How to approach this two dimensional array problem? - algorithm

Problem (THE QUESTION)
Strokes to paint
Alex wants to paint a picture. In one stroke, Alex can only paint the same colored cells which are joined via some edge.
Given the painting as a 2-dimensional array of letters indicating colors, determine the minimum number of strokes to completely paint the picture.
Example: The canvas height, h = 3 and width, w = 5 is to be painted with picture=["aabba", "aabba", "aaacb"]. The diagram below shows the 5 strokes needed to paint the canvas. It takes two strokes each for colors a and b, and one for c
a a b b a
a a b b a
a a a c b
Function Description Complete the function strokesRequired in the editor below. The function must return an integer, the minimum number of strokes required to paint the canvas.
strokesRequired has the following parameter(s): picture[picture[0],...picture[h-1]] an array of strings where each string represents one row of the picture to be painted
Constraints
1 <= h <= 10^5
1<= w <= 10^5
1 <= h*w <= 10^5
len(pictureffl) = w (where 0 <= i < h)
picture[i][j] <- (a, b, c) (where 0 <= i < h and 0 <= j < w)
Hello.. so i attended one company interview and they asked me this problem and iam not getting any ideas please help

class Paint:
def __init__(self, row, col, arr):
self.ROW = row
self.COL = col
self.arr = arr
def visit(self, i, j, visited):
ele = self.arr[i][j]
for k in range(i,self.ROW):
for l in range(j, self.COL):
if self.arr[k][l]==ele:
visited[k][l]=True
v=l
if l>0 and self.arr[k][l-1]==ele and not visited[k][l-1]:
self.visit(k, l-1, visited)
if k>0 and self.arr[k-1][l]==ele and not visited[k-1][l]:
self.visit(k-1, l, visited)
elif l>=v:
break
# 2D matrix
def count_cells(self):
# Make an array to mark visited cells.
# Initially all cells are unvisited
visited = [[False for j in range(self.COL)]for i in range(self.ROW)]
# Initialize count as 0 and travese
count = 0
for i in range(self.ROW):
for j in range(self.COL):
# If a cell value false then not visited yet
# then visit
if visited[i][j] == False:
# Visit all cells in the array
self.visit(i, j, visited)
print(visited)
count += 1
return count
arr = ["aabba", "aabba", "aaacb"]
row = len(arr)
col = len(arr[0])
p = Paint(row, col, arr)
print (p.count_cells())

function visit(picture, i, j, visitedBoxes) {
const currentElem = picture[i][j];
if (picture[i][j] === currentElem) {
visitedBoxes[i][j] = true;
// go in four directions
// south
if (i + 1 < picture.length && picture[i+1][j] === currentElem && visitedBoxes[i+1][j] === false) {
visit(picture, i+1, j, visitedBoxes);
}
// west
if (j+ 1 < picture[i].length && picture[i][j+1] === currentElem && visitedBoxes[i][j+1] === false) {
visit(picture, i, j+1, visitedBoxes);
}
// north
if (i > 0 && picture[i-1][j] === currentElem && visitedBoxes[i-1][j] === false) {
visit(picture, i-1, j, visitedBoxes);
}
// west
if (j > 0 && picture[i, j-1] === currentElem && visitedBoxes[i, j-1] === false) {
visit(picture, i, j-1, visitedBoxes);
}
}
}
function countStrokes(picture) {
const visitedBoxes = [];
for (let i = 0; i < picture.length; i++) {
visitedBoxes[i] = [];
for(let j = 0; j < picture[i].length; j++) {
visitedBoxes[i][j] = false;
}
}
let srokesCount = 0;
for (let i = 0; i < picture.length; i++) {
for (let j = 0; j < picture[i].length; j++) {
if (!visitedBoxes[i][j]) {
visit(picture, i, j, visitedBoxes);
srokesCount++;
}
}
}
console.log('Strokes Count', srokesCount);
}
countStrokes(['aaaba', 'ababa', 'aacba']);
This will output 5.
Also you can use
function printVisited(visitedBoxes) {
for (let i = 0; i < visitedBoxes.length; i++) {
let str = ''
for(let j = 0; j < visitedBoxes[i].length; j++) {
str += visitedBoxes[i][j] ? '1 ': '0 ';
}
console.log(str);
}
console.log('-------------');
}
to print after each loop.
Output
1 1 1 0 0
1 0 1 0 0
1 1 0 0 0
-------------
1 1 1 1 0
1 0 1 1 0
1 1 0 1 0
-------------
1 1 1 1 1
1 0 1 1 1
1 1 0 1 1
-------------
1 1 1 1 1
1 1 1 1 1
1 1 0 1 1
-------------
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
-------------
Strokes Count 5

Related

How would one solve the staircase problem recursively with a variable number of steps?

The problem of determining the n amount of ways to climb a staircase given you can take 1 or 2 steps is well known with the Fibonacci sequencing solution being very clear. However how exactly could one solve this recursively if you also assume that you can take a variable M amount of steps?
I tried to make a quick mockup of this algorithm in typescript with
function counter(n: number, h: number){
console.log(`counter(n=${n},h=${h})`);
let sum = 0
if(h<1) return 0;
sum = 1
if (n>h) {
n = h;
}
if (n==h) {
sum = Math.pow(2, h-1)
console.log(`return sum=${sum}, pow(2,${h-1}) `)
return sum
}
for (let c = 1; c <= h; c++) {
console.log(`c=${c}`)
sum += counter(n, h-c);
console.log(`sum=${sum}`)
}
console.log(`return sum=${sum}`)
return sum;
}
let result = counter (2, 4);
console.log(`result=${result}`)
but unfortunately this doesn't seem to work for most cases where the height is not equal to the number of steps one could take.
I think this could be solved with recursive DP.
vector<vector<int>> dp2; //[stair count][number of jumps]
int stair(int c, int p) {
int& ret = dp2[c][p];
if (ret != -1) return ret; //If you've already done same search, return saved result
if (c == n) { //If you hit the last stair, return 1
return ret = 1;
}
int s1 = 0, s2 = 0;
if (p < m) { //If you can do more jumps, make recursive call
s1 = stair(c + 1, p + 1);
if (c + 2 <= n) { //+2 stairs can jump over the last stair. That shouldn't happen.
s2 = stair(c + 2, p + 1);
}
}
return ret = s1 + s2; //Final result will be addition of +1 stair methods and +2 methods
}
int main()
{
ios::sync_with_stdio(0); cin.tie(0); cout.tie(0);
cin >> n >> m; dp2 = vector<vector<int>>(n + 1, vector<int>(m + 1, -1));
for (int i = 1; i <= m; i++) {
dp2[n][i] = 1; //All last stair method count should be 1, because there is no more after.
}
cout << stair(0, 0) << "\n";
return 0;
}
Example IO 1
5 5
8
// 1 1 1 1 1
// 1 1 1 2
// 1 1 2 1
// 1 2 1 1
// 2 1 1 1
// 1 2 2
// 2 1 2
// 2 2 1
Example IO 2
5 4
7
// 1 1 1 2
// 1 1 2 1
// 1 2 1 1
// 2 1 1 1
// 1 2 2
// 2 1 2
// 2 2 1
Example IO 3
5 3
3
// 1 2 2
// 2 1 2
// 2 2 1

How to calculate cost to Convert an N X M binary matrix to all 0 matrix with only allowed moves as square-matrix toggle?

Given an N X M binary matrix ( every element is either 1 or 0) , find the minimum number of moves to convert it to an all 0 matrix.
For converting a matrix, one can choose squares of any size and convert the value of that square. '1' changes to '0' and '0' changes to '1'.This process can be done multiple times with square of the same size. Converting any square counts as 1 move.
Calculate minimum number of moves required..
Example :
input matrix
0 1 1
0 0 0
0 1 1
we need to calculate minimum moves to convert this to all '0' matrix
0 0 0
0 0 0
0 0 0
Here,
For square of size 1 ( 1 X 1 or single element sub-matrix), the total number of moves required to convert this matrix is 4 . he converts elements for position (1,2),(1,3),(3,2),(3,3)
For square of size 2 ( 2 X 2 or single element sub-matrix), it takes 2 moves to convert the matrix
First we can convert elements from (1,2) to (2,3) and the matrix becomes, {{0 0 0}, {0 1 1}, {0 1 1}}
And then we convert elements from (2,2)to (3,3) and the matrix becomes ``{{0 0 0}, {0 0 0}, {0 0 0}}```
So minimum is 2.
Could some help in designing an approach to this ?
I attempted to solve it using Gaussian elimination for every possible square size. But the result is not correct here. There must be some gap in my approach to this problem.
package com.practice.hustle;
import java.util.Arrays;
public class GaussianElimination {
public static void main(String[] args) {
int countMoves = Integer.MAX_VALUE;
byte[][] inputMatrix = new byte[3][3];
inputMatrix[0][0] = 0;
inputMatrix[0][1] = 1;
inputMatrix[0][2] = 1;
inputMatrix[1][0] = 0;
inputMatrix[1][1] = 0;
inputMatrix[1][2] = 0;
inputMatrix[2][0] = 0;
inputMatrix[2][1] = 1;
inputMatrix[2][2] = 1;
int N = inputMatrix.length;
int M = inputMatrix[0].length;
int maxSize = Math.min(N, M);
for (int j = 2; j <= maxSize; ++j) { // loop for every possible square size
byte[][] a = new byte[N * M][(N * M) + 1];
for (int i = 0; i < N * M; i++) { // logic for square wise toggle for every element of N*M elements
byte seq[] = new byte[N * M + 1];
int index_i = i / M;
int index_j = i % M;
if (index_i <= N - j && index_j <= M - j) {
for (int c = 0; c < j; c++) {
for (int k = 0; k < j; k++) {
seq[i + k + M * c] = 1;
}
}
a[i] = seq;
} else {
if (index_i > N - j) {
seq = Arrays.copyOf(a[i - M], N * M + 1);
} else {
seq = Arrays.copyOf(a[i - 1], N * M + 1);
}
}
seq[N * M] = inputMatrix[index_i][index_j];
a[i] = seq;
}
System.out.println("\nSolving for square size = " + j);
print(a, N * M);
int movesPerSquareSize = gaussian(a);
if (movesPerSquareSize != 0) { // to calculate minimum moves
countMoves = Math.min(countMoves, movesPerSquareSize);
}
}
System.out.println(countMoves);
}
public static int gaussian(byte a[][]) {
// n X n+1 matrix
int N = a.length;
for (int k = 0; k < N - 1; k++) {
// Finding pivot element
int max_i = k, max_value = a[k][k];
for (int i = k + 1; i < N; i++) {
if (a[i][k] > max_value) {
max_value = a[i][k];
max_i = i;
}
}
// swap max row with kth row
byte[] temp = a[k];
a[k] = a[max_i];
a[max_i] = temp;
// convert to 0 all cells below pivot in the column
for (int i = k+1; i < N; i++) {
// int scalar = a[i][k] + a[k][k]; // probability of a divide by zero
if (a[i][k] == 1) {
for (int j = 0; j <= N; j++) {
if (a[i][j] == a[k][j]) {
a[i][j] = 0;
} else {
a[i][j] = 1;
}
}
}
}
}
System.out.println("\n\tAfter applying gaussian elimination : ");
print(a, N);
int count = 0;
for (int i = 0; i < N; i++) {
if (a[i][N] == 1)
++count;
}
return count;
}
private static void print(byte[][] a, int N) {
for (int i = 0; i < N; i++) {
System.out.print("\t ");
for (int j = 0; j < N + 1; j++) {
System.out.print(a[i][j] + " ");
}
System.out.println(" ");
}
}
}
Its giving final reduced Euler matrix formed is incorrect and thereby the result is also incorrect.
I think its failing due to the logic used for element like - the cell at index-(2,3) , for that we are not sure which square would it be a part of ( either the square from (1,2) to (2,3) or the square from ( 2,2) to (3,3))
here the input matrix to Gaussian algo is having exactly same sequence at 2nd and 3rd row which could be the culprit of incorrect results.
1 1 0 1 1 0 0 0 0 0
* 0 1 1 0 1 1 0 0 0 1 *
* 0 1 1 0 1 1 0 0 0 1 *
0 0 0 1 1 0 1 1 0 0
0 0 0 0 1 1 0 1 1 0
0 0 0 0 1 1 0 1 1 0
0 0 0 1 1 0 1 1 0 0
0 0 0 0 1 1 0 1 1 1
0 0 0 0 1 1 0 1 1 1
for a sqaure size 2, the above program prints :
Solving for square size = 2
The input to Gaussian algo :
1 1 0 1 1 0 0 0 0 0
0 1 1 0 1 1 0 0 0 1
0 1 1 0 1 1 0 0 0 1
0 0 0 1 1 0 1 1 0 0
0 0 0 0 1 1 0 1 1 0
0 0 0 0 1 1 0 1 1 0
0 0 0 1 1 0 1 1 0 0
0 0 0 0 1 1 0 1 1 1
0 0 0 0 1 1 0 1 1 1
After applying gaussian elimination :
1 0 1 0 0 0 1 0 1 1
0 1 1 0 0 0 0 1 1 0
0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 1 1 0 1 0
0 0 0 0 1 1 0 1 1 1
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 1

Maximum length of zigzag sequence

A sequence of integers is called zigzag sequence if each of its elements is either strictly less or strictly greater than its neighbors.
Example : The sequence 4 2 3 1 5 3 forms a zigzag, but 7 3 5 5 2 and 3 8 6 4 5 don't.
For a given array of integers we need to find the length of its largest (contiguous) sub-array that forms a zigzag sequence.
Can this be done in O(N) ?
Currently my solution is O(N^2) which is just simply taking every two points and checking each possible sub-array if it satisfies the condition or not.
I claim that the length of overlapping sequence of any 2 zigzag sub-sequences is a most 1
Proof by contradiction:
Assume a_i .. a_j is the longest zigzag sub-sequence, and there is another zigzag sub-sequence b_m...b_n overlapping it.
without losing of generality, let's say the overlapping part is
a_i ... a_k...a_j
--------b_m...b_k'...b_n
a_k = b_m, a_k+1 = b_m+1....a_j = b_k' where k'-m = j-k > 0 (at least 2 elements are overlapping)
Then they can merge to form a longer zig-zag sequence, contradiction.
This means the only case they can be overlapping each other is like
3 5 3 2 3 2 3
3 5 3 and 3 2 3 2 3 is overlapping at 1 element
This can still be solved in O(N) I believe, like just greedily increase the zig-zag length whenever possible. If fails, move iterator 1 element back and treat it as a new zig-zag starting point
Keep record the latest and longest zig-zag length you have found
Walk along the array and see if the current item belongs to (fits a definition of) a zigzag. Remember the las zigzag start, which is either the array's start or the first zigzag element after the most recent non-zigzag element. This and the current item define some zigzag subarray. When it appears longer than the previously found, store the new longest zigzag length. Proceed till the end of array and you should complete the task in O(N).
Sorry I use perl to write this.
#!/usr/bin/perl
#a = ( 5, 4, 2, 3, 1, 5, 3, 7, 3, 5, 5, 2, 3, 8, 6, 4, 5 );
$n = scalar #a;
$best_start = 0;
$best_end = 1;
$best_length = 2;
$start = 0;
$end = 1;
$direction = ($a[0] > $a[1]) ? 1 : ($a[0] < $a[1]) ? -1 : 0;
for($i=2; $i<$n; $i++) {
// a trick here, same value make $new_direction = $direction
$new_direction = ($a[$i-1] > $a[$i]) ? 1 : ($a[$i-1] < $a[$i]) ? -1 : $direction;
print "$a[$i-1] > $a[$i] : direction $new_direction Vs $direction\n";
if ($direction != $new_direction) {
$end = $i;
} else {
$this_length = $end - $start + 1;
if ($this_length > $best_length) {
$best_start = $start;
$best_end = $end;
$best_length = $this_length;
}
$start = $i-1;
$end = $i;
}
$direction = $new_direction;
}
$this_length = $end - $start + 1;
if ($this_length > $best_length) {
$best_start = $start;
$best_end = $end;
$best_length = $this_length;
}
print "BEST $best_start to $best_end length $best_length\n";
for ($i=$best_start; $i <= $best_end; $i++) {
print $a[$i], " ";
}
print "\n";
For each index i, you can find the smallest j such that the subarray with index j,j+1,...,i-1,i is a zigzag. This can be done in two phases:
Find the longest "increasing" zig zag (starts with a[1]>a[0]):
start = 0
increasing[0] = 0
sign = true
for (int i = 1; i < n; i ++)
if ((arr[i] > arr[i-1] && sign) || )arr[i] < arr[i-1] && !sign)) {
increasing[i] = start
sign = !sign
} else if (arr[i-1] < arr[i]) { //increasing and started last element
start = i-1
sign = false
increasing[i] = i-1
} else { //started this element
start = i
sign = true
increasing[i] = i
}
}
Do similarly for "decreasing" zig-zag, and you can find for each index the "earliest" possible start for a zig-zag subarray.
From there, finding the maximal possible zig-zag is easy.
Since all oporations are done in O(n), and you basically do one after the other, this is your complexity.
You can combine the both "increasing" and "decreasing" to one go:
start = 0
maxZigZagStart[0] = 0
sign = true
for (int i = 1; i < n; i ++)
if ((arr[i] > arr[i-1] && sign) || )arr[i] < arr[i-1] && !sign)) {
maxZigZagStart[i] = start
sign = !sign
} else if (arr[i-1] > arr[i]) { //decreasing:
start = i-1
sign = false
maxZigZagStart[i] = i-1
} else if (arr[i-1] < arr[i]) { //increasing:
start = i-1
sign = true
maxZigZagStart[i] = i-1
} else { //equality
start = i
//guess it is increasing, if it is not - will be taken care of next iteration
sign = true
maxZigZagStart[i] = i
}
}
You can see that you can actually even let go of maxZigZagStart aux array and stored local maximal length instead.
A sketch of simple one-pass algorithm. Cmp compares neighbour elements, returning -1, 0, 1 for less, equal and greater cases.
Zigzag ends for cases of Cmp transitions:
0 0
-1 0
1 0
Zigzag ends and new series starts:
0 -1
0 1
-1 -1
1 1
Zigzag series continues for transitions
-1 1
1 -1
Algo:
Start = 0
LastCmp = - Compare(A[i], A[i - 1]) //prepare to use the first element individually
MaxLen = 0
for i = 1 to N - 1 do
Cmp = Compare(A[i], A[i - 1]) //returns -1, 0, 1 for less, equal and greater cases
if Abs(Cmp - LastCmp) <> 2 then
//zigzag condition is violated, series ends, new series starts
MaxLen = Max(MaxLen, i - 1 - Start)
Start = i
//else series continues, nothing to do
LastCmp = Cmp
//check for ending zigzag
if LastCmp <> 0 then
MaxLen = Max(MaxLen, N - Start)
examples of output:
2 6 7 1 7 0 7 3 1 1 7 4
5 (7 1 7 0 7)
8 0 0 3 5 8
1
0 0 7 0
2
1 2 0 7 9
3
8 3 5 2
4
1 3 7 1 6 6
2
1 4 0 6 6 3 4 3 8 0 9 9
5
Lets consider sequence 5 9 3 4 5 4 2 3 6 5 2 1 3 as an example. You have a condition which every internal element of subsequence should satisfy (element is strictly less or strictly greater than its neighbors). Lets compute this condition for every element of the whole sequence:
5 9 3 6 5 7 2 3 6 5 2 1 3
0 1 1 1 1 1 1 0 1 0 0 1 0
The condition is undefined for outermost elements because they have only one neighbor each. But I defined it as 0 for convenience.
The longest subsequence of 1's (9 3 6 5 7 2) is the internal part of the longest zigzag subsequence (5 9 3 6 5 7 2 3). So the algorithm is:
Find the longest subsequence of elements satisfying condition.
Add to it one element to each side.
The first step can be done in O(n) by the following algorithm:
max_length = 0
current_length = 0
for i from 2 to len(a) - 1:
if a[i - 1] < a[i] > a[i + 1] or a[i - 1] > a[i] < a[i + 1]:
current_length += 1
else:
max_length = max(max_length, current_length)
current_length = 0
max_length = max(max_length, current_length)
The only special case is if the sequence total length is 0 or 1. Then the whole sequence would be the longest zigzag subsequence.
#include "iostream"
using namespace std ;
int main(){
int t ; scanf("%d",&t) ;
while(t--){
int n ; scanf("%d",&n) ;
int size1 = 1 , size2 = 1 , seq1 , seq2 , x ;
bool flag1 = true , flag2 = true ;
for(int i=1 ; i<=n ; i++){
scanf("%d",&x) ;
if( i== 1 )seq1 = seq2 = x ;
else {
if( flag1 ){
if( x>seq1){
size1++ ;
seq1 = x ;
flag1 = !flag1 ;
}
else if( x < seq1 )
seq1 = x ;
}
else{
if( x<seq1){
size1++ ;
seq1=x ;
flag1 = !flag1 ;
}
else if( x > seq1 )
seq1 = x ;
}
if( flag2 ){
if( x < seq2 ){
size2++ ;
seq2=x ;
flag2 = !flag2 ;
}
else if( x > seq2 )
seq2 = x ;
}
else {
if( x > seq2 ){
size2++ ;
seq2 = x ;
flag2 = !flag2 ;
}
else if( x < seq2 )
seq2 = x ;
}
}
}
printf("%d\n",max(size1,size2)) ;
}
return 0 ;
}

Caculating total combinations

I don't know how to go about this programming problem.
Given two integers n and m, how many numbers exist such that all numbers have all digits from 0 to n-1 and the difference between two adjacent digits is exactly 1 and the number of digits in the number is atmost 'm'.
What is the best way to solve this problem? Is there a direct mathematical formula?
Edit: The number cannot start with 0.
Example:
for n = 3 and m = 6 there are 18 such numbers (210, 2101, 21012, 210121 ... etc)
Update (some people have encountered an ambiguity):
All digits from 0 to n-1 must be present.
This Python code computes the answer in O(nm) by keeping track of the numbers ending with a particular digit.
Different arrays (A,B,C,D) are used to track numbers that have hit the maximum or minimum of the range.
n=3
m=6
A=[1]*n # Number of ways of being at digit i and never being to min or max
B=[0]*n # number of ways with minimum being observed
C=[0]*n # number of ways with maximum being observed
D=[0]*n # number of ways with both being observed
A[0]=0 # Cannot start with 0
A[n-1]=0 # Have seen max so this 1 moves from A to C
C[n-1]=1 # Have seen max if start with highest digit
t=0
for k in range(m-1):
A2=[0]*n
B2=[0]*n
C2=[0]*n
D2=[0]*n
for i in range(1,n-1):
A2[i]=A[i+1]+A[i-1]
B2[i]=B[i+1]+B[i-1]
C2[i]=C[i+1]+C[i-1]
D2[i]=D[i+1]+D[i-1]
B2[0]=A[1]+B[1]
C2[n-1]=A[n-2]+C[n-2]
D2[0]=C[1]+D[1]
D2[n-1]=B[n-2]+D[n-2]
A=A2
B=B2
C=C2
D=D2
x=sum(d for d in D2)
t+=x
print t
After doing some more research, I think there may actually be a mathematical approach after all, although the math is advanced for me. Douglas S. Stones pointed me in the direction of Joseph Myers' (2008) article, BMO 2008–2009 Round 1 Problem 1—Generalisation, which derives formulas for calculating the number of zig-zag paths across a rectangular board.
As I understand it, in Anirudh's example, our board would have 6 rows of length 3 (I believe this would mean n=3 and r=6 in the article's terms). We can visualize our board so:
0 1 2 example zig-zag path: 0
0 1 2 1
0 1 2 0
0 1 2 1
0 1 2 2
0 1 2 1
Since Myers' formula m(n,r) would generate the number for all the zig-zag paths, that is, the number of all 6-digit numbers where all adjacent digits are consecutive and digits are chosen from (0,1,2), we would still need to determine and subtract those that begin with zero and those that do not include all digits.
If I understand correctly, we may do this in the following way for our example, although generalizing the concept to arbitrary m and n may prove more complicated:
Let m(3,6) equal the number of 6-digit numbers where all adjacent digits
are consecutive and digits are chosen from (0,1,2). According to Myers,
m(3,r) is given by formula and also equals OEIS sequence A029744 at
index r+2, so we have
m(3,6) = 16
How many of these numbers start with zero? Myers describes c(n,r) as the
number of zig-zag paths whose colour is that of the square in the top
right corner of the board. In our case, c(3,6) would include the total
for starting-digit 0 as well as starting-digit 2. He gives c(3,2r) as 2^r,
so we have
c(3,6) = 8. For starting-digit 0 only, we divide by two to get 4.
Now we need to obtain only those numbers that include all the digits in
the range, but how? We can do this be subtracting m(n-1,r) from m(n,r).
In our case, we have all the m(2,6) that would include only 0's and 1's,
and all the m(2,6) that would include 1's and 2's. Myers gives
m(2,anything) as 2, so we have
2*m(2,6) = 2*2 = 4
But we must remember that one of the zero-starting numbers is included
in our total for 2*m(2,6), namely 010101. So all together we have
m(3,6) - c(3,6)/2 - 4 + 1
= 16 - 4 - 4 + 1
= 9
To complete our example, we must follow a similar process for m(3,5),
m(3,4) and m(3,3). Since it's late here, I might follow up tomorrow...
One approach could be to program it recursively, calling the function to add as well as subtract from the last digit.
Haskell code:
import Data.List (sort,nub)
f n m = concatMap (combs n) [n..m]
combs n m = concatMap (\x -> combs' 1 [x]) [1..n - 1] where
combs' count result
| count == m = if test then [concatMap show result] else []
| otherwise = combs' (count + 1) (result ++ [r + 1])
++ combs' (count + 1) (result ++ [r - 1])
where r = last result
test = (nub . sort $ result) == [0..n - 1]
Output:
*Main> f 3 6
["210","1210","1012","2101","12101","10121","21210","21012"
,"21010","121210","121012","121010","101212","101210","101012"
,"212101","210121","210101"]
In response to Anirudh Rayabharam's comment, I hope the following code will be more 'pseudocode' like. When the total number of digits reaches m, the function g outputs 1 if the solution has hashed all [0..n-1], and 0 if not. The function f accumulates the results for g for starting digits [1..n-1] and total number of digits [n..m].
Haskell code:
import qualified Data.Set as S
g :: Int -> Int -> Int -> Int -> (S.Set Int, Int) -> Int
g n m digitCount lastDigit (hash,hashCount)
| digitCount == m = if test then 1 else 0
| otherwise =
if lastDigit == 0
then g n m d' (lastDigit + 1) (hash'',hashCount')
else if lastDigit == n - 1
then g n m d' (lastDigit - 1) (hash'',hashCount')
else g n m d' (lastDigit + 1) (hash'',hashCount')
+ g n m d' (lastDigit - 1) (hash'',hashCount')
where test = hashCount' == n
d' = digitCount + 1
hash'' = if test then S.empty else hash'
(hash',hashCount')
| hashCount == n = (S.empty,hashCount)
| S.member lastDigit hash = (hash,hashCount)
| otherwise = (S.insert lastDigit hash,hashCount + 1)
f n m = foldr forEachNumDigits 0 [n..m] where
forEachNumDigits numDigits accumulator =
accumulator + foldr forEachStartingDigit 0 [1..n - 1] where
forEachStartingDigit startingDigit accumulator' =
accumulator' + g n numDigits 1 startingDigit (S.empty,0)
Output:
*Main> f 3 6
18
(0.01 secs, 571980 bytes)
*Main> f 4 20
62784
(1.23 secs, 97795656 bytes)
*Main> f 4 25
762465
(11.73 secs, 1068373268 bytes)
model your problem as 2 superimposed lattices in 2 dimensions, specifically as pairs (i,j) interconnected with oriented edges ((i0,j0),(i1,j1)) where i1 = i0 + 1, |j1 - j0| = 1, modified as follows:
dropping all pairs (i,j) with j > 9 and its incident edges
dropping all pairs (i,j) with i > m-1 and its incident edges
dropping edge ((0,0), (1,1))
this construction results in a structure like in this diagram:
:
the requested numbers map to paths in the lattice starting at one of the green elements ((0,j), j=1..min(n-1,9)) that contain at least one pink and one red element ((i,0), i=1..m-1, (i,n-1), i=0..m-1 ). to see this, identify the i-th digit j of a given number with point (i,j). including pink and red elements ('extremal digits') guarantee that all available diguts are represented in the number.
Analysis
for convenience, let q1, q2 denote the position-1.
let q1 be the position of a number's first digit being either 0 or min(n-1,9).
let q2 be the position of a number's first 0 if the digit at position q1 is min(n-1,9) and vv.
case 1: first extremal digit is 0
the number of valid prefixes containing no 0 can be expressed as sum_{k=1..min(n-1,9)} (paths_to_0(k,1,q1), the function paths_to_0 being recursively defined as
paths_to_0(0,q1-1,q1) = 0;
paths_to_0(1,q1-1,q1) = 1;
paths_to_0(digit,i,q1) = 0; if q1-i < digit;
paths_to_0(x,_,_) = 0; if x >= min(n-1,9)
// x=min(n-1,9) mustn't occur before position q2,
// x > min(n-1,9) not at all
paths_to_0(x,_,_) = 0; if x <= 0;
// x=0 mustn't occur before position q1,
// x < 0 not at all
and else paths_to_0(digit,i,q1) =
paths_to_0(digit+1,i+1,q1) + paths_to_0(digit-1,i+1,q1);
similarly we have
paths_to_max(min(n-1,9),q2-1,q2) = 0;
paths_to_max(min(n-2,8),q2-1,q2) = 1;
paths_to_max(digit,i,q2) = 0 if q2-i < n-1;
paths_to_max(x,_,_) = 0; if x >= min(n-1,9)
// x=min(n-1,9) mustn't occur before
// position q2,
// x > min(n-1,9) not at all
paths_to_max(x,_,_) = 0; if x < 0;
and else paths_to_max(digit,q1,q2) =
paths_max(digit+1,q1+1,q2) + paths_to_max(digit-1,q1+1,q2);
and finally
paths_suffix(digit,length-1,length) = 2; if digit > 0 and digit < min(n-1,9)
paths_suffix(digit,length-1,length) = 1; if digit = 0 or digit = min(n-1,9)
paths_suffix(digit,k,length) = 0; if length > m-1
or length < q2
or k > length
paths_suffix(digit,k,0) = 1; // the empty path
and else paths_suffix(digit,k,length) =
paths_suffix(digit+1,k+1,length) + paths_suffix(digit-1,k+1,length);
... for a grand total of
number_count_case_1(n, m) =
sum_{first=1..min(n-1,9), q1=1..m-1-(n-1), q2=q1..m-1, l_suffix=0..m-1-q2} (
paths_to_0(first,1,q1)
+ paths_to_max(0,q1,q2)
+ paths_suffix(min(n-1,9),q2,l_suffix+q2)
)
case 2: first extremal digit is min(n-1,9)
case 2.1: initial digit is not min(n-1,9)
this is symmetrical to case 1 with all digits d replaced by min(n,10) - d. as the lattice structure is symmetrical, this means number_count_case_2_1 = number_count_case_1.
case 2.2: initial digit is min(n-1,9)
note that q1 is 1 and the second digit must be min(n-2,8).
thus
number_count_case_2_2 (n, m) =
sum_{q2=1..m-2, l_suffix=0..m-2-q2} (
paths_to_max(1,1,q2)
+ paths_suffix(min(n-1,9),q2,l_suffix+q2)
)
so the grand grand total will be
number_count ( n, m ) = 2 * number_count_case_1 (n, m) + number_count_case_2_2 (n, m);
Code
i don't know whether a closed expression for number_count exists, but the following perl code will compute it (the code is but a proof of concept as it does not use memoization techniques to avoid recomputing results already obtained):
use strict;
use warnings;
my ($n, $m) = ( 5, 7 ); # for example
$n = ($n > 10) ? 10 : $n; # cutoff
sub min
sub paths_to_0 ($$$) {
my (
$d
, $at
, $until
) = #_;
#
if (($d == 0) && ($at == $until - 1)) { return 0; }
if (($d == 1) && ($at == $until - 1)) { return 1; }
if ($until - $at < $d) { return 0; }
if (($d <= 0) || ($d >= $n))) { return 0; }
return paths_to_0($d+1, $at+1, $until) + paths_to_0($d-1, $at+1, $until);
} # paths_to_0
sub paths_to_max ($$$) {
my (
$d
, $at
, $until
) = #_;
#
if (($d == $n-1) && ($at == $until - 1)) { return 0; }
if (($d == $n-2) && ($at == $until - 1)) { return 1; }
if ($until - $at < $n-1) { return 0; }
if (($d < 0) || ($d >= $n-1)) { return 0; }
return paths_to_max($d+1, $at+1, $until) + paths_to_max($d-1, $at+1, $until);
} # paths_to_max
sub paths_suffix ($$$) {
my (
$d
, $at
, $until
) = #_;
#
if (($d < $n-1) && ($d > 0) && ($at == $until - 1)) { return 2; }
if ((($d == $n-1) && ($d == 0)) && ($at == $until - 1)) { return 1; }
if (($until > $m-1) || ($at > $until)) { return 0; }
if ($until == 0) { return 1; }
return paths_suffix($d+1, $at+1, $until) + paths_suffix($d-1, $at+1, $until);
} # paths_suffix
#
# main
#
number_count =
sum_{first=1..min(n-1,9), q1=1..m-1-(n-1), q2=q1..m-1, l_suffix=0..m-1-q2} (
paths_to_0(first,1,q1)
+ paths_to_max(0,q1,q2)
+ paths_suffix(min(n-1,9),q2,l_suffix+q2)
)
my ($number_count, $number_count_2_2) = (0, 0);
my ($first, $q1, i, $l_suffix);
for ($first = 1; $first <= $n-1; $first++) {
for ($q1 = 1; $q1 <= $m-1 - ($n-1); $q1++) {
for ($q2 = $q1; $q2 <= $m-1; $q2++) {
for ($l_suffix = 0; $l_suffix <= $m-1 - $q2; $l_suffix++) {
$number_count =
$number_count
+ paths_to_0($first,1,$q1)
+ paths_to_max(0,$q1,$q2)
+ paths_suffix($n-1,$q2,$l_suffix+$q2)
;
}
}
}
}
#
# case 2.2
#
for ($q2 = 1; $q2 <= $m-2; $q2++) {
for ($l_suffix = 0; $l_suffix <= $m-2 - $q2; $l_suffix++) {
$number_count_2_2 =
$number_count_2_2
+ paths_to_max(1,1,$q2)
+ paths_suffix($n-1,$q2,$l_suffix+$q2)
;
}
}
$number_count = 2 * $number_count + number_count_2_2;

Finding a minimal set of rectangles covering a binary matrix [duplicate]

This question already has answers here:
Algorithm for finding the fewest rectangles to cover a set of rectangles without overlapping
(2 answers)
Closed 5 years ago.
Say I have the following binary matrix:
0 0 0 1 1 1 0
0 0 0 1 1 1 0
0 0 0 1 1 1 0
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
0 0 0 1 1 1 0
0 0 0 1 1 1 0
0 0 0 1 1 1 0
I want to find the set of rectangles parallel to the x and y axis that covers every 1 at least once and covers not a single 0 which has minimal cardinality (the least amount of rectangles). In the example above this would be the rectangles ((0, 3), (6, 5)) and ((3, 0), (5, 8)) (notation is in the form (topleft, bottomright)) - the minimal solution is using two rectangles.
My previous attempt was finding the rectangle with the largest area covering only 1's, adding that rectangle to the set and then marking all those 1's as 0's, until all 1's are gone. While this set would cover every 1 and not a single 0, it won't necessarily have minimal cardinality (this algorithm will fail on the example above).
I think you should replace covered 1's with 2's instead of 0's. This way you can include the 2's when covering 1's and still not cover any 0's.
Here's what I came up with:
#include <stdio.h>
#include <stdlib.h>
struct board {
int **data;
int w,h;
};
int load_board(char *, struct board *);
void print_board(struct board *);
int max_height_with_fixed_w(struct board *board, int i, int j, int w) {
int jj = -1, ii;
if (board->data[j][i] != 0) {
for (jj = j; jj < board->h && board->data[jj][i] != 0; jj++) {
for (ii = i; ii - i < w; ii++) {
if (board->data[jj][ii] == 0)
return jj - j;
}
}
printf("maximum height = %d\n", jj);
}
return jj - j;
}
void find_largest_rect_from(
struct board *board,
int i, int j, int *ei, int *ej) {
int max_w = 0, max_h = 0, max_a = 0;
*ei = *ej = -1;
for (max_w = 0; max_w < board->w - i &&
(board->data[j][i + max_w] != 0);
max_w++) {
int max_aa;
int max_hh = max_height_with_fixed_w(board, i, j, max_w + 1);
if (max_hh > max_h) {
max_h = max_hh;
}
max_aa = max_hh * (max_w + 1);
printf(" area: %d x %d = %d\n", max_hh, max_w + 1, max_aa);
if (max_aa > max_a) {
max_a = max_aa;
*ei = i + max_w;
*ej = j + max_hh - 1;
}
}
printf("max width : %d\n", max_w);
printf("max height: %d\n", max_h);
printf("max area : %d\n", max_a);
}
int main(int arc, char **argv) {
struct board board;
int jj, ii, i = 0, j = 0;
int total_rects = 0;
if(load_board(argv[1], &board)) return 1;
print_board(&board);
for (j = 0; j < board.h; j++) {
for (i = 0; i < board.w; i++) {
if (board.data[j][i] == 1) {
find_largest_rect_from(&board, i, j, &ii, &jj);
printf("largest from %d, %d ends at %d,%d\n", i, j, ii, jj);
int marki, markj;
total_rects++;
for (markj = j; markj <= jj; markj++) {
for (marki = i; marki <= ii; marki++) {
board.data[markj][marki] = 2;
}
}
print_board(&board);
}
}
}
printf("minimum %d rects are required\n", total_rects);
return 0;
}
int load_board(char *fname, struct board *board) {
FILE *file = fopen(fname, "r");
int j,i;
if (!file) return 1;
fscanf(file, "%d %d", &board->w, &board->h);
board->data = (int**)malloc(sizeof(int*)*board->h);
for (j = 0; j < board->h; j++) {
board->data[j] = (int*)malloc(sizeof(int)*board->w);
for (i = 0; i < board->w; i++) {
fscanf(file, "%d", &board->data[j][i]);
}
}
return 0;
}
void print_board(struct board *board) {
int i,j;
printf("board size: %d, %d\n", board->w, board->h);
for (j = 0; j < board->h; j++) {
for (i = 0; i < board->w; i++) {
printf("%d ", board->data[j][i]);
} printf("\n");
}
}
Example input 1:
7 9
0 0 0 1 1 1 0
0 0 0 1 1 1 0
0 0 0 1 1 1 0
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
0 0 0 1 1 1 0
0 0 0 1 1 1 0
0 0 0 1 1 1 0
Example input 2:
7 7
0 0 0 1 0 0 0
0 0 1 1 1 0 0
0 1 1 1 1 1 0
1 1 1 1 1 1 1
0 1 1 1 1 1 0
0 0 1 1 1 0 0
0 0 0 1 0 0 0
Idea for an algorithm:
As long as there are 1s in the matrix do:
For every 1 that doesn't have 1 above it AND doesn't have 1 to its left, do:
Go greedy: start from this 1 and go in diagonal to the right and down, as long as there are 1s on the way - create a rectangle and change the 1s of the created rectangle into 0s.
I'd go for an algorithm that picks points and expands until it comsumed all possible space, and then picks more, until all points on the grid have been consumed.
For your example, say we're consuming 1s.
I'll pick (0,3), the uppermost of the leftmost 1s. My rectangle would start at a size of 0,0. I'd expand it right and down until it grew to a size of 6,2. At this point, I'd mark those points as occupied.
I'd then pick another point, say (3,0), with a rectangle of size 0,0. I'd grow it down and right until it took up the largest available space, at a size of 2,6.
Consider the following:
0 0 0 1 0 0 0
0 0 1 1 1 0 0
0 1 1 1 1 1 0
1 1 1 1 1 1 1
0 1 1 1 1 1 0
0 0 1 1 1 0 0
0 0 0 1 0 0 0
You can easily determine that for any random starting points, it will always take 4 rectangles.
In order to mark points as "occupied", you should mark them differently than those marked "unconsumable". You can then differentiate between unconsumable (which cannot be expanded into) and "occupied" (which may be expanded into, but do not have to be since they already have been).

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