There is a matrix which contains white cells(represented as 1) , black cells(represented as 0) and only one gray cell(represented as 2), need to go from (0,0) to (N-1, N-1) in Array[N][N].
Constraints:
1) The path should cover only white cells and must go via grey cell (this grey cell can be anywhere in the array)
2) The node once visited cannot be visited again.
Below is the typical maze problem solution but this solution doesn't handle the specific case of traversing GREY cell...so can you please help me in modifying the below code to handle the specific case.
My problem is that I am not sure how to put a check for grey cell?
#include "stdafx.h"
#include "algorithm"
#include <iostream>
#include <fstream>
using namespace std;
#include<stdio.h>
// Maze size
#define N 4
bool solveMazeUtil(int maze[N][N], int x, int y, int sol[N][N]);
/* A utility function to print solution matrix sol[N][N] */
void printSolution(int sol[N][N])
{
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
printf(" %d ", sol[i][j]);
printf("\n");
}
}
/* A utility function to check if x,y is valid index for N*N maze */
bool isSafe(int maze[N][N], int x, int y)
{
//solveMazeUtil() to solve the problem. It returns false if no path is possible,
//otherwise return true and prints the path in the form of 1s. Please note that
//there may be more than one solutions, this function prints one of the feasible
if(x >= 0 && x < N && y >= 0 && y < N && maze[x][y] == 1)
// if (x,y outside maze) return false
return true;
return false;
}
/* This function solves the Maze problem using Backtracking. It mainly uses
solutions.*/
bool solveMaze(int maze[N][N])
{
int sol[N][N] = { {0, 0, 0, 0},
{0, 0, 0, 0},
{0, 0, 0, 0},
{0, 0, 0, 0}
};
if(solveMazeUtil(maze, 0, 0, sol) == false)
{
printf("Solution doesn't exist");
return false;
}
printSolution(sol);
return true;
}
/* A recursive utility function to solve Maze problem */
bool solveMazeUtil(int maze[N][N], int x, int y, int sol[N][N])
{
// if (x,y is goal) return true
if(x == N-1 && y == N-1)
{
sol[x][y] = 1;
return true;
}
// Check if maze[x][y] is valid
if(isSafe(maze, x, y) == true)
{
// mark x,y as part of solution path
sol[x][y] = 1;
/* Move forward in x direction */
if (solveMazeUtil(maze, x+1, y, sol) == true)
return true;
/* If x moving in x direction doesn't give solution then
Move down in y direction */
if (solveMazeUtil(maze, x, y+1, sol) == true)
return true;
/* If none of the above movements work then BACKTRACK:
unmark x,y as part of solution path */
sol[x][y] = 0;
return false;
}
return false;
}
// driver program to test above function
int main()
{
int maze[N][N] = { {1, 0, 0, 0},
{1, 1, 0, 1},
{0, 1, 0, 0},
{1, 1, 1, 1}
};
solveMaze(maze);
getchar();
return 0;
}
One solution what I am thinking is:
Produce all the possible paths (which traverse through either 1 or 2).
Then, find out which of the path is having 2 in it. and then print that path as the output.
But I don't think this will be the good approach...So, please let me know how to achieve my goal in a decent way.
Thanks
Since in your code you are only using two possible movements: down and right then this is a DAG. A DAG is suitable for a dynamic programming approach: each cell has two possibilities to get there, one is coming from above and the other is coming from left. Thus the minimum distance for a cell is:
cost[i][j] = min(cost[i][j-1],cost[i-1][j]) + 1
That is considering that the cost to do a movement is 1. If the cell is black you can give it an infinite cost, and you only need to find a path from P1(start) to P2(gray cell) and then a path from P2 to P3(goal).
For reconstructing the path, you can create another matrix of parents pi[N][N], if shortest path is coming from above then pi[i][j] = (i-1, j) if is coming from left pi[i][j] = (i, j-1) if is impossible to reach that cell pi[i][j] = null(whatever you want).
In general, my approach would be:
Generate a graph where each cell is a vertex, and connect with an edge vertices that represent adjacent white/grey cells in the maze.
Find the shortest path P1 between the the starting vertex (the one representing Array[0][0]) and the grey vertex (A* would be recommended).
Find the shortest path P2 between the the grey vertex and the end vertex (the one representing Array[N-1][N-1]).
P1 and P2 may intesect only once (as they represent the shortest paths), if they do intersect, from this point and on the will represent the same path. thus:
if P1 and P2 don't intersect, then P1 followed by P2 is the optimal solution.
if P1 and P2 do intersect, then delete the vertices in the intersected part, peform actions 2 and 3 again to find new shortest paths P3 and P4 respectively. the optimal solution is the minimum between P1 followed by P4 and P3 followed by P2.
Related
The above parallelize code is taking much more time as compare to the original one. I have used bfs approach to solve the problem. I am getting the correct output but it is taking too much time.
(x, y) represents matrix cell coordinates, and
dist represents their minimum distance from the source
struct Node
{
int x, y, dist;
};
\\Below arrays detail all four possible movements from a cell
int row[] = { -1, 0, 0, 1 };
int col[] = { 0, -1, 1, 0 };
Function to check if it is possible to go to position (row, col)
from the current position. The function returns false if (row, col)
is not a valid position or has a value 0 or already visited.
bool isValid(vector<vector<int>> const &mat, vector<vector<bool>> &visited, int row, int col)
{
return (row >= 0 && row < mat.size()) && (col >= 0 && col < mat[0].size())
&& mat[row][col] && !visited[row][col];
}
Find the shortest possible route in a matrix mat from source
cell (i, j) to destination cell (x, y)
int findShortestPathLength(vector<vector<int>> const &mat, pair<int, int> &src,
pair<int, int> &dest)
{
if (mat.size() == 0 || mat[src.first][src.second] == 0 ||
mat[dest.first][dest.second] == 0) {
return -1;
}
// `M × N` matrix
int M = mat.size();
int N = mat[0].size();
// construct a `M × N` matrix to keep track of visited cells
vector<vector<bool>> visited;
visited.resize(M, vector<bool>(N));
// create an empty queue
queue<Node> q;
// get source cell (i, j)
int i = src.first;
int j = src.second;
// mark the source cell as visited and enqueue the source node
visited[i][j] = true;
q.push({i, j, 0});
// stores length of the longest path from source to destination
int min_dist = INT_MAX;
// loop till queue is empty
while (!q.empty())
{
// dequeue front node and process it
Node node = q.front();
q.pop();
// (i, j) represents a current cell, and `dist` stores its
// minimum distance from the source
int i = node.x, j = node.y, dist = node.dist;
// if the destination is found, update `min_dist` and stop
if (i == dest.first && j == dest.second)
{
min_dist = dist;
break;
}
// check for all four possible movements from the current cell
// and enqueue each valid movement
#pragma omp parallel for
for (int k = 0; k < 4; k++)
{
// check if it is possible to go to position
// (i + row[k], j + col[k]) from current position
#pragma omp task shared(i,visited,j)
{
if (isValid(mat, visited, i + row[k], j + col[k]))
{
// mark next cell as visited and enqueue it
visited[i + row[k]][j + col[k]] = true;
q.push({ i + row[k], j + col[k], dist + 1 });
}
}
}
}
if (min_dist != INT_MAX) {
return min_dist;
}
return -1;
}
main part of the code only contains a matrix and source and destination coordinates
int main()
{
vector<vector<int>> mat =
{
{ 1, 1, 1, 1, 1, 0, 0, 1, 1, 1 },
{ 0, 1, 1, 1, 1, 1, 0, 1, 0, 1 },
{ 0, 0, 1, 0, 1, 1, 1, 0, 0, 1 },
{ 1, 0, 1, 1, 1, 0, 1, 1, 0, 1 },
{ 0, 0, 0, 1, 0, 0, 0, 1, 0, 1 },
{ 1, 0, 1, 1, 1, 0, 0, 1, 1, 0 },
{ 0, 0, 0, 0, 1, 0, 0, 1, 0, 1 },
{ 0, 1, 1, 1, 1, 1, 1, 1, 0, 0 },
{ 1, 1, 1, 1, 1, 0, 0, 1, 1, 1 },
{ 0, 0, 1, 0, 0, 1, 1, 0, 0, 1 },
};
pair<int, int> src = make_pair(0, 0);
pair<int, int> dest = make_pair(7, 5);
int min_dist = findShortestPathLength(mat, src, dest);
if (min_dist != -1)
{
cout << min_dist<<endl;
}
else {
cout << "Destination cannot be reached from a given source"<<endl;
}
return 0;
}
I have used shared variable but it is taking too much time.
Can anyone help me?
As people have remarked, you only get parallelism over the 4 directions; a better approach is to keep a set of sets of points: first the starting point, then all points you can reach from there in 1 step, then the ones you can reach in two steps.
With this approach you get more parallelism: you're building what's known as a "wave front" and all the points can be tackled simultaneously.
auto last_level = reachable.back();
vector<int> newly_reachable;
for ( auto n : last_level ) {
const auto& row = graph.row(n);
for ( auto j : row ) {
if ( not reachable.has(j)
and not any_of
( newly_reachable.begin(),newly_reachable.end(),
[j] (int i) { return i==j; } ) )
newly_reachable.push_back(j);
}
}
if (newly_reachable.size()>0)
reachable.push_back(newly_reachable);
(I have written this for a general DAG; writing your maze as a DAG is an exercise for the reader.)
However, this approach still has big problems: if two points on the current wave front decide to add the same new point, you have to resolve that.
For a very parallel approach you need to abandon the "push" model of adding new points altogether, and to go a "pull" model: in every "distance" iteration you loop over all points and ask, if I am not reachable, was one of my neighbors reachable? If so, mark me as reachable in one step more than that neighbor.
If you think about that last approach for a second (or two) you'll see that you are essentially doing a sequence of matrix-vector products, with the adjacency matrix and the currently reachable set. Except that you replace the scalar "+" operation by "min" and the scalar "*" by "+1". Read any tutorial about the interpretation of graph operations as linear algebra. Except that it's not really linear.
The problem I am facing is the following:
I have a function based on the BFS search algorithm that I use in a NxM grid, the mission of this function is to return the following Direction from a set of possible Directions = {Up, Down, Left , Right} (No diagonal moves!)to which a player has to move, so that in each "round / frame" where there is a type of item of a game (For example, in this specific case, a bazooka) is closer to the item.
To address the problem, I have created a Map class made of vector <vector <Cell> > where vector is from the standard library and Cell is what the grid is made of and has some consulting methods on what is in one of the NxM cells (if there is a building, an enemy, a Bazooka, etc.)
So, for implementing a solution for this, I made a struct TrackingBFS to reconstruct the path of the BFS search:
struct TrackingBFS {
pair <int,int> anterior;
bool visited;
};
And this is the BFS search implementation:
//Pre: origen is a valid position on the grid where the player is
//Post:Returns a pair of bool and a direction to the closest bazooka. If we have access to a bazooka, then we will return a pair (true,Dir) where Dir is a direction to take to be closer to the bazooka else a pair (false, Dir) where dir is just the same direction as origen.
pair <bool,Dir> direction_to_closest_gun (const Pos& origen) {
//R = board_rows() C = board_cols()
//m = mapa
//sr,sc = origin positions
int sr = origen.i;
int sc = origen.j;
//Variables para mantener tracking de los pasos cogidos
queue <int> rq; //Queue of x-row coordinates
queue <int> cq; //Queue of y-row coordinates
int move_count = 0; //Number of steps
int nodes_left_in_layer = 1; //How many nodes we need to de-queue before doing a step
int nodes_in_next_layer = 0; //How many nodes we add to the expansio of the BFS so we can upgrade nodes_left_in_layer in the next iteration
bool arma_mejor_encontrada = false;
//Visited is a MxN matrix of TrackingBFS that for every i,j stores the following information:
//If we visited the cell at position visited [i][j], then the TrackingBFS struct will be visited = true and last_node = (k,l) where k and l are the components of the LAST cell on the grid we visited in the BFS search.
//Else if we didn't visited the cell at [i][j], the TrackingBFS struct will be visited = true and last_node (-1,-1).
TrackingBFS aux;
aux.last_node = make_pair(-1,-1);
aux.visited = false;
//We create a Matrix of TrackingBFS named visited of NxM filled with the unvisited cells
vector < vector <TrackingBFS> > visited (board_rows(), vector <TrackingBFS> (board_cols(),aux));
//--------------------------------SOLVE---------------------------------
rq.push(sr);
cq.push(sc);
visited[sr][sc].visited = true;
visited[sr][sc].last_node = make_pair(sr,sc);
int xfound;
int yfound;
while (!rq.empty()) {
int r = rq.front();
int c = cq.front();
rq.pop();
cq.pop();
if (mapa[r][c].weapon == Bazooka) {
arma_mejor_encontrada = true;
xfound = r;
yfound = c;
break;
}
//Explore neighbours
Pos p (r,c);
for (Dir d : dirs) {
Pos searching = p + d;
int rr = searching.i;
int cc = searching.j;
//If the position we are searching is out of range or it's been visited before or there is a obstacle then continue
if (!pos_ok(searching) or visited[rr][cc].visited or mapa[rr][cc].type == Building or mapa[rr][cc].resistance != -1 or mapa[rr][cc].id != -1) {
//NADA
}
//Else we add the visited node to the queue, and fill the information on visited[rr][cc] with the node we are coming and mark it as visited
else {
rq.push(rr);
cq.push(cc);
visited[rr][cc].visited = true;
visited[rr][cc].last_node = make_pair (r,c);
nodes_in_next_layer++;
}
}
nodes_left_in_layer--;
if (nodes_left_in_layer == 0) {
nodes_left_in_layer = nodes_in_next_layer;
nodes_in_next_layer = 0;
move_count++;
}
}
//If we found the Bazooka
if (arma_mejor_encontrada) {
//Return the pair (true,next direction of the player at position (sr,sc) to approach the bazooka)
return make_pair(arma_mejor_encontrada, reconstruir_camino(visited,xfound,yfound,sr,sc));
}
else {
//If there is no bazooka we return (false,Up (this second component is meaningless))
return make_pair(arma_mejor_encontrada, Up);
}
}
The reconstruir_camino (recosntruct_path in english) implementation:
//This function is made to reconstruct the path from where we found de bazooka (x,y) to where our player is (ox,oy), whe are only interested in the next position of because this is run each frame, so we are constantly refreshing the state of the map.
Dir reconstruir_camino(const vector < vector <TrackingBFS> >& visited, const int& x, const int& y, const int& ox, const int& oy) {
//In v we save the pair of coordinates of our path, this was done only for debuging and in debug_matriz_tracking(visited) is also only for debuging
vector <pair<int,int>> path;
debug_matriz_tracking(visited);
//
int i = visited[x][y].last_node.first;
int j = visited[x][y].last_node.second;
while (not (i == ox and j == oy)) { //While the next node is not iqual as the original node we started de search (The one where our player is)
path.push_back(make_pair(i,j)); //Just for debuging
i = visited[i][j].last_node.first;
j = visited[i][j].last_node.second;
}
//So now in path we have the start and end nodes of every edge on the path
int siguiente_x = path.back().first;
int siguiente_y = path.back().second;
debug_camino(path);
return direccion_contiguos(ox,oy,siguiente_x,siguiente_y);
}
And direccion_contiguos (contiguous direction / relative direction in english) implementation:
//Returns the RELATIVE direction to go if we want to go from (ox, oy) to (dx, dy) being these two contiguous positions, that is, (dx, dy) is in Up, Down, Left or Right with respect to (ox, oy) IT WORKS FINE, NOTHING WRONG WITH THIS DON'T TOUCH
Dir direccion_contiguos (const int& ox, const int& oy, const int& dx, const int& dy) {
Pos o (ox,oy);
Pos d (dx,dy);
if (o + Up == d) {
return Up;
}
else if (o + Down == d){
return Down;
}
else if (o + Left == d) {
return Left;
}
else if (o + Right == d) {
return Right;
}
return Down;
}
So now in visited, we have the information to reconstruct the path, in fact I debuged it (it's kinda messy i know, sorry), in a visual way so this is what I got for a Player in origen = (7,10) and bazooka at position = (4,11):
[Imgur link of the Visual representation of the Matrix for reconstructing the path from origin to bazooka][1]
To read this image, at the top and left there are the coordinates of every cell of the visited matrix, the ones with green font, are the ones that have been visited, and they store THE NEXT cell/vertex of the path, and the ones with (-1,-1) are the ones that have not been visited by the BFS algorithm and thus they don't have any previous node and are in white.
So, NICE! It seems to work, at least the visited matrix.
My problem is when I debug the vector of edges/directions of the graph/grid, this is what I used in the example of the image:
void debug_camino(const vector <pair<int,int>>& v) {
cerr << "----------------------CAMINO-------------------- DEBUG_CAMINO" << endl;
for (long unsigned int i = 0; i < v.size(); ++i) {
cerr << "(" << v[i].first << "," << v[i].second << "),";
}
cerr << endl;
}
And when I executed the program, this is the path that I got with debug_camino():
If you see the image attached you can see that that's almost the path but not quite yet.
(4,12),(4,13),(4,14),(3,15),(3,16),(4,16),(5,16),(6,16),(7,15),(7,14),(7,13),(7,12),(7,11)
These ones bolded are not real (even valid because they are diagonal moves) reconstructions of the path and I don't really know WHY this is happening, but it's provoking my player to not following right path, and I want to fix the error and I'm kinda desperate because I don't really know where the error is and I've been trying for days :( ! I hope somebody can help me with this. Thanks for reading all this and sorry if the code is in some parts in Spanish or if it's not all that readable.
[1]: https://i.stack.imgur.com/vZ2Go.png
Okay, I actually managed to fix this error, i was overwriting the i variable so that was causing the error.
//This function is made to reconstruct the path from where we found de bazooka (x,y) to where our player is (ox,oy), whe are only interested in the next position of because this is run each frame, so we are constantly refreshing the state of the map.
Dir reconstruir_camino(const vector < vector <TrackingBFS> >& visited, const int& x, const int& y, const int& ox, const int& oy) {
//In v we save the pair of coordinates of our path, this was done only for debuging and in debug_matriz_tracking(visited) is also only for debuging
vector <pair<int,int>> path;
debug_matriz_tracking(visited);
//
int i = visited[x][y].last_node.first;
int j = visited[x][y].last_node.second;
while (not (i == ox and j == oy)) { //While the next node is not iqual as the original node we started de search (The one where our player is)
path.push_back(make_pair(i,j)); //Just for debuging
**i** = visited[i][j].last_node.first;
j = visited[**i**][j].last_node.second;
}
//So now in path we have the start and end nodes of every edge on the path
int siguiente_x = path.back().first;
int siguiente_y = path.back().second;
debug_camino(path);
return direccion_contiguos(ox,oy,siguiente_x,siguiente_y);
}
It's already fixed
recently moved from C# to C++ so I'm new to pointers and references and so on.
I've a pointer-to-pointer array declared like this
enum Type
{
Void,
DeepWater,
Water,
... etc }
Tile::Type** tiles;
TileManager::TileManager(int width, int height)
{
this->tiles = new Tile::Type*[width];
for (int w = 0; w < width; w++)
{
tiles[w] = new Tile::Type[height];
for (int h = 0; h < height; h++)
{
tiles[w][h] = Tile::Type::Dirt;
}
}
}
Now I'm putting together a method that returns the neighbours of a cell in the tiles array and checking if each neighbour is not-equal to NULL.
However even when checking whether it's null or not seems to throw an error, so I'm stumped.
Tile::Type * TileManager::GetNeighbours(int x, int y)
{
Tile::Type neighbours[8];
if(tiles[x][y+1] != NULL)
neighbours[0] = tiles[x ][y + 1];
...etc
if (tiles[x - 1][y - 1] != NULL) //<-- Error fires here
neighbours[5] = tiles[x - 1][y - 1];
return neighbours;
}
I know why it's throwing the error but shy of checking X and Y to see if they go over the limit or below 0... I figure there's a more practical way to prevent this so thought I'd best ask.
Edit:
Thank you, user4581301. I found most of this code elsewhere and adapted it to reflect the changes you suggested.
std::array<Tile::Type, 8> TileManager::GetNeighbours(int c, int r)
{
std::array<Tile::Type, 8> neighbours;
const int y[] = { -1, -1, -1, 1, 1, 1, 0, 0 };// 8 shifts to neighbors
const int x[] = { -1, 0, 1, -1, 0, 1, -1, 1 };// used in functions
for (int i = 0; i < 8; ++i)// visit the 8 spaces around it
if (inField(r + y[i], c + x[i]))
neighbours[i] = tiles[r + y[i]][c + x[i]];
else
neighbours[i] = Tile::Type::Void;
return neighbours;
}
bool TileManager::inField(int r, int c)
{
if (r < 0 || r >= 25) return false;
if (c < 0 || c >= 25) return false;
return true;
}
tiles[x][y+1], if y is the maximum valid value, will not be NULL except by the grace of . This goes out of bounds and as soon as you go out of bounds all bets are off. You've invoked Undefined Behaviour and pretty much anything can happen. Even what you expected to happen.
The same applies to the reported crash site, tiles[x - 1][y - 1].
Edit: Left out solution. Not helpful.
The only way, short of taking off and nuking the entire site from orbit, is to test the index to make sure it does not puncture the array bounds before using the index on the array. You'll probably want a function to handle this.
void assign_if(Type & neighbour, int x, int y)
{
if(x >= 0 && x < width && y >= 0 && y < height)
neighbour = tiles[x][y];
}
and call it
assign_if(neighbours[0], x, y+1);
and later
assign_if(neighbours[0], x-1, y-1);
Edit: Stealing this from Bob__ for completeness
It is impossible to return a raw array from a function. The array goes out of scope and the pointer to it becomes invalid. Either pass in the array as another parameter or use a std::array or std::vector, both of which can be returned. Thanks to Copy Elision, a smart compiler will likely eliminate the copying costs.
Example:
std::array<Tile::Type, 8> TileManager::GetNeighbours(int x, int y)
{
std::array<Tile::Type, 8> neighbours;
...
return neighbours;
}
Edit by original poster. Here is my solution:
std::array<Tile::Type, 8> TileManager::GetNeighbours(int c, int r)
{
std::array<Tile::Type, 8> neighbours;
const int y[] = { -1, -1, -1, 1, 1, 1, 0, 0 };// 8 shifts to neighbors
const int x[] = { -1, 0, 1, -1, 0, 1, -1, 1 };// used in functions
for (int i = 0; i < 8; ++i)// visit the 8 spaces around it
if (inField(r + y[i], c + x[i]))
neighbours[i] = tiles[r + y[i]][c + x[i]];
else
neighbours[i] = Tile::Type::Void;
return neighbours;
}
bool TileManager::inField(int r, int c)
{
if (r < 0 || r >= 25) return false;
if (c < 0 || c >= 25) return false;
return true;
}
Edit: Caveat
This answer deals directly with solving the problem as asked. See the answer by Kaz for a description of a more practical solution that trades a bit of memory to completely eliminate the need for testing and generating the neighbours array.
The more "practical" way (shorter code that avoids conditional checks) is to create the tile array so that it's it contains an additional "border" of tiles around the valid area. If any tile position is in the valid area, then is valid and so is .
You can have a special type for the border tiles which only they have, and simply include those tiles in the "neighbors" list. If your world has walls, then the border can consist of wall material.
Needless to say, you must never ask for the list of neighbors of a border tile. This is ensured by logic such as not allowing a border tile to be the valid position for anything.
This tile is in the valid area within the border" is a condition that is easier to check, in fewer places, and your program can be structured so that this check is actually just a removable assertion (a check for a situation that should not happen if the program is correct, rather than a check for an expected situation).
In C and C++, we can displace the pointers so that position [0][0] is still the corner of the valid area, yet the out-of-bounds coordinates [-1][-1] are valid indices, as are [w][h].
Firstly, the column array is allocated two elements larger than necessary, and the pointer is the incremented by one. Then the columns are allocated two elements larger, and each pointer is incremented by one before being assigned into the main array.
When freeing the arrays with delete [], you have to remember to decrement each pointer by one.
Given N boxes. How can i find the tallest tower made with them in the given order ? (Given order means that the first box must be at the base of the tower and so on). All boxes must be used to make a valid tower.
It is possible to rotate the box on any axis in a way that any of its 6 faces gets parallel to the ground, however the perimeter of such face must be completely restrained inside the perimeter of the superior face of the box below it. In the case of the first box it is possible to choose any face, because the ground is big enough.
To solve this problem i've tried the following:
- Firstly the code generates the rotations for each rectangle (just a permutation of the dimensions)
- secondly constructing a dynamic programming solution for each box and each possible rotation
- finally search for the highest tower made (in the dp table)
But my algorithm is taking wrong answer in unknown test cases. What is wrong with it ? Dynamic programming is the best approach to solve this problem ?
Here is my code:
#include <cstdio>
#include <vector>
#include <algorithm>
#include <cstdlib>
#include <cstring>
struct rectangle{
int coords[3];
rectangle(){ coords[0] = coords[1] = coords[2] = 0; }
rectangle(int a, int b, int c){coords[0] = a; coords[1] = b; coords[2] = c; }
};
bool canStack(rectangle ¤t_rectangle, rectangle &last_rectangle){
for (int i = 0; i < 2; ++i)
if(current_rectangle.coords[i] > last_rectangle.coords[i])
return false;
return true;
}
//six is the number of rotations for each rectangle
int dp(std::vector< std::vector<rectangle> > &v){
int memoization[6][v.size()];
memset(memoization, -1, sizeof(memoization));
//all rotations of the first rectangle can be used
for (int i = 0; i < 6; ++i) {
memoization[i][0] = v[0][i].coords[2];
}
//for each rectangle
for (int i = 1; i < v.size(); ++i) {
//for each possible permutation of the current rectangle
for (int j = 0; j < 6; ++j) {
//for each permutation of the previous rectangle
for (int k = 0; k < 6; ++k) {
rectangle &prev = v[i - 1][k];
rectangle &curr = v[i][j];
//is possible to put the current rectangle with the previous rectangle ?
if( canStack(curr, prev) ) {
memoization[j][i] = std::max(memoization[j][i], curr.coords[2] + memoization[k][i-1]);
}
}
}
}
//what is the best solution ?
int ret = -1;
for (int i = 0; i < 6; ++i) {
ret = std::max(memoization[i][v.size()-1], ret);
}
return ret;
}
int main ( void ) {
int n;
scanf("%d", &n);
std::vector< std::vector<rectangle> > v(n);
for (int i = 0; i < n; ++i) {
rectangle r;
scanf("%d %d %d", &r.coords[0], &r.coords[1], &r.coords[2]);
//generate all rotations with the given rectangle (all combinations of the coordinates)
for (int j = 0; j < 3; ++j)
for (int k = 0; k < 3; ++k)
if(j != k) //micro optimization disease
for (int l = 0; l < 3; ++l)
if(l != j && l != k)
v[i].push_back( rectangle(r.coords[j], r.coords[k], r.coords[l]) );
}
printf("%d\n", dp(v));
}
Input Description
A test case starts with an integer N, representing the number of boxes (1 ≤ N ≤ 10^5).
Following there will be N rows, each containing three integers, A, B and C, representing the dimensions of the boxes (1 ≤ A, B, C ≤ 10^4).
Output Description
Print one row containing one integer, representing the maximum height of the stack if it’s possible to pile all the N boxes, or -1 otherwise.
Sample Input
2
5 2 2
1 3 4
Sample Output
6
Sample image for the given input and output.
Usually you're given the test case that made you fail. Otherwise, finding the problem is a lot harder.
You can always approach it from a different angle! I'm going to leave out the boring parts that are easily replicated.
struct Box { unsigned int dim[3]; };
Box will store the dimensions of each... box. When it comes time to read the dimensions, it needs to be sorted so that dim[0] >= dim[1] >= dim[2].
The idea is to loop and read the next box each iteration. It then compares the second largest dimension of the new box with the second largest dimension of the last box, and same with the third largest. If in either case the newer box is larger, it adjusts the older box to compare the first largest and third largest dimension. If that fails too, then the first and second largest. This way, it always prefers using a larger dimension as the vertical one.
If it had to rotate a box, it goes to the next box down and checks that the rotation doesn't need to be adjusted there too. It continues until there are no more boxes or it didn't need to rotate the next box. If at any time, all three rotations for a box failed to make it large enough, it stops because there is no solution.
Once all the boxes are in place, it just sums up each one's vertical dimension.
int main()
{
unsigned int size; //num boxes
std::cin >> size;
std::vector<Box> boxes(size); //all boxes
std::vector<unsigned char> pos(size, 0); //index of vertical dimension
//gets the index of dimension that isn't vertical
//largest indicates if it should pick the larger or smaller one
auto get = [](unsigned char x, bool largest) { if (largest) return x == 0 ? 1 : 0; return x == 2 ? 1 : 2; };
//check will compare the dimensions of two boxes and return true if the smaller one is under the larger one
auto check = [&boxes, &pos, &get](unsigned int x, bool largest) { return boxes[x - 1].dim[get(pos[x - 1], largest)] < boxes[x].dim[get(pos[x], largest)]; };
unsigned int x = 0, y; //indexing variables
unsigned char change; //detects box rotation change
bool fail = false; //if it cannot be solved
for (x = 0; x < size && !fail; ++x)
{
//read in the next three dimensions
//make sure dim[0] >= dim[1] >= dim[2]
//simple enough to write
//mine was too ugly and I didn't want to be embarrassed
y = x;
while (y && !fail) //when y == 0, no more boxes to check
{
change = pos[y - 1];
while (check(y, true) || check(y, false)) //while invalid rotation
{
if (++pos[y - 1] == 3) //rotate, when pos == 3, no solution
{
fail = true;
break;
}
}
if (change != pos[y - 1]) //if rotated box
--y;
else
break;
}
}
if (fail)
{
std::cout << -1;
}
else
{
unsigned long long max = 0;
for (x = 0; x < size; ++x)
max += boxes[x].dim[pos[x]];
std::cout << max;
}
return 0;
}
It works for the test cases I've written, but given that I don't know what caused yours to fail, I can't tell you what mine does differently (assuming it also doesn't fail your test conditions).
If you are allowed, this problem might benefit from a tree data structure.
First, define the three possible cases of block:
1) Cube - there is only one possible option for orientation, since every orientation results in the same height (applied toward total height) and the same footprint (applied to the restriction that the footprint of each block is completely contained by the block below it).
2) Square Rectangle - there are three possible orientations for this rectangle with two equal dimensions (for examples, a 4x4x1 or a 4x4x7 would both fit this).
3) All Different Dimensions - there are six possible orientations for this shape, where each side is different from the rest.
For the first box, choose how many orientations its shape allows, and create corresponding nodes at the first level (a root node with zero height will allow using simple binary trees, rather than requiring a more complicated type of tree that allows multiple elements within each node). Then, for each orientation, choose how many orientations the next box allows but only create nodes for those that are valid for the given orientation of the current box. If no orientations are possible given the orientation of the current box, remove that entire unique branch of orientations (the first parent node with multiple valid orientations will have one orientation removed by this pruning, but that parent node and all of its ancestors will be preserved otherwise).
By doing this, you can check for sets of boxes that have no solution by checking whether there are any elements below the root node, since an empty tree indicates that all possible orientations have been pruned away by invalid combinations.
If the tree is not empty, then just walk the tree to find the highest sum of heights within each branch of the tree, recursively up the tree to the root - the sum value is your maximum height, such as the following pseudocode:
std::size_t maximum_height() const{
if(leftnode == nullptr || rightnode == nullptr)
return this_node_box_height;
else{
auto leftheight = leftnode->maximum_height() + this_node_box_height;
auto rightheight = rightnode->maximum_height() + this_node_box_height;
if(leftheight >= rightheight)
return leftheight;
else
return rightheight;
}
}
The benefits of using a tree data structure are
1) You will greatly reduce the number of possible combinations you have to store and check, because in a tree, the invalid orientations will be eliminated at the earliest possible point - for example, using your 2x2x5 first box, with three possible orientations (as a Square Rectangle), only two orientations are possible because there is no possible way to orient it on its 2x2 end and still fit the 4x3x1 block on it. If on average only two orientations are possible for each block, you will need a much smaller number of nodes than if you compute every possible orientation and then filter them as a second step.
2) Detecting sets of blocks where there is no solution is much easier, because the data structure will only contain valid combinations.
3) Working with the finished tree will be much easier - for example, to find the sequence of orientations of the highest, rather than just the actual height, you could pass an empty std::vector to a modified highest() implementation, and let it append the actual orientation of each highest node as it walks the tree, in addition to returning the height.
I have m*n table which each entry have a value .
start position is at top left corner and I can go right or down until I reach lower right corner.
I want a path that if I multiply numbers on that path I get a number that have minimum number of zeros in it's right side .
example:
1 2 100
5 5 4
possible paths :
1*2*100*4=800
1*2*5*4= 40
1*5*5*4= 100
Solution : 1*2*5*4= 40 because 40 have 1 zero but other paths have 2 zero.
easiest way is using dfs and calculate all paths. but it's not efficient.
I'm looking for an optimal substructure for solving it using dynammic programming.
After thinking for a while I came up to this equation :
T(i,j) = CountZeros(T(i-1,j)*table[i,j]) < CountZeros(T(i,j-1)*table[i,j]) ?
T(i-1,j)*table[i,j] : T(i,j-1)*table[i,j]
Code :
#include <iostream>
#include <vector>
#include <algorithm>
#include <numeric>
using namespace std;
using Table = vector<vector<int>>;
const int rows = 2;
const int cols = 3;
Table memo(rows, vector<int>(cols, -1));
int CountZeros(int number)
{
if (number < 0)
return numeric_limits<int>::max();
int res = 0;
while (number != 0)
{
if (number % 10 == 0)
res++;
else break;
number /= 10;
}
return res;
}
int solve(int i, int j, const Table& table)
{
if (i < 0 || j < 0)
return -1;
if (memo[i][j] != -1)
return memo[i][j];
int up = solve(i - 1, j, table)*table[i][j];
int left = solve(i, j - 1, table)*table[i][j];
memo[i][j] = CountZeros(up) < CountZeros(left) ? up : left;
return memo[i][j];
}
int main()
{
Table table =
{
{ 1, 2, 100 },
{ 5, 5, 4 }
};
memo[0][0] = table[0][0];
cout << solve(1, 2, table);
}
(Run )
But it is not optimal (for example in above example it give 100 )
Any idea for better optimal sub-structure ? can I solve it with dynammic programming ?!
Let's reconsider the Bellman optimality equation for your task. I consider this as a systematic approach to such problems (whereas I often don't understand DP one-liners). My reference is the book of Sutton and Barto.
The state in which your system is can be described by a triple of integer numbers (i,j,r) (which is modeled as a std::array<int,3>). Here, i and j denote column and row in your rectangle M = m_{i,j}, whereas r denotes the multiplication result.
Your actions in state (i,j,r) are given by going right, with which you end in state (i, j+1, r*m_{i,j+1}) or by going down which leads to the state (i+1, j, r*m_{i+1,j}).
Then, the Bellman equation is given by
v(i,j,r) = min{ NullsIn(r*m_{i+1,j}) - NullsIn(r) + v_(i+1,j, r*m_{i+1,j})
NullsIn(r*m_{i,j+1}) - NullsIn(r) + v_(i,j+1, r*m_{i,j+1}) }
The rationale behind this equation is the following: NullsIn(r*m_{i+1,j}) - NullsIn(r) denotes the zeros you have to add when you take one of the two actions, i.e. the instant penalty. v_(i+1,j, r*m_{i+1,j}) denotes the zeros in the state you get to when you take this action. Now one wants to take the action which minimizes both contributions.
What you need further is only a function int NullsIn(int) which returns the nulls in a given integer. Here is my attempt:
int NullsIn(int r)
{
int ret=0;
for(int j=10; j<=r; j*=10)
{
if((r/j) * j == r)
++ret;
}
return ret;
}
For convenience I further defined a NullsDifference function:
int NullsDifference(int r, int m)
{
return NullsIn(r*m) - NullsIn(r);
}
Now, one has to do a backwards iteration starting from the initial state in the right bottom element of the matrix.
int backwardIteration(std::array<int,3> state, std::vector<std::vector<int> > const& m)
{
static std::map<std::array<int,3>, int> memoization;
auto it=memoization.find(state);
if(it!=memoization.end())
return it->second;
int i=state[0];
int j=state[1];
int r=state[2];
int ret=0;
if(i>0 && j>0)
{
int inew=i-1;
int jnew=j-1;
ret=std::min(NullsDifference(r, m[inew][j]) + backwardIteration({inew,j,r*m[inew][j]}, m),
NullsDifference(r, m[i][jnew]) + backwardIteration({i,jnew,r*m[i][jnew]}, m));
}
else if(i>0)
{
int inew=i-1;
ret= NullsDifference(r, m[inew][j]) + backwardIteration({inew,j,r*m[inew][j]}, m);
}
else if(j>0)
{
int jnew=j-1;
ret= NullsDifference(r, m[i][jnew]) + backwardIteration({i,jnew,r*m[i][jnew]}, m);
}
memoization[state]=ret;
return ret;
}
This routine is called via
int main()
{
int ncols=2;
int nrows=3;
std::vector<std::vector<int> > m={{1,2,100}, {5,5,4}};
std::array<int,3> initialState = {ncols-1, nrows -1, m[ncols-1][nrows - 1]};
std::cout<<"Minimum number of zeros: "backwardIteration(initialState, m)<<"\n"<<std::endl;
}
For your array, it prints out the desired 1 for the number of zeros.
Here is a live demo on Coliru.
EDIT
Here is an important thing: in production, you usually don't call backwardIteration as I did because it takes an exponentially increasing number of recursive calls. Rather, you start in the top left and call it, then store the result. Next you go left and down and each time call backwardIteration where you now use the previously stored result. And so on.
In order to do this, one needs a memoization concept within the function backwardIteration, which returns the already stored result instead of invoking another recursive call.
I've added memoization in the function call above. Now you can loop through the array from left top to right bottom in any way you like -- but prefereably take small steps, such as row-by-row, column-by-column, or rectangle-for-rectangle.
In fact, this and only this is the spirit of Dynamic Programming.