public Distribution(String DistributionName, params double[] vs) // 构造函数 根据名字初始化分布
{
switch (DistributionName.ToUpper())
{
case "NORMAL":
normalDis = new Normal(vs[0], vs[1]); // double mean, double stddev
break;
case "CONTINUOUS":
continuousUniformDis = new ContinuousUniform(vs[0], vs[1]); // int lower, int upper
break;
case "TRIANGULAR":
triangularDis = new Triangular(vs[0], vs[1], vs[2]); //double lower, double upper, double mode (lower ≤ mode ≤ upper)
break;
case "STUDENTT":
studentTDis = new StudentT(vs[0], vs[1], vs[2]); //double location, double scale, double freedom
break;
case "BERNOULLI":
bernoulliDis = new Bernoulli(vs[0]);
break;
case "DISCRETEUNIFORM":
discreteUniform = new DiscreteUniform((int)vs[0], (int)vs[1]); // int lower, int upper
break;
}
this.DistributionName = DistributionName;
}
public void SetupDistributions()
{
dists = new IDistribution[8];
dists[0] = new Beta(1.0, 1.0);
dists[1] = new ContinuousUniform(0.0, 1.0);
dists[2] = new Gamma(1.0, 1.0);
dists[3] = new Normal(0.0, 1.0);
dists[4] = new Bernoulli(0.6);
dists[5] = new Weibull(1.0, 1.0);
dists[6] = new DiscreteUniform(1, 10);
dists[7] = new LogNormal(1.0, 1.0);
}
/// <summary>
/// Generates A random change to the specified point
/// </summary>
/// <param name="reference">The reference point</param>
public void GenerateChange(Point reference)
{
Bernoulli rBernoulli = new Bernoulli(0.5);
double percentage = (rBernoulli.Sample() > 0.5 ? -1 : 1) * Simulation.Default.Data_Change_Maximum_Percentage * random.NextDouble();
GenerateChange(reference, percentage);
}
/// <summary>
/// Generates A random change in the field. Both the coordinates and the
/// </summary>
public void GenerateChange()
{
Bernoulli rBernoulli = new Bernoulli(0.5); // Generate A random sign
double percentage = (rBernoulli.Sample() > 0.5 ? -1 : 1) * Simulation.Default.Data_Change_Maximum_Percentage * random.NextDouble();
GenerateChange(new Point(random.Next(field.Width), random.Next(field.Height)), percentage);
}
protected override bool ShouldRunActivity()
{
var bernoulli = new Bernoulli((double) Context.GlobalSettings.SendEmailPercentage/100);
return bernoulli.Sample() == 1;
}
public void ValidateSkewness(double p)
{
var b = new Bernoulli(p);
Assert.AreEqual((1.0 - (2.0 * p)) / Math.Sqrt(p * (1.0 - p)), b.Skewness);
}
public void ValidateEntropy(double p)
{
var b = new Bernoulli(p);
AssertHelpers.AlmostEqualRelative(-((1.0 - p) * Math.Log(1.0 - p)) - (p * Math.Log(p)), b.Entropy, 14);
}
public void ValidateMode(double p, double m)
{
var b = new Bernoulli(p);
Assert.AreEqual(m, b.Mode);
}
public void CanCreateBernoulli(double p)
{
var bernoulli = new Bernoulli(p);
Assert.AreEqual(p, bernoulli.P);
}
public void ValidateCumulativeDistribution(double p, double x, double cdf)
{
var b = new Bernoulli(p);
Assert.AreEqual(cdf, b.CumulativeDistribution(x));
}
public void CanSampleSequence()
{
var n = new Bernoulli(0.3);
var ied = n.Samples();
GC.KeepAlive(ied.Take(5).ToArray());
}
public void CanSample()
{
var n = new Bernoulli(0.3);
n.Sample();
}
public void ValidateProbabilityLn(double p, int x, double dln)
{
var b = new Bernoulli(p);
Assert.AreEqual(dln, b.ProbabilityLn(x));
}
public void ValidateMaximum()
{
var b = new Bernoulli(0.3);
Assert.AreEqual(1.0, b.Maximum);
}
public void ValidateToString()
{
var b = new Bernoulli(0.3);
Assert.AreEqual("Bernoulli(p = 0.3)", b.ToString());
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Bernoulli_distribution">Bernoulli distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Bernoulli distribution class with parameter P = 0.2
var bernoulli = new Bernoulli(0.2);
Console.WriteLine(@"1. Initialize the new instance of the Bernoulli distribution class with parameter P = {0}", bernoulli.P);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", bernoulli);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", bernoulli.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", bernoulli.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", bernoulli.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", bernoulli.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", bernoulli.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", bernoulli.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", bernoulli.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", bernoulli.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", bernoulli.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", bernoulli.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", bernoulli.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Bernoulli distribution
Console.WriteLine(@"3. Generate 10 samples of the Bernoulli distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(bernoulli.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Bernoulli(0.2) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Bernoulli(0.2) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = bernoulli.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Bernoulli(4) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Bernoulli(0.9) distribution and display histogram");
bernoulli.P = 0.9;
for (var i = 0; i < data.Length; i++)
{
data[i] = bernoulli.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Bernoulli(8) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Bernoulli(0.5) distribution and display histogram");
bernoulli.P = 0.5;
for (var i = 0; i < data.Length; i++)
{
data[i] = bernoulli.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
static void Main(string[] args)
{
try
{
Application oXL = new Application();
oXL.DisplayAlerts = false;
#if DEBUG
oXL.Visible = true;
#else
oXL.Visible = false;
#endif
//Open the Excel File
Workbook oWB = oXL.Workbooks.Open(inputFile);
_Worksheet oSheet = oWB.Sheets[SheetName1];
oSheet = oWB.Sheets[1]; // Gives the reference to the first sheet
oSheet = oWB.ActiveSheet; // Gives the reference to the current opened sheet
oSheet.Activate();
oSheet.Copy(Type.Missing, Type.Missing);
_Worksheet oSheet1 = oXL.Workbooks[2].Sheets[1];
oSheet1.Name = SheetName2;
try
{
//We already know the Address Range of the cells
String start_range = "A2";
String end_range = "A164";
Object[,] valuesA = oSheet1.get_Range(start_range, end_range).Value2;
start_range = "B2";
end_range = "B164";
Object[,] valuesB = oSheet.get_Range(start_range, end_range).Value2;
start_range = "C2";
end_range = "C164";
Object[,] valuesC = oSheet.get_Range(start_range, end_range).Value2;
start_range = "D2";
end_range = "D164";
Object[,] valuesD = oSheet.get_Range(start_range, end_range).Value2;
//int t = valuesB.GetLength(0);
object duplicate = null;
int index = 1;
int count = 0;
int i = 1;
for (i = 1; i <= valuesA.GetLength(0); i++)
{
if (duplicate == null)
{
duplicate = valuesA[i, 1];
index = i;
}
if (Convert.ToInt64(valuesA[i, 1]) == Convert.ToInt64(duplicate))
{
count++;
duplicate = valuesA[i, 1];
continue;
}
double[] probSuc = new double[count];
//double max = Double.MinValue;
int r = 1;
int[] successes = new int[count];
int[] failures = new int[count];
int[] trials = new int[count];
for (int k = index; k <= (index + count - 1); k++)
{
double s = Convert.ToInt32(valuesC[k, 1]);
double n = Convert.ToInt32(valuesD[k, 1]);
double f = Convert.ToInt32(valuesD[k, 1]) - s;
trials[r - 1] = Convert.ToInt32(n);
successes[r - 1] = Convert.ToInt32(s);
failures[r - 1] = Convert.ToInt32(f);
probSuc[r - 1] = s / n;
r++;
}
r = 1;
for (int k = index; k <= (index + count - 1); k++)
{
var samples = new Binomial(probSuc[r-1], trials[r-1]);
var samples1 = new Bernoulli(probSuc[r - 1]);
double prob = samples.Probability(0);
if (prob < 0.05)
{
oSheet1.Cells[k + 1, 5] = "WINNER";
}
else
{
oSheet1.Cells[k + 1, 5] = "LOSSER";
}
r++;
}
i--;
duplicate = null;
count = 0;
}
if (count > 0 && index <= valuesA.GetLength(0))
{
double[] probSuc = new double[count];
//double max = Double.MinValue;
int r = 1;
int[] successes = new int[count];
int[] failures = new int[count];
int[] trials = new int[count];
for (int k = index; k <= (index + count - 1); k++)
{
double s = Convert.ToInt32(valuesC[k, 1]);
double n = Convert.ToInt32(valuesD[k, 1]);
double f = Convert.ToInt32(valuesD[k, 1]) - s;
trials[r - 1] = Convert.ToInt32(n);
successes[r - 1] = Convert.ToInt32(s);
failures[r - 1] = Convert.ToInt32(f);
probSuc[r - 1] = s / n;
r++;
}
r = 1;
for (int k = index; k <= (index + count - 1); k++)
{
var samples = new Binomial(probSuc[r - 1], trials[r - 1]);
double prob = samples.Probability(0);
if (prob < 0.05)
{
oSheet1.Cells[k + 1, 5] = "WINNER";
}
else
{
oSheet1.Cells[k + 1, 5] = "LOSSER";
}
}
}
}
catch (Exception e)
{
var errors = e.Message;
}
finally
{
oSheet1.SaveAs(OutputFile);
oXL.Quit();
Marshal.ReleaseComObject(oSheet);
Marshal.ReleaseComObject(oSheet1);
Marshal.ReleaseComObject(oWB);
Marshal.ReleaseComObject(oXL);
oSheet = null;
oSheet1 = null;
oWB = null;
oXL = null;
GC.GetTotalMemory(false);
GC.Collect();
GC.WaitForPendingFinalizers();
GC.Collect();
GC.GetTotalMemory(true);
}
}
catch (Exception e)
{
var errors = e.Message;
}
}
