}
else{
checking+=", "+a[n][m];
}
}
}
System.out.print(" "+checking);
System.out.println();
}
}
for (int i=1;i<=a.length*a[1].length;i++){
if(checking.contains(Integer.toString(i))){
}
else{
checkers=false;
}
}
}
checkers=checkCol(a);
if(checking.contains("-")&&!checking.contains("--")){
checkers=false;
}
System.out.println();
if(checkers==true){
System.out.println("This is correct! YAY!");
}
else{
System.out.println("Sorry, it's not right. :-(");
}
}
private static boolean checkCol(int[][]a){
boolean checkers=true;
int[][]col=new int[][]{{0,0,0},{0,0,0},{0,0,0}};
for(int i=0; i<a.length; i++){
for(int j=0; j<a[i].length; j++){
if(a[i][j]>9||a[i][j]<1){
checkers=false;
break;
}
else{
col[a[i].length-j][i]=a[i][j];
}
}
}
String alia="";
for(int i=0; i<col.length; i++){
for(int j=1; j<=col[i].length; j++){
alia=a[i].toString();
if(alia.contains(""+j)){
alia=col[i].toString();
if(alia.contains(""+j)){}
else{
checkers=false;
}
}
else{
checkers=false;
}
}
}
return checkers;
}
You can check if sudoku is solved, in these two similar ways:
Check if the number is unique in each row, column and block.
A naive solution would be to iterate trough every square and check if the number is unique in the row, column block that number occupies.
But there is a better way.
Sudoku is solved if every row, column and block contains a permutation of the numbers (1 trough 9)
This only requires to check every row, column and block, instead of doing that for every number. A simple implementation would be to have a bitfield of numbers 1 trough 9 and remove them when you iterate the columns, rows and blocks. If you try to remove a missing number or if the field isn't empty when you finish then sudoku isn't correctly solved.
Here's a very concise version in Swift, that only uses an array of Ints to track the groups of 9 numbers, and only iterates over the sudoku once.
import UIKit
func check(_ sudoku:[[Int]]) -> Bool {
var groups = Array(repeating: 0, count: 27)
for x in 0...8 {
for y in 0...8 {
groups[x] += 1 << sudoku[x][y] // Column (group 0 - 8)
groups[y + 9] += 1 << sudoku[x][y] // Row (group 9 - 17)
groups[(x + y * 9) / 9 + 18] += 1 << sudoku[x][y] // Box (group 18 - 27)
}
}
return groups.filter{ $0 != 1022 }.count == 0
}
let sudoku = [
[7, 5, 1, 8, 4, 3, 9, 2, 6],
[8, 9, 3, 6, 2, 5, 1, 7, 4],
[6, 4, 2, 1, 7, 9, 5, 8, 3],
[4, 2, 5, 3, 1, 6, 7, 9, 8],
[1, 7, 6, 9, 8, 2, 3, 4, 5],
[9, 3, 8, 7, 5, 4, 6, 1, 2],
[3, 6, 4, 2, 9, 7, 8, 5, 1],
[2, 8, 9, 5, 3, 1, 4, 6, 7],
[5, 1, 7, 4, 6, 8, 2, 3, 9]
]
if check(sudoku) {
print("Pass")
} else {
print("Fail")
}
One minor optimization you can make is that you can check for duplicates in a row, column, or box in O(n) time rather than O(n^2): as you iterate through the set of numbers, you add each one to a hashset. Depending on the language, you may actually be able to use a true hashset, which is constant time lookup and insertion; then checking for duplicates can be done in the same step by seeing if the insertion was successful or not. It's a minor improvement in the code, but going from O(n^2) to O(n) is a significant optimization.