Develop Reference

Swapping data in arrays

I made Twitch bot for points and i need now !top10 command,i made it to read my points.ini file and if contains channelName that i use to store number of points and usernames in usernamesTop10 and pointsTop10 arrays.
I need now to cycle trough whole document and compare points and then swap points and username if newUser has more points.
These are steps:
1) get first username in file.
2) get points of that user.
3) record both in respective arrays.
4) get next username.
5) get points of that user.
6) compare points vs entire array and insert at point where they
have more points than the last person and fewer points than the next
person Repeat steps 4 to 6 until end of file
Here is code : http://pastebin.com/PztMj9Nw (I couldn't past it here, dunno why)
It's not finished, so please, could you help me with it? I'm not that smart i guess...
Thanks in advance, i appreciate any help!

Perhaps some help from a design standpoint might help.
I'm assuming you are storing your current top 10 in some array?
If so you should access this top 10 array and find the user with the lowest points. Keep track of this value. Lets say we call this variable 'threshold'.
To update your top 10, loop through your file calling GetPoints on every user. If you find a user with points > threshold, kick the lowest user out of the top10 and replace them with this new user. Be sure to update your new threshold value.
Repeat this until you can no longer find any users with more points than your threshold.
Additionally, if you want to keep your top 10 in order you can implement a simple sort algorithm.

Traveling salesman prob on 2d map with walls (obstacles) so pathfinding needed

I need to find optimal path between a number of points on a 2d map.
The 2d map is of a building and will simply have where you can not go (through walls) and all the points on the map. So it's not really a map, rather lines you cannot go through with points to pass through.
I have a number of points, say between 20 and 500
I start with one that I select and then need the route calculated for most optimal path.
I would love hints for where to look for this travelling salesman problem with obstacles. Or even better, done library for doing it.
Bonuses
Things like doors can be weighted as they are less fun to pass through back and forth.
Possibility of prioritizing/Weighting the ability to end close to where you started.
Selecting areas as passable but annoying (weighting down)
.Net/C# code that I can use, I want to use this both on .NET MVC project and Xamarin mobile project so .net code would be great (if code exists)
Update example
In my example here we have an office. Now I have not thought every detail out so this is merely an example.
All the purple dots need to be checked
Yellow area could mean annoying to pass through but doable
Red could mean not active but can be passed if no other option exists.
Blue (walls) are impenetrable and can not be passed.
Green is doors, weighted down possibly as it's annoying to go trough closed doors (usually this would probably make sense anyway as the dots in a room would be easiest to check together.
The user would go to one dot, check it, then the software should tell him which one to do next until he is done.
Bonus could be given for ending close to start place. So for instance in this example, if the red area was normal and contained dots it would have been easy to make it a loop. (So the user comes back close to where he started)
Finally I suppose it would also be smart to differentiate outdoors areas as you would need to get dressed for outdoors, so you only want to go out once.
Also it could be smart to be able to prioritize ending on a point close to stairwell to next floor if they intend to check multiple floors at once.
Of course would have more more complex and larger plans the this exmaple.
Again sorry for just brainstorming out ideas but I have never done this kind of work and is happy for any pointers :-)

Let N be the set of nodes to visit (purple points). For each i and j in N, let c(i,j) be the distance (or travel time) to get from i to j. These can be pre-computed based on actual distances plus walls, doors, other barriers, etc.
Now, you could then add a penalty to c(i,j) if the path from i to j goes through a door, "annoying" area, etc. But a more flexible way might be as follows:
Let k = 1,...,K be the various types of undesirable route attributes (doors, annoying areas, etc.). Let a_k(i,j) be the amount of each of these attributes on the path from i to j. (For example, suppose k=1 represents door, k=2 represents yellow areas, k=3 represents outside. Then from an i in the break area to j in the bathroom might have a_1(i,j) = 1, and from an i to a j both in the yellow areas would have a_2(i,j) = 0.5 or 2.0 or however annoying that area is, etc.)
Then, let p_k be a penalty for each unit of undesirable attribute k -- maybe p_1 = 0.1 if you don't mind going through doors too much but p_2 = 3.0 if you really don't like yellow areas.
Then, let c'(i,j) = c(i,j) + sum{k=1,...,K} p_k * a_k(i,j). In other words, replace the actual distance with the distance plus penalties for all the annoyances. The user can set the p_k values before the optimization in order to express his/her preferences among these. The final penalties p_k * a_k(i,j) should be commensurate with the distance units used for c(i,j), though -- you don't want distances of 100m but penalties of 1,000,000.
Now solve a TSP with distances given by c'(i,j).
The TSP requires you to start and end at the same node, so that preference is really a constraint. If you're going to solve for multiple floors simultaneously, then the stairway times would be in the c(i,j) so there's no need to explicitly encourage routes that end near a stairway -- the solution would tend to do that anyway since stairs are slow. If you're going to solve each floor independently, then just set the start node for each floor equal to the stairway.
I wouldn't do anything about the red (allowable but unused) areas -- that would already be baked into the c(i,j) calculations.
Hope this helps.

Optimize Chart with 10000+ points

I have chart that can contain a lot of points (10000 +)
When I scale the chart in order to see all points in screen, it takes some time to draw them
Can You advice me some optimization, in order not to draw all points

I'm not an expert with listed technologies, but I would solve this by 'bucketing' your data points.
Your X axis is time, so determine the resolution point for the current chart size. IE, if you are seeing the entire chart you will only need a data point per day for example. If you are zoomed in a long way, you might want a point per hour.
Now you have determined resolution, go through your chart, and find all the data that exists between the resolution points, IE, all data that is > 20th April 2011 at 4pm and < 20th April 2011 at 5pm if you are on an hourly resolution.
Depending on the type of data you are using, will determine if you want to average all the data point you have collected, or find the median (or some other method, such as a candle stick chart to show the max/min values). Either way, pick the most relevant method, repeat for all points and render the result with your new data.
Hope that's what you meant.

Seems like you should use some sort of level of detail (LoD) algorithm.
For example:
Always use a maximum given set of points to represent all your actual points. By calculating local minima and maxima you can create a proper representation of the given point set for a certain 'detail', depending on how far you are zoomed in.
Calculating these extrema can still prove to be slow, so you might need to cache them. You can calculate and cache this on the fly as new data arrives.

In addition to the other good suggestions, I would
Do some random-pausing on it, to see if it's spending much time doing something else that could be avoided, such as maybe allocating new point structures all the time.
Rather than paint directly to the window, paint to a bitmap, and copy that to the window. It always looks faster, and sometimes it even is faster. (Be sure to stub out the method that clears the window background.)

I had experienced a severe performance problem with thousands of Series added to the chart rather than thousands of Points. The solution that worked for me was a flavor of the Flyweight pattern:
Instead of adding 1000-s of series, add just a single one.
At the end of a virtual series, i.e., when all points of the series have been added and it's time to move on to the next one, insert an empty point:
series.Points.Add(new DataPoint(0, 0) { IsEmpty = true });
Hope that helps somebody.

Permutation/Algorithm to Solve Conditional Fill Puzzle

I've been digging around to see if something similar has been done previously, but have not seen anything with the mirrored conditions. To make swallowing the problem a little easier to understand, I'm going to apply it in the context of filling a baseball team roster.
The given roster structure is organized as such: C, 1B, 2B, 3B, SS, 2B/SS (either or), 1B/3B, OF, OF, OF, OF, UT (can be any position)
Every player has at least one of the non-backup positions (positions that allow more than one position) where they're eligible and in many cases more than one (i.e. a player that can play 1B and OF, etc.). Say that you are manager of a team, which already has some players on it and you want to see if you have room for a particular player at any of your slots or if you can move one or more players around to open up a slot where he is eligible.
My initial attempts were to use a conditional permutation and collect in a list all the possible unique "lineups" for each player, updating the open slots before moving to the next player. This also required (since the order that the player was moved would affect what positions were available for the next player) that the list being looped through was reordered and then looped through again. I still think that this is the way to go, but there are a number of pitfalls that have snagged the function.
The data to start the loop that you assume is given is:
1. List of positions the player being evaluated can player (the one being checked if he can fit)
2. List of players currently on the roster and the positions each of those is eligible at (I'm currently storing a list of lists and using the list index as the unique identifier of the player)
3. A list of the positions open as the roster currently is
It's proven a bigger headache than I originally anticipated. It was even suggested to me by a colleague that the situation I have (which involves, on a much larger scale, conditional assignments for each object) was NP-complete. I am certain that it is not, since once a player has been repositioned in a particular lineup being tested, the entire roster should not need to be iterated over again once another player has moved. That's the long and short of it and I finally decided to open it up to the forums.
Thanks for any help anyone can provide. Due to restrictions, I can't post portions of code (some of it is legacy). It is, however, being translated in .NET (C# at the moment). If there's additional information necessary, I'll try and rewrite some of the short pieces of the function for post.
Joseph G.
EDITED 07/24/2010
Thank you very much for the responses. I actually did look into using a genetic algorithm, but ultimately abandoned it because of the amount of work that would go into the determination of ordinal results was superfluous. The ultimate aim of the test is to determine if there is, in fact, a scenario that returns a positive. There's no need to determine the relative benefit of each working solution.
I appreciate the feedback on the likely lack of familiarity with the context I presented the problem. The actual model is in the distribution of build commands across multiple platform-specific build servers. It's accessible, but I'd rather not get into why certain build tasks can only be executed on certain systems and why certain systems can only execute certain types of build commands.
It appears that you have gotten the gist of what I was presenting, but here's a different model that's a little less specific. There are a set of discrete positions in an ordered array of lists as such (I'll refer to these as "positions"):
((2), (2), (3), (4), (5), (6), (4, 6), (3, 5), (7), (7), (7), (7), (7), (2, 3, 4, 5, 6, 7))
Additionally, there is a an unordered array of lists (I'll refer to as "employees") that can only occupy one of the slots if its array has a member in common with the ordered list to which it would be assigned. After the initial assignments have been made, if an additional employee comes along, I need to determine if he can fill one of the open positions, and if not, if the current employees can be rearranged to allow one of the positions the employee CAN fill to be made available.
Brute force is something I'd like to avoid, because with this being on the order of 40 - 50 objects (and soon to be increasing), individual determinations will be very expensive to calculate at runtime.

I don't understand baseball at all so sorry if I'm on the wrong track. I do like rounders though, but there are only 2 positions to play in rounders, a batter or everyone else.
Have you considered using Genetic Algorithms to solve this problem? They are very good at solving NP hard problems and work surprisingly well for rota and time schedule type problems as well.
You have a solution model which can easily be scored and easily manipulated which is a great start for a genetic algorithm.
For more complex problems where the total permutations are too large to calculate a genetic algorithm should find a near optimum or excellent solution (along with lots and lots of other valid solutions) in a fairly short amount of time. Although if you wish the find the optimum solution every time, you are going to have to brute force it in all likelihood (I have only skimmed the problem so this may not be the case but it sounds like it probably is).
In your example, you would have a solution class, this represents a solution, IE a line-up for the baseball team. You randomly generate say 20 solutions, regardless if they are valid or not, then you have a rating algorithm that rates the solution. In your case, a better player in the line-up would score more than a worse player, and any invalid line-ups (for whatever reason) would force a score of 0.
Any 0 scoring solutions are killed off, and replaced with new random ones, and the rest of the solutions breed together to form new solutions. Theoretically and after enough time the pool of solutions should improve.
This has the benefit of not only finding lots of valid unique line-ups, but also rating them. You didn't specify in your problem the need to rate the solutions, but it offers plenty of benefits (for example if a player is injured, he can be temporarily rated as a -10 or whatever). All other players score based on their quantifiable stats.
It's scalable and performs well.

It sounds as though you have a bipartite matching problem. One partition has a vertex for each player on the roster. The other has a vertex for each roster position. There is an edge between a player vertex and a position vertex if and only if the player can play that position. You are interested in matchings: collections of edges such that no endpoint is repeated.
Given an assignment of players to positions (a matching) and a new player to be accommodated, there is a simple algorithm to determine if it can be done. Direct each edge in the current matching from the position to the player; direct the others from the player to the position. Now, using breadth-first search, look for a path from the new player to an unassigned position. If you find one, it tells you one possible series of reassignments. If you don't, there's no matching with all of the players.
For example, suppose player A can play positions 1 or 2
A--1
\
\
2
We provisionally assign A to 2. Now B shows up and can only play 2. Direct the graph:
A->1
<
\
B->2
We find a path B->2->A->1, which means "assign B to 2, displacing A to 1".
There is lots of pretty theory for dealing with hypothetical matchings. Genetic algorithms need not apply.
EDIT: I should add that because of the use of BFS, it computes the least disruptive sequence of reassignments.

Finding a Position on a 2D Map that meets several criteria

as my personal project i develop a game to which users can join at any time.
I have a tiled worldmap that is created from a simple Bitmap which has resources at random positions all over the map except for oceans.
When a player joins i want to create his starting position at a place that has at least 1 tile of each of the 4 resources in range (circle with a still to decide diameter, i think about 3-4 tiles) but no ocean tiles (Tile.Type != "ocean") and not conflicting with a field belonging to another player (Tile.Owner == null).
The map size can vary, currently it's 600x450 and it's implemented as a simple Array: Tile[][] with Tile.Resource being either null or having Tile.Resource.Type as a string of the resource name (as it's configurable by plaintext files to fit any scenery i want to put it in, so no built-in enums possible).
I currently have a loop that simple goes through every possible position, checks every field in range and counts the number of each resource field and discards it if there are none for one of them or if one of them belongs to a player or is an ocean field.
I would prefer if it finds a random position but thats not a requirement, mono-compatibility however is a requirement.
What would be the best way to implement an algorithm for that in C#?
Edit
The Area of players can and will increase/change and resources can be used up and may even appear randomly (=> "Your prospectors found a new goldmine") so pre-calculated positions will propably not work.

Instead of looping through all your positions, why don't you loop through all your resources? Your resources are likely to be more scant. Then pick one of the sets of resources that meet your clustering criterion.

You might consider simulated annealing ... it's not very complex to implement. You have a set of criteria with certain weight, and randomly "shake" the position at a certain "temperature" (the higher the temp, the greater the radius the position may randomly move within, from it's previous position), then when it "cools" you measure the value of the position based on the total weights and subtract negative things, like spawning too close to where they died, or next to other players, etc..., if the value is not within a certain range, you decrease the temperature, but "shake" the positions again, cool down, check weights and overall value, repeat until you get an acceptable solution.
Simulated annealing is used in map making, to label cities and features with maximum clarity, while staying within range and minimizing overlap. Since it's a heuristic approach there is no guarantee that there will be an optimal solution, so you keep "lowering the temp" and eventually just choose the best result.

Let's suppose that once your map is created you don't have to create a new one often.
Just add the following to each Tile and calculate them once after your map was generated:
-int NrOceanTiles
-int NrResourceA
-int ...
Now when you want to get a tile you can do it quite a bit faster:
IEnumerable<Tiles> goodTiles = tiles.Where(tile => tile.NrResourceA >= 1 && tile.NrResourceB >= 2);
Tile goodTile = goodTiles.ElementAt(randomI);

Predefined data would still be the best way forward.
As modifying the map size, and adding/losing resources would not happen as often, just update this data table when they do happen. Perhaps you could do the map/resource changes once per day, and have everything done in a daily database update.
In this way, finding a valid location would be far faster than any algorithm you implement to search all the tiles around it.

If the game isn't going to be designed for a huge number of players, most games implement "start spots" on the map. You'd hand-pick them and record the positions in your map somehow, probably similar to how you're implementing the map resources (i.e., on that spot, there exists an item you can pick up, but on top of the tile map).
Since the resources spawn at random, you could either not spawn resources on the start spots (which could be visible or not), or simply not spawn a player at a start spot on which there is a resource (or look within a 9-cell box to find a close alternate location).
Certainly you would want to hold the set of possible starting locations and update it as resources are created and consumed.

It seems like your best bet is to calculate open locations at map generation. Have your start location calculation function optionally take grid location and size or rectangle corners.
Have a list for Free locations and Occupied locations. Player occupies territory? Move resources in range to the Occupied list. Player gets crushed mercilessly? Move resources in range to the Free list. Resource eliminated? Delete any locations that used it in your Open/Occupied lists. Resource added? Recalculate using your effect radius to determine effected area. When your map area expands, just run the initial calculations on the new section of your grid + effect radius and add the new locations.
Then you just have to set the events up and pick a random Free value when someone joins.