public double TakeSamples()
{
var dateTimeElapsed = 0.0;
var dateTime = DateTime.Now;
if (this.DistributionName == "Binomial")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}-Trials:{TrialsNumber}";
var binomaial = new MathNet.Numerics.Distributions.Binomial(0.5, this.TrialsNumber);
var generatedsamples = binomaial.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Geometric")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var geometric = new MathNet.Numerics.Distributions.Geometric(0.5);
var generatedsamples = geometric.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Poisson")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var poisson = new MathNet.Numerics.Distributions.Poisson(0.5);
var generatedsamples = poisson.Samples().Take(SamplesNumber).ToArray();
}
else if (this.DistributionName == "Normal")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var normal = new MathNet.Numerics.Distributions.Normal(0.5, 2);
var generatedsamples = normal.Samples().Take(SamplesNumber).ToArray();
}
dateTimeElapsed = (DateTime.Now - dateTime).TotalMilliseconds;
return(dateTimeElapsed);
}
public double TakeSamples()
{
var dateTimeElapsed = 0.0;
var dateTime = DateTime.Now;
//IEnumerable<int> generatedSamplesEnumerable = Enumerable.Empty<int>();
//IEnumerable<double> generatedSamplesDoubleEnumerable = Enumerable.Empty<double>();
if (this.DistributionName == "Binomial")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}-Trials:{TrialsNumber}";
var binomaial = new MathNet.Numerics.Distributions.Binomial(0.5, this.TrialsNumber);
var generatedsamples = binomaial.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = binomaial.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Geometric")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var geometric = new MathNet.Numerics.Distributions.Geometric(0.5);
var generatedsamples = geometric.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = geometric.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Poisson")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var poisson = new MathNet.Numerics.Distributions.Poisson(0.5);
var generatedsamples = poisson.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesEnumerable = poisson.Samples().Take(SamplesNumber);
//foreach (var item in generatedSamplesEnumerable)
//{
// var test = item;
//}
}
else if (this.DistributionName == "Normal")
{
fullName = $"{DistributionName}-Samples:{SamplesNumber}";
var normal = new MathNet.Numerics.Distributions.Normal(0.5, 2);
var generatedsamples = normal.Samples().Take(SamplesNumber).ToArray();
//generatedSamplesDoubleEnumerable = normal.Samples().Take(SamplesNumber);
//foreach(var item in generatedSamplesDoubleEnumerable)
//{
// var test = item;
//}
}
dateTimeElapsed = (DateTime.Now - dateTime).TotalMilliseconds;
return(dateTimeElapsed);
}
public void LocateControls(Form form, ConsoleHandler console)
{
form.Text = "Задание № 1";
form.SetDefaultVals(new System.Drawing.Size(800, 500));
form.Controls.Add(BeautyfyForms.AddButton(" Суть ", new Point(370, 10), (o, k) =>
{
MessageBox.Show("Задача № 1. Игра с Геометрическим, Биномиальным и Клиновидным распределениями!");
}));
logGeometric = BeautyfyForms.AddListBox(new Point(10, 100), new Size(200, 450));
form.Controls.Add(logGeometric);
_geometric = BeautyfyForms.CreateTextBox(new Point(40, 420), true);
form.Controls.Add(_geometric);
logBinominal = BeautyfyForms.AddListBox(new Point(300, 100), new Size(200, 450));
form.Controls.Add(logBinominal);
_binominal = BeautyfyForms.CreateTextBox(new Point(340, 420), true);
form.Controls.Add(_binominal);
logWedgeShaped = BeautyfyForms.AddListBox(new Point(590, 100), new Size(200, 450));
form.Controls.Add(logWedgeShaped);
_wedgeShaped = BeautyfyForms.CreateTextBox(new Point(625, 420), true);
form.Controls.Add(_wedgeShaped);
form.Controls.Add(BeautyfyForms.AddButton("Геометрическое", new Point(30, 70), (o, k) =>
{
logGeometric.Items.Clear();
Task.Run(() =>
{
var listOfElements = new List <KeyValueItem>();
var geom = new MathNet.Numerics.Distributions.Geometric(0.6);
for (int i = 0; i <= 1000; i++)
{
listOfElements.Add(new KeyValueItem(i, geom.Probability(i)));
}
var sum = (from element in listOfElements.AsParallel() select element.Probability).Sum();
listOfElements = (from element in listOfElements.AsParallel() orderby element.Probability descending select element).ToList();
foreach (var item in listOfElements)
{
logGeometric.BeginInvoke(new MethodInvoker(() => logGeometric.Items.Add(item)));
}
double average = 0;
for (int i = 0; i < 100000; i++)
{
var search = geom.Sample();
for (int j = 0; j < listOfElements.Count; j++)
{
if (listOfElements[j].Key == search)
{
average += j;
}
}
}
average /= 100000;
_geometric.BeginInvoke(new MethodInvoker(() => _geometric.Text = Math.Round(average).ToString()));
});
}));
form.Controls.Add(BeautyfyForms.AddButton("Биноминальное", new Point(335, 70), (o, k) =>
{
logBinominal.Items.Clear();
Task.Run(() =>
{
var listOfElements = new List <KeyValueItem>();
var binom = new MathNet.Numerics.Distributions.Binomial(0.4, 1000);
for (int i = 0; i <= 1000; i++)
{
listOfElements.Add(new KeyValueItem(i, binom.Probability(i)));
}
listOfElements = (from element in listOfElements.AsParallel() orderby element.Probability descending select element).ToList();
foreach (var item in listOfElements)
{
logBinominal.BeginInvoke(new MethodInvoker(() => logBinominal.Items.Add(item)));
}
long average = 0;
for (int i = 0; i < 100000; i++)
{
var search = binom.Sample();
for (int j = 0; j < listOfElements.Count; j++)
{
if (listOfElements[j].Key == search)
{
average += j;
}
}
}
average /= 100000;
_binominal.BeginInvoke(new MethodInvoker(() => _binominal.Text = Math.Round((decimal)average).ToString()));
});
}));
form.Controls.Add(BeautyfyForms.AddButton("Клиновидное", new Point(630, 70), (o, k) =>
{
logWedgeShaped.Items.Clear();
Task.Run(() =>
{
var listOfElements = new List <KeyValueItem>();
Random random = new Random((int)DateTime.Now.ToBinary());
int N = 1000;
for (int i = 0; i < 1000; i++)
{
listOfElements.Add(new KeyValueItem(i, (N - i) * (2.0 / (N * (N + 1)))));
}
var sum = (from element in listOfElements.AsParallel() select element.Probability).Sum();
listOfElements = (from element in listOfElements.AsParallel() orderby element.Probability descending select element).ToList();
foreach (var item in listOfElements)
{
logWedgeShaped.BeginInvoke(new MethodInvoker(() => logWedgeShaped.Items.Add(item)));
}
double average = 0;
for (int i = 0; i < 100000; i++)
{
var p = (N - random.Next(0, 1000)) * (2.0 / (N * (N + 1)));
var search = -(1 / 2.0) * N * (N * p + p - 2);
for (int j = 0; j < listOfElements.Count; j++)
{
if (listOfElements[j].Key == search)
{
average += j;
}
}
}
average /= 100000;
_wedgeShaped.BeginInvoke(new MethodInvoker(() => _wedgeShaped.Text = Math.Round(average).ToString()));
});
}));
}
