public RandomRecommender(DataModel dataModel)
: base(dataModel)
{
this.random = RandomUtils.getRandom();
float negativeInfinity = float.NegativeInfinity;
float positiveInfinity = float.PositiveInfinity;
IEnumerator <long> enumerator = dataModel.getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
PreferenceArray array = dataModel.getPreferencesFromUser(current);
for (int i = 0; i < array.length(); i++)
{
float num5 = array.getValue(i);
if (num5 < positiveInfinity)
{
positiveInfinity = num5;
}
if (num5 > negativeInfinity)
{
negativeInfinity = num5;
}
}
}
this.minPref = positiveInfinity;
this.maxPref = negativeInfinity;
}
public Features(ALSWRFactorizer factorizer)
{
this.dataModel = factorizer.dataModel;
this.numFeatures = factorizer.numFeatures;
RandomWrapper wrapper = RandomUtils.getRandom();
this.M = new double[this.dataModel.getNumItems()][];
IEnumerator <long> enumerator = this.dataModel.getItemIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
int index = factorizer.itemIndex(current);
this.M[index] = new double[this.numFeatures];
this.M[index][0] = this.averateRating(current);
for (int j = 1; j < this.numFeatures; j++)
{
this.M[index][j] = wrapper.nextDouble() * 0.1;
}
}
this.U = new double[this.dataModel.getNumUsers()][];
for (int i = 0; i < this.U.Length; i++)
{
this.U[i] = new double[this.numFeatures];
}
}
protected void initialize()
{
int num3;
RandomWrapper wrapper = RandomUtils.getRandom();
this.userVectors = new double[this.dataModel.getNumUsers()][];
this.itemVectors = new double[this.dataModel.getNumItems()][];
double num = this.getAveragePreference();
for (int i = 0; i < this.userVectors.Length; i++)
{
this.userVectors[i] = new double[this.rank];
this.userVectors[i][0] = num;
this.userVectors[i][USER_BIAS_INDEX] = 0.0;
this.userVectors[i][ITEM_BIAS_INDEX] = 1.0;
num3 = FEATURE_OFFSET;
while (num3 < this.rank)
{
this.userVectors[i][num3] = wrapper.nextGaussian() * NOISE;
num3++;
}
}
for (int j = 0; j < this.itemVectors.Length; j++)
{
this.itemVectors[j] = new double[this.rank];
this.itemVectors[j][0] = 1.0;
this.itemVectors[j][USER_BIAS_INDEX] = 1.0;
this.itemVectors[j][ITEM_BIAS_INDEX] = 0.0;
for (num3 = FEATURE_OFFSET; num3 < this.rank; num3++)
{
this.itemVectors[j][num3] = wrapper.nextGaussian() * NOISE;
}
}
}
public ShipFactoryIntegrationTests()
{
_configMock = new Mock <IOptions <AppSettings> >(MockBehavior.Strict);
var randomWrapper = new RandomWrapper();
_sut = new ShipFactory(_configMock.Object, randomWrapper);
}
public FixedSizeSamplingIterator(int size, IEnumerator <T> source)
{
this.buf = new List <T>(size);
int n = 0;
RandomWrapper wrapper = RandomUtils.getRandom();
while (source.MoveNext())
{
T current = source.Current;
n++;
if (this.buf.Count < size)
{
this.buf.Add(current);
}
else
{
int num2 = wrapper.nextInt(n);
if (num2 < this.buf.Count)
{
this.buf[num2] = current;
}
}
}
this.enumerator = this.buf.GetEnumerator();
}
private static void Main(string[] args)
{
var seed = new Random().Next();
var rndSource = new RandomWrapper(seed);
var treeBuilder = new TreeBuilder(rndSource);
var tree = treeBuilder.BuildTree();
}
protected void initialize()
{
RandomWrapper random = RandomUtils.getRandom();
userVectors = new double[dataModel.GetNumUsers()][];
itemVectors = new double[dataModel.GetNumItems()][];
double globalAverage = getAveragePreference();
for (int userIndex = 0; userIndex < userVectors.Length; userIndex++)
{
userVectors[userIndex] = new double[rank];
userVectors[userIndex][0] = globalAverage;
userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
for (int feature = FEATURE_OFFSET; feature < rank; feature++)
{
userVectors[userIndex][feature] = random.nextGaussian() * NOISE;
}
}
for (int itemIndex = 0; itemIndex < itemVectors.Length; itemIndex++)
{
itemVectors[itemIndex] = new double[rank];
itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
for (int feature = FEATURE_OFFSET; feature < rank; feature++)
{
itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE;
}
}
}
public EntityBase(Color foreground, Color background, int glyph, Map map, bool haveVision = false, int visionRange = 20) : base(1, 1)
{
Animation.CurrentFrame[0].Foreground = foreground;
Animation.CurrentFrame[0].Background = background;
Animation.CurrentFrame[0].Glyph = glyph;
EntityStatus = new EntityStatus();
Inventory = new Inventory(EntityStatus.MaxWeight);
this.map = map;
HaveVision = haveVision;
if (HaveVision)
{
fovmap = new FOV(map);
VisionRange = visionRange;
}
Equip = new ItemBase("Pickaxe", 10f);
Equip.ItemBehaviour = ItemBehaviourHelper.Mine();
random = RandomNumberServiceLocator.GetService();
logger = LoggingServiceLocator.GetService();
ID = random.NextUint();
}
private void UpdateSeed(int newSeed)
{
var treeRndSource = new RandomWrapper(newSeed);
var treeBuilder = new TreeBuilder(treeRndSource);
trunk = treeBuilder.BuildTree();
grassRandom = new RandomWrapper(newSeed);
}
public TreeBehaviourEngine(TreeEnvironmentConfig config, TreeState treeState)
{
this.config = config ?? throw new System.ArgumentNullException(nameof(config));
this.TreeState = treeState ?? throw new System.ArgumentNullException(nameof(treeState));
WaterDelta = 0.125;
rndSource = new RandomWrapper(treeState.Seed);
}
protected override void prepareTraining()
{
int num;
int num2;
base.prepareTraining();
RandomWrapper wrapper = RandomUtils.getRandom();
this.p = new double[base.dataModel.getNumUsers()][];
for (num = 0; num < this.p.Length; num++)
{
this.p[num] = new double[base.numFeatures];
num2 = 0;
while (num2 < RatingSGDFactorizer.FEATURE_OFFSET)
{
this.p[num][num2] = 0.0;
num2++;
}
num2 = RatingSGDFactorizer.FEATURE_OFFSET;
while (num2 < base.numFeatures)
{
this.p[num][num2] = wrapper.nextGaussian() * base.randomNoise;
num2++;
}
}
this.y = new double[base.dataModel.getNumItems()][];
for (num = 0; num < this.y.Length; num++)
{
this.y[num] = new double[base.numFeatures];
num2 = 0;
while (num2 < RatingSGDFactorizer.FEATURE_OFFSET)
{
this.y[num][num2] = 0.0;
num2++;
}
for (num2 = RatingSGDFactorizer.FEATURE_OFFSET; num2 < base.numFeatures; num2++)
{
this.y[num][num2] = wrapper.nextGaussian() * base.randomNoise;
}
}
this.itemsByUser = new Dictionary <int, List <int> >();
IEnumerator <long> enumerator = base.dataModel.getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
int num4 = base.userIndex(current);
FastIDSet set = base.dataModel.getItemIDsFromUser(current);
List <int> list = new List <int>(set.size());
this.itemsByUser[num4] = list;
foreach (long num5 in set)
{
int item = base.itemIndex(num5);
list.Add(item);
}
}
}
public GridBuilderIntegrationTests()
{
_configMock = new Mock <IOptions <AppSettings> >(MockBehavior.Strict);
var randomWrapper = new RandomWrapper();
var shipFactory = new ShipFactory(_configMock.Object, randomWrapper);
var grid = new Logic.Grid.Grid();
_sut = new GridBuilder(_configMock.Object, shipFactory, grid);
}
public float Execute()
{
Debug.Log("DamagePercentageUseCase");
float damage = -target.finalMaxHP * percentage * RandomWrapper.Range(0.2f);
new ModifyHPUseCase(target, damage).Execute();
DamageLogger.Add(target.unitId, (int)damage);
return(damage);
}
protected void shufflePreferences()
{
RandomWrapper wrapper = RandomUtils.getRandom();
for (int i = this.cachedUserIDs.Length - 1; i > 0; i--)
{
int posB = wrapper.nextInt(i + 1);
this.swapCachedPreferences(i, posB);
}
}
protected void shufflePreferences()
{
RandomWrapper random = RandomUtils.getRandom();
/// Durstenfeld shuffle
for (int currentPos = cachedUserIDs.Length - 1; currentPos > 0; currentPos--)
{
int swapPos = random.nextInt(currentPos + 1);
swapCachedPreferences(currentPos, swapPos);
}
}
public void ChooseRandomWeighted_ReturnsRandomObjectBasedOnWeight()
{
var listOfObjects = new List <WeightedTestObject>
{
new WeightedTestObject {
Name = TestConst.Heavy, Weight = TestConst.WeightHeavy
},
new WeightedTestObject {
Name = TestConst.Medium, Weight = TestConst.WeightMedium
},
new WeightedTestObject {
Name = TestConst.Light, Weight = TestConst.WeightLight
},
new WeightedTestObject {
Name = TestConst.Empty, Weight = TestConst.WeightNone
},
};
int testRuns = 100;
var stats = new Dictionary <string, int>();
var rand = new RandomWrapper();
for (int i = 0; i < testRuns; i++)
{
var selection = rand.ChooseRandomWeighted(listOfObjects);
Assert.IsNotNull(selection);
Debug.Print("Random select: {0}", selection.Name);
if (stats.ContainsKey(selection.Name))
{
stats[selection.Name] += 1;
}
else
{
stats.Add(selection.Name, 1);
}
}
foreach (var item in stats)
{
Debug.Print("{0}: {1}", item.Key, item.Value);
}
Assert.IsTrue(stats.ContainsKey(TestConst.Heavy));
Assert.IsTrue(stats.ContainsKey(TestConst.Medium));
Assert.IsTrue(stats.ContainsKey(TestConst.Light));
Assert.IsTrue(stats.ContainsKey(TestConst.Empty) == false);
Assert.IsTrue(stats[TestConst.Heavy] > stats[TestConst.Medium]);
Assert.IsTrue(stats[TestConst.Medium] > stats[TestConst.Light]);
}
void Start()
{
rb2D = GetComponent <Rigidbody2D>();
rng = new RandomWrapper();
foreach (float degreeDirection in possibleDirectionsAsDegrees)
{
float radian = degreeDirection * Mathf.Deg2Rad;
possibleVectorDirections.Add(new Vector2(
Mathf.Cos(radian), Mathf.Sin(radian)
));
}
MoveToRandomDirection();
}
public void ChooseRandomWeighted_WithZeroWeightedItemsNotAllowed_ReturnsNull()
{
var listOfObjects = new List <WeightedTestObject>
{
new WeightedTestObject {
Name = TestConst.Empty, Weight = TestConst.WeightNone
}
};
var selection = new RandomWrapper().ChooseRandomWeighted(listOfObjects, itemsWithZeroWeightCanBeSelected: false);
Assert.IsNull(selection);
}
public GameLogicIntegrationTests()
{
_configMock = new Mock <IOptions <AppSettings> >(MockBehavior.Strict);
var settingsChecker = new SettingsChecker(_configMock.Object);
var randomWrapper = new RandomWrapper();
var shipFactory = new ShipFactory(_configMock.Object, randomWrapper);
var grid = new Logic.Grid.Grid();
var gridBuilder = new GridBuilder(_configMock.Object, shipFactory, grid);
var coordinateValidator = new CoordinateValidator();
var coordinateParser = new CoordinateParser(coordinateValidator);
_sut = new GameLogic(settingsChecker, gridBuilder, coordinateParser);
}
public SamplingLongPrimitiveIterator(RandomWrapper random, IEnumerator <long> enumerator, double samplingRate)
{
if (enumerator == null)
{
throw new ArgumentException("enumerator");
}
if ((samplingRate <= 0.0) || (samplingRate > 1.0))
{
throw new ArgumentException("samplingRate");
}
this.geometricDistribution = new PascalDistribution(random.getRandomGenerator(), 1, samplingRate);
this.enumerator = enumerator;
this.SkipNext();
}
public SamplinglongPrimitiveIterator(RandomWrapper random, IEnumerator <long> enumerator, double samplingRate)
{
if (enumerator == null)
{
throw new ArgumentException("enumerator");
}
if (!(samplingRate > 0.0 && samplingRate <= 1.0))
{
throw new ArgumentException("samplingRate");
}
//Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0, "Must be: 0.0 < samplingRate <= 1.0");
// Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
geometricDistribution = new PascalDistribution(random.getRandomGenerator(), 1, samplingRate);
this.enumerator = enumerator;
SkipNext();
}
/// <summary>
/// LoadContent will be called once per game and is the place to load
/// all of your content.
/// </summary>
protected override void LoadContent()
{
// Create a new SpriteBatch, which can be used to draw textures.
spriteBatch = new SpriteBatch(GraphicsDevice);
// TODO: use this.Content to load your game content here
var actions = new List <IAction>
{
new MoveAction(),
new ShrinkSizeAction(),
new ChangeColorAction(new ColorRgba(255, 255, 0), new ColorRgba(0, 0, 255)),
new FadeAction(255, 0)
};
var randomGenerator = new RandomWrapper();
_particleEmitter1 = new Emitter(
new EmitterConfig
{
EmitterShape = new PointEmitter(new ThreeSixty(50, 100, randomGenerator)),
InitialColorCalculator = new Varied(new ColorRgba(255, 255, 0), new ColorRgba(20, 20, 20), randomGenerator),
FinalColorCalculator = new Varied(new ColorRgba(0, 0, 255), new ColorRgba(20, 20, 20), randomGenerator),
InitialSizeCalculator = new RandomBetweenXAndY(0.1f, 2.0f, randomGenerator),
EmissionRatePerSecond = 200,
MaximumNumberOfParticles = 1000,
Actions = actions
});
_particleEmitter1.Location = new SneakyParticleSystem.Vector2(100.0f, 100.0f);
_particleEmitter2 = new Emitter(
new EmitterConfig
{
EmitterShape = new CircleEmitter(100.0f, new Normal(1, 100, randomGenerator), randomGenerator),
InitialColorCalculator = new Simple(new ColorRgba(255, 0, 0)),
FinalColorCalculator = new Simple(new ColorRgba(255, 255, 0)),
InitialSizeCalculator = new RandomBetweenXAndY(0.1f, 2.0f, randomGenerator),
EmissionRatePerSecond = 200,
MaximumNumberOfParticles = 1000,
Actions = actions
});
_particleEmitter2.Location = new SneakyParticleSystem.Vector2(400.0f, 300.0f);
_particleSystemRenderer = new ParticleSystemRenderer();
_particleSystemRenderer.LoadContent(Content);
}
/**
* @param dataModel
* {@link DataModel} which provides users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param clusteringThreshold
* clustering similarity threshold; clusters will be aggregated into larger clusters until the next
* two nearest clusters' similarity drops below this threshold
* @param samplingRate
* percentage of all cluster-cluster pairs to consider when finding next-most-similar clusters.
* Decreasing this value from 1.0 can increase performance at the cost of accuracy
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code clusteringThreshold} is {@link Double#NaN},
* or samplingRate is {@link Double#NaN} or nonpositive or greater than 1.0
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
double clusteringThreshold,
double samplingRate)
: base(dataModel)
{
//Preconditions.checkArgument(!Double.IsNaN(clusteringThreshold), "clusteringThreshold must not be NaN");
//Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0, "samplingRate is invalid: %f", samplingRate);
random = RandomUtils.getRandom();
this.clusterSimilarity = clusterSimilarity; //Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = int.MinValue; //Integer.MIN_VALUE;
this.clusteringThreshold = clusteringThreshold;
this.clusteringByThreshold = true;
this.samplingRate = samplingRate;
this.refreshHelper = new RefreshHelper(buildClusters);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
public static void FuckUpSteamData()
{
if (STEAM_NAME == null || STEAM_ID_HOLDER == null || STEAM_ID_FIELD == null)
{
Console.WriteLine("ERROR: {0} {1} {2}", STEAM_NAME != null, STEAM_ID_HOLDER != null, STEAM_ID_FIELD != null);
return;
}
STEAM_NAME.SetValue(null, RandomWrapper.GenerateString(8), null);
if (holder != null)
{
var id = (CSteamID)STEAM_ID_FIELD.GetValue(holder, null);
var oldId = id.m_SteamID;
id = new CSteamID(new AccountID_t((uint)UnityEngine.Random.Range(0, int.MaxValue)), id.GetEUniverse(), id.GetEAccountType());
CurrentID = id.m_SteamID;
STEAM_ID_FIELD.SetValue(holder, id, null);
}
}
/**
* @param dataModel
* {@link DataModel} which provdes users
* @param clusterSimilarity
* {@link ClusterSimilarity} used to compute cluster similarity
* @param numClusters
* desired number of clusters to create
* @param samplingRate
* percentage of all cluster-cluster pairs to consider when finding next-most-similar clusters.
* Decreasing this value from 1.0 can increase performance at the cost of accuracy
* @throws IllegalArgumentException
* if arguments are {@code null}, or {@code numClusters} is less than 2, or samplingRate
* is {@link Double#NaN} or nonpositive or greater than 1.0
*/
public TreeClusteringRecommender(DataModel dataModel,
ClusterSimilarity clusterSimilarity,
int numClusters,
double samplingRate)
: base(dataModel)
{
//Preconditions.checkArgument(numClusters >= 2, "numClusters must be at least 2");
//Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
// "samplingRate is invalid: %f", samplingRate);
random = RandomUtils.getRandom();
this.clusterSimilarity = clusterSimilarity;//Preconditions.checkNotNull(clusterSimilarity);
this.numClusters = numClusters;
this.clusteringThreshold = Double.NaN;
this.clusteringByThreshold = false;
this.samplingRate = samplingRate;
this.refreshHelper = new RefreshHelper(buildClusters);
refreshHelper.addDependency(dataModel);
refreshHelper.addDependency(clusterSimilarity);
buildClusters();
}
protected override void Initialize()
{
// Generally you don't want to hide the mouse from the user
IsMouseVisible = true;
// Finish the initialization of SadConsole
base.Initialize();
RandomWrapper randomWrapper = new RandomWrapper(2);
RandomNumberServiceLocator.Provide(randomWrapper);
// Create the map
AdventureScreen = new AdventureScreen();
AdventureScreen.LoadMap(MapGenerator.Generate(200, 200));
AdventureScreen.SpawnPlayer();
SadConsole.ControlsConsole startingConsole = new SadConsole.ControlsConsole(SCREEN_WIDTH, SCREEN_HEIGHT);
var bt1 = new SadConsole.Controls.Button(5);
startingConsole.Add(bt1);
SadConsole.Global.CurrentScreen = startingConsole;
SadConsole.Global.CurrentScreen.Children.Add(AdventureScreen);
//SimplexNoiseViewer simplexNoiseViewer = new SimplexNoiseViewer(0, 0, SCREEN_WIDTH, SCREEN_HEIGHT);
//simplexNoiseViewer.LoadMap(200, 200, 0.03f);
//SadConsole.Global.CurrentScreen = simplexNoiseViewer;
//SadConsole.Window window = new SadConsole.Window(10, 10);
//window.Title = "status";
//window.Dragable = true;
//window.Show();
//SadConsole.Global.CurrentScreen = startingConsole;
//SadConsole.Global.CurrentScreen.Children.Add(window);
}
public static GameState MakeInitialGameState(List <Player> players, TurnCount totalRounds, GridSize gridSize, ICollisions iCollisions, IEndGame iEndGame)
{
var gameState = new GameState()
{
Round = 1,
TotalRounds = totalRounds,
GameOver = false,
GridSize = gridSize,
Players = new List <Player>(),
Collisions = iCollisions,
EndGame = iEndGame
};
foreach (var player in players)
{
int x = RandomWrapper.RandomNumber(0, gameState.GridSize.Cols);
int y = RandomWrapper.RandomNumber(0, gameState.GridSize.Rows);
player.Destroyed = false;
player.Position = new Position(x, y);
gameState.Players.Add(player);
}
return(gameState);
}
protected virtual void prepareTraining()
{
RandomWrapper random = RandomUtils.getRandom();
userVectors = new double[dataModel.GetNumUsers()][]; //numFeatures
itemVectors = new double[dataModel.GetNumItems()][];
double globalAverage = getAveragePreference();
for (int userIndex = 0; userIndex < userVectors.Length; userIndex++)
{
userVectors[userIndex] = new double[numFeatures];
userVectors[userIndex][0] = globalAverage;
userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++)
{
userVectors[userIndex][feature] = random.nextGaussian() * randomNoise;
}
}
for (int itemIndex = 0; itemIndex < itemVectors.Length; itemIndex++)
{
itemVectors[itemIndex] = new double[numFeatures];
itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++)
{
itemVectors[itemIndex][feature] = random.nextGaussian() * randomNoise;
}
}
cachePreferences();
shufflePreferences();
}
protected virtual void prepareTraining()
{
int num3;
RandomWrapper wrapper = RandomUtils.getRandom();
this.userVectors = new double[this.dataModel.getNumUsers()][];
this.itemVectors = new double[this.dataModel.getNumItems()][];
double num = this.getAveragePreference();
for (int i = 0; i < this.userVectors.Length; i++)
{
this.userVectors[i] = new double[this.numFeatures];
this.userVectors[i][0] = num;
this.userVectors[i][USER_BIAS_INDEX] = 0.0;
this.userVectors[i][ITEM_BIAS_INDEX] = 1.0;
num3 = FEATURE_OFFSET;
while (num3 < this.numFeatures)
{
this.userVectors[i][num3] = wrapper.nextGaussian() * this.randomNoise;
num3++;
}
}
for (int j = 0; j < this.itemVectors.Length; j++)
{
this.itemVectors[j] = new double[this.numFeatures];
this.itemVectors[j][0] = 1.0;
this.itemVectors[j][USER_BIAS_INDEX] = 1.0;
this.itemVectors[j][ITEM_BIAS_INDEX] = 0.0;
for (num3 = FEATURE_OFFSET; num3 < this.numFeatures; num3++)
{
this.itemVectors[j][num3] = wrapper.nextGaussian() * this.randomNoise;
}
}
this.cachePreferences();
this.shufflePreferences();
}
void Awake()
{
main = this;
rng = new RandomWrapper();
}