Find points with maximum visibility in a room-as-grid - c#

I have a 2D grid of cells, like so:
I want to find the "centroids," or the places in each room that can see the most other cells. For example, "centroid" 1 can see 500 other cells, "centroid" 2 can see 400 other cells (NOT included in the first 500), and so on (if there's a better name for this let me know).
I'm currently doing this with the code below.
public void SetCentroids(CellWalkableState[,] grid)
{
centroids = new List<((int, int), int)>();
List<(int, int)> cellsCopy = new List<(int, int)>();
for (int i = 0; i < cells.Count; i++)
{
cellsCopy.Add(cells[i]);
}
Debug.Log(DateTime.Now.ToString("o") + " - Setting centroids for room with " + cells.Count + " cells");
var perCellInView = cellsCopy.AsParallel().Select(x => (x, StaticClass.FindInView(x, grid))).ToList();
var force_start = perCellInView.First();
Debug.Log(DateTime.Now.ToString("o") + " - got in view");
var perCellInViewOrdered = perCellInView.AsParallel().OrderByDescending(xxx => xxx.Item2.Count);
var force_start_1 = perCellInViewOrdered.First();
Debug.Log(DateTime.Now.ToString("o") + " - sorted");
List<(int, int)> roomCellsAdded = new List<(int, int)>();
while(roomCellsAdded.Count < (cells.Count*0.9))
{
if(cellsCopy.Count == 0)
{
Debug.LogError("something is wrong here.");
}
var centroid = perCellInViewOrdered.First().x;
var centroidCells = perCellInViewOrdered.First().Item2;
if(centroidCells.Count == 0)
{
Debug.Log("this shouldnt be happening");
break;
}
roomCellsAdded.AddRange(centroidCells);
centroids.Add((centroid, centroidCells.Count));
Debug.Log(DateTime.Now.ToString("o") + " - added centroids, " + roomCellsAdded.Count + " cells in view");
var loopPerCellInView = perCellInView.AsParallel().Where(x => centroids.Select(y => y.Item1).Contains(x.x) == false).Select(x => (x.x, x.Item2.Except(roomCellsAdded).ToList())).ToList();
Debug.Log(DateTime.Now.ToString("o") + " - excluded");
perCellInViewOrdered = loopPerCellInView.AsParallel().OrderByDescending(xxx => xxx.Item2.Count);
Debug.Log(DateTime.Now.ToString("o") + " - resorted");
}
}
public static List<(int, int)> FindInView((int,int) start, CellWalkableState[,] grid)
{
List<(int, int)> visible = new List<(int, int)>() { start };
bool alive = true;
int r = 1;
var length_x = grid.GetLength(0);
var length_y = grid.GetLength(1);
List<(int, int)> searched = new List<(int, int)>();
List<double> angles = new List<double>();
while(alive)
{
//alive = false;
int newR = r;
int count = CountFromR(newR);
var angleInc = 360.0 / count;
var rNexts = Enumerable.Repeat(1, count).ToArray();
for (int i = 0; i < count; i++)
{
var angle = angleInc * i;
if(angles.Contains(angle) == false)
{
angles.Add(angle);
float cos = Mathf.Cos((float)(Mathf.Deg2Rad * angle));
float sin = Mathf.Sin((float)(Mathf.Deg2Rad * angle));
var b = r;
var p = i % (r * 2);
var d = math.sqrt(math.pow(b, 2) + math.pow(p, 2));
var dScaled = d / r;
bool keepGoing = true;
while(keepGoing)
{
var rCur = dScaled * (rNexts[i]);
var loc = (start.Item1 + Mathf.RoundToInt(rCur * cos), start.Item2 + Mathf.RoundToInt(rCur * sin));
if (searched.Contains(loc) == false)
{
searched.Add(loc);
if (loc.Item1 >= 0 && loc.Item1 < length_x && loc.Item2 >= 0 && loc.Item2 < length_y)
{
if (grid[loc.Item1, loc.Item2] == CellWalkableState.Interactive || grid[loc.Item1, loc.Item2] == CellWalkableState.Walkable)
{
visible.Add(loc);
}
else
{
keepGoing = false;
}
}
else
{
keepGoing = false; // invalid, stop
}
}
else
{
if (visible.Contains(loc) == false)
{
keepGoing = false; // can stop, because we can't see past this place
}
}
if(keepGoing)
{
rNexts[i]++;
}
}
}
}
angles = angles.Distinct().ToList();
searched = searched.Distinct().ToList();
visible = visible.Distinct().ToList();
if(rNexts.All(x => x <= r))
{
alive = false;
}
else
{
r = rNexts.Max();
}
}
return visible;
}
static int CountFromR(int r)
{
return 8 * r;
}
The "short" summary of the code above is that each location first determines what cells around itself it can see. That becomes a list of tuples, List<((int,int), List<(int,int)>)>, where the first item is the location and the second is all cells it views. That main list is sorted by the count of the sublist, such that the item with the most cells-it-can-vew is first. That's added as a centroid, and all cells it can view are added to a second ("already handled") list. A modified "main list" is formed, with each sublist now excluding anything in the second list. It loops doing this until 90% of the cells have been added.
Some output:
2021-04-27T15:24:39.8678545-04:00 - Setting centroids for room with 7129 cells
2021-04-27T15:45:26.4418515-04:00 - got in view
2021-04-27T15:45:26.4578551-04:00 - sorted
2021-04-27T15:45:27.3168517-04:00 - added centroids, 4756 cells in view
2021-04-27T15:45:27.9868523-04:00 - excluded
2021-04-27T15:45:27.9868523-04:00 - resorted
2021-04-27T15:45:28.1058514-04:00 - added centroids, 6838 cells in view
2021-04-27T15:45:28.2513513-04:00 - excluded
2021-04-27T15:45:28.2513513-04:00 - resorted
2021-04-27T15:45:28.2523509-04:00 - Setting centroids for room with 20671 cells
This is just too slow for my purposes. Can anyone suggest alternate methods of doing this? For all of the cells essentially the only information one has is whether they're "open" or one can see through them or not (vs something like a wall).

An approximate solution with fixed integer slopes
For a big speedup (from quadratic in the number of rooms to linear), you could decide to check just a few integer slopes at each point. These are equivalence classes of visibility, i.e., if cell x can see cell y along such a line, and cell y can see cell z along the same line, then all 3 cells can see each other. Then you only need to compute each "visibility interval" along a particular line once, rather than per-cell.
You would probably want to check at least horizontal, vertical and both 45-degree diagonal slopes. For horizontal, start at cell (1, 1), and move right until you hit a wall, let's say at (5, 1). Then the cells (1, 1), (2, 1), (3, 1) and (4, 1) can all see each other along this slope, so although you started at (1, 1), there's no need to repeat the computation for the other 3 cells -- just add a copy of this list (or even a pointer to it, which is faster) to the visibility lists for all 4 cells. Keep heading right, and repeat the process as soon as you hit a non-wall. Then begin again on the next row.
Visibility checking for 45-degree diagonals is slightly harder, but not much, and checking for other slopes in which we advance 1 horizontally and some k vertically (or vice versa) is about the same. (Checking for for general slopes, like for every 2 steps right go up 3, is perhaps a bit trickier.)
Provided you use pointers rather than list copies, for a given slope, this approach spends amortised constant time per cell: Although finding the k horizontally-visible neighbours of some cell takes O(k) time, it means no further horizontal processing needs to be done for any of them. So if you check a constant number of slopes (e.g., the four I suggested), this is O(n) time overall to process n cells. In contrast, I think your current approach takes at least O(nq) time, where q is the average number of cells visible to a cell.

Related

C# WinsForm, Frequency Distribution Table [Updated]

Update 01
Thanks to Caius, found the main problem, the logic on the "if" was wrong, now fixed and giving the correct results. The loop still create more positions than needed on the secondary List, an extra position for each number on the main List.
I've updated the code bellow for refence for the following question:
-001 I can figure out why it create positions that needed, the for loop should run only after the foreach finishes its loops correct?
-002 To kind of solving this issue, I've used a List.Remove() to remove all the 0's, so far no crashes, but, the fact that I'm creating the extra indexes, and than removing them, does means a big performance down if I have large list of numbers? Or is an acceptable solution?
Description
It supposed to read all numbers in a central List1 (numberList), and count how many numbers are inside a certain (0|-15 / 15|-20) range, for that I use another List, that each range is a position on the List2 (numberSubList), where each number on List2, tells how many numbers exists inside that range.
-The range changes as the numbers grows or decrease
Code:
void Frequency()
{
int minNumb = numberList.Min();
int maxNumb = numberList.Max();
int size = numberList.Count();
numberSubList.Clear();
dGrdVFrequency.Rows.Clear();
dGrdVFrequency.Refresh();
double k = (1 + 3.3 * Math.Log10(size));
double h = (maxNumb - minNumb) / k;
lblH.Text = $"H: {Math.Round(h, 2)} / Rounded = {Math.Round(h / 5) * 5}";
lblK.Text = $"K: {Math.Round(k, 4)}";
if (h <= 5) { h = 5; }
else { h = Math.Round(h / 5) * 5; }
int counter = 1;
for (int i = 0; i < size; i++)
{
numberSubList.Add(0); // 001 HERE, creating more positions than needed, each per number.
foreach (int number in numberList)
{
if (number >= (h * i) + minNumb && number < (h * (i + 1)) + minNumb)
{
numberSubList[i] = counter++;
}
}
numberSubList.Remove(0); // 002-This to remove all the extra 0's that are created.
counter = 1;
}
txtBoxSubNum.Clear();
foreach (int number in numberSubList)
{
txtBoxSubNum.AppendText($"{number.ToString()} , ");
}
lblSubTotalIndex.Text = $"Total in List: {numberSubList.Count()}";
lblSubSumIndex.Text = $"Sum of List: {numberSubList.Sum()}";
int inc = 0;
int sum = 0;
foreach (int number in numberSubList)
{
sum = sum + number;
int n = dGrdVFrequency.Rows.Add();
dGrdVFrequency.Rows[n].Cells[0].Value = $"{(h * inc) + minNumb} |- {(h * (1 + inc)) + minNumb}";
dGrdVFrequency.Rows[n].Cells[1].Value = $"{number}";
dGrdVFrequency.Rows[n].Cells[2].Value = $"{sum}";
dGrdVFrequency.Rows[n].Cells[3].Value = $"{(number * 100) / size} %";
dGrdVFrequency.Rows[n].Cells[4].Value = $"{(sum * 100) / size} %";
inc++;
}
}
Screen shot showing the updated version.
I think, if your aim is to only store eg 17 in the "15 to 25" slot, this is wonky:
if (number <= (h * i) + minNumb) // Check if number is smaller than the range limit
Because it's found inside a loop that will move on to the next range, "25 to 35" and it only asks if the number 17 is less than the upper limit (and 17 is less than 35) so 17 is accorded to the 25-35 range too
FWIW the range a number should be in can be derived from the number, with (number - min) / number_of_ranges - at the moment you create your eg 10 ranges and then you visit each number 10 times looking to put it in a range, so you do 9 times more operations than you really need to

Check if 2 objects from an array overlap and change their position on y if they do

So how can I update the position every time I call the StartRandomizingRightSpikePosition
private bool CheckOverlap(GameObject o1, GameObject o2)
{
return spikeRight.Select(t => t.GetComponent<Collider>().bounds.Intersects(t.GetComponent<Collider>().bounds)).FirstOrDefault();
}
public void StartRandomizingRightSpikesPosition()
{
foreach (var t in spikeRight)
{
foreach (var t1 in spikeRight)
{
if (t == t1) continue;
if (!CheckOverlap(t, t1)) continue;
yPosition = Random.Range(-7, 7);
var position = t1.transform.position;
desiredPosition = new Vector3(position.x, yPosition, position.z);
t1.transform.position = desiredPosition;
Debug.Log(t.gameObject + " intersects " + t1.gameObject);
}
}
}
The short answer is yes but I'm not sure you would want too. I'm not sure you're going to find a way to do this efficiently and you might be better off finding a way to generate the objects such that this step is not necessary.
I can't tell from your question how the objects are actually stored so I'm going to provide some sample code that just deals with a simple array of Rectangles. You should be able to adapt it to your specifics.
I tried to make it slightly more efficient by not checking both t1 == t and t == t1.
const int Bounds = 1000;
static bool RemovedOverlappingRects(Rectangle[] rects)
{
for (int pos = 0; pos < rects.Length; ++pos)
{
for (int check = pos +1; check < rects.Length; ++check)
{
var r1 = rects[pos];
var r2 = rects[check];
if (r1.IntersectsWith(r2))
{
r2.Y = Rng.Next(1, Bounds);
rects[check] = r2;
Console.WriteLine($"{pos} overlaps with {check}");
return true;
}
}
}
return false;
}
Once we've randomly generated a new rectangle we have to start over. Which means invoking the above method in a loop.
var rects = GetRandomeRects(20).ToArray();
while (RemovedOverlappingRects(rects))
;
Because of the random movement I'm not certain you can guarantee this will always end. If you can deal with the non-random look of the results changing it to stack the overlaps would I believe always finish. That would be this:
r2.Y = r1.Y + r1.Height + 1;
in place of
r2.Y = Rng.Next(1, Bounds);
But even then you're still dealing with a very unpredictable run time due to the random input.
Maybe someone else can show us a more efficient way...

Selecting data intervals on Datachart with Mouse

I use stacked area chart and I want to select a datapoint interval with the mouse like below.
I know that some applications offer this feature however, I couldn't find how to do it.
Could you please show me the right way?
The term you needed is DataVisualization.Charting.Cursor
You can use this combination of properties:
// a few short references:
ChartArea ca = chart1.ChartAreas[0];
Axis ax = ca.AxisX;
var cx = ca.CursorX;
cx.IsUserEnabled = true; // allow a cursor to be placed
cx.IsUserSelectionEnabled = true; // allow it to be used for selecting
ax.ScaleView.Zoomable = false; // prevent from automatically zooming in
Here are the first and last values selected:
var x1 = cx.SelectionStart;
var x2 = cx.SelectionEnd;
Here are the first and last DataPoints selected:
var p1 = s.Points.Select(x => x).Where(x => x.XValue >= x1).First();
var p2 = s.Points.Select(x => x).Where(x => x.XValue <= x2).Last();
And the indices of the first and last DataPointsselected:
var i1 = s.Points.IndexOf(p1);
var i2 = s.Points.IndexOf(p2);
Now you can tell which points were selected:
textBox1.Text += (i2 - i1) + " points selected.\r\n\r\n";
for (int i = i1; i < i2; i++)
{
textBox1.Text += i + ". " + chart1.Series[0].Points[i].ToString() + "\r\n";
chart1.Series[0].Points[i].Color = Color.Red;
}
Note: The code to identify the starting and end points assumes that all DataPoints are added in increasing x-value order. Since you can add DataPoints in any order it will fail for instance when you insert out of order points! In that case you would instead collect the points in the selection (testing for both sides) in a List<DataPoint> and then enumerate this list.

C# for skipping some

I am asking a question today because I look is skipping certain items and I think it is because its running too fast and doing too much, is there a way to fix this without manually putting a thread.sleep of like 10ms after each loop element?
Is this a known issue? any help would be appreciated! The for loop I am talking about is the 3rd one, the one after the two nested set of for's
There is about 104 items in squares after the first 2 foreach's are ran, there could be up to 1000, what is the best way to stop it skipping?
If I don't put a thread.sleep there, only the first occurence works, the rest of the items in the foreach DONT work
If I do 10ms, 3-4 miss
If I do 15ms they all work
List<string> squares = new List<string>();
var minX = 1;
var minY = 1;
var maxX = room.Model.MapSizeX;
var maxY = room.Model.MapSizeY;
for (var currentX = minX; currentX <= maxX; ++currentX)
{
for (var currentY = minY; currentY <= maxY; ++currentY)
{
if (!room.GetGameMap().ItemCanBePlacedHere(currentX, currentY))
{
continue;
}
squares.Add(currentX + "," + currentY);
}
}
foreach (string tileString in squares)
{
var x = Convert.ToInt32(tileString.Split(',')[0]);
var y = Convert.ToInt32(tileString.Split(',')[1]);
session.SendWhisper("Attempting to place an item in X" + x + " and Y" + y);
if (!room.GetGameMap().ItemCanBePlacedHere(x, y))
{
session.SendWhisper("Can't place an item in X" + x + " Y" + y);
continue;
}
var newItem = new Item(new Random().Next(), room.RoomId, 1536, "", x, y, 0, 0, session.GetPlayerData().Id, 0, 0, 0, "", room);
newItem.ExtraData = "1";
newItem.UpdateState();
room.SendMessage(new ObjectAddComposer(newItem, room));
room.GetGameMap().AddToMap(newItem);
// I only put this here because it skips
// This slows it down a little and doesnt
// Make it skip 3-5 of the items
System.Threading.Thread.Sleep(15);
}

Calculate all possible permutations/combinations, then check if the result is equal to a value

Best way I can explain it is using an example:
You are visiting a shop with $2000, your goal is to have $0 at the end of your trip.
You do not know how many items are going to be available, nor how much they cost.
Say that there are currently 3 items costing $1000, $750, $500.
(The point is to calculate all possible solutions, not the most efficient one.)
You can spend $2000, this means:
You can buy the $1000 item 0, 1 or 2 times.
You can buy the $750 item 0, 1 or 2 times.
You can buy the $500 item 0, 1, 2, 3 or 4 times.
At the end I need to be able to have all solutions, in this case it will be
2*$1000
1*$1000 and 2*$500
2*$750 and 1*$500
4*$500
Side note: you can't have a duplicate solution (like this)
1*$1000 and 2*$500
2*$500 and 1*$1000
This is what I tried:
You first call this function using
goalmoney = convert.ToInt32(goalMoneyTextBox.Text);
totalmoney = Convert.ToInt32(totalMoneyTextBox.Text);
int[] list = new int[usingListBox.Items.Count];
Calculate(0, currentmoney, list);
The function:
public void Calculate(int level, int money, int[] list)
{
string item = usingListBox.Items[level].ToString();
int cost = ItemDict[item];
for (int i = 0; i <= (totalmoney / cost); i++)
{
int[] templist = list;
int tempmoney = money - (cost * i);
templist[level] = i;
if (tempmoney == goalmoney)
{
resultsFound++;
}
if (level < usingListBox.Items.Count - 1 && tempmoney != goalmoney) Calculate(level + 1, tempmoney, templist);
}
}
Your problem can be reduced to a well known mathematical problem labeled Frobenius equation which is closely related to the well known Coin problem. Suppose you have N items, where i-th item costs c[i] and you need to spent exactly S$. So you need to find all non negative integer solutions (or decide whether there are no solutions at all) of equation
c[1]*n[1] + c[2]*n[2] + ... + c[N]*n[N] = S
where all n[i] are unknown variables and each n[i] is the number of bought items of i-th type.
This equation can be solved in a various ways. The following function allSolutions (I suppose it can be additionally simplified) finds all solutions of a given equation:
public static List<int[]> allSolutions(int[] system, int total) {
ArrayList<int[]> all = new ArrayList<>();
int[] solution = new int[system.length];//initialized by zeros
int pointer = system.length - 1, temp;
out:
while (true) {
do { //the following loop can be optimized by calculation of remainder
++solution[pointer];
} while ((temp = total(system, solution)) < total);
if (temp == total && pointer != 0)
all.add(solution.clone());
do {
if (pointer == 0) {
if (temp == total) //not lose the last solution!
all.add(solution.clone());
break out;
}
for (int i = pointer; i < system.length; ++i)
solution[i] = 0;
++solution[--pointer];
} while ((temp = total(system, solution)) > total);
pointer = system.length - 1;
if (temp == total)
all.add(solution.clone());
}
return all;
}
public static int total(int[] system, int[] solution) {
int total = 0;
for (int i = 0; i < system.length; ++i)
total += system[i] * solution[i];
return total;
}
In the above code system is array of coefficients c[i] and total is S. There is an obvious restriction: system should have no any zero elements (this lead to infinite number of solutions). A slight modification of the above code avoids this restriction.
Assuming you have class Product which exposes a property called Price, this is a way to do it:
public List<List<Product>> GetAffordableCombinations(double availableMoney, List<Product> availableProducts)
{
List<Product> sortedProducts = availableProducts.OrderByDescending(p => p.Price).ToList();
//we have to cycle through the list multiple times while keeping track of the current
//position in each subsequent cycle. we're using a list of integers to save these positions
List<int> layerPointer = new List<int>();
layerPointer.Add(0);
int currentLayer = 0;
List<List<Product>> affordableCombinations = new List<List<Product>>();
List<Product> tempList = new List<Product>();
//when we went through all product on the top layer, we're done
while (layerPointer[0] < sortedProducts.Count)
{
//take the product in the current position on the current layer
var currentProduct = sortedProducts[layerPointer[currentLayer]];
var currentSum = tempList.Sum(p => p.Price);
if ((currentSum + currentProduct.Price) <= availableMoney)
{
//if the sum doesn't exeed our maximum we add that prod to a temp list
tempList.Add(currentProduct);
//then we advance to the next layer
currentLayer++;
//if it doesn't exist, we create it and set the 'start product' on that layer
//to the current product of the current layer
if (currentLayer >= layerPointer.Count)
layerPointer.Add(layerPointer[currentLayer - 1]);
}
else
{
//if the sum would exeed our maximum we move to the next prod on the current layer
layerPointer[currentLayer]++;
if (layerPointer[currentLayer] >= sortedProducts.Count)
{
//if we've reached the end of the list on the current layer,
//there are no more cheaper products to add, and this cycle is complete
//so we add the list we have so far to the possible combinations
affordableCombinations.Add(tempList);
tempList = new List<Product>();
//move to the next product on the top layer
layerPointer[0]++;
currentLayer = 0;
//set the current products on each subsequent layer to the current of the top layer
for (int i = 1; i < layerPointer.Count; i++)
{
layerPointer[i] = layerPointer[0];
}
}
}
}
return affordableCombinations;
}

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