/// <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;
}
