/// <summary> /// Вероятности принадлежности к классу /// </summary> /// <param name="vect"></param> /// <param name="sm"></param> void GetProbability(double[] vect, SModel sm) { for (int i = 0; i < vect.Length; i++) { sm[i].pr = DistributionFunc.GaussNorm1(vect[i], sm[i]._e, sm[i]._sco); } sm.CalculateProb(); }
/// <summary> /// /// </summary> /// <param name="dataset"></param> /// <returns></returns> public static Vector ImportanceSign(Vector[] dataset) { Vector dispers = Statistic.EnsembleDispersion(dataset); double m = Statistic.ExpectedValue(dispers); double std = Statistic.Std(dispers); Vector Y = DistributionFunc.GaussNorm1(dispers, m, std); Vector X = MathFunc.GenerateTheSequence(0, 1, Y.N); RBFGauss regr = new RBFGauss(X, Y, 25); return(regr.Predict(X)); }
public UserGroup(User founder) { Members = new List <User>(); ClassCount = new Dictionary <Class, uint>(); foreach (var cl in Enum.GetValues(typeof(Class)).Cast <Class>()) { ClassCount[cl] = 0; } Logger.InfoFormat("Creating new group with {0} as founder.", founder.Name); // Distribute full experience to everyone with a bonus if a member of each // class is present. ExperienceDistributionFunc = Distribution_AllClassBonus; Add(founder); CreatedOn = DateTime.Now; }
public UserGroup(User founder) { Members = new List<User>(); ClassCount = new Dictionary<Class, uint>(); foreach (var cl in Enum.GetValues(typeof(Class)).Cast<Class>()) { ClassCount[cl] = 0; } Logger.InfoFormat("Creating new group with {0} as founder.", founder.Name); // Distribute full experience to everyone with a bonus if a member of each // class is present. ExperienceDistributionFunc = Distribution_AllClassBonus; Add(founder); CreatedOn = DateTime.Now; }