It's a Google interview question. There's a list of "T" and "F" only. All denotes a position such that T means position is occupied by a flower pot and F means pot is not there, so you can put another pot at this position. Find the number of pots that can be placed in a given arrangement such that no two pots are adjacent to each other(they can be adjacent in the given arrangement). If a position at the beginning is unoccupied then a pot can be placed if second position is also unoccupied and if the last position is unoccupied than a pot can be placed if second last position is also unoccupied. For ex.
TFFFTFFTFFFFT - returns 2
FFTTFFFFFTTFF - returns 4
I tried solving it by looking at adjacent values for every position with value F. Increased the counter if both adjacent positions were F and set this position as T. I need a better solution or any other solution(if any).
Let's analyse what has to be done.
So first we probably need to visit and examine each place. That suggests loop of some sort. E.g.:
for (int i = 0; i < myPlaces.Length; ++i)
When we are at a spot we have to check if it's occupied
if (place[i] == 'F')
but that's not enough to place the flower pot there. We have to check if the next and previous place is free
place[i-1]
place[i+1]
If all tree contain F you can put the flower pot there and move to next field
Now, we also have some exceptions from the rule. Beginning and end of the list. So you have to deal with them separately. E.g
if (i == 0)
{
// only check current position and next position
}
if (i == myPlaces.Length - 1) // minus 1 because indexing usually starts from 0
{
// only check current position and previous position
}
After that you can perform the checks mentioned previously.
Now let's think of the input data. Generally, it's a good habit not to modify the input data but make a copy and work on the copy. Also some data structures work better than the others for different tasks. Here you can use simple string to keep entry values. But I would say an array of chars would be a better option because then, when you find a place where you can put a flower pot you can actually replace the F with the T in an array. Then when you move to new spot your data structers knows that there is already a pot in the previous position so your algorithm won't put an adjacent one.
You would not be able to do that with string as strings are immutable and you would need to generate a new string each time.
Note that it's only a naive algorithm with a lot of scope for improvement and optimization. But my goal was rather to give some idea how to approach this kind of problems in general. I'll leave implementing of the details to you as an afternoon exercise before targeting a job at Google.
You may be able to do this with a modified Mergesort. Consider the flowerpots that can be placed in the singletons, then the flowerpots that can be placed in the doubleton merges of those singletons, up the tree to the full arrangement. It would complete in O(n lg n) for a list of n flowerpots.
There is certainly a way to do this with a modified Rod Cutting algorithm with complexity O(n^2). The subproblem is whether or not an open "false set" exists in the substring being considered. The "closed false sets" already have some maximum value computed for them. So, when a new character is added, it either increases the amount of flowerpots that can be inserted, or "locks in" the maximum quantity of available flowerpots for the substring.
Also, you know that the maximum flowerpots that can be placed in a set of n open positions bound by closed positions is n - 2 (else n-1 if only bracketed on one side, i.e. the string begins or ends with a "false set". The base condition (the first position is open, or the first position is closed) can calculated upon reaching the second flowerpot.
So, we can build up to the total number of flowerpots that can be inserted into the whole arrangement in terms of the maximum number of flowerpots that can be inserted into smaller subarrangements that have been previously calculated. By storing our previous calculations in an array, we reduce the amount of time necessary to calculate the maximum for the next subarrangement to a single array lookup and some constant-time calculations. This is the essence of dynamic programming!
EDIT: I updated the answer to provide a description of the Dynamic Programming approach. Please consider working through the interactive textbook I mentioned in the comments! http://interactivepython.org/runestone/static/pythonds/index.html
I would approach the problem like this. You need FFF to have one more pot, FFFFF for two pots, etc. To handle the end cases, add an F at each end.
Because this is very similar to a 16-bit integer, the algorithm should use tricks like binary arithmetic operations.
Here is an implementation in Python that uses bit masking (value & 1), bit shifting (value >>= 1) and math ((zeros - 1) / 2) to count empty slots and calculate how many flower pots could fit.
#value = 0b1000100100001
value = 0b0011000001100
width = 13
print bin(value)
pots = 0 # number of flower pots possible
zeros = 1 # number of zero bits in a row, start with one leading zero
for i in range(width):
if value & 1: # bit is one, count the number of zeros
if zeros > 0:
pots += (zeros - 1) / 2
zeros = 0
else: # bit is zero, increment the number found
zeros += 1
value >>= 1 # shift the bits to the right
zeros += 1 # add one trailing zero
pots += (zeros - 1) / 2
print pots, "flower pots"
The solution is really simple, check the previous and current value of the position and mark the position as plantable (or puttable) and increment the count. Read the next value, if it is already is planted, (backtrack and) change the previous value and decrement the count. The complexity is O(n). What we really want to check is the occurrence of 1001. Following is the implementation of the algorithm in Java.
public boolean canPlaceFlowers(List<Boolean> flowerbed, int numberToPlace) {
Boolean previous = false;
boolean puttable = false;
boolean prevChanged = false;
int planted = 0;
for (Boolean current : flowerbed) {
if (previous == false && current == false) {
puttable = true;
}
if (prevChanged == true && current == true) {
planted--;
}
if (puttable) {
previous = true;
prevChanged = true;
planted++;
puttable = false;
} else {
previous = current;
prevChanged = false;
}
}
if (planted >= numberToPlace) {
return true;
}
return false;
}
private static void canPlaceOneFlower(List<Boolean> flowerbed, FlowerBed fb) {
boolean result;
result = fb.canPlaceFlowers(flowerbed, 1);
System.out.println("Can place 1 flower");
if (result) {
System.out.println("-->Yes");
} else {
System.out.println("-->No");
}
}
private static void canPlaceTwoFlowers(List<Boolean> flowerbed, FlowerBed fb) {
boolean result;
result = fb.canPlaceFlowers(flowerbed, 2);
System.out.println("Can place 2 flowers");
if (result) {
System.out.println("-->Yes");
} else {
System.out.println("-->No");
}
}
private static void canPlaceThreeFlowers(List<Boolean> flowerbed, FlowerBed fb) {
boolean result;
result = fb.canPlaceFlowers(flowerbed, 3);
System.out.println("Can place 3 flowers");
if (result) {
System.out.println("-->Yes");
} else {
System.out.println("-->No");
}
}
private static void canPlaceFourFlowers(List<Boolean> flowerbed, FlowerBed fb) {
boolean result;
result = fb.canPlaceFlowers(flowerbed, 4);
System.out.println("Can place 4 flowers");
if (result) {
System.out.println("-->Yes");
} else {
System.out.println("-->No");
}
}
public static void main(String[] args) {
List<Boolean> flowerbed = makeBed(new int[] { 0, 0, 0, 0, 0, 0, 0 });
FlowerBed fb = new FlowerBed();
canPlaceFourFlowers(flowerbed, fb);
canPlaceThreeFlowers(flowerbed, fb);
flowerbed = makeBed(new int[] { 0, 0, 0, 1, 0, 0, 0 });
canPlaceFourFlowers(flowerbed, fb);
canPlaceThreeFlowers(flowerbed, fb);
canPlaceTwoFlowers(flowerbed, fb);
flowerbed = makeBed(new int[] { 1, 0, 0, 1, 0, 0, 0, 1 });
canPlaceFourFlowers(flowerbed, fb);
canPlaceThreeFlowers(flowerbed, fb);
canPlaceTwoFlowers(flowerbed, fb);
canPlaceOneFlower(flowerbed, fb);
}
My solution using dynamic programming.
ar is array in the form of ['F','T','F'].
import numpy as np
def pot(ar):
s = len(ar)
rt = np.zeros((s,s))
for k in range(0,s):
for i in range(s-k):
for j in range(i,i+k+1):
left = 0
right = 0
if ar[j] != 'F':
continue
if j-1 >= i and ar[j-1] == 'T':
continue
else:
left = 0
if j+1 <= i+k and ar[j+1] == 'T':
continue
else:
right = 0
if j-2 >= i:
left = rt[i][j-2]
if j+2 <= i+k:
right = rt[j+2][i+k]
rt[i][i+k] = max(rt[i][i+k], left+right+1)
return rt[0][len(ar)-1]
My solution written in C#
private static int CheckAvailableSlots(string str)
{
int counter = 0;
char[] chrs = str.ToCharArray();
if (chrs.FirstOrDefault().Equals('F'))
if (chrs.Length == 1)
counter++;
else if (chrs.Skip(1).FirstOrDefault().Equals('F'))
counter++;
if (chrs.LastOrDefault().Equals('F') && chrs.Reverse().Skip(1).FirstOrDefault().Equals('F'))
counter++;
for (int i = 1; i < chrs.Length - 2; i++)
{
if (chrs[i - 1].Equals('T'))
continue;
else if (chrs[i].Equals('F') && chrs[i + 1].Equals('F'))
{
chrs[i] = 'T';
counter++;
i++;
}
else
i++;
}
return counter;
}
// 1='T'
// 0='F'
int[] flowerbed = new int[] {1,0,0,0,0,1};
public boolean canPlaceFlowers(int[] flowerbed, int n) {
int tg = 0;
for (int i = 0, g = 1; i < flowerbed.length && tg < n; i++) {
g += flowerbed[i] == 0 ? flowerbed.length - 1 == i ? 2 : 1 : 0;
if (flowerbed[i] == 1 || i == flowerbed.length - 1) {
tg += g / 2 - (g % 2 == 0 ? 1 : 0);
g = 0;
}
}
return tg >= n;
}
Most of these answers (unless they alter the array or traverse and a copy) dont consider the situation where the first 3 (or last 3) pots are empty. These solutions will incorrectly determine that FFFT will contain 2 spaces, rather than just one. We therefore need to start at the third element (rather than then second) and end at index length - 3 (rather than length - 2). Also, while looping through the array, if an eligible index is found, the index just be incremented by 2, otherwise TTFFFFT would give 2 available plots instead of one. This is true unless you alter the array while looping or use a copy of the array and alter it.
Edit: this holds true unless the question is how many spaces are available for planting, rather than how many total plants can be added
Related
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");
}
}
I recently stumbled upon an interesting problem, an I am wondering if my solution is optimal.
You are given an array of zeros and ones. The goal is to return the
amount zeros and the amount of ones in the most expensive sub-array.
The cost of an array is the amount of 1s divided by amount of 0s. In
case there are no zeros in the sub-array, the cost is zero.
At first I tried brute-forcing, but for an array of 10,000 elements it was far too slow and I ran out of memory.
My second idea was instead of creating those sub-arrays, to remember the start and the end of the sub-array. That way I saved a lot of memory, but the complexity was still O(n2).
My final solution that I came up is I think O(n). It goes like this:
Start at the beginning of the array, for each element, calculate the cost of the sub-arrays starting from 1, ending at the current index. So we would start with a sub-array consisting of the first element, then first and second etc. Since the only thing that we need to calculate the cost, is the amount of 1s and 0s in the sub-array, I could find the optimal end of the sub-array.
The second step was to start from the end of the sub-array from step one, and repeat the same to find the optimal beginning. That way I am sure that there is no better combination in the whole array.
Is this solution correct? If not, is there a counter-example that will show that this solution is incorrect?
Edit
For clarity:
Let's say our input array is 0101.
There are 10 subarrays:
0,1,0,1,01,10,01,010,101 and 0101.
The cost of the most expensive subarray would be 2 since 101 is the most expensive subarray. So the algorithm should return 1,2
Edit 2
There is one more thing that I forgot, if 2 sub-arrays have the same cost, the longer one is "more expensive".
Let me sketch a proof for my assumption:
(a = whole array, *=zero or more, +=one or more, {n}=exactly n)
Cases a=0* and a=1+ : c=0
Cases a=01+ and a=1+0 : conforms to 1*0{1,2}1*, a is optimum
For the normal case, a contains one or more 0s and 1s.
This means there is some optimum sub-array of non-zero cost.
(S) Assume s is an optimum sub-array of a.
It contains one or more zeros. (Otherwise its cost would be zero).
(T) Let t be the longest `1*0{1,2}+1*` sequence within s
(and among the equally long the one with with most 1s).
(Note: There is always one such, e.g. `10` or `01`.)
Let N be the number of 1s in t.
Now, we prove that always t = s.
By showing it is not possible to add adjacent parts of s to t if (S).
(E) Assume t shorter than s.
We cannot add 1s at either side, otherwise not (T).
For each 0 we add from s, we have to add at least N more 1s
later to get at least the same cost as our `1*0+1*`.
This means: We have to add at least one run of N 1s.
If we add some run of N+1, N+2 ... somewhere than not (T).
If we add consecutive zeros, we need to compensate
with longer runs of 1s, thus not (T).
This leaves us with the only option of adding single zeors and runs of N 1s each.
This would give (symmetry) `1{n}*0{1,2}1{m}01{n+m}...`
If m>0 then `1{m}01{n+m}` is longer than `1{n}0{1,2}1{m}`, thus not (T).
If m=0 then we get `1{n}001{n}`, thus not (T).
So assumption (E) must be wrong.
Conclusion: The optimum sub-array must conform to 1*0{1,2}1*.
Here is my O(n) impl in Java according to the assumption in my last comment (1*01* or 1*001*):
public class Q19596345 {
public static void main(String[] args) {
try {
String array = "0101001110111100111111001111110";
System.out.println("array=" + array);
SubArray current = new SubArray();
current.array = array;
SubArray best = (SubArray) current.clone();
for (int i = 0; i < array.length(); i++) {
current.accept(array.charAt(i));
SubArray candidate = (SubArray) current.clone();
candidate.trim();
if (candidate.cost() > best.cost()) {
best = candidate;
System.out.println("better: " + candidate);
}
}
System.out.println("best: " + best);
} catch (Exception ex) { ex.printStackTrace(System.err); }
}
static class SubArray implements Cloneable {
String array;
int start, leftOnes, zeros, rightOnes;
// optimize 1*0*1* by cutting
void trim() {
if (zeros > 1) {
if (leftOnes < rightOnes) {
start += leftOnes + (zeros - 1);
leftOnes = 0;
zeros = 1;
} else if (leftOnes > rightOnes) {
zeros = 1;
rightOnes = 0;
}
}
}
double cost() {
if (zeros == 0) return 0;
else return (leftOnes + rightOnes) / (double) zeros +
(leftOnes + zeros + rightOnes) * 0.00001;
}
void accept(char c) {
if (c == '1') {
if (zeros == 0) leftOnes++;
else rightOnes++;
} else {
if (rightOnes > 0) {
start += leftOnes + zeros;
leftOnes = rightOnes;
zeros = 0;
rightOnes = 0;
}
zeros++;
}
}
public Object clone() throws CloneNotSupportedException { return super.clone(); }
public String toString() { return String.format("%s at %d with cost %.3f with zeros,ones=%d,%d",
array.substring(start, start + leftOnes + zeros + rightOnes), start, cost(), zeros, leftOnes + rightOnes);
}
}
}
If we can show the max array is always 1+0+1+, 1+0, or 01+ (Regular expression notation then we can calculate the number of runs
So for the array (010011), we have (always starting with a run of 1s)
0,1,1,2,2
so the ratios are (0, 1, 0.3, 1.5, 1), which leads to an array of 10011 as the final result, ignoring the one runs
Cost of the left edge is 0
Cost of the right edge is 2
So in this case, the right edge is the correct answer -- 011
I haven't yet been able to come up with a counterexample, but the proof isn't obvious either. Hopefully we can crowd source one :)
The degenerate cases are simpler
All 1's and 0's are obvious, as they all have the same cost.
A string of just 1+,0+ or vice versa is all the 1's and a single 0.
How about this? As a C# programmer, I am thinking we can use something like Dictionary of <int,int,int>.
The first int would be use as key, second as subarray number and the third would be for the elements of sub-array.
For your example
key|Sub-array number|elements
1|1|0
2|2|1
3|3|0
4|4|1
5|5|0
6|5|1
7|6|1
8|6|0
9|7|0
10|7|1
11|8|0
12|8|1
13|8|0
14|9|1
15|9|0
16|9|1
17|10|0
18|10|1
19|10|0
20|10|1
Then you can run through the dictionary and store the highest in a variable.
var maxcost=0
var arrnumber=1;
var zeros=0;
var ones=0;
var cost=0;
for (var i=1;i++;i<=20+1)
{
if ( dictionary.arraynumber[i]!=dictionary.arraynumber[i-1])
{
zeros=0;
ones=0;
cost=0;
if (cost>maxcost)
{
maxcost=cost;
}
}
else
{
if (dictionary.values[i]==0)
{
zeros++;
}
else
{
ones++;
}
cost=ones/zeros;
}
}
This will be log(n^2), i hope and u just need 3n size of memory of the array?
I think we can modify the maximal subarray problem to fit to this question. Here's my attempt at it:
void FindMaxRatio(int[] array, out maxNumOnes, out maxNumZeros)
{
maxNumOnes = 0;
maxNumZeros = 0;
int numOnes = 0;
int numZeros = 0;
double maxSoFar = 0;
double maxEndingHere = 0;
for(int i = 0; i < array.Size; i++){
if(array[i] == 0) numZeros++;
if(array[i] == 1) numOnes++;
if(numZeros == 0) maxEndingHere = 0;
else maxEndingHere = numOnes/(double)numZeros;
if(maxEndingHere < 1 && maxEndingHere > 0) {
numZeros = 0;
numOnes = 0;
}
if(maxSoFar < maxEndingHere){
maxSoFar = maxEndingHere;
maxNumOnes = numOnes;
maxNumZeros = numZeros;
}
}
}
I think the key is if the ratio is less then 1, we can disregard that subsequence because
there will always be a subsequence 01 or 10 whose ratio is 1. This seemed to work for 010011.
What I am trying to do is make it so that the game I am creating will randomly change characters every 5 seconds.
I got this working via a timer, the only problem is I don't want them repeating, I'm currently working on dummy code so it's just changing the screen colour, but how can I make it so that it doesn't repeat the number it just called?
if (timer <= 0)
{
num = rand.Next(2);
timer = 5.0f;
}
That is the current code and then in the draw I've literally just done "if num equals a certain number then change background colour".
I tried adding a prev_num checker but I can't get it to work properly (here it is)
if (timer <= 0)
{
prev_number = num;
num = rand.Next(2);
if (prev_number == num)
{
num = rand.Next(2);
}
else
{
timer = 5.0f;
}
}
Consider that if you're picking (for example) a random number from 1-5 then there are five possible outcomes, so you would use rand.Next(5) to select the zero-based "ordinal" or index of the outcome, then convert it into the range you actually want (in this case, by adding one).
If you want a random number from 0-4, excluding the number you just picked, then there are only four possible outcomes, not five - if the previous number was 3, then the possible outcomes are 0, 1, 2 or 4. You can then simplify your algorithm by choosing one of those four outcomes (rand.Next(4)) and mapping that ordinal to your desired range. A simple mapping would be to say if the new random number is below the previous number, return it as-is, otherwise (if equal or greater) add one.
int new_num = rand.Next(4);
if(new_num >= prev_num)
{
new_num++;
}
Your new number is now guaranteed to be in the same range as the previous number, but not equal to it.
Maybe just put it into a loop instead of a single check?
Also, I think because your timer was inside the else then it was not always
updated correctly.
if (timer <= 0)
{
tempNum = rand.Next(2);
do
{
tempNum = rand.Next(2);
}
while (tempNum == num)
num = tempNum;
timer = 5.0f;
}
Create an array of sequential numbers and then shuffle them (like a deck of cards) when your application begins.
int[] numbers = new int[100];
for(int i = 0; i < numbers.Length; i++)
numbers[i] = i;
Shuffle(numbers);
Using a function to shuffle the list:
public static void Shuffle<T>(IList<T> list)
{
Random rng = new Random();
int n = list.Count;
while (n > 1) {
n--;
int k = rng.Next(n + 1);
T value = list[k];
list[k] = list[n];
list[n] = value;
}
}
You can then access them sequentially out of the list. They will be random as the list was shuffled, but you won't have any repetitions since each number only exists once in the list.
if (timer <= 0)
{
num = numbers[index];
index++;
timer = 5.0f;
}
There are ones, zeroes and ‘U’s in a particular order. (E.g. “1001UU0011”) The number of ones and zeroes are the same, and there’s always two ‘U’s next to each other. You can swap the pair of ‘U’s with any pair of adjacent digits. Here’s a sample move:
__
/ \
1100UU0011 --> 11001100UU
The task is to put all the zeroes before the ones.
Here's a sample solution:
First step:
__
/ \
1100UU0011
Second step:
____
/ \
UU00110011
000011UU11 --> DONE
It’s pretty easy to create a brute-force algorithm. But with that it takes hundreds or even thousands of moves to solve a simple one like my example. So I’m looking for something more “clever” algorithm.
It's not homework; it was a task in a competition. The contest is over but I can’t find the solution for this.
Edit: The task here is the create an algorithm that can sort those 0s and 1s - not just output N 0s and N 1s and 2 Us. You have to show the steps somehow, like in my example.
Edit 2: The task didn't ask for the result with the least moves or anything like that. But personally I would love the see an algorithm that provides that :)
I think this should work:
Iterate once to find the position of
the U's. If they don't occupy the last
two spots, move them there by
swapping with the last two.
Create a
variable to track the currently
sorted elements, initially set to
array.length - 1, meaning anything
after it is sorted.
Iterate
backwards. Every time you encounter a
1:
swap the the one and its element before it with the U's.
swap the U's back to the the currently sorted elements tracker -1, update variable
Continue until the beginning of the array.
This is quite an interesting problem - so let's try to solve it. I will start with an precise analysis of the problem and see what one can find out. I will add piece by piece to this answer over the next days. Any help is welcome.
A problem of size n is a problem with exactly exactly n zeros, n ones, and two Us, hence it consists of 2n+2 symbols.
There are
(2n)!
-----
(n!)²
different sequences of exactly n zeros and nones. Then there are 2n+1 possible positions to insert the two Us, hence there are
(2n)! (2n+1)!
-----(2n+1) = -------
(n!)² (n!)²
problem instances of size n.
Next I am looking for a way to assign a score to each problem instance and how this score changes under all possible moves hoping to find out what the minimal number of required moves is.
Instance of size one are either already sorted
--01 0--1 01--
(I think I will use hyphens instead of Us because they are easier to recognize) or cannot be sorted.
--10 ==only valid move==> 10--
-10- no valid move
10-- ==only valid move==> --10
In consequence I will assume n >= 2.
I am thinking about the inverse problem - what unordered sequences can be reached starting from an ordered sequence. The ordered sequences are determined up to the location of the both hyphens - so the next question is if it is possible to reach every ordered sequence from every other order sequence. Because a sequence of moves can be performed forward and backward it is sufficient to show that one specific ordered sequence is reachable from all other. I choose (0|n)(1|n)--. ((0|x) represents exactly x zeros. If x is not of the form n-m zero or more is assumed. There may be additional constraints like a+b+2=n not explicitly stated. ^^ indicates the swap position. The 0/1 border is obviously between the last zero and first one.)
// n >= 2, at least two zeros between -- and the 0/1 border
(0|a)--(0|b)00(1|n) => (0|n)--(1|n-2)11 => (0|n)(1|n)--
^^ ^^
// n >= 3, one zero between -- and 0/1 boarder
(0|n-1)--01(1|n-1) => (0|n)1--(1|n-3)11 => (0|n)(1|n)--
^^ ^^
// n >= 2, -- after last zero but at least two ones after --
(0|n)(1|a)--(1|b)11 => (0|n)(1|n)--
^^
// n >= 3, exactly one one after --
(0|n)(1|n-3)11--1 => (0|n)(1|n-3)--111 => (0|n)(1|n)--
^^ ^^
// n >= 0, nothing to move
(0|n)(1|n)--
For the remaining two problems of size two - 0--011 and 001--1 - it seems not to be possible to reach 0011--. So for n >= 3 it is possible to reach every ordered sequence from every other ordered sequence in at most four moves (Probably less in all cases because I think it would have been better to choose (0|n)--(1|n) but I leave this for tomorrow.). The preliminary goal is to find out at what rate and under what conditions one can create (and in consequence remove) 010 and 101 because they seem to be the hard cases as already mentioned by others.
If you use a WIDTH-first brute force, it's still brute force, but at least you are guaranteed to come up with the shortest sequence of moves, if there is an answer at all. Here's a quick Python solution using a width-first search.
from time import time
def generate(c):
sep = "UU"
c1, c2 = c.split(sep)
for a in range(len(c1)-1):
yield c1[0:a]+sep+c1[(a+2):]+c1[a:(a+2)]+c2
for a in range(len(c2)-1):
yield c1+c2[a:(a+2)]+c2[0:a]+sep+c2[(a+2):]
def solve(moves,used):
solved = [cl for cl in moves if cl[-1].rindex('0') < cl[-1].index('1')]
if len(solved) > 0: return solved[0]
return solve([cl+[d] for cl in moves for d in generate(cl[-1]) if d not in used and not used.add(d)],used)
code = raw_input('enter the code:')
a = time()
print solve([[code]],set())
print "elapsed time:",(time()-a),"seconds"
Well, the first thing that gets up to my mind is top-down dynamic programming approach. It's kind of easy to understand but could eat a lot of memory. While I'm trying to apply a bottom-up approach you can try this one:
Idea is simple - cache all of the results for the brute-force search. It will become something like this:
function findBestStep(currentArray, cache) {
if (!cache.contains(currentArray)) {
for (all possible moves) {
find best move recursively
}
cache.set(currentArray, bestMove);
}
return cache.get(currentArray);
}
This method complexity would be... O(2^n) which is creepy. However I see no logical way it can be smaller as any move is allowed.
If if find a way to apply bottom-up algorithm it could be a little faster (it does not need a cache) but it will still have O(2^n) complexity.
Added:
Ok, I've implemented this thing in Java. Code is long, as it always is in Java, so don't get scared of it's size. The main algorithm is pretty simple and can be found at the bottom. I don't think there can be any way faster than this (this is more of a mathematical question if it can be faster). It eats tonns of memory but still computes it all pretty fast.
This 0,1,0,1,0,1,0,1,0,1,0,1,0,1,2,2 computes in 1 second, eating ~60mb memory resulting in 7 step sorting.
public class Main {
public static final int UU_CODE = 2;
public static void main(String[] args) {
new Main();
}
private static class NumberSet {
private final int uuPosition;
private final int[] numberSet;
private final NumberSet parent;
public NumberSet(int[] numberSet) {
this(numberSet, null, findUUPosition(numberSet));
}
public NumberSet(int[] numberSet, NumberSet parent, int uuPosition) {
this.numberSet = numberSet;
this.parent = parent;
this.uuPosition = uuPosition;
}
public static int findUUPosition(int[] numberSet) {
for (int i=0;i<numberSet.length;i++) {
if (numberSet[i] == UU_CODE) {
return i;
}
}
return -1;
}
protected NumberSet getNextNumberSet(int uuMovePos) {
final int[] nextNumberSet = new int[numberSet.length];
System.arraycopy(numberSet, 0, nextNumberSet, 0, numberSet.length);
System.arraycopy(this.getNumberSet(), uuMovePos, nextNumberSet, uuPosition, 2);
System.arraycopy(this.getNumberSet(), uuPosition, nextNumberSet, uuMovePos, 2);
return new NumberSet(nextNumberSet, this, uuMovePos);
}
public Collection<NumberSet> getNextPositionalSteps() {
final Collection<NumberSet> result = new LinkedList<NumberSet>();
for (int i=0;i<=numberSet.length;i++) {
final int[] nextNumberSet = new int[numberSet.length+2];
System.arraycopy(numberSet, 0, nextNumberSet, 0, i);
Arrays.fill(nextNumberSet, i, i+2, UU_CODE);
System.arraycopy(numberSet, i, nextNumberSet, i+2, numberSet.length-i);
result.add(new NumberSet(nextNumberSet, this, i));
}
return result;
}
public Collection<NumberSet> getNextSteps() {
final Collection<NumberSet> result = new LinkedList<NumberSet>();
for (int i=0;i<=uuPosition-2;i++) {
result.add(getNextNumberSet(i));
}
for (int i=uuPosition+2;i<numberSet.length-1;i++) {
result.add(getNextNumberSet(i));
}
return result;
}
public boolean isFinished() {
boolean ones = false;
for (int i=0;i<numberSet.length;i++) {
if (numberSet[i] == 1)
ones = true;
else if (numberSet[i] == 0 && ones)
return false;
}
return true;
}
#Override
public boolean equals(Object obj) {
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
final NumberSet other = (NumberSet) obj;
if (!Arrays.equals(this.numberSet, other.numberSet)) {
return false;
}
return true;
}
#Override
public int hashCode() {
int hash = 7;
hash = 83 * hash + Arrays.hashCode(this.numberSet);
return hash;
}
public int[] getNumberSet() {
return this.numberSet;
}
public NumberSet getParent() {
return parent;
}
public int getUUPosition() {
return uuPosition;
}
}
void precacheNumberMap(Map<NumberSet, NumberSet> setMap, int length, NumberSet endSet) {
int[] startArray = new int[length*2];
for (int i=0;i<length;i++) startArray[i]=0;
for (int i=length;i<length*2;i++) startArray[i]=1;
NumberSet currentSet = new NumberSet(startArray);
Collection<NumberSet> nextSteps = currentSet.getNextPositionalSteps();
List<NumberSet> nextNextSteps = new LinkedList<NumberSet>();
int depth = 1;
while (nextSteps.size() > 0) {
for (NumberSet nextSet : nextSteps) {
if (!setMap.containsKey(nextSet)) {
setMap.put(nextSet, nextSet);
nextNextSteps.addAll(nextSet.getNextSteps());
if (nextSet.equals(endSet)) {
return;
}
}
}
nextSteps = nextNextSteps;
nextNextSteps = new LinkedList<NumberSet>();
depth++;
}
}
public Main() {
final Map<NumberSet, NumberSet> cache = new HashMap<NumberSet, NumberSet>();
final NumberSet startSet = new NumberSet(new int[] {0,1,0,1,0,1,0,1,0,1,0,1,0,1,2,2});
precacheNumberMap(cache, (startSet.getNumberSet().length-2)/2, startSet);
if (cache.containsKey(startSet) == false) {
System.out.println("No solutions");
} else {
NumberSet cachedSet = cache.get(startSet).getParent();
while (cachedSet != null && cachedSet.parent != null) {
System.out.println(cachedSet.getUUPosition());
cachedSet = cachedSet.getParent();
}
}
}
}
Here's a try:
Start:
let c1 = the total number of 1s
let c0 = the total number of 0s
if the UU is at the right end of the string, goto StartFromLeft
StartFromRight
starting at the right end of the string, move left, counting 1s,
until you reach a 0 or the UU.
If you've reached the UU, goto StartFromLeft.
If the count of 1s equals c1, you are done.
Else, swap UU with the 0 and its left neighbor if possible.
If not, goto StartFromLeft.
StartFromLeft
starting at the left end of the string, move right, counting 0s,
until you reach a 1 or the UU.
If you've reached the UU, goto StartFromRight.
If the count of 0s equals c0, you are done.
Else, swap UU with the 1 and its right neighbor, if possible.
If not, goto StartFromRight
Then goto StartFromRight.
So, for the original 1100UU0011:
1100UU0011 - original
110000UU11 - start from right, swap UU with 00
UU00001111 - start from left, swap UU with 11
For the trickier 0101UU01
0101UU01 - original
0UU11001 - start from right, can't swap UU with U0, so start from left and swap UU with 10
00011UU1 - start from right, swap UU with 00
However, this won't solve something like 01UU0...but that could be fixed by a flag - if you've gone through the whole algorithm once, made no swaps and it isn't solved...do something.
About the question... It never asked for the optimal solution and these types of questions do not want that. You need to write a general purpose algorithm to handle this problem and a brute-force search to find the best solution is not feasible for strings that may be megabytes in length. Also I noticed late that there are guaranteed to be the same number of 0s and 1s, but I think it's more interesting to work with the general case where there may be different numbers of 0s and 1s. There actually isn't guaranteed to be a solution in every case if the length of the input string is less than 7, even in the case where you have 2 0s and 2 1s.
Size 3: Only one digit so it is sorted by definition (UU0 UU1 0UU 1UU)
Size 4: No way to alter the order. There are no moves if UU is in the middle, and only swap with both digits if it is at an end (1UU0 no moves, UU10->10UU->UU10, etc)
Size 5: UU in the middle can only move to the far end and not change the order of the 0s and 1s (1UU10->110UU). UU at an end can move to middle and not change order, but only move back to the same end so there is no use for it (UU110->11UU0->UU110). The only way to change digits is if the UU is at an end and to swap with the opposite end. (UUABC->BCAUU or ABCUU->UUCAB). This means that if UU is at positions 0 or 2 it can solve if 0 is in the middle (UU101->011UU or UU100->001UU) and if UU is at positions 1 or 3 it can solve if 1 is in the middle (010UU->UU001 or 110UU->UU011). Anything else is already solved or is unsolvable. If we need to handle this case, I would say hard-code it. If sorted, return result (no moves). If UU is in the middle somewhere, move it to the end. Swap from the end to the other end and that is the only possible swap whether it is now sorted or not.
Size 6: Now we get so a position where we can have a string specified according to the rules where we can make moves but where there can be no solution. This is the problem point with any algorithm, because I would think a condition of any solution should be that it will let you know if it cannot be solved. For instance 0010, 0100, 1000, 1011, 1100, 1101, and 1110 can be solved no matter where the UU is placed and the worst cases take 4 moves to solve. 0101 and 1010 can only be solved if UU is in an odd position. 0110 and 1001 can only be solved if UU is in an even position (either end or middle).
I think the best way will be something like the following, but I haven't written it yet. First, make sure you place a '1' at the end of the list. If the end is currently 0, move UU to the end then move it to the last '1' position - 1. After that you continually move UU to the first '1', then to the first '0' after the new UU. This will move all the 0s to the start of the list. I've seen a similar answer the other way, but it didn't take into account the final character on either end. This can run into issues with small values still (i.e. 001UU01, cannot move to first 1, move to end 00101UU lets us move to start but leaves 0 at end 00UU110).
My guess is that you can hard-code special cases like that. I'm thinking there may be a better algorithm though. For instance you could use the first two characters as a 'temporary swap variable. You would put UU there and then do combinations of operations on others to leave UY back at the start. For instance, UUABCDE can swap AB with CD or DE or BC WITH DE (BCAUUDE->BCADEUU->UUADEBC).
Another possible thing would be to treat the characters as two blocks of two base-3 bits
0101UU0101 will show up as 11C11 or 3593. Maybe also something like a combination of hard-coded swaps. For instance if you ever see 11UU, move UU left 2. If you ever see UU00, move UU right two. If you see UU100, or UU101, move UU right 2 to get 001UU or 011UU.
Maybe another possibility would be some algorithm to move 0s left of center and 1s right of center (if it is given that there are the same number of 0s and 1s.
Maybe it would be better to work on an a structure that contained only 0s and 1s with a position for UU.
Maybe look at the resulting condition better, allowing for UU to be anywhere in the string, these conditions MUST be satisfied:
No 0s after Length/2
No 1s before (Length/2-1)
Maybe there are more general rules, like it's really good to swap UU with 10 in this case '10111UU0' because a '0' is after UU now and that would let you move the new 00 back to where the 10 was (10111UU0->UU111100->001111UU).
Anyway, here's the brute force code in C#. The input is a string and an empty Dictionary. It fills the dictionary with every possible resulting string as the keys and the list of shortest steps to get there as the value:
Call:
m_Steps = new Dictionary<string, List<string>>();
DoSort("UU1010011101", new List<string>);
It includes DoTests() which calls DoSort for every possible string with the given number of digits (not including UU):
Dictionary<string, List<string>> m_Steps = new Dictionary<string, List<string>>();
public void DoStep(string state, List<string> moves) {
if (m_Steps.ContainsKey(state) && m_Steps[state].Count <= moves.Count + 1) // have better already
return;
// we have a better (or new) solution to get to this state, so set it to the moves we used to get here
List<string> newMoves = new List<string>(moves);
newMoves.Add(state);
m_Steps[state] = newMoves;
// if the state is a valid solution, stop here
if (state.IndexOf('1') > state.LastIndexOf('0'))
return;
// try all moves
int upos = state.IndexOf('U');
for (int i = 0; i < state.Length - 1; i++) {
// need to be at least 2 before or 2 after the UU position (00UU11 upos is 2, so can only move to 0 or 4)
if (i > upos - 2 && i < upos + 2)
continue;
char[] chars = state.ToCharArray();
chars[upos] = chars[i];
chars[upos + 1] = chars[i + 1];
chars[i] = chars[i + 1] = 'U';
DoStep(new String(chars), newMoves);
}
}
public void DoTests(int digits) { // try all combinations
char[] chars = new char[digits + 2];
for (int value = 0; value < (2 << digits); value++) {
for (int uupos = 0; uupos < chars.Length - 1; uupos++) {
for (int i = 0; i < chars.Length; i++) {
if (i < uupos)
chars[i] = ((value >> i) & 0x01) > 0 ? '1' : '0';
else if (i > uupos + 1)
chars[i] = ((value >> (i - 2)) & 0x01) > 0 ? '1' : '0';
else
chars[i] = 'U';
}
m_Steps = new Dictionary<string, List<string>>();
DoSort(new string(chars), new List<string>);
foreach (string key in m_Steps.AllKeys))
if (key.IndexOf('1') > key.LastIndexOf('0')) { // winner
foreach (string step in m_Steps[key])
Console.Write("{0}\t", step);
Console.WriteLine();
}
}
}
}
Counting sort.
If A is the number of 0s, A is also the number of 1s, and U is the number of Us:
for(int i=0; i<A; i++)
data[i] = '0';
for(int i=0; i<A; i++)
data[A+i] = '1';
for(int i=0; i<U; i++)
data[A+A+i] = 'U';
There are only 2 Us?
Why not just count the number of 0s and store the position of the us:
numberOfZeros = 0
uPosition = []
for i, value in enumerate(sample):
if value = 0:
numberOfZeros += 1
if value = U
uPosition.append(i)
result = []
for i in range(len(sample)):
if i = uPosition[0]
result.append('U')
uPosition.pop(0)
continue
if numberOfZeros > 0:
result.append('0')
numberOfZeros -= 1
continue
result.append('1')
Would result in a runtime of O(n)
Or even better:
result = []
numberOfZeros = (len(sample)-2)/2
for i, value in enumerate(sample):
if value = U
result.append('U')
continue
if numberOfZeros > 0:
result.append(0)
numberOfZeros -= 1
continue
result.append(1)
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();
}
}
}