/// <summary>
/// Construct a cross validation trainer.
/// </summary>
///
/// <param name="train">The training</param>
/// <param name="k">The number of folds.</param>
public CrossValidationKFold(IMLTrain train, int k) : base(train.Method, (FoldedDataSet)train.Training)
{
_train = train;
Folded.Fold(k);
_flatNetwork = ((BasicNetwork)train.Method).Structure.Flat;
_networks = new NetworkFold[k];
for (int i = 0; i < _networks.Length; i++)
{
_networks[i] = new NetworkFold(_flatNetwork);
}
}
/// <summary>
/// Construct a cross validation trainer.
/// </summary>
/// <param name="train">The training.</param>
/// <param name="k">The number of folds.</param>
public CrossValidationKFold(ITrain train, int k)
: base(train.Network, (FoldedDataSet)train.Training)
{
this.train = train;
Folded.Fold(k);
this.flatNetwork = train.Network.Structure.Flat;
this.networks = new NetworkFold[k];
for (int i = 0; i < networks.Length; i++)
{
this.networks[i] = new NetworkFold(flatNetwork);
}
}
private static void folded_normal_cdf_test()
//****************************************************************************80
//
// Purpose:
//
// FOLDED_NORMAL_CDF_TEST tests FOLDED_NORMAL_CDF.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 05 April 2016
//
// Author:
//
// John Burkardt
//
{
int i;
int seed = 123456789;
Console.WriteLine("");
Console.WriteLine("FOLDED_NORMAL_CDF_TEST");
Console.WriteLine(" FOLDED_NORMAL_CDF evaluates the Folded Normal CDF;");
Console.WriteLine(" FOLDED_NORMAL_CDF_INV inverts the Folded Normal CDF.");
Console.WriteLine(" FOLDED_NORMAL_PDF evaluates the Folded Normal PDF;");
const double a = 2.0;
const double b = 3.0;
Console.WriteLine("");
Console.WriteLine(" PDF parameter A = " + a + "");
Console.WriteLine(" PDF parameter B = " + b + "");
if (!Folded.folded_normal_check(a, b))
{
Console.WriteLine("");
Console.WriteLine("FOLDED_NORMAL_CDF_TEST - Fatal error!");
Console.WriteLine(" The parameters are not legal.");
return;
}
Console.WriteLine("");
Console.WriteLine(" X PDF CDF CDF_INV");
Console.WriteLine("");
for (i = 1; i <= 10; i++)
{
double x = Folded.folded_normal_sample(a, b, ref seed);
double pdf = Folded.folded_normal_pdf(x, a, b);
double cdf = Folded.folded_normal_cdf(x, a, b);
double x2 = Folded.folded_normal_cdf_inv(cdf, a, b);
Console.WriteLine(" "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ pdf.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ cdf.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ x2.ToString(CultureInfo.InvariantCulture).PadLeft(12) + "");
}
}
private static void folded_normal_sample_test()
//****************************************************************************80
//
// Purpose:
//
// FOLDED_NORMAL_SAMPLE_TEST tests FOLDED_NORMAL_SAMPLE.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 05 April 2016
//
// Author:
//
// John Burkardt
//
{
const int SAMPLE_NUM = 1000;
int i;
int seed = 123456789;
double[] x = new double [SAMPLE_NUM];
Console.WriteLine("");
Console.WriteLine("FOLDED_NORMAL_SAMPLE_TEST");
Console.WriteLine(" FOLDED_NORMAL_MEAN computes the Folded Normal mean;");
Console.WriteLine(" FOLDED_NORMAL_SAMPLE samples the Folded Normal distribution;");
Console.WriteLine(" FOLDED_NORMAL_VARIANCE computes the Folded Normal variance;");
const double a = 2.0;
const double b = 3.0;
Console.WriteLine("");
Console.WriteLine(" PDF parameter A = " + a + "");
Console.WriteLine(" PDF parameter B = " + b + "");
if (!Folded.folded_normal_check(a, b))
{
Console.WriteLine("");
Console.WriteLine("FOLDED_NORMAL_SAMPLE_TEST - Fatal error!");
Console.WriteLine(" The parameters are not legal.");
return;
}
double mean = Folded.folded_normal_mean(a, b);
double variance = Folded.folded_normal_variance(a, b);
Console.WriteLine("");
Console.WriteLine(" PDF mean = " + mean + "");
Console.WriteLine(" PDF variance = " + variance + "");
for (i = 0; i < SAMPLE_NUM; i++)
{
x[i] = Folded.folded_normal_sample(a, b, ref seed);
}
mean = typeMethods.r8vec_mean(SAMPLE_NUM, x);
variance = typeMethods.r8vec_variance(SAMPLE_NUM, x);
double xmax = typeMethods.r8vec_max(SAMPLE_NUM, x);
double xmin = typeMethods.r8vec_min(SAMPLE_NUM, x);
Console.WriteLine("");
Console.WriteLine(" Sample size = " + SAMPLE_NUM + "");
Console.WriteLine(" Sample mean = " + mean + "");
Console.WriteLine(" Sample variance = " + variance + "");
Console.WriteLine(" Sample maximum = " + xmax + "");
Console.WriteLine(" Sample minimum = " + xmin + "");
}
