public void ValidateProbability(double p, int n, int x, double d)
{
var b = new Binomial(p,n);
AssertHelpers.AlmostEqual(d, b.Probability(x), 14);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Binomial_distribution">Binomial distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Binomial distribution class with parameters P = 0.2, N = 20
var binomial = new Binomial(0.2, 20);
Console.WriteLine(@"1. Initialize the new instance of the Binomial distribution class with parameters P = {0}, N = {1}", binomial.P, binomial.N);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", binomial);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", binomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", binomial.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Binomial distribution
Console.WriteLine(@"3. Generate 10 samples of the Binomial distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(binomial.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Binomial(0.2, 20) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Binomial(0.2, 20) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Binomial(0.7, 20) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Binomial(0.7, 20) distribution and display histogram");
binomial.P = 0.7;
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Binomial(0.5, 40) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Binomial(0.5, 40) distribution and display histogram");
binomial.P = 0.5;
binomial.N = 40;
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.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;
}
}