/*
* This algorithm begins by assuming the player has access to all items, which implies all
* locations are reachable, so R is initialized to the entire game and I contains all items. A random
* item is selected from I and is removed from the player’s assumed item list, meaning that some
* locations may become unreachable, and removed from R. A random, empty location which is
* still reachable will then be selected and the previously removed item will be placed there. The
* way this algorithm initially assumes all items are available and slowly removes them reducing
* reachable areas, it can be thought of as a reverse fill.
*/
public WorldGraph AssumedFill(WorldGraph world, List <Item> itempool)
{
List <Item> owneditems = itempool; //In contrast to other two algos, I is initialized to all items and itempool is empty
itempool = new List <Item>();
WorldGraph reachable = searcher.GetReachableLocationsAssumed(world, owneditems); //Initially R should equal all locations in the game
List <Location> reachablelocations = reachable.GetAllEmptyLocations();
helper.Shuffle(owneditems);
while (reachablelocations.Count > 0 && owneditems.Count > 0)
{
Item item = helper.Pop(owneditems); //Pop random item from I, R will shrink
helper.Shuffle(owneditems);
reachable = searcher.GetReachableLocationsAssumed(world, owneditems); //Recalculate R now that less items are owned
reachablelocations = reachable.GetAllEmptyLocations(); //Get empty locations which are reachable
helper.Shuffle(reachablelocations);
if (reachablelocations.Count() == 0) //If this happens, means there are no reachable locations left and must return, usually indicates uncompletable permutation
{
break;
}
Location location = helper.Pop(reachablelocations); //Remove location from list
world.Place(location, item); //Place random item in random location
itempool.Add(item); //Add item to item pool
}
return(world); //World has been filled with items, return
}
//Utilizes the recusive DFS for exit score function to return a score for this exit if its requirement is met, otherwise returns -1
private double ExitScore(WorldGraph world, List <Item> owneditems, Region current, List <Region> traversed, Exit exit)
{
if (parser.RequirementsMet(exit.Requirements, owneditems))
{
double score = 0;
List <Region> visited = new List <Region>();
visited.Add(current); //Do this so path does not go through current region
Region exitto = world.Regions.First(x => x.Name == exit.ToRegionName); //Get the region this edge leads to
maxtraversed = 0;
score += RecursiveDFSForExitScore(world, exitto, visited, owneditems, 1); //Add score from a recursive search which scores item locations, scored lower more regions traversed
int multiplier = maxtraversed == 1 ? 2 : 1; //Give a multiplier if this edge leads to a single, dead-end region
//Give divider based on how recently the region was visited
int divider = 1;
int lastindex = traversed.FindLastIndex(x => x.Name == exit.ToRegionName);
if (lastindex > -1)
{
int howrecent = traversed.Count - 2 - lastindex;
divider = 16 - howrecent; //Most recent region divided by 8, 2nd most recent divided by 7, etc to minimum of 1
divider = divider < 1 ? 1 : divider;
}
return(score * multiplier / divider);
}
else //Can not traverse exit
{
return(-1); //Return -1 to indicate it cannot be crossed
}
}
/*
* Sphere Search is done iteratively in “Spheres” and is used to attempt to trace a path
* from the beginning to the end of the game. The first sphere s is simply all locations which are
* reachable from the beginning of the game. As it searches these locations, it adds key items
* found to a temporary set; we do not want those items to affect reachability until the next sphere
* iteration so we do not yet add them to I. After all reachable locations have been found, sphere s
* is added to the list of spheres S, and all items in the temporary set are added to I. It then
* iterates again with a new sphere s.
*/
public SphereSearchInfo SphereSearch(WorldGraph world)
{
SphereSearchInfo output = new SphereSearchInfo();
output.Spheres = new List <WorldGraph>();
List <Item> owneditems = new List <Item>();
//Initial sphere s0 includes items reachable from the start of the game
WorldGraph s0 = GetReachableLocations(world, owneditems);
owneditems = s0.CollectMajorItems(); //Collect all major items reachable from the start of the game
output.Spheres.Add(s0); //Add initial sphere to sphere list
//sx indicates every sphere after the first. Any major items found in s0 means sx should be bigger.
WorldGraph sx = GetReachableLocations(world, owneditems);
int temp = owneditems.Where(x => x.Importance >= 2).Count(); //Temp is the count of previously owned major items
owneditems = sx.CollectAllItems(); //Used to find new count of major items
//If counts are equal then no new major items found, stop searching
while (owneditems.Where(x => x.Importance >= 2).Count() > temp) //If new count is not greater than old count, that means all currently reachable locations have been found
{
output.Spheres.Add(sx); //If new locations found, add to sphere list
//Take the same steps taken before the loop: Get new reachable locations, collect new major items, and check to see if new count is larger than old count
sx = GetReachableLocations(world, owneditems);
temp = owneditems.Where(x => x.Importance >= 2).Count(); //Only want to consider count of major items
owneditems = sx.CollectAllItems();
}
//At this point, either a dead end has been found or all locations have been discovered
//If the goal item is in the list of owned items, means the end has been found and thus the game is completable
output.Completable = owneditems.Count(x => x.Name == world.GoalItemName) > 0;
return(output);
}
//Constructor from a previous world graph
//Copies the start region and goal but new items and regions
//Used when constructing partial graph of world
public WorldGraph(WorldGraph world)
{
StartRegionName = world.StartRegionName;
GoalItemName = world.GoalItemName;
Regions = new HashSet <Region>();
Items = new List <Item>();
}
public WorldGraph Generated; //Result of world generation
//Constructor which only specifies a number of items which are then generated
public WorldGenerator(int regions, int items)
{
rng = new Random();
helper = new Helpers();
Regions = regions;
MajorItemList = GenItemList(items);
Generated = new WorldGraph();
}
//Constructor which specifies an item list
public WorldGenerator(int regions, List <Item> itemlist)
{
rng = new Random();
helper = new Helpers();
Regions = regions;
MajorItemList = itemlist;
Generated = new WorldGraph();
}
//Calculates the bias for a given permutation of items in the world graph
public BiasOutput CalcDistributionBias(WorldGraph world)
{
//Get total counts for majors and items so a percent can be calculated
int totalmajorcount = world.Items.Where(x => x.Importance >= 2).Count();
int totallocationcount = world.GetLocationCount();
//Find spheres in randomized world
Search searcher = new Search();
List <WorldGraph> spheres = searcher.SphereSearch(world).Spheres;
//Initialize variables to use in the loop
double[] spherebias = new double[spheres.Count];
int rollingmajorcount = 0;
int rollinglocationcount = 0;
for (int i = 0; i < spheres.Count; i++)
{
WorldGraph sphere = spheres[i];
//Check the number of major items in the sphere, not counting those in previous spheres
int majoritemcount = sphere.CollectMajorItems().Count - rollingmajorcount;
rollingmajorcount += majoritemcount;
//Check the number of locations in the sphere, not counting those in previous spheres
int locationcount = sphere.GetLocationCount() - rollinglocationcount;
rollinglocationcount += locationcount;
//Find the percentage of major items and locations in this sphere
double majorpercent = majoritemcount / (double)totalmajorcount;
double locationpercent = locationcount / (double)totallocationcount;
//Now find the difference between the two percentages
double difference = majorpercent - locationpercent;
spherebias[i] = difference;
}
//Now that we have a list of biases find the sum of their absolute values to determine absolute bias
//Also use the positivity of bias before and after the median to determine bias direction
double overallsum = 0;
double beforesum = 0; //Sums bias before median so a bias direction can be computed
double aftersum = 0; //Sums bias after median so a bias direction can be computed
bool even = spherebias.Length % 2 == 0; //Want to check if even so can determine when after the median is
int median = spherebias.Length / 2; //Use median to determine bias direction
for (int i = 0; i < spherebias.Length; i++)
{
overallsum += Math.Abs(spherebias[i]);
if (i < median) //Before median, add to that sum
{
beforesum += spherebias[i];
}
else if ((i >= median && even) || (i > median && !even)) //After median, add to that sum. If it's even then >= makes sense so every index is checked, if odd then skip middle
{
aftersum = spherebias[i];
}
}
//Package output and return
BiasOutput output = new BiasOutput();
output.biasvalue = overallsum / spherebias.Length; //Get average of absolute value to determine overall bias
output.direction = beforesum < aftersum; //If bias is more positive before the median, the direction is toward the beginning, otherwise toward end
return(output);
}
//Utilizes BFS search algorithm to find all locations in the world which are reachable with the current item set
//In this algorithm, we want to check for items which have been removed from I but are still contained within R, so an initial search is done to collect items,
//then repeated iteratively until no new items are found, at which point the final reachability graph is returned.
public WorldGraph GetReachableLocationsAssumed(WorldGraph world, List <Item> owneditems)
{
WorldGraph copy = world.Copy(); //Used so items may be removed from world at will
List <Item> newitems = ItemSearch(copy, owneditems); //Find items within R
List <Item> combined = owneditems.ToList(); //Copy list
while (newitems.Count > 0)
{
combined.AddRange(newitems); //Add items to currently used items
newitems = ItemSearch(copy, combined); //Find items within R
}
return(GetReachableLocations(world, combined)); //Use that combined list to find final search result
}
//Calculate the average complexity from generating many worlds with a specific regioncount and itemcount
static List <TestComplexityOutput> AverageComplexity(int regioncount, int itemcount)
{
//First do x trials to determine an average complexity
int gentrials = 5;
List <TestComplexityOutput> outputs = new List <TestComplexityOutput>();
for (int i = 0; i < gentrials; i++)
{
WorldGenerator generator = new WorldGenerator(regioncount, itemcount);
WorldGraph generated = generator.Generate();
outputs.Add(generator.GetComplexity());
}
return(outputs); //Determine average complexity, we want the goal complexity to be within some% of this
}
private List <List <Region> > paths = new List <List <Region> >(); //Declared outside of function scope so multiple instances of following two functions can access
//Use DFS to find all possible paths from the root to the specified region
//Not including paths that go back on themselves
public List <List <Region> > PathsToRegion(WorldGraph world, Region dest)
{
Region root = world.Regions.First(x => x.Name == world.StartRegionName);
List <Region> visited = new List <Region>();
paths = new List <List <Region> >();
if (root == dest) //If root and dest equal, return empty list
{
return(paths);
}
RecursiveDFSForPathList(world, root, dest, visited); //Recursively run DFS, when dest found add the path to paths var
return(paths); //Return list of paths
}
//Calculate info about human-like playthrough and then return the score
public InterestingnessOutput CalcDistributionInterestingness(WorldGraph world)
{
PlaythroughInfo info = new PlaythroughInfo();
try
{
info = searcher.PlaythroughSearch(world.Copy());
}
catch
{
throw new Exception(); //Something went wrong, have calling code retry
}
BiasOutput biasinfo = CalcDistributionBias(world);
return(ScorePlaythrough(world, info, biasinfo));
}
//Initially, went to collect all items which are reachable with the current item set and not already contained within the item set
public List <Item> ItemSearch(WorldGraph world, List <Item> owneditems)
{
List <Item> newitems = new List <Item>();
Region root = world.Regions.First(x => x.Name == world.StartRegionName);
Queue <Region> Q = new Queue <Region>();
HashSet <Region> visited = new HashSet <Region>();
Q.Enqueue(root);
visited.Add(root);
//Implementation of BFS
while (Q.Count > 0)
{
Region r = Q.Dequeue();
foreach (Exit e in r.Exits)
{
//Normally in BFS, all exits would be added
//But in this case, we only want to add exits which are reachable
if (parser.RequirementsMet(e.Requirements, owneditems))
{
Region exitto = world.Regions.First(x => x.Name == e.ToRegionName); //Get the region this edge leads to
if (!visited.Contains(exitto)) //Don't revisit already visited nodes on this path
{
Q.Enqueue(exitto);
visited.Add(exitto);
}
}
}
//Subsearch to check each edge to a location in the current region
//If requirement is met, add it to reachable locations
foreach (Location l in r.Locations)
{
if (parser.RequirementsMet(l.Requirements, owneditems))
{
if (l.Item.Importance == 2) //If location contains a major item
{
newitems.Add(l.Item);
l.Item = new Item(); //Remove item so it isn't added again in future iterations
}
}
}
}
return(newitems);
}
//Recursively check exits with copy of visited list
//It's done this way so that after the destination or a dead end is met, the code flow "backs up"
public void RecursiveDFSForPathList(WorldGraph world, Region r, Region dest, List <Region> visited)
{
visited.Add(r); //Add to visited list
if (r == dest)
{
paths.Add(visited); //If this is the dest, then visited currently equals a possible path
return;
}
foreach (Exit e in r.Exits)
{
Region exitto = world.Regions.First(x => x.Name == e.ToRegionName); //Get the region this edge leads to
if (!visited.Contains(exitto)) //Don't revisit already visited nodes on this path
{
List <Region> copy = new List <Region>(visited); //If don't do this List is passed by reference, algo doesn't work
RecursiveDFSForPathList(world, exitto, dest, copy);
}
}
}
//Utilizes BFS search algorithm to find all locations in the world which are reachable with the current item set
//Important note is that throughout this function the owned items are static, they are not collected throughout (as they are in sphere search)
//It is used for forward search, where there is no need to check for items currently within R, as well as other places such as sphere search
public WorldGraph GetReachableLocations(WorldGraph world, List <Item> owneditems)
{
WorldGraph reachable = new WorldGraph(world);
Region root = world.Regions.First(x => x.Name == world.StartRegionName);
Queue <Region> Q = new Queue <Region>();
HashSet <Region> visited = new HashSet <Region>();
Q.Enqueue(root);
visited.Add(root);
//Implementation of BFS
while (Q.Count > 0)
{
Region r = Q.Dequeue();
Region toadd = new Region(r);
foreach (Exit e in r.Exits)
{
//Normally in BFS, all exits would be added
//But in this case, we only want to add exits which are reachable
if (parser.RequirementsMet(e.Requirements, owneditems))
{
toadd.Exits.Add(e);
Region exitto = world.Regions.First(x => x.Name == e.ToRegionName); //Get the region this edge leads to
if (!visited.Contains(exitto)) //Don't revisit already visited nodes on this path
{
Q.Enqueue(exitto);
visited.Add(exitto);
}
}
}
//Subsearch to check each edge to a location in the current region
//If requirement is met, add it to reachable locations
foreach (Location l in r.Locations)
{
if (parser.RequirementsMet(l.Requirements, owneditems))
{
toadd.Locations.Add(l);
}
}
reachable.Regions.Add(toadd); //Add every reachable exit and location discovered in this iteration
}
return(reachable); //Return graph of reachable locations
}
//Copy all properties of graph and return
//Used so that input graph is not copied by reference and overwritten
public WorldGraph Copy()
{
WorldGraph copy = new WorldGraph();
copy.StartRegionName = StartRegionName;
copy.GoalItemName = GoalItemName;
copy.Regions = new HashSet <Region>();
foreach (Region r in Regions)
{
Region rcopy = new Region();
rcopy.Name = r.Name;
rcopy.Exits = new HashSet <Exit>();
foreach (Exit e in r.Exits)
{
Exit ecopy = new Exit();
ecopy.ToRegionName = e.ToRegionName;
ecopy.Requirements = e.Requirements;
rcopy.Exits.Add(e);
}
rcopy.Locations = new HashSet <Location>();
foreach (Location l in r.Locations)
{
Location lcopy = new Location();
lcopy.Name = l.Name;
lcopy.Requirements = l.Requirements;
Item icopy = new Item();
icopy.Importance = l.Item.Importance;
icopy.Name = l.Item.Name;
lcopy.Item = icopy;
rcopy.Locations.Add(lcopy);
}
copy.Regions.Add(rcopy);
}
copy.Items = new List <Item>();
foreach (Item i in Items)
{
Item icopy = new Item();
icopy.Importance = i.Importance;
icopy.Name = i.Name;
copy.Items.Add(icopy);
}
return(copy);
}
//G: Graph of world locations (called world in code)
// A node in G initially has a null value, this value can be filled with a key item
// An edge in G may require certain items to traverse
//R: Graph of reachable locations (called reachable in code)
//I: Set of items owned, determines R, called owneditems in code
//I*: Set of items not owned, inverse of I, called itempool in code
//Goal: A specific item in G which signifies the end of the game
//Start: A specific region in G which the player starts in
/*
* This algorithm simply places a random key item in a random location until either of these
* sets are empty (Usually items). After placing all a check is done to see if the game is beatable. If
* not it runs the algorithm again. In complex world this could potentially take hundreds of attempts.
*/
public WorldGraph RandomFill(WorldGraph world, List <Item> itempool)
{
//Initialize owneditems to empty and locations to all that are empty
List <Item> owneditems = new List <Item>();
List <Location> locations = world.GetAllEmptyLocations();
helper.Shuffle(locations);
helper.Shuffle(itempool);
while (locations.Count > 0 && itempool.Count > 0)
{
Location location = helper.Pop(locations); //Select random location
helper.Shuffle(locations);
Item item = helper.Pop(itempool); //Take random item from item pool
helper.Shuffle(itempool);
world.Place(location, item); //Place random item in random location
owneditems.Add(item); //Add to owned items
}
return(world); //World has been filled with items, return
}
//Generates a world with a specific count of regions and items
//First generates many worlds to determine an average complexity then returns a world generated with a certain tolerance of that complexity
//This takes a while to run, mainly because each complexity calculation takes ~2 seconds due to running the external python script
static string GenerateWorld(int regioncount, int itemcount)
{
double goalcomplexity = AverageComplexity(regioncount, itemcount).Average(x => x.top50);
//Now generate worlds until one is generated within a certain tolerance of the average
double tolerance = .10; //10%
while (true)
{
//Generate a world, check its complexity
WorldGenerator generator = new WorldGenerator(regioncount, itemcount);
WorldGraph generated = generator.Generate();
int test = generated.GetLocationCount();
double complexity = generator.GetComplexity().top50;
if (goalcomplexity * (1 - tolerance) < complexity && complexity < goalcomplexity * (1 + tolerance))
{
//Once complexity within x% of average has been generated, return json of the world so it can be saved
return(generated.ToJson());
}
}
}
/*
* This algorithm initializes set R to be the reachable locations from the start of the game. It
* then chooses an item from the item pool I* and places it in a random location in set R, meaning
* it is also added to I. This location is then removed from consideration and all locations that
* become reachable are added to R. Repeat until R or I* is empty. To be clear, items related to
* progression are placed first, and then everything else is filled in with helpful or junk items. In
* fact, usually the helpful and junk items are placed using random fill since it’s faster and
* placement doesn’t matter.
*/
public WorldGraph ForwardFill(WorldGraph world, List <Item> itempool)
{
List <Item> owneditems = new List <Item>(); //Initialize owneditems to empty
WorldGraph reachable = searcher.GetReachableLocations(world, owneditems); //Initially R should only equal locations reachable from the start of the game
List <Location> locations = reachable.GetAllEmptyLocations();
helper.Shuffle(locations);
helper.Shuffle(itempool);
while (locations.Count > 0 && itempool.Count > 0)
{
Location location = helper.Pop(locations); //Get random location and item
Item item = helper.Pop(itempool);
helper.Shuffle(itempool);
world.Place(location, item); //Place random item in random reachable location
owneditems.Add(item); //Add new item to owned items, R will expand
reachable = searcher.GetReachableLocations(world, owneditems); //Recalculate R now that more items are owned
locations = reachable.GetAllEmptyLocations();
helper.Shuffle(locations);
}
return(world); //World has been filled with items, return
}
//Scores an exit by recurisvely searching for items, adding score for every available location, with less weight if farther away
public double RecursiveDFSForExitScore(WorldGraph world, Region r, List <Region> visited, List <Item> owneditems, int traversed)
{
maxtraversed = Math.Max(traversed, maxtraversed);
visited.Add(r); //Add to visited list
double score = 0;
double multiplier = Math.Max(1 / 8, 1 / (double)traversed); //Max multiplier is 1, Minimum multiplier is 1/8
//First look at all locations in region to add to score
foreach (Location l in r.Locations)
{
if (parser.RequirementsMet(l.Requirements, owneditems)) //Only want to consider available locations
{
if (l.Item.Importance == 3) //Path contains goal item, add large amount to score, add 100 / traversed so that shorter paths to goal preferred
{
score += 10000000000 + (100 / traversed);
}
else if (l.Item.Importance > -1) //Else just add 1 if item isn't already collected (remember, although player knows there is a location here, they don't know what item it is unless it's the goal)
{
score += 1 * multiplier;
}
}
}
//Now recursively look through each exit
foreach (Exit e in r.Exits)
{
if (parser.RequirementsMet(e.Requirements, owneditems)) //Only consider exit if it can be traversed
{
Region exitto = world.Regions.First(x => x.Name == e.ToRegionName); //Get the region this edge leads to
if (!visited.Contains(exitto)) //Don't revisit already visited nodes on this path
{
//Recursively call this function, adding 1 to traversed, score will be added to our score and returned
//We purposely pass visited by reference rather than by value, ensuring that locations are only visited once
score += RecursiveDFSForExitScore(world, exitto, visited, owneditems, traversed + 1);
}
}
}
return(score);
}
//Experiment space
static void Main(string[] args)
{
Fill filler = new Fill();
Search searcher = new Search();
Statistics stats = new Statistics();
//string testjsontext = File.ReadAllText("../../../WorldGraphs/World3.json");
//WorldGraph testworld = JsonConvert.DeserializeObject<WorldGraph>(testjsontext);
//double[] testaverages = new double[5];
//for (int regioncount = 10; regioncount <= 50; regioncount += 5)
//{
// for (int itemcount = 5; itemcount <= Math.Min(regioncount, 30); itemcount += 5)
// {
// List<TestComplexityOutput> complexity = AverageComplexity(regioncount, itemcount);
// Console.WriteLine("Regions: " + regioncount + ", Items: " + itemcount);
// Console.WriteLine("Sum: " + complexity.Average(x => x.sum));
// Console.WriteLine("Avg: " + complexity.Average(x => x.average));
// Console.WriteLine("Max: " + complexity.Average(x => x.max));
// Console.WriteLine("SOS: " + complexity.Average(x => x.sumofsquares));
// Console.WriteLine("Avg50: " + complexity.Average(x => x.top50));
// Console.WriteLine("Avg75: " + complexity.Average(x => x.top75));
// Console.Write(Environment.NewLine);
// }
//}
//string generatedjson = GenerateWorld(50, 30);
//string jsontest = File.ReadAllText("../../../WorldGraphs/World5.json");
//WorldGraph testworld = JsonConvert.DeserializeObject<WorldGraph>(jsontest);
//int testlocationcount = testworld.GetLocationCount();
//Search testsearcher = new Search();
//testsearcher.PathsToRegion(world, world.Regions.First(x => x.Name == "Waterfall"));
//Parser testparse = new Parser();
//string result = testparse.Simplify("(Sword and Bow and Bow) or Has(Key,2)"); //Should be simplified to something like (Sword and Bow) or Has(Key,2)
//string result2 = testparse.Simplify("Sword or Sword and Bow"); //Should be simplified to Sword
////majoritempool.RemoveAt(8);
////majoritempool.RemoveAt(0);
//bool result = testparse.RequirementsMet("(Sword and Bow) or Has(Key,2)", majoritempool);
//string testjsontext = File.ReadAllText("../../../WorldGraphs/TestWorldOriginal.json");
//WorldGraph testworld = JsonConvert.DeserializeObject<WorldGraph>(testjsontext);
//SphereSearchInfo testoutput = searcher.SphereSearch(testworld);
//Print_Spheres(testoutput);
string[] algos = { "Random", "Forward", "Assumed" };
foreach (string worldname in testworlds)
{
DateTime expstart = DateTime.Now;
string jsontext = File.ReadAllText("../../../WorldGraphs/" + worldname + ".json");
WorldGraph world = JsonConvert.DeserializeObject <WorldGraph>(jsontext);
int l = world.GetLocationCount();
//Loop to perform fill
for (int i = 0; i < 3; i++) //0 = Random, 1 = Forward, 2 = assumed
{
if (dotests[i])
{
//List<InterestingnessOutput> intstats = new List<InterestingnessOutput>();
//double totaltime = 0;
int savecounter = 0;
int countofexp = db.Results.Count(x => x.Algorithm == algos[i] && x.World == worldname);
//int countofexp = 0;
while (countofexp < trials) //Go until there are trial number of records in db
{
InterestingnessOutput intstat = new InterestingnessOutput();
double difference = -1;
while (true) //If something goes wrong in playthrough search, may need to retry
{
WorldGraph input = world.Copy(); //Copy so that world is not passed by reference and overwritten
List <Item> majoritempool = input.Items.Where(x => x.Importance == 2).ToList();
List <Item> minoritempool = input.Items.Where(x => x.Importance < 2).ToList();
WorldGraph randomizedgraph = new WorldGraph();
DateTime start = DateTime.Now; //Start timing right before algorithm
//Decide which algo to use based on i
switch (i)
{
case 0:
randomizedgraph = filler.RandomFill(input, majoritempool);
break;
case 1:
randomizedgraph = filler.ForwardFill(input, majoritempool);
break;
case 2:
randomizedgraph = filler.AssumedFill(input, majoritempool);
break;
}
randomizedgraph = filler.RandomFill(randomizedgraph, minoritempool); //Use random for minor items always since they don't matter
//Calculate metrics
DateTime end = DateTime.Now;
difference = (end - start).TotalMilliseconds;
//totaltime += difference;
//string randomizedjson = JsonConvert.SerializeObject(randomizedgraph);
//SphereSearchInfo output = searcher.SphereSearch(randomizedgraph);
//Print_Spheres(output);
try
{
intstat = stats.CalcDistributionInterestingness(randomizedgraph);
break; //Was successful, continue
}
catch { } //Something went wrong, retry fill from scratch
}
//intstats.Add(intstat);
//Store result in database
Result result = new Result();
result.Algorithm = algos[i];
result.World = worldname;
result.Completable = intstat.completable;
result.ExecutionTime = difference;
result.Bias = intstat.bias.biasvalue;
result.BiasDirection = intstat.bias.direction;
result.Interestingness = intstat.interestingness;
result.Fun = intstat.fun;
result.Challenge = intstat.challenge;
result.Satisfyingness = intstat.satisfyingness;
result.Boredom = intstat.boredom;
db.Entry(result).State = EntityState.Added;
savecounter++;
if (savecounter >= 1000) //Save every 1000 results processed
{
db.SaveChanges();
savecounter = 0;
}
countofexp++;
}
//double avgint = intstats.Where(x => x.completable).Average(x => x.interestingness);
//Console.WriteLine("Average interestingness for " + algos[i] + " Fill in world " + worldname + ": " + avgint);
//double avgbias = intstats.Where(x => x.completable).Average(x => x.bias.biasvalue);
//Console.WriteLine("Average bias for " + algos[i] + " Fill in world " + worldname + ": " + avgbias);
//double avgfun = intstats.Where(x => x.completable).Average(x => x.fun);
//Console.WriteLine("Average fun for " + algos[i] + " Fill in world " + worldname + ": " + avgfun);
//double avgchal = intstats.Where(x => x.completable).Average(x => x.challenge);
//Console.WriteLine("Average challenge for " + algos[i] + " Fill in world " + worldname + ": " + avgchal);
//double avgsat = intstats.Where(x => x.completable).Average(x => x.satisfyingness);
//Console.WriteLine("Average satisfyingness for " + algos[i] + " Fill in world " + worldname + ": " + avgsat);
//double avgbore = intstats.Where(x => x.completable).Average(x => x.boredom);
//Console.WriteLine("Average boredom for " + algos[i] + " Fill in world " + worldname + ": " + avgbore);
//double avgtime = totaltime / trials;
//Console.WriteLine("Average time to generate for " + algos[i] + " Fill in world " + worldname + ": " + avgtime + "ms");
db.SaveChanges(); //Save changes when combo of algo and world is done
}
//Console.Write(Environment.NewLine);
}
//Console.Write(Environment.NewLine);
//Console.Write(Environment.NewLine);
DateTime expend = DateTime.Now;
double expdifference = (expend - expstart).TotalMinutes;
Console.WriteLine("Time to perform " + trials + " iterations for world " + worldname + ": " + expdifference + " minutes");
}
Console.ReadLine();
}
//Experiment space
static void Main(string[] args)
{
Fill filler = new Fill();
Search searcher = new Search();
Statistics stats = new Statistics();
////Uncomment to test different complexity measures and generate many worlds with different parameters.
//double[] testaverages = new double[5];
//for (int regioncount = 10; regioncount <= 50; regioncount += 5)
//{
// for (int itemcount = 5; itemcount <= Math.Min(regioncount, 30); itemcount += 5)
// {
// List<TestComplexityOutput> complexity = AverageComplexity(regioncount, itemcount);
// Console.WriteLine("Regions: " + regioncount + ", Items: " + itemcount);
// Console.WriteLine("Sum: " + complexity.Average(x => x.sum));
// Console.WriteLine("Avg: " + complexity.Average(x => x.average));
// Console.WriteLine("Max: " + complexity.Average(x => x.max));
// Console.WriteLine("SOS: " + complexity.Average(x => x.sumofsquares));
// Console.WriteLine("Avg50: " + complexity.Average(x => x.top50));
// Console.WriteLine("Avg75: " + complexity.Average(x => x.top75));
// Console.Write(Environment.NewLine);
// }
//}
//Loop through each algorithm set to be used and each world in the list, performing specified algorithm on specified world and recording information about the result.
string[] algos = { "Random", "Forward", "Assumed" };
foreach (string worldname in testworlds)
{
DateTime expstart = DateTime.Now;
string jsontext = File.ReadAllText("../../../WorldGraphs/" + worldname + ".json");
WorldGraph world = JsonConvert.DeserializeObject <WorldGraph>(jsontext);
//Loop to perform fill algorithms
for (int i = 0; i < 3; i++) //0 = Random, 1 = Forward, 2 = Assumed
{
if (dotests[i])
{
int savecounter = 0;
int countofexp = db.Results.Count(x => x.Algorithm == algos[i] && x.World == worldname);
while (countofexp < trials) //Go until there are trial number of records in db
{
InterestingnessOutput intstat = new InterestingnessOutput();
double difference = -1;
while (true) //If something goes wrong in playthrough search, may need to retry
{
WorldGraph input = world.Copy(); //Copy so that world is not passed by reference and overwritten
List <Item> majoritempool = input.Items.Where(x => x.Importance == 2).ToList();
List <Item> minoritempool = input.Items.Where(x => x.Importance < 2).ToList();
WorldGraph randomizedgraph = new WorldGraph();
DateTime start = DateTime.Now; //Start timing right before algorithm
//Decide which algo to use based on i
switch (i)
{
case 0:
randomizedgraph = filler.RandomFill(input, majoritempool);
break;
case 1:
randomizedgraph = filler.ForwardFill(input, majoritempool);
break;
case 2:
randomizedgraph = filler.AssumedFill(input, majoritempool);
break;
}
randomizedgraph = filler.RandomFill(randomizedgraph, minoritempool); //Use random for minor items always since they don't matter
//Calculate metrics
DateTime end = DateTime.Now;
difference = (end - start).TotalMilliseconds;
try
{
intstat = stats.CalcDistributionInterestingness(randomizedgraph);
break; //Was successful, continue
}
catch { } //Something went wrong, retry fill from scratch
////Uncomment to print the spheres of the result.
//SphereSearchInfo output = searcher.SphereSearch(randomizedgraph);
//Print_Spheres(output);
}
//Store result in database
Result result = new Result();
result.Algorithm = algos[i];
result.World = worldname;
result.Completable = intstat.completable;
result.ExecutionTime = difference;
result.Bias = intstat.bias.biasvalue;
result.BiasDirection = intstat.bias.direction;
result.Interestingness = intstat.interestingness;
result.Fun = intstat.fun;
result.Challenge = intstat.challenge;
result.Satisfyingness = intstat.satisfyingness;
result.Boredom = intstat.boredom;
db.Entry(result).State = EntityState.Added;
savecounter++;
if (savecounter >= 1000) //Save every 1000 results processed
{
db.SaveChanges();
savecounter = 0;
}
countofexp++;
}
db.SaveChanges(); //Save changes when combo of algo and world is done
}
}
DateTime expend = DateTime.Now;
double expdifference = (expend - expstart).TotalMinutes;
Console.WriteLine("Time to perform " + trials + " iterations for world " + worldname + ": " + expdifference + " minutes"); //Print how long this world took to do
}
Console.ReadLine();
}
//Generate a random worldgraph using the specified number of regions and item list
public WorldGraph Generate()
{
HashSet <Region> regions = new HashSet <Region>(); //Set of all regions in the world
for (int i = 0; i < Regions; i++)
{
Region r = new Region("Region-" + i.ToString()); //Each region is named Region_x. So Region-1, Region-2, etc.
regions.Add(r);
}
//Not must loop through each region to add exits
//Separate loop from the previous so that all regions are available to add as exits
foreach (Region r in regions)
{
//The first region has some specific conditions:
// 1. First exit is guaranteed to have no requirement and goes to hub region
// 2. There is a guaranteed second exit, that will have a single item requirement
// 3. There is a 50% chance to have a third exit, which has a 50% chance between a single item and no item
if (r.Name == "Region-0")
{
//Add exit to hub region with no requirement
List <string> currentexits = new List <string>();
Region hub = regions.First(x => x.Name == "Region-1");
AddExitsNoRequirement(regions, r, hub);
currentexits.Add("Region-1");
//Add exit to 2nd region with single requirement
Region second = GetRandomAvailableRegion(regions, r, currentexits);
AddExitsOneRequirement(regions, r, second);
currentexits.Add(second.Name);
//50% chance to add a third region
int random = rng.Next(1, 3); //Either 1 or 2
if (random == 2)
{
Region third = GetRandomAvailableRegion(regions, r, currentexits);
random = rng.Next(1, 3);
//50% chance to have 1 requirement, 50% chance to have none
if (random == 2)
{
AddExitsNoRequirement(regions, r, third);
}
else
{
AddExitsOneRequirement(regions, r, third);
}
}
}
//The second region is the hub region and also has some specific conditions:
// 1. Will connect to 5 regions besides the start region
// 2. Half of its exits will have no item requirement, the other half will have one
else if (r.Name == "Region-1")
{
List <string> currentexits = new List <string>();
currentexits.Add("Region-0");
for (int i = 0; i < 5; i++) //Run for 5 iterations
{
Region to = GetRandomAvailableRegion(regions, r, currentexits);
if (i < 2) //First 3 exits (including start region) have no requirement
{
AddExitsNoRequirement(regions, r, to);
}
else //Next 3 iterations will have 1 requirement
{
AddExitsOneRequirement(regions, r, to);
}
currentexits.Add(to.Name);
}
}
//Every other region will have a number of exits in [1, 4], however max of 2 chosen at generation, 2 more can be added by a later region
else
{
int ExitNum = rng.Next(1, 3); //Generate random number in [1, 2]
//In case r already has exits, create a list which contains all its current exits
List <string> currentexits = new List <string>();
foreach (Exit e in r.Exits)
{
currentexits.Add(e.ToRegionName);
}
while (r.Exits.Count < ExitNum) //Possible that location already has specified number of exits, no big deal if so
{
Region to = GetRandomAvailableRegion(regions, r, currentexits);
if (!string.IsNullOrEmpty(to.Name))
{
AddExits(regions, r, to); //Add exit from r to the random region
currentexits.Add(to.Name); //Also add dest region to list so it does not get added twice
}
else //Don't want to do this if r has 0 exits, but that logic is handled in GetRandomAvailableRegion
{
break;
}
}
}
}
//Must make sure all locations are reachable
Generated = new WorldGraph("Region-0", "Goal", regions.ToHashSet(), MajorItemList);
List <Region> unreachable = Generated.GetUnreachableRegions();
while (unreachable.Count > 0) //At least one reachable location
{
//Create a connection from a random reachable location to a random unreachable location
List <Region> regionscopy = regions.ToList();
helper.Shuffle(regionscopy);
Region from = regionscopy.First(x => !unreachable.Contains(x)); //Not in unreachable, so it is reachable
helper.Shuffle(unreachable);
Region to = unreachable.First(); //Unreachable
AddExits(regions, from, to); //Add connection between two regions to join subgraphs
Generated = new WorldGraph("Region-0", "Goal", regions.ToHashSet(), MajorItemList);
unreachable = Generated.GetUnreachableRegions(); //Recompute reachability
}
//Now before adding items, we will get the last region and place the goal there- No other items will be placed there
Generated = new WorldGraph("Region-0", "Goal", regions.ToHashSet(), MajorItemList);
Region goalregion = Generated.Regions.Last();
Item goalitem = new Item("Goal", 3); //Create goal item
Location goallocation = new Location("Final Boss", "None", goalitem); //Create location for goal item with no requirement since entrance to region will have full requirement
//We want all exits to the goal region to require every item so that they will all be required to complete the game
string fullrequirement = "";
foreach (Item i in MajorItemList)
{
fullrequirement += i.Name + " and ";
}
fullrequirement = fullrequirement.Substring(0, fullrequirement.Length - 5); //Remove final " and "
regions.First(x => x == goalregion).Locations.Add(goallocation);
foreach (Exit e in regions.First(x => x == goalregion).Exits)
{
e.Requirements = fullrequirement;
}
//Must also write to requirements leading into final region
foreach (Region r in regions)
{
foreach (Exit e in r.Exits)
{
if (e.ToRegionName == goalregion.Name)
{
e.Requirements = fullrequirement;
}
}
}
//Finally, generate item locations and place the location in the region
foreach (Region r in regions)
{
//The starting region has some specific conditions:
// 1. Three locations with no requirement
// 2. 50% chance of a 4th location with one requirement
if (r.Name == "Region-0")
{
int random = rng.Next(3, 5);
for (int i = 0; i < random; i++)
{
if (i < random - 1) //Guaranteed 3 locations with no requirement
{
Location l = new Location("Region-0_Location-" + i.ToString(), "None", new Item());
regions.First(x => x == r).Locations.Add(l); //Add generated location to region
}
else //Possible 4th location, have 1 requirement
{
Location l = new Location("Region-0_Location-" + i.ToString(), GenerateOneRandomRequirement(), new Item());
regions.First(x => x == r).Locations.Add(l); //Add generated location to region
}
}
}
//The second region is the hub region and also has some specific conditions:
// 1. Two locations with no requirement
// 2. One location with one requirement
// 3. One location with two requirements
else if (r.Name == "Region-1")
{
for (int i = 0; i < 4; i++)
{
if (i < 2)
{
Location l = new Location("Region-1_Location-" + i.ToString(), "None", new Item());
regions.First(x => x == r).Locations.Add(l); //Add generated location to region
}
else if (i == 2)
{
Location l = new Location("Region-1_Location-" + i.ToString(), GenerateOneRandomRequirement(), new Item());
regions.First(x => x == r).Locations.Add(l); //Add generated location to region
}
else if (i == 3)
{
Location l = new Location("Region-1_Location-" + i.ToString(), GenerateTwoRandomRequirements(), new Item());
regions.First(x => x == r).Locations.Add(l); //Add generated location to region
}
}
}
//Every other region will generate 2 to 4 locations, unless region contains goal, in which case we want that to be the only location in that region
else if (r != goalregion)
{
//Generate 2 to 4 locations per region
int random = rng.Next(2, 5);
for (int i = 0; i < random; i++)
{
//Generate a location with:
// Name: Region-x_Location-y, ex Region-5_Location-2
// Requirement: Randomly Generated
// Item: null item
Location l = new Location(r.Name + "_Location-" + i.ToString(), GenerateRandomRequirement(false), new Item());
regions.First(x => x == r).Locations.Add(l); //Add generated location to region
}
}
}
//Now that we have a total number of regions and a count of major items, must generate junk items to fill out the item list
List <Item> ItemList = MajorItemList; //Copy major item list and add goal item
ItemList.Add(goalitem);
Generated = new WorldGraph("Region-0", "Goal", regions.ToHashSet(), ItemList.OrderByDescending(x => x.Importance).ThenBy(x => x.Name).ToList()); //Remake generated now that items have been added
int locationcount = Generated.GetLocationCount(); //Get location count and find difference so we know how many junk items to generate
int difference = locationcount - MajorItemList.Count();
for (int i = 0; i < difference; i++)
{
//For a junk item, importance will be either 0 or 1, so generate one of those numbers randomly
int importance = rng.Next(0, 2);
Item newitem = new Item("JunkItem" + importance.ToString(), importance); //Name will either be JunkItem0 or JunkItem1
ItemList.Add(newitem);
}
Generated = new WorldGraph("Region-0", "Goal", regions.ToHashSet(), ItemList.OrderByDescending(x => x.Importance).ThenBy(x => x.Name).ToList()); //Remake generated now that items have been added
return(Generated);
}
/*
* The goal of this function is to traverse the game world like a player of the game rather than like an algorithm.
* It's assumed that the player has decent knowledge of the game, meaning they know where locations are and
* wether or not those locations and regions are accessible.
* Therefore a heuristic is used to score each posisble exit a player could take based on number of item locations
* and how close they are to the current location.
* Whichever exit has the maximum score (meaning maximum potential to gain new items) is taken.
* We keep several counts which can be used to gauge interestingness of a seed:
* 1. Number of locations collected for each region traversed
* 2. Number of regions traversed between finding major or helpful items
* 3. Number of regions traversed between finding major items
*/
public PlaythroughInfo PlaythroughSearch(WorldGraph world)
{
List <int> BetweenMajorOrHelpfulList = new List <int>(); //This did not end up being utilized.
List <int> BetweenMajorList = new List <int>();
List <int> LocationsPerTraversal = new List <int>();
List <int> LocationsUnlockedPerMajorFound = new List <int>();
Region current = world.Regions.First(x => x.Name == world.StartRegionName);
Region previous = new Region();
List <Item> owneditems = new List <Item>();
List <Region> traversed = new List <Region>();
int BetweenMajorOrHelpfulCount = 0;
int BetweenMajorCount = 0;
bool gomode = false;
int prevlocationsunlocked = GetReachableLocations(world, owneditems).GetLocationCount(); //Initial count of unlocked locations
int initial = prevlocationsunlocked; //Saved to add to output later
while (owneditems.Count(x => x.Importance == 3) < 1) //Loop until goal item found, or dead end reached (see break statement below)
{
//If count gets this high usually indicates search is stuck in a loop... not great but just retry with a new permutation...
//Seems to happen roughly one in every 5000 permutations of most complex world (World5) so fairly rare occurrence
if (traversed.Count > world.Regions.Count() * 20)
{
throw new Exception();
}
traversed.Add(current);
int checkcount = 0;
int prevcount = -1;
while (prevcount < owneditems.Count()) //While loop to re-check locations if major item is found in this region
{
prevcount = owneditems.Count();
//First, check each location in the current region, if accessible and not already searched check it for major items
foreach (Location l in current.Locations)
{
if (l.Item.Importance > -1 && parser.RequirementsMet(l.Requirements, owneditems))
{
checkcount++; //Add to check count
Item i = l.Item;
l.Item = new Item(); //Remove item, location importance set to -1
if (i.Importance == 1) //Helpful item, did not end up being utilized.
{
//Update helpful list only and reset counts
BetweenMajorOrHelpfulList.Add(BetweenMajorOrHelpfulCount);
BetweenMajorOrHelpfulCount = 0;
}
else if (i.Importance == 2) //Major item
{
owneditems.Add(i); //Collect item
//Update both lists and reset counts
BetweenMajorOrHelpfulList.Add(BetweenMajorOrHelpfulCount);
BetweenMajorList.Add(BetweenMajorCount);
BetweenMajorOrHelpfulCount = 0;
BetweenMajorCount = 0;
//Find number of locations unlocked, add to list, update count of locations unlocked
int locationsunlocked = GetReachableLocations(world, owneditems).GetLocationCount();
int newlocations = locationsunlocked - prevlocationsunlocked;
LocationsUnlockedPerMajorFound.Add(newlocations);
prevlocationsunlocked = locationsunlocked;
}
else if (i.Importance == 3) //Goal item, break loop here
{
owneditems.Add(i); //Collect goal item, indicates successful completion
}
}
}
}
if (!gomode) //Update this traversal with the number of locations checked within it, unless player is in go mode
{
LocationsPerTraversal.Add(checkcount);
}
List <double> exitscores = new List <double>();
if (current.Exits.Count == 1) //Only one exit, take it unless need to break
{
double score = ExitScore(world, owneditems, current, traversed, current.Exits.First());
if (score > 0) //If score is -1 and this is the only exit, then a dead end has been reached; if score is 0, then there are no items left to find; otherwise simply take exit since it is the only one
{
current = world.Regions.First(x => x.Name == current.Exits.First().ToRegionName); //Move to region
if (score >= 10000000000) //Score this high indicates player is in "go mode" where they are now rushing the end of the game
{
gomode = true;
}
//Update count of regions between finding items
BetweenMajorOrHelpfulCount++;
BetweenMajorCount++;
}
else
{
break; //Break loop in failure
}
}
else
{
List <double> scores = new List <double>();
//Calculate score for each exit
foreach (Exit e in current.Exits)
{
scores.Add(ExitScore(world, owneditems, current, traversed, e));
}
if (scores.Count(x => x > 0) > 0) //If none of the scores are greater than 0, all exits are either untraversable or have no available items, indicating dead end has been reached
{
int maxindex = scores.IndexOf(scores.Max()); //Get index of the maximum score
previous = current;
current = world.Regions.First(x => x.Name == current.Exits.ElementAt(maxindex).ToRegionName); //Move to region with maximum score
if (scores.Max() >= 10000000000) //Score this high indicates player is in "go mode" where they are now rushing the end of the game
{
gomode = true;
}
//Update count of regions between finding items
BetweenMajorOrHelpfulCount++;
BetweenMajorCount++;
}
else
{
break; //Break loop in failure
}
}
}
//Package all lists into list of lists and return
PlaythroughInfo output = new PlaythroughInfo();
output.BetweenMajorOrHelpfulList = BetweenMajorOrHelpfulList;
output.BetweenMajorList = BetweenMajorList;
output.LocationsPerTraversal = LocationsPerTraversal;
output.LocationsUnlockedPerMajorFound = LocationsUnlockedPerMajorFound;
output.Traversed = traversed;
output.Completable = owneditems.Count(x => x.Importance == 3) > 0; //Has goal item, so game is completable
output.InitialReachableCount = initial;
return(output);
}
/*
* Score info about human-like playthrough
* Several considerations:
* 1. Number of locations collected for each region traversed
* 2. Number of regions traversed between finding major or helpful items
* 3. Number of regions traversed between finding major items
*/
public InterestingnessOutput ScorePlaythrough(WorldGraph world, PlaythroughInfo input, BiasOutput biasinfo)
{
//First, calculate fun metric, which desires a consistently high rate of checking item locations
Queue <int> RollingAvg = new Queue <int>();
List <double> avgs = new List <double>();
List <bool> highavg = new List <bool>();
foreach (int num in input.LocationsPerTraversal)
{
if (RollingAvg.Count == 5) //Rolling average of last 5 values
{
RollingAvg.Dequeue();
}
RollingAvg.Enqueue(num);
double avg = RollingAvg.Average();
highavg.Add(avg >= 1); //If average is above 1, considered high enough to be fun, so add true to list, else add false
avgs.Add(avg);
}
double fun = (double)highavg.Count(x => x) / highavg.Count(); //Our "Fun" score is the percentage of high values in the list
//Next calculate challenge metric, which desires rate at which items are found to be within some optimal range so that it is not too often or too rare
double LocationToItemRatio = (double)world.GetLocationCount() / world.Items.Where(x => x.Importance == 2).Count();
int low = (int)Math.Floor(LocationToItemRatio * .5);
int high = (int)Math.Ceiling(LocationToItemRatio * 1.5);
RollingAvg = new Queue <int>();
avgs = new List <double>();
List <bool> avginrange = new List <bool>();
foreach (int num in input.BetweenMajorList)
{
if (RollingAvg.Count == 3) //Tighter rolling average of last 3 values
{
RollingAvg.Dequeue();
}
RollingAvg.Enqueue(num);
double avg = RollingAvg.Average();
avginrange.Add(low <= avg && avg <= high); //If value is within range rather than too high or too low, add true to list to indicate it is within a good range
avgs.Add(avg);
}
double challenge = (double)avginrange.Count(x => x) / avginrange.Count(); //Our "Challenge" score is the percentage of values in the list within desirable range
//Next calculate satisfyingness metric based on how many locations are unlocked when an item is found
double LocationToItemRatioWithoutInitial = (double)(world.GetLocationCount() - input.InitialReachableCount) / world.Items.Where(x => x.Importance == 2).Count();
int satthreshold = (int)Math.Floor(LocationToItemRatioWithoutInitial); //Set threshold as number of not-immediately-accessible locations divided by number of major items
List <bool> SatisfyingReachesThreshold = new List <bool>();
foreach (int num in input.LocationsUnlockedPerMajorFound)
{
SatisfyingReachesThreshold.Add(num >= satthreshold);
}
double satisfyingness = (double)SatisfyingReachesThreshold.Count(x => x) / SatisfyingReachesThreshold.Count(); //Our "Satisfyingness" score is the percentage of values above the desired threshold
//Finally calculate boredom by observing regions which were visited more often than is expected
//First get a count of how many times each region was visited
List <int> visitcounts = new List <int>();
foreach (Region r in world.Regions)
{
visitcounts.Add(input.Traversed.Count(x => x.Name == r.Name));
}
//Calculate threshold with max number of times region should be visited being the number of traversals divided by number of regions
double TraversedToRegionRatio = (double)input.Traversed.Count() / world.Regions.Count();
int borethreshold = (int)Math.Ceiling(TraversedToRegionRatio);
List <bool> VisitsAboveThreshold = new List <bool>();
foreach (int num in visitcounts)
{
VisitsAboveThreshold.Add(num > borethreshold); //Again as before, add list of bool when value is above threshold
}
double boredom = (double)VisitsAboveThreshold.Count(x => x) / VisitsAboveThreshold.Count(); //Our "Boredom" score is the percentage of values above the desired threshold
//Add calculated stats to output. If a result is NaN (possible when not completable) save as -1
InterestingnessOutput output = new InterestingnessOutput();
output.bias = biasinfo;
output.fun = double.IsNaN(fun) ? -1 : fun;
output.challenge = double.IsNaN(challenge) ? -1 : challenge;
output.satisfyingness = double.IsNaN(satisfyingness) ? -1 : satisfyingness;
output.boredom = double.IsNaN(boredom) ? -1 : boredom;
//Use stats to calculate final interestingness score
//Each score is a double in the range [0, 1]
//Multiply each score (or its 1 - score if low score is desirable) by its percentage share of the total
double biasscore = (1 - output.bias.biasvalue) * .2;
double funscore = output.fun * .2;
double challengescore = output.challenge * .2;
double satscore = output.satisfyingness * .2;
double borescore = (1 - output.boredom) * .2;
double intscore = biasscore + funscore + challengescore + satscore + borescore;
//If any components are NaN, consider interestingness as NaN as well
if (double.IsNaN(fun) || double.IsNaN(challenge) || double.IsNaN(satisfyingness) || double.IsNaN(boredom))
{
output.interestingness = -1;
}
else
{
output.interestingness = intscore;
}
output.completable = input.Completable;
return(output);
}
//Calculate complexity of the base graph (Not considering items, only rules for location reachability)
public TestComplexityOutput CalcWorldComplexity(WorldGraph world)
{
List <string> totalrules = new List <string>();
/*
* For each location, calculate a total rule
* Total rule meaning it includes every possible path to get there plus the rule for the location itself
* ex, 2 paths to location: Region A -> Region B -> Region C, Region A -> Region C
* Location has Rule X
* Then the total rule will equal:
* ((A->B and B->C) or (A->C)) and Rule X
*/
foreach (Region r in world.Regions)
{
//Must calculate every possible path (that doesn't go back on itself) from root to the region r
List <List <Region> > paths = searcher.PathsToRegion(world, r);
string regionstring = "";
if (paths.Count > 0) //If it equals 0, current region is root, do not need region string
{
//Go through each path and calculate the rule for that path to construct an absolute rule for the region
for (int i = 0; i < paths.Count; i++)
{
List <Region> path = paths[i];
string pathstring = "";
for (int j = 0; j < path.Count - 1; j++) //Last region is dest, don't need to check thus paths.Count - 1
{
if (j > 0)
{
pathstring += " and "; // Every requirement on this path must be met, so use "and"
}
pathstring += "(" + path[j].Exits.First(x => x.ToRegionName == path[j + 1].Name).Requirements + ")";
}
if (i > 0)
{
regionstring += " or "; //The pathstrings are different options, so use "or"
}
regionstring += "(" + pathstring + ")";
}
}
//Now calculate the total rule for each location
foreach (Location l in r.Locations)
{
string totalrule = "";
if (string.IsNullOrEmpty(regionstring)) //For when region is root
{
totalrule = l.Requirements;
}
else
{
totalrule = "(" + regionstring + ") and " + l.Requirements; //Must meet at least one path requirement to reach the region and the location requirement
}
totalrule = parser.Simplify(totalrule.Replace("None", "true")); //Simplifies the boolean expression
totalrules.Add(totalrule);
}
}
//We now have a list for the total rule of every location in the game
//Calculate score for each rule and add them all to list
List <double> scores = new List <double>();
foreach (string rule in totalrules)
{
scores.Add(parser.CalcRuleScore(rule));
}
//Use list of scores to calculate final score and return
return(ComplexityScoreCalculation(scores));
}
