Given a grid of open spots, and a certain number of tiles to place in those spots, what function f(openSpots, tilesToPlace) will give you the number of continuous paths you can form?
Continuous paths are placements of the tiles such that each tile shares an edge with another. (Only corners touching is not good enough. So (0, 1) and (0, 0) are legal, but (1, 1) and (2, 2) is not.)
I already have a function that will find all these paths. However, it only works for small numbers. For larger values, all I need is a count of how many could possibly exist. Here is some data:
For 1 tiles, there are 1 paths.
For 2 tiles, there are 4 paths.
For 3 tiles, there are 22 paths.
For 4 tiles, there are 89 paths.
For 5 tiles, there are 390 paths.
For 6 tiles, there are 1476 paths.
For 7 tiles, there are 5616 paths.
For 8 tiles, there are 19734 paths.
For 9 tiles, there are 69555 paths.
This gets really slow to calculate as the puzzle size increases. I think the asymptotic complexity of my path finding solution is pretty bad.
If there are n tiles, the grid is at most n spots long and wide.
Your problem seems to be at least as difficult as enumerating polyominoes. There are no known fast algorithms for doing this, and the best known algorithms struggle after n=50. I doubt there is a fast way to solve this problem.
I'm not even going to pretend that this is an optimal solution but it might be useful as a reference solution. I think it at least gives the correct answer, although it takes some time. It solves the problem recursively by finding all paths of length n-1, then checking for all possible places it can add one more tile and removing duplicate solutions. It has a particularly ugly part where it checks for duplicate by converting the path to a string and comparing the strings, but it was fast to write.
Here's the output it generates:
n = 1, number of paths found = 1
n = 2, number of paths found = 4
n = 3, number of paths found = 22
n = 4, number of paths found = 113
n = 5, number of paths found = 571
n = 6, number of paths found = 2816
n = 7, number of paths found = 13616
n = 8, number of paths found = 64678
n = 9, number of paths found = 302574
And here's the code:
using System;
using System.Collections.Generic;
using System.Linq;
public struct Tile
{
public Tile(int x, int y) { X = x; Y = y; }
public readonly int X;
public readonly int Y;
public IEnumerable<Tile> GetNeighbours(int gridSize)
{
if (X > 0)
yield return new Tile(X - 1, Y);
if (X < gridSize - 1)
yield return new Tile(X + 1, Y);
if (Y > 0)
yield return new Tile(X, Y - 1);
if (Y < gridSize - 1)
yield return new Tile(X, Y + 1);
}
public override string ToString()
{
return string.Format("({0},{1})", X, Y);
}
}
public class Path
{
public Path(Tile[] tiles) { Tiles = tiles; }
public Tile[] Tiles { get; private set; }
public override string ToString()
{
return string.Join("", Tiles.Select(tile => tile.ToString()).ToArray());
}
}
public class PathFinder
{
public IEnumerable<Path> FindPaths(int n, int gridSize)
{
if (n == 1)
{
for (int x = 0; x < gridSize; ++x)
for (int y = 0; y < gridSize; ++y)
yield return new Path(new Tile[] { new Tile(x, y) });
}
else
{
Dictionary<string, object> pathsSeen = new Dictionary<string, object>();
foreach (Path shortPath in FindPaths(n - 1, gridSize))
{
foreach (Tile tile in shortPath.Tiles)
{
foreach (Tile neighbour in tile.GetNeighbours(gridSize))
{
// Ignore tiles that are already included in the path.
if (shortPath.Tiles.Contains(neighbour))
continue;
Path newPath = new Path(shortPath.Tiles
.Concat(new Tile[] { neighbour })
.OrderBy(t => t.X)
.ThenBy(t => t.Y)
.ToArray());
string pathKey = newPath.ToString();
if (!pathsSeen.ContainsKey(pathKey))
{
pathsSeen[pathKey] = null;
yield return newPath;
}
}
}
}
}
}
static void Main()
{
PathFinder pathFinder = new PathFinder();
for (int n = 1; n <= 9; ++n)
{
List<Path> paths = pathFinder.FindPaths(n, n).ToList();
Console.WriteLine("n = {0}, number of paths found = {1}", n, paths.Count);
//foreach (Path path in paths)
// Console.WriteLine(path.ToString());
}
}
}
Related
Imagine you're given an unsorted array [5,11,7,4,19,8,11,6,17] and a max weight 23. [similar to Two Sum Problem by a bit different]
One needs to find two optimal weights (by which I mean if two weights that are (almost or not) half of the weight you're trying to find) in this case [5,17], [3,19], [11,11] so I need to return [11,11].
I was taken back by the problem, and could not solve it. [I was not allowed to use structs]
I tried to sort [3, 5, 6, 7, 8, 11, 11, 17, 19] and search from both ends and store indexes of values that were <= max weight in a vector as a pair (like v[i], v[i+1] and check them later by their pairs) then return a pair with both largest vals, but got confused.
[although, weights were doubles and I did not see duplicates at that set I did not use unsorted_map(hashMap), might it've worked?]
Can anyone suggest how should I go about this problem? is it similar to "knapsack problem"? Thank you
You can use Two Pointer Approach for the problem.
Algorithm:
Sort the array.
Have two pointers startIndex and endIndex to 0 and arraySize-1.
sum = arr[startIndex] + arr[endIndex]
If sum is less than or equal to 23, increment startIndex else decrement endIndex.
keep track of closest value using a variable.
finish when startIndex == endIndex
Code in Java:
public class Solution {
private ArrayList<Integer> twoSumClosest(ArrayList<Integer> a, int s) {
// Sort the arraylist
Collections.sort(a);
// closests sum we got till now
int sumClosest = Integer.MIN_VALUE;
// indexes used to traverse
int startIndex = 0;
int endIndex = a.size() - 1 ;
// answer Indexes
int firstIndex = 1;
int secondIndex = a.size() - 1;
while( startIndex < endIndex ) {
if( a.get(startIndex) + a.get(endIndex) > s) {
endIndex--;
continue;
} else {
if( a.get(startIndex) + a.get(endIndex) > sumClosest ) {
sumClosest = a.get(startIndex) + a.get(endIndex);
firstIndex = startIndex;
secondIndex = endIndex;
}
startIndex++;
}
}
ArrayList<Integer> ans = new ArrayList<>();
ans.add(firstIndex);
ans.add(secondIndex);
return ans;
}
}
Time Complexity: O(nlogn)
O(n) if array was already sorted
Extra Space Needed: O(1)
I ran into a tricky question from an interview prep book which goes..
You have a 3 by 3 matrix containing integers 1 to 9 as shown below
1 2 3
4 5 6
7 8 9
How do you get unique 7 digit number combos with the first numbers all starting with 4 (matrix[1][0]). The traversal is meant to be like that of a rook on a chess board.. 1 way either horizontally or vertically...(Having 4125874 is valid 7 digit combo btw).
I tried writing some code and doing regular 2D matrix traversal with a boolean visited flag here to get an answer and storing each combo in a hashSet to ensure uniqueness but I am stuck. Any kind comments, hints and code revisions to get me code working would be appreciated.
class Ideone
{
void dfs(int[][] matrix, boolean visited) //considered dfs with a boolean visited flag but I am stuck. I want to make my solution recursive
{
boolean visited = false;
}
public static HashSet<String> get7DigitCombo(int[][] matrix)
{
String result = "";
int[][] cache = matrix.clone();
Set<String> comboSet = new HashSet<String>();
boolean visited = false;
int resultStart = matrix[1][0];
for(int row = 1; row < matrix.length; row++)
{
for(int col = 0; col < matrix[0].length; col++)
{
if (visited == false & result.length < 7)
{
result += "" + (matrix[row + 1][col] || matrix[row -1][col] || matrix[row][col+1] || matrix[row][col-1]);
}
}
}
comboSet.add(result);
return comboSet;
}
public static void main (String[] args) throws java.lang.Exception
{
// your code goes here
int[][] matrix = {{1, 2, 3},
{4, 5, 6},
{7, 8, 9},
};
HashSet<String> comboSet = get7DigitCombo(matrix);
System.out.print(comboSet);
}
}
The following mcve demonstrates recursively getting neighbors and accumulating then into
unique combinations.
The code is documented with comments:
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
class Ideone
{
private static final int SIZE = 7; //size of combo
private static int[][] directions = { //represents moving directions
{-1, 0}, //up
{ 0,-1}, //left
{ 0, 1}, //right
{ 1, 0} //down
};
public static void main (String[] args) throws java.lang.Exception
{
int[][] matrix = {{1, 2, 3},
{4, 5, 6},
{7, 8, 9},
};
Set<String> comboSet = get7DigitCombo(matrix);
System.out.print(comboSet.size());
}
public static Set<String> get7DigitCombo(int[][] matrix)
{
Set<String> comboSet = new HashSet<>();
get7DigitCombo(1, 0, matrix, String.valueOf(matrix[1][0]), comboSet);
return comboSet;
}
//recursively get all neighbors. generate combos by appending each neighbor
//combo represents a single combination. combos accumulates combination
static void get7DigitCombo(int row, int col, int[][] matrix, String combo, Set<String> combos){
if(combo !=null && combo.length() == SIZE) { //when combo reached the right size, add it
//System.out.println(combo);
combos.add(combo);
return;
}
//get and iterate over all adjacent neighbors
for(int[] neighbor : getNeighbors(row, col, matrix)){
get7DigitCombo(neighbor[0], neighbor[1], matrix, combo+neighbor[2], combos);
}
}
//return list of adjacent neighbors. each neighbor is represented by
//int[3]: row, column, value
private static List<int[]> getNeighbors(int row, int col, int[][] matrix) {
List<int[]> neighbors = new ArrayList<>();
for(int[] dir : directions){
int newRow = row + dir[0] ; int newCol = col + dir[1];
if(isValidAddress(newRow, newCol, matrix)) {
neighbors.add( new int[]{newRow,newCol, matrix[newRow][newCol]});
}
}
return neighbors;
}
private static boolean isValidAddress(int row, int col, int[][] matrix) {
if(row < 0 || col < 0) return false;
if(row >= matrix.length || col >= matrix[row].length) return false;
return true;
}
}
This is a pacman problem.
You must look for or define the neighbors of each matrix value.
Then cross the matrix fallowing the neighbors of each matrix value.
It usually resolves with recursive functions.
I think you code must be change from the ground using a different approach.
I am trying to scale a skeleton to match to the sizes of another skeleton.
My algoritm do the following:
Find the distance between two joints of the origin skeleton and the destiny skeleton using phytagorean teorem
divide this two distances to find a multiply factor.
Multiply each joint by this factor.
Here is my actual code:
public static Skeleton ScaleToMatch(this Skeleton skToBeScaled, Skeleton skDestiny)
{
Joint newJoint = new Joint();
double distanciaOrigem = 0;
double distanciaDestino = 0;
double fator = 1;
SkeletonPoint pos = new SkeletonPoint();
foreach (BoneOrientation bo in skToBeScaled.BoneOrientations)
{
distanciaOrigem = FisioKinectCalcs.Distance3DBetweenJoint(skToBeScaled.Joints[bo.StartJoint], skToBeScaled.Joints[bo.EndJoint]);
distanciaDestino = FisioKinectCalcs.Distance3DBetweenJoint(skDestiny.Joints[bo.StartJoint], skDestiny.Joints[bo.EndJoint]);
if (distanciaOrigem > 0 && distanciaDestino > 0)
{
fator = (distanciaDestino / distanciaOrigem);
newJoint = skToBeScaled.Joints[bo.EndJoint]; // escaling only the end joint as the BoneOrientatios starts from HipCenter, i am scaling from center to edges.
// applying the new values to the joint
pos = new SkeletonPoint()
{
X = (float)(newJoint.Position.X * fator),
Y = (float)(newJoint.Position.Y * fator),
Z = (float)(newJoint.Position.Z * fator)
};
newJoint.Position = pos;
skToBeScaled.Joints[bo.EndJoint] = newJoint;
}
}
return skToBeScaled;
}
Every seems to work fine except for the hands and foots
Look at this images
I have my own skeleton over me, and my skeleton scaled to the sizes of another person, but the hands and foots still crazy. (but code looks right)
Any suggestion?
It's hard to say without running the code, but it somewhat "looks good".
What I would validate though, is your
if (distanciaOrigem > 0 && distanciaDestino > 0)
If distanciaOrigem is very close to 0, but even just epsilon away from 0, it won't be picked up by the if, and then
fator = (distanciaDestino / distanciaOrigem);
Will result in a very large number!
I would suggest to smooth the factor so it generally fits the proper scale. Try this code:
private static Dictionary<JointType, double> jointFactors = null;
static CalibrationUtils()
{
InitJointFactors();
}
public static class EnumUtil
{
public static IEnumerable<T> GetValues<T>()
{
return Enum.GetValues(typeof(T)).Cast<T>();
}
}
private static void InitJointFactors()
{
var jointTypes = EnumUtil.GetValues<JointType>();
jointFactors = new Dictionary<JointType, double>();
foreach(JointType type in jointTypes)
{
jointFactors.Add(type, 0);
}
}
private static double SmoothenFactor(JointType jointType, double factor, int weight)
{
double currentValue = jointFactors[jointType];
double newValue = 0;
if(currentValue != 0)
newValue = (weight * currentValue + factor) / (weight + 1);
else
newValue = factor;
jointFactors[jointType] = newValue;
return newValue;
}
When it comes to factor usage just use the SmoothenFactor method first:
public static Skeleton ScaleToMatch(this Skeleton skToBeScaled, Skeleton skDestiny, double additionalFactor = 1)
{
Joint newJoint = new Joint();
double distanceToScale = 0;
double distanceDestiny = 0;
double factor = 1;
int weight = 500;
SkeletonPoint pos = new SkeletonPoint();
Skeleton newSkeleton = null;
KinectHelper.CopySkeleton(skToBeScaled, ref newSkeleton);
SkeletonPoint hipCenterPosition = newSkeleton.Joints[JointType.HipCenter].Position;
foreach(BoneOrientation bo in skToBeScaled.BoneOrientations)
{
distanceToScale = Distance3DBetweenJoints(skToBeScaled.Joints[bo.StartJoint], skToBeScaled.Joints[bo.EndJoint]);
distanceDestiny = Distance3DBetweenJoints(skDestiny.Joints[bo.StartJoint], skDestiny.Joints[bo.EndJoint]);
if(distanceToScale > 0 && distanceDestiny > 0)
{
factor = (distanceDestiny / distanceToScale) * additionalFactor;
newJoint = skToBeScaled.Joints[bo.EndJoint]; // escaling only the end joint as the BoneOrientatios starts from HipCenter, i am scaling from center to edges.
factor = SmoothenFactor(newJoint.JointType, factor, weight);
pos = new SkeletonPoint()
{
X = (float)((newJoint.Position.X - hipCenterPosition.X) * factor + hipCenterPosition.X),
Y = (float)((newJoint.Position.Y - hipCenterPosition.Y) * factor + hipCenterPosition.Y),
Z = (float)((newJoint.Position.Z - hipCenterPosition.Z) * factor + hipCenterPosition.Z)
};
newJoint.Position = pos;
newSkeleton.Joints[bo.EndJoint] = newJoint;
}
}
return newSkeleton;
}
I also modified your ScaleToMatch method as you see. There was a need to move joints in relation to HipCenter position. Also new positions are saved to a new Skeleton instance so they are not used in further vector calculations.
Experiment with the weight but since our bones length is constant you can use big numbers like 100 and more to be sure that wrong Kinect readings do not disturb the correct scale.
Here's an example of how it helped with scaling HandRight joint position:
The weight was set to 500. The resulting factor is supposed to be around 2 (because the base skeleton was purposely downscaled by a factor of 2).
I hope it helps!
I'm working on a game which has tank battles on a tiled map. If a tank is on a cell, that cell is considered unpassable in the A* algorithm, therefore, whenever an unit needs to attack another, I need to plan a path which brings the attacker into range (if range=1, then next to the target).
Currently, I use an iterative approach with increasing radius to find a path to a nearby cell and choose a cell which minimizes the A-Cell-B distance. Unfortunately, this is slow for one unit, not to mention for 50 units.
Is there a way to extract a partial path from a regular A* search data structures?
Just for reference, here is the implementation I have.
Set<T> closedSet = U.newHashSet();
Map<T, T> cameFrom = U.newHashMap();
final Map<T, Integer> gScore = U.newHashMap();
final Map<T, Integer> hScore = U.newHashMap();
final Map<T, Integer> fScore = U.newHashMap();
final Comparator<T> smallestF = new Comparator<T>() {
#Override
public int compare(T o1, T o2) {
int g1 = fScore.get(o1);
int g2 = fScore.get(o2);
return g1 < g2 ? -1 : (g1 > g2 ? 1 : 0);
}
};
Set<T> openSet2 = U.newHashSet();
List<T> openSet = U.newArrayList();
gScore.put(initial, 0);
hScore.put(initial, estimation.invoke(initial, destination));
fScore.put(initial, gScore.get(initial) + hScore.get(initial));
openSet.add(initial);
openSet2.add(initial);
while (!openSet.isEmpty()) {
T current = openSet.get(0);
if (current.equals(destination)) {
return reconstructPath(cameFrom, destination);
}
openSet.remove(0);
openSet2.remove(current);
closedSet.add(current);
for (T loc : neighbors.invoke(current)) {
if (!closedSet.contains(loc)) {
int tentativeScore = gScore.get(current)
+ distance.invoke(current, loc);
if (!openSet2.contains(loc)) {
cameFrom.put(loc, current);
gScore.put(loc, tentativeScore);
hScore.put(loc, estimation.invoke(loc, destination));
fScore.put(loc, gScore.get(loc) + hScore.get(loc));
openSet.add(loc);
Collections.sort(openSet, smallestF);
openSet2.add(loc);
} else
if (tentativeScore < gScore.get(loc)) {
cameFrom.put(loc, current);
gScore.put(loc, tentativeScore);
hScore.put(loc, estimation.invoke(loc, destination));
fScore.put(loc, gScore.get(loc) + hScore.get(loc));
Collections.sort(openSet, smallestF);
}
}
}
}
return Collections.emptyList();
A solution that seems to work (replacing the last return Collections.emptyList();):
// if we get here, there was no direct path available
// find a target location which minimizes initial-L-destination
if (closedSet.isEmpty()) {
return Pair.of(false, Collections.<T>emptyList());
}
T nearest = Collections.min(closedSet, new Comparator<T>() {
#Override
public int compare(T o1, T o2) {
int d1 = trueDistance.invoke(destination, o1);
int d2 = trueDistance.invoke(destination, o2);
int c = U.compare(d1, d2);
if (c == 0) {
d1 = trueDistance.invoke(initial, o1);
d2 = trueDistance.invoke(initial, o2);
c = U.compare(d1, d2);
}
return c;
}
});
return Pair.of(true, reconstructPath(cameFrom, nearest));
Where the trueDistance gives the eucleidian distance of two points. (The base algorithm uses a simpler function yielding 1000 for X-X or YY neightbor, 1414 for XY neighbor).
Project Euler problem 14:
The following iterative sequence is
defined for the set of positive
integers:
n → n/2 (n is even) n → 3n + 1 (n is
odd)
Using the rule above and starting with
13, we generate the following
sequence: 13 → 40 → 20 → 10 → 5 → 16 →
8 → 4 → 2 → 1
It can be seen that this sequence
(starting at 13 and finishing at 1)
contains 10 terms. Although it has not
been proved yet (Collatz Problem), it
is thought that all starting numbers
finish at 1.
Which starting number, under one
million, produces the longest chain?
My first instinct is to create a function to calculate the chains, and run it with every number between 1 and 1 million. Obviously, that takes a long time. Way longer than solving this should take, according to Project Euler's "About" page. I've found several problems on Project Euler that involve large groups of numbers that a program running for hours didn't finish. Clearly, I'm doing something wrong.
How can I handle large groups of numbers quickly?
What am I missing here?
Have a read about memoization. The key insight is that if you've got a sequence starting A that has length 1001, and then you get a sequence B that produces an A, you don't to repeat all that work again.
This is the code in Mathematica, using memoization and recursion. Just four lines :)
f[x_] := f[x] = If[x == 1, 1, 1 + f[If[EvenQ[x], x/2, (3 x + 1)]]];
Block[{$RecursionLimit = 1000, a = 0, j},
Do[If[a < f[i], a = f[i]; j = i], {i, Reverse#Range#10^6}];
Print#a; Print[j];
]
Output .... chain length´525´ and the number is ... ohhhh ... font too small ! :)
BTW, here you can see a plot of the frequency for each chain length
Starting with 1,000,000, generate the chain. Keep track of each number that was generated in the chain, as you know for sure that their chain is smaller than the chain for the starting number. Once you reach 1, store the starting number along with its chain length. Take the next biggest number that has not being generated before, and repeat the process.
This will give you the list of numbers and chain length. Take the greatest chain length, and that's your answer.
I'll make some code to clarify.
public static long nextInChain(long n) {
if (n==1) return 1;
if (n%2==0) {
return n/2;
} else {
return (3 * n) + 1;
}
}
public static void main(String[] args) {
long iniTime=System.currentTimeMillis();
HashSet<Long> numbers=new HashSet<Long>();
HashMap<Long,Long> lenghts=new HashMap<Long, Long>();
long currentTry=1000000l;
int i=0;
do {
doTry(currentTry,numbers, lenghts);
currentTry=findNext(currentTry,numbers);
i++;
} while (currentTry!=0);
Set<Long> longs = lenghts.keySet();
long max=0;
long key=0;
for (Long aLong : longs) {
if (max < lenghts.get(aLong)) {
key = aLong;
max = lenghts.get(aLong);
}
}
System.out.println("number = " + key);
System.out.println("chain lenght = " + max);
System.out.println("Elapsed = " + ((System.currentTimeMillis()-iniTime)/1000));
}
private static long findNext(long currentTry, HashSet<Long> numbers) {
for(currentTry=currentTry-1;currentTry>=0;currentTry--) {
if (!numbers.contains(currentTry)) return currentTry;
}
return 0;
}
private static void doTry(Long tryNumber,HashSet<Long> numbers, HashMap<Long, Long> lenghts) {
long i=1;
long n=tryNumber;
do {
numbers.add(n);
n=nextInChain(n);
i++;
} while (n!=1);
lenghts.put(tryNumber,i);
}
Suppose you have a function CalcDistance(i) that calculates the "distance" to 1. For instance, CalcDistance(1) == 0 and CalcDistance(13) == 9. Here is a naive recursive implementation of this function (in C#):
public static int CalcDistance(long i)
{
if (i == 1)
return 0;
return (i % 2 == 0) ? CalcDistance(i / 2) + 1 : CalcDistance(3 * i + 1) + 1;
}
The problem is that this function has to calculate the distance of many numbers over and over again. You can make it a little bit smarter (and a lot faster) by giving it a memory. For instance, lets create a static array that can store the distance for the first million numbers:
static int[] list = new int[1000000];
We prefill each value in the list with -1 to indicate that the value for that position is not yet calculated. After this, we can optimize the CalcDistance() function:
public static int CalcDistance(long i)
{
if (i == 1)
return 0;
if (i >= 1000000)
return (i % 2 == 0) ? CalcDistance(i / 2) + 1 : CalcDistance(3 * i + 1) + 1;
if (list[i] == -1)
list[i] = (i % 2 == 0) ? CalcDistance(i / 2) + 1: CalcDistance(3 * i + 1) + 1;
return list[i];
}
If i >= 1000000, then we cannot use our list, so we must always calculate it. If i < 1000000, then we check if the value is in the list. If not, we calculate it first and store it in the list. Otherwise we just return the value from the list. With this code, it took about ~120ms to process all million numbers.
This is a very simple example of memoization. I use a simple list to store intermediate values in this example. You can use more advanced data structures like hashtables, vectors or graphs when appropriate.
Minimize how many levels deep your loops are, and use an efficient data structure such as IList or IDictionary, that can auto-resize itself when it needs to expand. If you use plain arrays they need to be copied to larger arrays as they expand - not nearly as efficient.
This variant doesn't use an HashMap but tries only to not repeat the first 1000000 numbers. I don't use an hashmap because the biggest number found is around 56 billions, and an hash map could crash.
I have already done some premature optimization. Instead of / I use >>, instead of % I use &. Instead of * I use some +.
void Main()
{
var elements = new bool[1000000];
int longestStart = -1;
int longestRun = -1;
long biggest = 0;
for (int i = elements.Length - 1; i >= 1; i--) {
if (elements[i]) {
continue;
}
elements[i] = true;
int currentStart = i;
int currentRun = 1;
long current = i;
while (current != 1) {
if (current > biggest) {
biggest = current;
}
if ((current & 1) == 0) {
current = current >> 1;
} else {
current = current + current + current + 1;
}
currentRun++;
if (current < elements.Length) {
elements[current] = true;
}
}
if (currentRun > longestRun) {
longestStart = i;
longestRun = currentRun;
}
}
Console.WriteLine("Longest Start: {0}, Run {1}", longestStart, longestRun);
Console.WriteLine("Biggest number: {0}", biggest);
}