CodinGame: Let's Go To The Cinema - algorithm

It made already some months, I try to resolve the codingame "Let's Go To The Cinema" of the CodinGame platform, a problem of mid-level difficulty, but I can't still figure out the solution around.
The codingame lays on three concepts of recursion, simulation and stack and consist to fill a cinema where several groups want to sit.
The statement of the problem is as it follows :
There is a cinema with maxRow * maxColumn seats (all rows having same number of seats).
n groups of people are arriving in order and trying to sit down. For each group, you get as input the numPersons number of peoples it consists of, and the row and seat their ticket is issued for. (Meaning they purchased the seats from ( row , seat ) to ( row , seat + numPersons - 1), these seats included.) The same seat might have been sold several times!
You have to simulate and output how many groups and how many persons could sit on their original appointed seat.
If a group cannot sit to its place because it is already occupied, they try to find another place using a seat finding method:
The group sits ONLY if ALL of them can sit next to each other.
They try to shift together: left by 1, right by 1, left by 2, right by 2, etc.
If unsuccessful in the row, then try 1 row towards the front, then by 1 row to back, 2 rows to front, etc.
In each row use the same seat-finding procedure (starting at the same seat position).
If they tried every possible places but still without success:
They adapt to the situation and split into two subgroups of same size (the first subgroup shall be bigger, if the size of the group was odd.).
Now the first subgroup tries to sit on the original row seat position using the same seat-finding procedure as above.
As the group size is now smaller, they might succeed this time.
If they don't succeed, they keep splitting the subgroupsize by another half, and continue trying, BEFORE the original another half subgroup gets the chance to try.
Note: Latest when the subgroup size reaches 1 person, it will always succeed to find a place.
After the whole original group got seated, the next group arrives, and so on.
You have to output two numbers:
groupSuccess is the number of groups, who could sit instantly in their original places. (If a split subgroup can sit later on the original place, that does not count towards this counter.)
personSuccess is the number of individuals, who could find a place on their original group ticket. A person shall be counted as successful, no matter after how many iterations (s)he found the place, if and only if the seat is any of the places appointed to the original group: ( row , seat ) to ( row , seat + numPersons - 1).
In first place, I created a "checkPlaces" function which checks if places at row parameter and comprised between column and column + numPersons - 1 are occupied :
public static boolean checkPlaces(int numPersons, int row, int column) {
for(int i = column; i < column + numPersons - 1; i++) {
if(cinema[row][i] == 1) {
return false;
}
}
return true;
}
I create another function "analyzeRow" where I check in calling "checkPlaces" function several times if original reserved places are available and if not I check consecutively
in shifting from 1, 2, ..., n column to the left and to right if the group can sit elsewhere on the row.
public static boolean analyzeRow(int numPersons, int row, int column, int maxRow, int maxColumn) {
if(checkPlaces(numPersons, row, column)) {
for(int i = column; i < column + numPersons - 1; i++) {
cinema[row][i] = 1;
}
return true;
}
else {
for(int j = 1; j < maxColumn; j++) {
if(j < column) {
int col1 = shiftLeftColumn(column, j);
if(checkPlaces(numPersons, row, col1)) {
for(int i = column; i < column + numPersons - 1; i++) {
cinema[row][i] = 1;
}
return true;
}
}
if(j < maxColumn - column) {
int col2 = shiftRightColumn(column, j);
if(checkPlaces(numPersons, row, col2)) {
for(int i = column; i < column + numPersons - 1; i++) {
cinema[row][i] = 1;
}
return true;
}
}
}
}
return false;
}
A third function "checkGrid" has for purpose to check the entire cinema in changing row
when analyzeRow sends false on a particular row in shifting from 1, 2, ..., n to front and to back until borders of the cinema are reached out.
public static boolean checkGrid(int numPersons, int row, int column, int maxRow, int maxColumn) {
if(!analyzeRow(numPersons, row, column, maxRow, maxColumn)) {
for(int l = 1; l < maxRow; l++) {
if(l < maxRow - row) {
int row1 = moveFrontRow(row, l);
analyzeRow(numPersons, row1, column, maxRow, maxColumn);
}
if(l < row) {
int row2 = moveBackRow(row, l);
analyzeRow(numPersons, row2, column, maxRow, maxColumn);
}
}
}
return true;
}
I also created several functions to shift column or row to left, right, front, back :
public static int shiftLeftColumn(int column, int j) {
int columnReturn = column - j;
return columnReturn;
}
public static int shiftRightColumn(int column, int j) {
int columnReturn = column + j;
return columnReturn;
}
public static int moveFrontRow(int row, int i) {
int rowReturn = row + i;
return rowReturn;
}
public static int moveBackRow(int row, int i) {
int rowReturn = row - i;
return rowReturn;
}
However, I have the feeling to have understood the idea, however I have difficulties to bring my code until the end and I'm not totally sure how I must do to take account the "split groups" part of the exercize, as the statement advice to use the "stack" concept, but I would rather use a binary tree that I create at the beginning of my main function with a static function :
TreeNode root = TreeNode.buildTree(numPersons);
Given that the exercize ask to split the group of people in two parts and then examine a first group and divide again to examine the two split groups issued of this first group,
before to examine the second.
It seems like leafs of a tree.
public class TreeNode {
int value;
TreeNode left;
TreeNode right;
public TreeNode(int value) {
this.value = value;
}
public TreeNode(int value, TreeNode left, TreeNode right) {
this.value = value;
this.left = left;
this.right = right;
}
public static TreeNode buildTree(int numPersons) {
while(numPersons > 1) {
if(numPersons % 2 == 0) {
TreeNode A = new TreeNode(numPersons, buildTree(numPersons/2), buildTree(numPersons/2));
return A;
}
else {
TreeNode B = new TreeNode(numPersons, buildTree((numPersons / 2) + 1), buildTree(numPersons / 2));
return B;
}
}
return new TreeNode(numPersons, null, null);
}
}
It made a long time I think on this, but I can't find how to finish and I would like to know if the way of thinking is the good or if I'm totally aside of the plate concerning the resolution of this exercize and I failed to see some algorithmic subtlety.
Thanks in advance to who could guide me.

Related

Algorithm: use union find to count number of islands

Suppose you need to count the number of islands on a matrix
{1, 1, 0, 0, 0},
{0, 1, 0, 0, 1},
{1, 0, 0, 1, 1},
{0, 0, 0, 0, 0},
{1, 0, 1, 0, 1}
We could simply use DFS or BFS when the input matrix size can be fitting into the memory.
However, what do we do if the input matrix is really large which could not be fitting into the memory?
I could chunk/split the input matrix into different small files and read them respectively.
But how to merge them?
I got stuck at how to merge them. I have the idea that when merging them we have to read some overlapped portion. But what is a concrete way to do so?
Trying to understand Matt's solution.
When I drew the below sample on the whiteboard and process it row by row.
Merge left then merge top and it seems won't work.
From Matt's solution.
not sure what are topidx, botidx meaning
int topidx = col * 2;
int botidx = topidx + 1;
Using union-find, the basic algorithm (without worrying about memory) is:
Create a set for every 1
Merge the sets for every pair of adjacent 1s. It doesn't matter what order you find them in, so reading order is usually fine.
Count the number of root sets -- there will be one for every island.
Easy, and with a little care, you can do this using sequential access to the matrix and only 2 rows worth of memory:
Initialize the island count to 0
Read the first row, create a set for each 1, and merge sets in adjacent columns.
For each additional row:
Read the row, create a set for each 1, and merge sets in adjacent columns;
Merge sets in the new row with adjacent sets in the previous row. ALWAYS POINT THE LINKS DOWNWARD, so that you never end up with a set in the new row linked to a parent in the old row.
Count the remaining root sets in the previous row, and add the number to your island count. These will never be able to merge with anything else.
Discard all the sets in the previous row -- you're never going to need them again, because you already counted them and nothing links to them.
Finally, count the root sets in the last row and add them to your island count.
The key to this, of course, is always pointing the links downward whenever you link sets in different rows. This will not hurt the complexity of the algorithm, and if you're using your own union-find, then it is easy to accomplish. If you're using a library data structure then you can use it just for each row, and keep track of the links between root sets in different rows yourself.
Since this is actually one of my favorite algorithms, here is an implementation in Java. This is not the most readable implementation since it involves some low-level tricks, but is super-efficient and short -- the kind of thing I'd write where performance is very important:
import java.util.Arrays;
public class Islands
{
private static final String[] matrix=new String[] {
" ############# ### ",
" # ##### ## ",
" # ## ## # # ",
" ### ## # # ",
" # ######### ## ## ",
" ## ## ",
" ########## ",
};
// find with path compression.
// If sets[s] < 0 then it is a link to ~sets[s]. Otherwise it is size of set
static int find(int[] sets, int s)
{
int parent = ~sets[s];
if (parent>=0)
{
int root = find(sets, parent);
if (root != parent)
{
sets[s] = ~root;
}
return root;
}
return s;
}
// union-by-size
// If sets[s] < 0 then it is a link to ~sets[s]. Otherwise it is size of set
static boolean union(int[] sets, int x, int y)
{
x = find(sets,x);
y = find(sets,y);
if (x!=y)
{
if ((sets[x] < sets[y]))
{
sets[y] += sets[x];
sets[x] = ~y;
}
else
{
sets[x] += sets[y];
sets[y] = ~x;
}
return true;
}
return false;
}
// Count islands in matrix
public static void main(String[] args)
{
// two rows of union-find sets.
// top row is at even indexes, bottom row is at odd indexes. This arrangemnt is chosen just
// to make resizing this array easier.
// For each value x:
// x==0 => no set. x>0 => root set of size x. x<0 => link to ~x
int cols=4;
int[] setrows= new int[cols*2];
int islandCount = 0;
for (String s : matrix)
{
System.out.println(s);
//Make sure our rows are big enough
if (s.length() > cols)
{
cols=s.length();
if (setrows.length < cols*2)
{
int newlen = Math.max(cols,setrows.length)*2;
setrows = Arrays.copyOf(setrows, newlen);
}
}
//Create sets for land in bottom row, merging left
for (int col=0; col<s.length(); ++col)
{
if (!Character.isWhitespace(s.charAt(col)))
{
int idx = col*2+1;
setrows[idx]=1; //set of size 1
if (idx>=2 && setrows[idx-2]!=0)
{
union(setrows, idx, idx-2);
}
}
}
//merge up
for (int col=0; col<cols; ++col)
{
int topidx = col*2;
int botidx = topidx+1;
if (setrows[topidx]!=0 && setrows[botidx]!=0)
{
int toproot=find(setrows,topidx);
if ((toproot&1)!=0)
{
//top set is already linked down
union(setrows, toproot, botidx);
}
else
{
//link top root down. It does not matter that we aren't counting its size, since
//we will shortly throw it aaway
setrows[toproot] = ~botidx;
}
}
}
//count root sets, discard top row, and move bottom row up while fixing links
for (int col=0; col<cols; ++col)
{
int topidx = col * 2;
int botidx = topidx + 1;
if (setrows[topidx]>0)
{
++islandCount;
}
int v = setrows[botidx];
setrows[topidx] = (v>=0 ? v : v|1); //fix up link if necessary
setrows[botidx] = 0;
}
}
//count remaining root sets in top row
for (int col=0; col<cols; ++col)
{
if (setrows[col*2]>0)
{
++islandCount;
}
}
System.out.println("\nThere are "+islandCount+" islands there");
}
}

Optimum movie schedule considering duration and no overlap

I am struggling to find a good strategy to tackle this problem. I considered sorting the collection of movies, by duration, but that seems to obscure the overlap condition. Any ideas?
Another thing I was thinking about was sorting based on start month and then, approaching as an extension of this problem.
An actor is offered a set of movies for a certain (start month and
duration). You have to select the movies the actor should do so that
the actor can do the most number of movies in the year, without any
overlaps.
It is assumed that movies start on the first of a month, and end on
the last day. Months are of 30 days (should not be required?).
I wrote up some code to demonstrate a solution. The overall essence is similar to what Edward Peters mentioned. First thing is to sort the movies by their end times. Now, greedily the first option is always chosen.
Why does choosing the first option work?
In our case, our ordering A had the first movie, some a. Say there is another ordering B where the first movie is some b, such that b ≠ a. There must be an ordering (we already know about the existence of A) such that, A = {B – {b}} U {a}. Obviously, in the case of B, b would have the smallest time; since b ≠ a, duration(b) >= duration(a).
public class Optimum_Movie_Schedule {
public static void main(String[] args) {
Movie[] movies = new Movie[] {
new Movie("f", 5, 9),
new Movie("a", 1, 2),
new Movie("b", 3, 4),
new Movie("c", 0, 6),
new Movie("d", 5, 7),
new Movie("e", 8, 9)
};
// sort by endMonth; startMonth if endMonth clash
Arrays.sort(movies);
System.out.println(Arrays.toString(movies));
System.out.println("Final list: ");
System.out.print(movies[0] + " ");
int cur = 0;
for(int i = 1 ; i < movies.length ; i++) {
if(movies[i].startMonth > movies[cur].endMonth) {
System.out.print(movies[i] + " ");
cur = i;
}
}
}
static class Movie implements Comparable<Movie> {
String name;
int startMonth;
int endMonth;
int duration;
public Movie(String name, int start, int end) {
this.name = name;
this.startMonth = start;
this.endMonth = end;
this.duration = (end + 1) - start;
}
public int compareTo(Movie o) {
if(this.endMonth < o.endMonth)
return -1;
if(this.endMonth == o.endMonth)
if(this.startMonth < o.startMonth)
return -1;
return 1;
}
public String toString() {
return name + "("+ startMonth + ", " + endMonth + ")";
}
}
}
Take your list of movies, sorted by their end dates. For each movie, you want to solve "What are the maximum number of movies I could have acted in, by the end of this movie." This can be found recursively as the maximum of either the result for the most recent movie, or one plus the result for the most recent movie to end before this one began.
The first case corresponds to "I did not act in this movie, so would have been able to pursue other movies during that duration." The second case corresponds to "I acted in this movie, but was not available for any movies that did not end before this movie began."

Algorithm for seating firefighters in vehicle seats

I’m doing a project which involves placing volunteer firefighters, in seats when they arrive at the fire station to take the trucks.
Also some firefighters has drivers license some haven’t and some have a team leader education.
So my idea is to follow them by GPS and send the server a integer of distance to fire station and which education types each has.
Then every 5 sec, run the algorithm and based on the new GPS coordinates change their seats, until 1 is close to the station, and then mark is seat as taken.
This should happen until there is no empty seats or not anymore firefighters to call in or all called firefighters has arrived
The tricky thing I want help with (besides if my idea is the optimal), is to seat the firefighters most optimal.
I was thinking to make a prioritization list of possible roles.
And a prioritization list of vehicles which had to leave the station.
Then take the highest prioritized vehicle and the highest prioritized role, and fill it in with the closest firefighter which has the education.
But then if he is a driver but already set to the teamleaders seat, and only 1 more driver is coming, and there were more teamleaders coming, and two vehicles had to leave, it would be a wrong solution as the second vehicle couldn’t leave.
Again if Drivers and not teamleaders is the highest priority, then if the closest is set as driver, but is also the only one coming with a teamleader education, but more drivers are coming, then it would also be wrong.
Any ideas for the algorithm to work? Or does anybody knows an algorithm for this?
You're right, this type of greedy approach won't necessarily give you an optimal solution. Have you looked into/heard of Linear Programming or more specifically Integer programming: http://en.wikipedia.org/wiki/Integer_programming
In short integer programming is one technique for solving these types of scheduling problems. The problem has some objective you wish to maximise or minimise and is subject to various (linear) constraints. Integer because we can't have half a volunteer.
From your description the objective function could be to minimise the number of undeployed trucks and the total wait time. You could have different costs for different trucks to capture the different vehicle priorities.
Constraints would include each vehicle needing at least one driver, at least one team leader and that a volunteer can only be assigned to one vehicle. There might be other constraints you haven't described as well, such as no one can wait at the base for more than 20 minutes.
If you search for integer programming and scheduling you should find some code examples. You'll likely need a solver to assist. Wiki has a fairly comprehensive list, the choice will come down to your programming language preference and budget: http://en.wikipedia.org/wiki/Linear_programming#Solvers_and_scripting_.28programming.29_languages
How about something like this..
as you noticed the only conflicting situation is when you have a firefighter that can be driver and leader.. because the can only occupy one position.. but may be blocking another..
so don't start with them.. first start with the ones that have ether divers license OR Leaders education.. because those already have a predefined seat (the only they can take)..
after those are filled.. assign the ones that can do ether jobs, for the seats that are missing or replace some if they are nearer.
after having a queue of drivers and a queue of leaders.. sort them by distance to the firehouse and assign to trucks in pairs.. then fill the rest of the truck.. removing from a queue by order of ETA..
not sure if it will always give the best solution.. but seems quite optimal.. to to you wish some code? C#?
the question made me curious so. here is the code for what I was talking.. eve if you don't use it
using System;
using System.Collections.Generic;
using System.Diagnostics;
namespace FF
{
class Program
{
[Flags]
public enum Skill
{
None = 0,
Driver = 1,
TeamLeader = 2,
}
public class FireFighter : IComparable<FireFighter>
{
public int ID;
public Skill Skills;//the skills that he has
public Skill Assigned;//the one that he will be deployed with
public int ETA;
public override string ToString()
{
return ID + "(" + Skills + ")" + " # " + ETA + " minutes as " + this.Assigned;
}
int IComparable<FireFighter>.CompareTo(FireFighter other)
{
return this.ETA.CompareTo(other.ETA);
}
}
public class Truck
{
public int ID;
public int Capacity;
public List<FireFighter> Crew = new List<FireFighter>();
}
static int TotalStationTrucks = 8;
static List<Truck> Trucks = new List<Truck>();
static List<FireFighter> Team = new List<FireFighter>();
static Random Rnd = new Random();
static void Main(string[] args)
{
//create the sample data
int nAvailableSeats = 0;
for (int i = 0; i < TotalStationTrucks; i++)
{
Truck oTruck = new Truck();
oTruck.ID = i;
nAvailableSeats += oTruck.Capacity = Rnd.Next(4, 7);//seats between 4 - 6
Trucks.Add(oTruck);
}
for (int i = 0; i < nAvailableSeats * 2; i++)//add twice the amount of FF we need for all trucks
{
FireFighter oFireFighter = new FireFighter();
oFireFighter.ID = i;
oFireFighter.ETA = Rnd.Next(Int16.MaxValue);
Team.Add(oFireFighter);
}
for (int i = 0; i < Trucks.Count * 2; i++)//add twice the drivers we need
{
FireFighter oFireFighter = Team[Rnd.Next(Team.Count)];
oFireFighter.Skills |= Skill.Driver;
}
for (int i = 0; i < Trucks.Count * 2; i++)//add twice the leaders we need
{
FireFighter oFireFighter = Team[Rnd.Next(Team.Count)];
oFireFighter.Skills |= Skill.TeamLeader;
}
for (int i = 0; i < Trucks.Count * 2; i++)//add twice the multitaskers we need
{
FireFighter oFireFighter = Team[Rnd.Next(Team.Count)];
oFireFighter.Skills = (Skill.TeamLeader|Skill.Driver);
}
//Truck that are going to be deployed
int nTrucksToDeploy = Rnd.Next(2, Trucks.Count);
// distribute firefighter by ETA to minimize truck deploy
//*******************************************************
//Sort by ETA
Team.Sort();
//get top the ones that can only drive
List<FireFighter> oSelectedDrivers = Team.FindAll(delegate(FireFighter item) { return item.Skills == Skill.Driver; }).GetRange(0, nTrucksToDeploy);
oSelectedDrivers.ForEach(delegate(FireFighter item) { item.Assigned = Skill.Driver; });
//get top the ones that can only lead
List<FireFighter> oSelectedLeaders = Team.FindAll(delegate(FireFighter item) { return item.Skills == Skill.TeamLeader; }).GetRange(0, nTrucksToDeploy);
oSelectedLeaders.ForEach(delegate(FireFighter item) { item.Assigned = Skill.TeamLeader; });
//put them on a list of already assigned
List<FireFighter> oAssigned = new List<FireFighter>();
oAssigned.AddRange(oSelectedDrivers);
oAssigned.AddRange(oSelectedLeaders);
//get the ones that can do ether job
List<FireFighter> oMultitaskers = Team.FindAll(delegate(FireFighter item) { return item.Skills == (Skill.Driver | Skill.TeamLeader); });
//sort by ETA
oMultitaskers.Sort();
oAssigned.Sort();
//put a multitaskers in the queue if he is gonna arrive earlier than the most delayed
while (oMultitaskers[0].ETA < oAssigned[oAssigned.Count - 1].ETA)
{
FireFighter oIsOut = oAssigned[oAssigned.Count - 1];
FireFighter oIsIn = oMultitaskers[0];
//swap tasks
oIsIn.Assigned = oIsOut.Assigned;
oIsOut.Assigned = Skill.None;
//remove from respective queues
oAssigned.RemoveAt(oAssigned.Count - 1);
oMultitaskers.RemoveAt(0);
//Add the multitasker to queue
//and if you are questioning if the could get a better solution by choosing another assignment
//that as no influence.. the optmizing condition is removing the slowest that was on queue
oAssigned.Add(oIsIn);
//the out guy is not added for two reasons
//1st is not a multitasker
//2nd and most importante.. he was the one with the highest ETA, we will NEVER gain by replacing another driver with this one
//oMultitaskers.Add(oIsOut);
oMultitaskers.Sort();
oAssigned.Sort();
}
//start filling the trucks take one of each from top, wich means the first truck will have the earliest departure
for (int i = 0; i < nTrucksToDeploy; i++)
{
int nDriverIndex = oAssigned.FindIndex(delegate(FireFighter item) { return item.Assigned == Skill.Driver; });
Trucks[i].Crew.Add(oAssigned[nDriverIndex]);
oAssigned.RemoveAt(nDriverIndex);
int nLeaderIndex = oAssigned.FindIndex(delegate(FireFighter item) { return item.Assigned == Skill.TeamLeader; });
Trucks[i].Crew.Add(oAssigned[nLeaderIndex]);
oAssigned.RemoveAt(nLeaderIndex);
}
//now fill the rest of the crew.. also ordered by ETA
List<FireFighter> oUnassigned = Team.FindAll(delegate(FireFighter item) { return item.Assigned == Skill.None; });
oUnassigned.Sort();
for (int i = 0; i < nTrucksToDeploy; i++)
{
while (Trucks[i].Crew.Count < Trucks[i].Capacity)
{
Trucks[i].Crew.Add(oUnassigned[0]);
oUnassigned.RemoveAt(0);
}
}
//dump truck data
Trace.WriteLine(String.Format("{0} trucks to be deployed",nTrucksToDeploy));
for (int i = 0; i < nTrucksToDeploy; i++)
{
Trace.WriteLine(new String('-', 20));
Truck oTruck = Trucks[i];
oTruck.Crew.Sort();
Trace.WriteLine(String.Format("truck {0} estimated time of departure: {1}",oTruck.ID,oTruck.Crew[oTruck.Crew.Count-1].ETA));
for (int j = 0; j < oTruck.Crew.Count; j++)
{
FireFighter oFireFighter = oTruck.Crew[j];
Trace.WriteLine(String.Format("{0}({1})\t # {2} minutes as \t{3}", oFireFighter.ID, oFireFighter.Skills, oFireFighter.ETA, oFireFighter.Assigned));
}
}
Trace.WriteLine(new String('#', 20));
//list the team
for (int i = 0; i < Team.Count; i++)
{
FireFighter oFireFighter = Team[i];
Trace.WriteLine(String.Format("{0}({1})\t # {2} minutes as \t{3}", oFireFighter.ID, oFireFighter.Skills, oFireFighter.ETA, oFireFighter.Assigned));
}
}
}
}

What algorithm can I use to produce 'Random' value?

Say I have 4 possible results and the probabilities of each result appearing are
1 = 10%
2 = 20%
3 = 30%
4 = 40%
I'd like to write a method like GetRandomValue which if called 1000 times would return
1 x 100 times
2 x 200 times
3 x 300 times
4 x 400 times
Whats the name of an algorithm which would produce such results?
in your case you can generate a random number (int) within 1..10 and if it's 1 then select 1, if it's between 2-3 select 2 and if it's between 4..6 select 3 and if is between 7..10 select 4.
In all if you have some probabilities which sum to 1, you can have a random number within (0,1) distribute your generated result to related value (I simplified in your case within 1..10).
To get a random number you would use the Random class of .Net.
Something like the following would accomplish what you requested:
public class MyRandom
{
private Random m_rand = new Random();
public int GetNextValue()
{
// Gets a random value between 0-9 with equal probability
// and converts it to a number between 1-4 with the probablities requested.
switch (m_rand.Next(0, 9))
{
case 0:
return 1;
case 1: case 2:
return 2;
case 3: case 4: case 5:
return 3;
default:
return 4;
}
}
}
If you just want those probabilities in the long run, you can just get values by randomly selecting one element from the array {1,2,2,3,3,3,4,4,4,4}.
If you however need to retrieve exactly 1000 elements, in those specific quantities, you can try something like this (not C#, but shouldn't be a problem):
import java.util.Random;
import java.util.*;
class Thing{
Random r = new Random();
ArrayList<Integer> numbers=new ArrayList<Integer>();
ArrayList<Integer> counts=new ArrayList<Integer>();
int totalCount;
public void set(int i, int count){
numbers.add(i);
counts.add(count);
totalCount+=count;
}
public int getValue(){
if (totalCount==0)
throw new IllegalStateException();
double pos = r.nextDouble();
double z = 0;
int index = 0;
//we select elements using their remaining counts for probabilities
for (; index<counts.size(); index++){
z += counts.get(index) / ((double)totalCount);
if (pos<z)
break;
}
int result = numbers.get(index);
counts.set( index , counts.get(index)-1);
if (counts.get(index)==0){
counts.remove(index);
numbers.remove(index);
}
totalCount--;
return result;
}
}
class Test{
public static void main(String []args){
Thing t = new Thing(){{
set(1,100);
set(2,200);
set(3,300);
set(4,400);
}};
int[]hist=new int[4];
for (int i=0;i<1000;i++){
int value = t.getValue();
System.out.print(value);
hist[value-1]++;
}
System.out.println();
double sum=0;
for (int i=0;i<4;i++) sum+=hist[i];
for (int i=0;i<4;i++)
System.out.printf("%d: %d values, %f%%\n",i+1,hist[i], (100*hist[i]/sum));
}
}

ACM Problem: Coin-Flipping, help me identify the type of problem this is

I'm practicing for the upcoming ACM programming competition in a week and I've gotten stumped on this programming problem.
The problem is as follows:
You have a puzzle consisting of a square grid of size 4. Each grid square holds a single coin; each coin is showing either heads (H) and tails (T). One such puzzle is shown here:
H H H H
T T T T
H T H T
T T H T
Any coin that is current showing Tails (T) can be flipped to Heads (H). However, any time we flip a coin, we must also flip the adjacent coins direct above, below and to the left and right in the same row. Thus if we flip the second coin in the second row we must also flip 4 other coins, giving us this arrangment (coins that changed are shown in bold).
H T H H
H H H T
H H H T
T T H T
If a coin is at the edge of the puzzle, so there is no coin on one side or the other, then we flip fewer coins. We do not "wrap around" to the other side. For example, if we flipped the bottom right coin of the arragnement above we would get:
H T H H
H H H T
H H H H
T T T H
Note: Only coins showing (T) tails can be selected for flipping. However, anytime we flip such a coin, adjacent coins are also flipped, regardless of their state.
The goal of the puzzle is to have all coins show heads. While it is possible for some arragnements to not have solutions, all the problems given will have solutions. The answer we are looking for is, for any given 4x4 grid of coins what is the least number of flips in order to make the grid entirely heads.
For Example the grid:
H T H H
T T T H
H T H T
H H T T
The answer to this grid is: 2 flips.
What I have done so far:
I'm storing our grids as two-dimensional array of booleans. Heads = true, tails = false.
I have a flip(int row, int col) method that will flip the adjacent coins according the rules above and I have a isSolved() method that will determine if the puzzle is in a solved state (all heads). So we have our "mechanics" in place.
The part we are having problems with is how should we loop through, going an the least amount of times deep?
Your puzzle is a classic Breadth-First Search candidate. This is because you're looking for a solution with the fewest possible 'moves'.
If you knew the number of moves to the goal, then that would be ideal for a Depth-First Search.
Those Wikipedia articles contain plenty of information about the way the searches work, they even contain code samples in several languages.
Either search can be recursive, if you're sure you won't run out of stack space.
EDIT: I hadn't noticed that you can't use a coin as the primary move unless it's showing tails. That does indeed make order important. I'll leave this answer here, but look into writing another one as well.
No pseudo-code here, but think about this: can you ever imagine yourself flipping a coin twice? What would be the effect?
Alternative, write down some arbitrary board (literally, write it down). Set up some real world coins, and pick two arbitrary ones, X and Y. Do an "X flip", then a "Y flip" then another "X flip". Write down the result. Now reset the board to the starting version, and just do a "Y flip". Compare the results, and think about what's happened. Try it a few times, sometimes with X and Y close together, sometimes not. Become confident in your conclusion.
That line of thought should lead you to a way of determining a finite set of possible solutions. You can test all of them fairly easily.
Hope this hint wasn't too blatant - I'll keep an eye on this question to see if you need more help. It's a nice puzzle.
As for recursion: you could use recursion. Personally, I wouldn't in this case.
EDIT: Actually, on second thoughts I probably would use recursion. It could make life a lot simpler.
Okay, perhaps that wasn't obvious enough. Let's label the coins A-P, like this:
ABCD
EFGH
IJKL
MNOP
Flipping F will always involve the following coins changing state: BEFGJ.
Flipping J will always involve the following coins changing state: FIJKN.
What happens if you flip a coin twice? The two flips cancel each other out, no matter what other flips occur.
In other words, flipping F and then J is the same as flipping J and then F. Flipping F and then J and then F again is the same as just flipping J to start with.
So any solution isn't really a path of "flip A then F then J" - it's "flip <these coins>; don't flip <these coins>". (It's unfortunate that the word "flip" is used for both the primary coin to flip and the secondary coins which change state for a particular move, but never mind - hopefully it's clear what I mean.)
Each coin will either be used as a primary move or not, 0 or 1. There are 16 coins, so 2^16 possibilities. So 0 might represent "don't do anything"; 1 might represent "just A"; 2 might represent "just B"; 3 "A and B" etc.
Test each combination. If (somehow) there's more than one solution, count the number of bits in each solution to find the least number.
Implementation hint: the "current state" can be represented as a 16 bit number as well. Using a particular coin as a primary move will always XOR the current state with a fixed number (for that coin). This makes it really easy to work out the effect of any particular combination of moves.
Okay, here's the solution in C#. It shows how many moves were required for each solution it finds, but it doesn't keep track of which moves those were, or what the least number of moves is. That's a SMOP :)
The input is a list of which coins are showing tails to start with - so for the example in the question, you'd start the program with an argument of "BEFGJLOP". Code:
using System;
public class CoinFlip
{
// All ints could really be ushorts, but ints are easier
// to work with
static readonly int[] MoveTransitions = CalculateMoveTransitions();
static int[] CalculateMoveTransitions()
{
int[] ret = new int[16];
for (int i=0; i < 16; i++)
{
int row = i / 4;
int col = i % 4;
ret[i] = PositionToBit(row, col) +
PositionToBit(row-1, col) +
PositionToBit(row+1, col) +
PositionToBit(row, col-1) +
PositionToBit(row, col+1);
}
return ret;
}
static int PositionToBit(int row, int col)
{
if (row < 0 || row > 3 || col < 0 || col > 3)
{
// Makes edge detection easier
return 0;
}
return 1 << (row * 4 + col);
}
static void Main(string[] args)
{
int initial = 0;
foreach (char c in args[0])
{
initial += 1 << (c-'A');
}
Console.WriteLine("Initial = {0}", initial);
ChangeState(initial, 0, 0);
}
static void ChangeState(int current, int nextCoin, int currentFlips)
{
// Reached the end. Success?
if (nextCoin == 16)
{
if (current == 0)
{
// More work required if we want to display the solution :)
Console.WriteLine("Found solution with {0} flips", currentFlips);
}
}
else
{
// Don't flip this coin
ChangeState(current, nextCoin+1, currentFlips);
// Or do...
ChangeState(current ^ MoveTransitions[nextCoin], nextCoin+1, currentFlips+1);
}
}
}
I would suggest a breadth first search, as someone else already mentioned.
The big secret here is to have multiple copies of the game board. Don't think of "the board."
I suggest creating a data structure that contains a representation of a board, and an ordered list of moves that got to that board from the starting position. A move is the coordinates of the center coin in a set of flips. I'll call an instance of this data structure a "state" below.
My basic algorithm would look something like this:
Create a queue.
Create a state that contains the start position and an empty list of moves.
Put this state into the queue.
Loop forever:
Pull first state off of queue.
For each coin showing tails on the board:
Create a new state by flipping that coin and the appropriate others around it.
Add the coordinates of that coin to the list of moves in the new state.
If the new state shows all heads:
Rejoice, you are done.
Push the new state into the end of the queue.
If you like, you could add a limit to the length of the queue or the length of move lists, to pick a place to give up. You could also keep track of boards that you have already seen in order to detect loops. If the queue empties and you haven't found any solutions, then none exist.
Also, a few of the comments already made seem to ignore the fact that the problem only allows coins that show tails to be in the middle of a move. This means that order very much does matter. If the first move flips a coin from heads to tails, then that coin can be the center of the second move, but it could not have been the center of the first move. Similarly, if the first move flips a coin from tails to heads, then that coin cannot be the center of the second move, even though it could have been the center of the first move.
The grid, read in row-major order, is nothing more than a 16 bit integer. Both the grid given by the problem and the 16 possible moves (or "generators") can be stored as 16 bit integers, thus the problems amounts to find the least possible number of generators which, summed by means of bitwise XOR, gives the grid itself as the result. I wonder if there's a smarter alternative than trying all the 65536 possibilities.
EDIT: Indeed there is a convenient way to do bruteforcing. You can try all the 1-move patterns, then all the 2-moves patterns, and so on. When a n-moves pattern matches the grid, you can stop, exhibit the winning pattern and say that the solution requires at least n moves. Enumeration of all the n-moves patterns is a recursive problem.
EDIT2: You can bruteforce with something along the lines of the following (probably buggy) recursive pseudocode:
// Tries all the n bit patterns with k bits set to 1
tryAllPatterns(unsigned short n, unsigned short k, unsigned short commonAddend=0)
{
if(n == 0)
tryPattern(commonAddend);
else
{
// All the patterns that have the n-th bit set to 1 and k-1 bits
// set to 1 in the remaining
tryAllPatterns(n-1, k-1, (2^(n-1) xor commonAddend) );
// All the patterns that have the n-th bit set to 0 and k bits
// set to 1 in the remaining
tryAllPatterns(n-1, k, commonAddend );
}
}
To elaborate on Federico's suggestion, the problem is about finding a set of the 16 generators that xor'ed together gives the starting position.
But if we consider each generator as a vector of integers modulo 2, this becomes finding a linear combination of vectors, that equal the starting position.
Solving this should just be a matter of gaussian elimination (mod 2).
EDIT:
After thinking a bit more, I think this would work:
Build a binary matrix G of all the generators, and let s be the starting state. We are looking for vectors x satisfying Gx=s (mod 2). After doing gaussian elimination, we either end up with such a vector x or we find that there are no solutions.
The problem is then to find the vector y such that Gy = 0 and x^y has as few bits set as possible, and I think the easiest way to find this would be to try all such y. Since they only depend on G, they can be precomputed.
I admit that a brute-force search would be a lot easier to implement, though. =)
Okay, here's an answer now that I've read the rules properly :)
It's a breadth-first search using a queue of states and the moves taken to get there. It doesn't make any attempt to prevent cycles, but you have to specify a maximum number of iterations to try, so it can't go on forever.
This implementation creates a lot of strings - an immutable linked list of moves would be neater on this front, but I don't have time for that right now.
using System;
using System.Collections.Generic;
public class CoinFlip
{
struct Position
{
readonly string moves;
readonly int state;
public Position(string moves, int state)
{
this.moves = moves;
this.state = state;
}
public string Moves { get { return moves; } }
public int State { get { return state; } }
public IEnumerable<Position> GetNextPositions()
{
for (int move = 0; move < 16; move++)
{
if ((state & (1 << move)) == 0)
{
continue; // Not allowed - it's already heads
}
int newState = state ^ MoveTransitions[move];
yield return new Position(moves + (char)(move+'A'), newState);
}
}
}
// All ints could really be ushorts, but ints are easier
// to work with
static readonly int[] MoveTransitions = CalculateMoveTransitions();
static int[] CalculateMoveTransitions()
{
int[] ret = new int[16];
for (int i=0; i < 16; i++)
{
int row = i / 4;
int col = i % 4;
ret[i] = PositionToBit(row, col) +
PositionToBit(row-1, col) +
PositionToBit(row+1, col) +
PositionToBit(row, col-1) +
PositionToBit(row, col+1);
}
return ret;
}
static int PositionToBit(int row, int col)
{
if (row < 0 || row > 3 || col < 0 || col > 3)
{
return 0;
}
return 1 << (row * 4 + col);
}
static void Main(string[] args)
{
int initial = 0;
foreach (char c in args[0])
{
initial += 1 << (c-'A');
}
int maxDepth = int.Parse(args[1]);
Queue<Position> queue = new Queue<Position>();
queue.Enqueue(new Position("", initial));
while (queue.Count != 0)
{
Position current = queue.Dequeue();
if (current.State == 0)
{
Console.WriteLine("Found solution in {0} moves: {1}",
current.Moves.Length, current.Moves);
return;
}
if (current.Moves.Length == maxDepth)
{
continue;
}
// Shame Queue<T> doesn't have EnqueueRange :(
foreach (Position nextPosition in current.GetNextPositions())
{
queue.Enqueue(nextPosition);
}
}
Console.WriteLine("No solutions");
}
}
If you are practicing for the ACM, I would consider this puzzle also for non-trivial boards, say 1000x1000. Brute force / greedy may still work, but be careful to avoid exponential blow-up.
The is the classic "Lights Out" problem. There is actually an easy O(2^N) brute force solution, where N is either the width or the height, whichever is smaller.
Let's assume the following works on the width, since you can transpose it.
One observation is that you don't need to press the same button twice - it just cancels out.
The key concept is just that you only need to determine if you want to press the button for each item on the first row. Every other button press is uniquely determined by one thing - whether the light above the considered button is on. If you're looking at cell (x,y), and cell (x,y-1) is on, there's only one way to turn it off, by pressing (x,y). Iterate through the rows from top to bottom and if there are no lights left on at the end, you have a solution there. You can then take the min of all the tries.
It's a finite state machine, where each "state" is the 16 bit integer corresponding the the value of each coin.
Each state has 16 outbound transitions, corresponding to the state after you flip each coin.
Once you've mapped out all the states and transitions, you have to find the shortest path in the graph from your beginning state to state 1111 1111 1111 1111,
I sat down and attempted my own solution to this problem (based on the help I received in this thread). I'm using a 2d array of booleans, so it isn't as nice as the people using 16bit integers with bit manipulation.
In any case, here is my solution in Java:
import java.util.*;
class Node
{
public boolean[][] Value;
public Node Parent;
public Node (boolean[][] value, Node parent)
{
this.Value = value;
this.Parent = parent;
}
}
public class CoinFlip
{
public static void main(String[] args)
{
boolean[][] startState = {{true, false, true, true},
{false, false, false, true},
{true, false, true, false},
{true, true, false, false}};
List<boolean[][]> solutionPath = search(startState);
System.out.println("Solution Depth: " + solutionPath.size());
for(int i = 0; i < solutionPath.size(); i++)
{
System.out.println("Transition " + (i+1) + ":");
print2DArray(solutionPath.get(i));
}
}
public static List<boolean[][]> search(boolean[][] startState)
{
Queue<Node> Open = new LinkedList<Node>();
Queue<Node> Closed = new LinkedList<Node>();
Node StartNode = new Node(startState, null);
Open.add(StartNode);
while(!Open.isEmpty())
{
Node nextState = Open.remove();
System.out.println("Considering: ");
print2DArray(nextState.Value);
if (isComplete(nextState.Value))
{
System.out.println("Solution Found!");
return constructPath(nextState);
}
else
{
List<Node> children = generateChildren(nextState);
Closed.add(nextState);
for(Node child : children)
{
if (!Open.contains(child))
Open.add(child);
}
}
}
return new ArrayList<boolean[][]>();
}
public static List<boolean[][]> constructPath(Node node)
{
List<boolean[][]> solutionPath = new ArrayList<boolean[][]>();
while(node.Parent != null)
{
solutionPath.add(node.Value);
node = node.Parent;
}
Collections.reverse(solutionPath);
return solutionPath;
}
public static List<Node> generateChildren(Node parent)
{
System.out.println("Generating Children...");
List<Node> children = new ArrayList<Node>();
boolean[][] coinState = parent.Value;
for(int i = 0; i < coinState.length; i++)
{
for(int j = 0; j < coinState[i].length; j++)
{
if (!coinState[i][j])
{
boolean[][] child = arrayDeepCopy(coinState);
flip(child, i, j);
children.add(new Node(child, parent));
}
}
}
return children;
}
public static boolean[][] arrayDeepCopy(boolean[][] original)
{
boolean[][] r = new boolean[original.length][original[0].length];
for(int i=0; i < original.length; i++)
for (int j=0; j < original[0].length; j++)
r[i][j] = original[i][j];
return r;
}
public static void flip(boolean[][] grid, int i, int j)
{
//System.out.println("Flip("+i+","+j+")");
// if (i,j) is on the grid, and it is tails
if ((i >= 0 && i < grid.length) && (j >= 0 && j <= grid[i].length))
{
// flip (i,j)
grid[i][j] = !grid[i][j];
// flip 1 to the right
if (i+1 >= 0 && i+1 < grid.length) grid[i+1][j] = !grid[i+1][j];
// flip 1 down
if (j+1 >= 0 && j+1 < grid[i].length) grid[i][j+1] = !grid[i][j+1];
// flip 1 to the left
if (i-1 >= 0 && i-1 < grid.length) grid[i-1][j] = !grid[i-1][j];
// flip 1 up
if (j-1 >= 0 && j-1 < grid[i].length) grid[i][j-1] = !grid[i][j-1];
}
}
public static boolean isComplete(boolean[][] coins)
{
boolean complete = true;
for(int i = 0; i < coins.length; i++)
{
for(int j = 0; j < coins[i].length; j++)
{
if (coins[i][j] == false) complete = false;
}
}
return complete;
}
public static void print2DArray(boolean[][] array)
{
for (int row=0; row < array.length; row++)
{
for (int col=0; col < array[row].length; col++)
{
System.out.print((array[row][col] ? "H" : "T") + " ");
}
System.out.println();
}
}
}

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