I am working on image processing with C# and implementing integral histogram. I am not getting into the details, but assume that I have MxN matrix and each cell value is the sum of itself and its left and upper neighbor, minus left upper corner neighbor. This works fast but I want to make it faster for large images or for real time image processing performance.
matrix[i,j] += matrix[i-1,j] + matrix[i,j-1] - matrix[i-1,j-1];
The actual implementation is:
for (int i = 0; i < width; i++)
for (int j = 0; j < height; j++)
{
int left = 0, upper = 0, u_l_corner = 0;
if (j - 1 >= 0)
{
left = matrix[i, j - 1];
}
if (i - 1 >= 0)
{
upper = matrix[i - 1, j];
}
if (j - 1 >= 0 && i - 1 >= 0)
u_l_corner = matrix[i - 1, j - 1];
matrix[i, j] += left + upper - u_l_corner;
}
So the calculation is dependent on the previous values of cells. Therefore, it does not look like it can be implemented in parallel(at least to me). But still, just want to make sure before go any further..
Can this algorithm be implemented in parallel using Parallel.For or any other method in C#? If so a simple example is highly appreciated, but if not, I better work on to find a "parallel image histogram algorithm", if any exists.
Thanks in advance.
As far as I'm concerned it is possible to make this algorithm parallel but i find no use of doing so if your matrix is relatively small ( time to process is less than few miliseconds ).
If you are very interested in making this algorithm parallel you could make this task splitted to exactly "j" tasks ( number of items in "y" axis ).
Key to doing so is starting first thread to calculate points in first row of this matrice ( [i, 0] ) then starting second thread delayed - second thread should chase first thread - must never overtake preceding thread.
Related
It hasn't been long since I started studying algorithm coding tests, and I found it difficult to find regularity in Memoization.
Here are two problems.
Min Cost Climbing Stairs
You are given an integer array cost where cost[i] is the cost of ith step on a staircase. Once you pay the cost, you can either climb one or two steps.
You can either start from the step with index 0, or the step with index 1.
Return the minimum cost to reach the top of the floor.
Min Cost Climbing Stairs
Recurrence Relation Formula:
minimumCost(i) = min(cost[i - 1] + minimumCost(i - 1), cost[i - 2] + minimumCost(i - 2))
House Robber
You are a professional robber planning to rob houses along a street. Each house has a certain amount of money stashed, the only constraint stopping you from robbing each of them is that adjacent houses have security systems connected and it will automatically contact the police if two adjacent houses were broken into on the same night.
Given an integer array nums representing the amount of money of each house, return the maximum amount of money you can rob tonight without alerting the police.
House Robber
Recurrence Relation Formula:
robFrom(i) = max(robFrom(i + 1), robFrom(i + 2) + nums(i))
So as you can see, first problem consist of the previous, and second problem consist of the next.
Because of this, when I try to make recursion function, start numbers are different.
Start from n
int rec(int n, vector<int>& cost)
{
if(memo[n] == -1)
{
if(n <= 1)
{
memo[n] = 0;
} else
{
memo[n] = min(rec(n-1, cost) + cost[n-1], rec(n-2, cost) + cost[n-2]);
}
}
return memo[n];
}
int minCostClimbingStairs(vector<int>& cost) {
const int n = cost.size();
memo.assign(n+1,-1);
return rec(n, cost); // Start from n
}
Start from 0
int getrob(int n, vector<int>& nums)
{
if(how_much[n] == -1)
{
if(n >= nums.size())
{
return 0;
} else {
how_much[n] = max(getrob(n + 1, nums), getrob(n + 2, nums) + nums[n]);
}
}
return how_much[n];
}
int rob(vector<int>& nums) {
how_much.assign(nums.size() + 2, -1);
return getrob(0, nums); // Start from 0
}
How can I easily know which one need to be started from 0 or n? Is there some regularity?
Or should I just solve a lot of problems and increase my sense?
Your question is right, but somehow examples are not correct. Both the problems you shared can be done in both ways : 1. starting from top & 2. starting from bottom.
For example: Min Cost Climbing Stairs : solution that starts from 0.
int[] dp;
public int minCostClimbingStairs(int[] cost) {
int n = cost.length;
dp = new int[n];
for(int i=0; i<n; i++) {
dp[i] = -1;
}
rec(0, cost);
return Math.min(dp[0], dp[1]);
}
int rec(int in, int[] cost) {
if(in >= cost.length) {
return 0;
} else {
if(dp[in] == -1) {
dp[in] = cost[in] + Math.min(rec(in+1, cost), rec(in+2, cost));
}
return dp[in];
}
}
However, there are certain set of problems where this is not easy. Their structure is such that if you start in reverse, the computation could get complicated or mess up the future results:
Example: Reaching a target sum from numbers in an array using an index at max only 1 time. Reaching 10 in {3, 4, 6, 5, 2} : {4,6} is one answer but not {6, 2, 2} as you are using index (4) 2 times.
This can be done easily in top down way:
int m[M+10];
for(i=0; i<M+10; i++) m[i]=0;
m[0]=1;
for(i=0; i<n; i++)
for(j=M; j>=a[i]; j--)
m[j] |= m[j-a[i]];
If you try to implement in bottom up way, you will end up using a[i] multiple times. You can definitely do it bottom up way if you figure a out a way to tackle this messing up of states. Like using a queue to only store reached state in previous iterations and not use numbers reached in current iterations. Or even check if you keep a count in m[j] instead of just 1 and only use numbers where count is less than that of current iteration count. I think same thing should be valid for all DP.
I am currently working on simulating a cloth like material and then displaying the results via Open Scene Graph.
I've gotten the setup to display something cloth like, by just dumping all the vertices into 1 Vec3Array and then displaying them with a standard Point based DrawArrays. However I am looking into adding the faces between the vertices so that a further part of my application can visually see the cloth.
This is currently what I am attempting as for the PrimitiveSet
// create and add a DrawArray Primitive (see include/osg/Primitive). The first
// parameter passed to the DrawArrays constructor is the Primitive::Mode which
// in this case is POINTS (which has the same value GL_POINTS), the second
// parameter is the index position into the vertex array of the first point
// to draw, and the third parameter is the number of points to draw.
unsigned int k = CLOTH_SIZE_X;
unsigned int n = CLOTH_SIZE_Y;
osg::ref_ptr<osg::DrawElementsUInt> indices = new osg::DrawElementsUInt(GL_QUADS, (k) * (n));
for (uint y_i = 0; y_i < n - 1; y_i++) {
for (uint x_i = 0; x_i < k - 1; x_i++) {
(*indices)[y_i * k + x_i] = y_i * k + x_i;
(*indices)[y_i * (k + 1) + x_i] = y_i * (k + 1) + x_i;
(*indices)[y_i * (k + 1) + x_i + 1] = y_i * (k + 1) + x_i + 1;
(*indices)[y_i * k + x_i] = y_i * k + x_i + 1;
}
}
geom->addPrimitiveSet(indices.get());
This does however cause memory corruption when running, and I am not fluent enough in Assembly code to decipher what it is trying to do wrong when CLion gives me the disassembled code.
My thought was that I would iterate over each of the faces of my cloth and then select the 4 indices of the vertices that belong to it. The vertices are inputted from top left to bottom right in order. So:
0 1 2 3 ... k-1
k k+1 k+2 k+3 ... 2k-1
2k 2k+1 2k+2 2k+3 ... 3k-1
...
Has anyone come across this specific use-case before and does he/she perhaps have a solution for my problem? Any help would be greatly appreciated.
You might want to look into using DrawArrays with QUAD_STRIP (or TRIANGLE_STRIP because quads are frowned upon these days). There's an example here:
http://openscenegraph.sourceforge.net/documentation/OpenSceneGraph/examples/osggeometry/osggeometry.cpp
It's slightly less efficient than Elements/indices, but it's also less complicated to manage the relationship between the two related containers (the vertices and the indices).
If you really want to do the Elements/indices route, we'd probably need to see more repro code to see what's going on.
In my game,if I touch a particular object,coin objects will come out of them at random speeds and occupy random positions.
public void update(delta){
if(isTouched()&& getY()<Constants.WORLD_HEIGHT/2){
setY(getY()+(randomSpeed * delta));
setX(getX()-(randomSpeed/4 * delta));
}
}
Now I want to make this coins occupy positions in some patterns.Like if 3 coins come out,a triangle pattern or if 4 coins, rectangular pattern like that.
I tried to make it work,but coins are coming out and moved,but overlapping each other.Not able to create any patterns.
patterns like:
This is what I tried
int a = Math.abs(rndNo.nextInt() % 3)+1;//no of coins
int no =0;
float coinxPos = player.getX()-coins[0].getWidth()/2;
float coinyPos = player.getY();
int minCoinGap=20;
switch (a) {
case 1:
for (int i = 0; i < coins.length; i++) {
if (!coins[i].isCoinVisible() && no < a) {
coins[i].setCoinVisible(true);
coinxPos = coinxPos+rndNo.nextInt()%70;
coinyPos = coinyPos+rndNo.nextInt()%70;
coins[i].setPosition(coinxPos, coinyPos);
no++;
}
}
break;
case 2:
for (int i = 0; i < coins.length; i++) {
if (!coins[i].isCoinVisible() && no < a) {
coins[i].setCoinVisible(true);
coinxPos = coinxPos+minCoinGap+rndNo.nextInt()%70;
coinyPos = coinyPos+rndNo.nextInt()%150;
coins[i].setPosition(coinxPos, coinyPos);
no++;
}
}
break:
......
......
default:
break;
may be this is a simple logic to implement,but I wasted a lot of time on it and got confused of how to make it work.
Any help would be appreciated.
In my game, when I want some object at X,Y to reach some specific coordinates Xe,Ye at every frame I'm adding to it's coordinates difference between current and wanted position, divided by constant and multiplied by time passed from last frame. That way it starts moving quickly and goes slowly and slowly as it's closer, looks kinda cool.
X += ((Xe - X)* dt)/ CONST;
Y += ((Ye - Y)* dt)/ CONST;
You'll experimentally get that CONST value, bigger value means slower movement. If you want it to look even cooler you can add velocity variable and instead of changing directly coordinates depending on distance from end position you can adjust that velocity. That way even if object at some point reaches the end position it will still have some velocity and it will keep moving - it will have inertia. A bit more complex to achieve, but movement would be even wilder.
And if you want that Xe,Ye be some specific position (not random), then just set those constant values. No need to make it more complicated then that. Set like another constat OFFSET:
static final int OFFSET = 100;
Xe1 = X - OFFSET; // for first coin
Ye1 = Y - OFFSET;
Xe2 = X + OFFSET; // for second coin
Ye2 = Y - OFFSET;
...
Given two bit strings, x and y, with x longer than y, I'd like to compute a kind of asymmetric variant of the Levensthein distance between them. Starting with x, I'd like to know the minimum number of deletions and substitutions it takes to turn x into y.
Can I just use the usual Levensthein distance for this, or do I need I need to modify the algorithm somehow? In other words, with the usual set of edits of deletion, substitution, and addition, is it ever beneficial to delete more than the difference in lengths between the two strings and then add some bits back? I suspect the answer is no, but I'm not sure. If I'm wrong, and I do need to modify the definition of Levenshtein distance to disallow deletions, how do I do so?
Finally, I would expect intuitively that I'd get the same distance if I started with y (the shorter string) and only allowed additions and substitutions. Is this right? I've got a sense for what these answers are, I just can't prove them.
If i understand you correctly, I think the answer is yes, the Levenshtein edit distance could be different than an algorithm that only allows deletions and substitutions to the larger string. Because of this, you would need to modify, or create a different algorithm to get your limited version.
Consider the two strings "ABCD" and "ACDEF". The Levenshtein distance is 3 (ABCD->ACD->ACDE->ACDEF). If we start with the longer string, and limit ourselves to deletions and substitutions we must use 4 edits (1 deletion and 3 substitutions. The reason is that strings where deletions are applied to the smaller string to efficiently get to the larger string can't be achieved when starting with the longer string, because it does not have the complimentary insertion operation (since you're disallowing that).
Your last paragraph is true. If the path from shorter to longer uses only insertions and substitutions, then any allowed path can simply be reversed from the longer to the shorter. Substitutions are the same regardless of direction, but the inserts when going from small to large become deletions when reversed.
I haven't tested this thoroughly, but this modification shows the direction I would take, and appears to work with the values I've tested with it. It's written in c#, and follows the psuedo code in the wikipedia entry for Levenshtein distance. There are obvious optimizations that can be made, but I refrained from doing that so it was more obvious what changes I've made from the standard algorithm. An important observation is that (using your constraints) if the strings are the same length, then substitution is the only operation allowed.
static int LevenshteinDistance(string s, string t) {
int i, j;
int m = s.Length;
int n = t.Length;
// for all i and j, d[i,j] will hold the Levenshtein distance between
// the first i characters of s and the first j characters of t;
// note that d has (m+1)*(n+1) values
var d = new int[m + 1, n + 1];
// set each element to zero
// c# creates array already initialized to zero
// source prefixes can be transformed into empty string by
// dropping all characters
for (i = 0; i <= m; i++) d[i, 0] = i;
// target prefixes can be reached from empty source prefix
// by inserting every character
for (j = 0; j <= n; j++) d[0, j] = j;
for (j = 1; j <= n; j++) {
for (i = 1; i <= m; i++) {
if (s[i - 1] == t[j - 1])
d[i, j] = d[i - 1, j - 1]; // no operation required
else {
int del = d[i - 1, j] + 1; // a deletion
int ins = d[i, j - 1] + 1; // an insertion
int sub = d[i - 1, j - 1] + 1; // a substitution
// the next two lines are the modification I've made
//int insDel = (i < j) ? ins : del;
//d[i, j] = (i == j) ? sub : Math.Min(insDel, sub);
// the following 8 lines are a clearer version of the above 2 lines
if (i == j) {
d[i, j] = sub;
} else {
int insDel;
if (i < j) insDel = ins; else insDel = del;
// assign the smaller of insDel or sub
d[i, j] = Math.Min(insDel, sub);
}
}
}
}
return d[m, n];
}
Problem :
There is a stack consisting of N bricks. You and your friend decide to play a game using this stack. In this game, one can alternatively remove 1/2/3 bricks from the top and the numbers on the bricks removed by the player is added to his score. You have to play in such a way that you obtain maximum possible score while it is given that your friend will also play optimally and you make the first move.
Input Format
First line will contain an integer T i.e. number of test cases. There will be two lines corresponding to each test case, first line will contain a number N i.e. number of element in stack and next line will contain N numbers i.e. numbers written on bricks from top to bottom.
Output Format
For each test case, print a single line containing your maximum score.
I have tried with recursion but didn't work
int recurse(int length, int sequence[5], int i) {
if(length - i < 3) {
int sum = 0;
for(i; i < length; i++) sum += sequence[i];
return sum;
} else {
int sum1 = 0;
int sum2 = 0;
int sum3 = 0;
sum1 += recurse(length, sequence, i+1);
sum2 += recurse(length, sequence, i+2);
sum3 += recurse(length, sequence, i+3);
return max(max(sum1,sum2),sum3);
}
}
int main() {
int sequence[] = {0, 0, 9, 1, 999};
int length = 5;
cout << recurse(length, sequence, 0);
return 0;
}
My approach to solving this problem was as follows:
Both players play optimally.
So, the solution is to be built in a manner that need not take the player into account. This is because both players are going to pick the best choice available to them for any given state of the stack of bricks.
The base cases:
Either player, when left with the last one/two/three bricks, will choose to remove all bricks.
For the sake of convenience, let's assume that the array is actually in reverse order (i.e. a[0] is the value of the bottom-most brick in the stack) (This can easily be incorporated by performing a reverse operation on the array.)
So, the base cases are:
# Base Cases
dp[0] = a[0]
dp[1] = a[0]+a[1]
dp[2] = a[0]+a[1]+a[2]
Building the final solution:
Now, in each iteration, a player has 3 choices.
pick brick (i), or,
pick brick (i and i-1) , or,
pick brick (i,i-1 and i-2)
If the player opted for choice 1, the following would result:
player secures a[i] points from the brick (i) (+a[i])
will not be able to procure the points on the bricks removed by the opponent. This value is stored in dp[i-1] (which the opponent will end up scoring by virtue of this choice made by the player).
will surely procure the points on the bricks not removed by the opponent. (+ Sum of all the bricks up until brick (i-1) not removed by opponent )
A prefix array to store the partial sums of points of bricks can be computed as follows:
# build prefix sum array
pre = [a[0]]
for i in range(1,n):
pre.append(pre[-1]+a[i])
And, now, if player opted for choice 1, the score would be:
ans1 = a[i] + (pre[i-1] - dp[i-1])
Similarly, for choices 2 and 3. So, we get:
ans1 = a[i]+ (pre[i-1] - dp[i-1]) # if we pick only ith brick
ans2 = a[i]+a[i-1]+(pre[i-2] - dp[i-2]) # pick 2 bricks
ans3 = a[i]+a[i-1]+a[i-2]+(pre[i-3] - dp[i-3]) # pick 3 bricks
Now, each player wants to maximize this value. So, in each iteration, we pick the maximum among ans1, ans2 and ans3.
dp[i] = max(ans1, ans2, ans3)
Now, all we have to do is to iterate from 3 through to n-1 to get the required solution.
Here is the final snippet in python:
a = map(int, raw_input().split())
a.reverse() # so that a[0] is bottom brick of stack
dp = [0 for x1 in xrange(n)]
dp[0] = a[0]
dp[1] = a[0]+a[1]
dp[2] = a[0]+a[1]+a[2]
# build prefix sum array
pre = [a[0]]
for i in range(1,n):
pre.append(pre[-1]+a[i])
for i in xrange(3,n):
# We can pick brick i, (i,i-1) or (i,i-1,i-2)
ans1 = a[i]+ (pre[i-1] - dp[i-1]) # if we pick only ith brick
ans2 = a[i]+a[i-1]+(pre[i-2] - dp[i-2]) # pick 2
ans3 = a[i]+a[i-1]+a[i-2]+(pre[i-3] - dp[i-3]) #pick 3
# both players maximise this value. Doesn't matter who is playing
dp[i] = max(ans1, ans2, ans3)
print dp[n-1]
At a first sight your code seems totally wrong for a couple of reasons:
The player is not taken into account. You taking a brick or your friend taking a brick is not the same (you've to maximize your score, the total is of course always the total of the score on the bricks).
Looks just some form of recursion with no memoization and that approach will obviously explode to exponential computing time (you're using the "brute force" approach, enumerating all possible games).
A dynamic programming approach is clearly possible because the best possible continuation of a game doesn't depend on how you reached a certain state. For the state of the game you'd need
Who's next to play (you or your friend)
How many bricks are left on the stack
With these two input you can compute how much you can collect from that point to the end of the game. To do this there are two cases
1. It's your turn
You need to try to collect 1, 2 or 3 and call recursively on the next game state where the opponent will have to choose. Of the three cases you keep what is the highest result
2. It's opponent turn
You need to simulate collection of 1, 2 or 3 bricks and call recursively on next game state where you'll have to choose. Of the three cases you keep what is the lowest result (because the opponent is trying to maximize his/her result, not yours).
At the very begin of the function you just need to check if the same game state has been processed before, and when returning from a computation you need to store the result. Thanks to this lookup/memorization the search time will not be exponential, but linear in the number of distinct game states (just 2*N where N is the number of bricks).
In Python:
memory = {}
bricks = [0, 0, 9, 1, 999]
def maxResult(my_turn, index):
key = (my_turn, index)
if key in memory:
return memory[key]
if index == len(bricks):
result = 0
elif my_turn:
result = None
s = 0
for i in range(index, min(index+3, len(bricks))):
s += bricks[i]
x = s + maxResult(False, i+1)
if result is None or x > result:
result = x
else:
result = None
for i in range(index, min(index+3, len(bricks))):
x = maxResult(True, i+1)
if result is None or x < result:
result = x
memory[key] = result
return result
print maxResult(True, 0)
import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;
public class Solution {
public static void main(String[] args){
Scanner sc=new Scanner(System.in);
int noTest=sc.nextInt();
for(int i=0; i<noTest; i++){
int noBrick=sc.nextInt();
ArrayList<Integer> arr=new ArrayList<Integer>();
for (int j=0; j<noBrick; j++){
arr.add(sc.nextInt());
}
long sum[]= new long[noBrick];
sum[noBrick-1]= arr.get(noBrick-1);
for (int j=noBrick-2; j>=0; j--){
sum[j]= sum[j+1]+ arr.get(j);
}
long[] max=new long[noBrick];
if(noBrick>=1)
max[noBrick-1]=arr.get(noBrick-1);
if(noBrick>=2)
max[noBrick-2]=(int)Math.max(arr.get(noBrick-2),max[noBrick-1]+arr.get(noBrick-2));
if(noBrick>=3)
max[noBrick-3]=(int)Math.max(arr.get(noBrick-3),max[noBrick-2]+arr.get(noBrick-3));
if(noBrick>=4){
for (int j=noBrick-4; j>=0; j--){
long opt1= arr.get(j)+sum[j+1]-max[j+1];
long opt2= arr.get(j)+arr.get(j+1)+sum[j+2]-max[j+2];
long opt3= arr.get(j)+arr.get(j+1)+arr.get(j+2)+sum[j+3]-max[j+3];
max[j]= (long)Math.max(opt1,Math.max(opt2,opt3));
}
}
long cost= max[0];
System.out.println(cost);
}
}
}
I tried this using Java, seems to work alright.
here a better solution that i found on the internet without recursion.
#include <iostream>
#include <fstream>
#include <algorithm>
#define MAXINDEX 10001
using namespace std;
long long maxResult(int a[MAXINDEX], int LENGTH){
long long prefixSum [MAXINDEX] = {0};
prefixSum[0] = a[0];
for(int i = 1; i < LENGTH; i++){
prefixSum[i] += prefixSum[i-1] + a[i];
}
long long dp[MAXINDEX] = {0};
dp[0] = a[0];
dp[1] = dp[0] + a[1];
dp[2] = dp[1] + a[2];
for(int k = 3; k < LENGTH; k++){
long long x = prefixSum[k-1] + a[k] - dp[k-1];
long long y = prefixSum[k-2] + a[k] + a[k-1] - dp[k-2];
long long z = prefixSum[k-3] + a[k] + a[k-1] + a[k-2] - dp[k-3];
dp[k] = max(x,max(y,z));
}
return dp[LENGTH-1];
}
using namespace std;
int main(){
int cases;
int bricks[MAXINDEX];
ifstream fin("test.in");
fin >> cases;
for (int i = 0; i < cases; i++){
long n;
fin >> n;
for(int j = 0; j < n; j++) fin >> bricks[j];
reverse(bricks, bricks+n);
cout << maxResult(bricks, n)<< endl;
}
return 0;
}