/// <summary>
/// Constructs a new Principal Component Analysis.
/// </summary>
///
/// <param name="method">The analysis method to perform. Default is <see cref="AnalysisMethod.Center"/>.</param>
/// <param name="whiten">Whether to whiten the results or not. If set to <c>true</c> the generatred output
/// will be normalized to have unit standard deviation.</param>
/// <param name="numberOfOutputs">The maximum number of components that the analysis will be able to project data into.</param>
///
public PrincipalComponentAnalysis(PrincipalComponentMethod method = PrincipalComponentMethod.Center,
bool whiten = false, int numberOfOutputs = 0)
{
this.Method = method;
this.Whiten = whiten;
this.NumberOfOutputs = numberOfOutputs;
}
/// <summary>
/// Constructs the Kernel Principal Component Analysis.
/// </summary>
///
/// <param name="kernel">The kernel to be used in the analysis.</param>
/// <param name="method">The analysis method to perform.</param>
/// <param name="centerInFeatureSpace">True to center the data in feature space,
/// false otherwise. Default is true.</param>
/// <param name="numberOfOutputs">The maximum number of components that the analysis will be able to project data into.</param>
/// <param name="whiten">Whether to whiten the results or not. If set to <c>true</c> the generatred output
/// will be normalized to have unit standard deviation.</param>
///
public KernelPrincipalComponentAnalysis(IKernel kernel, PrincipalComponentMethod method = PrincipalComponentMethod.Center,
bool centerInFeatureSpace = true, int numberOfOutputs = 0, bool whiten = false)
{
if (kernel == null)
{
throw new ArgumentNullException("kernel");
}
this.Method = method;
this.kernel = kernel;
this.centerFeatureSpace = centerInFeatureSpace;
this.NumberOfOutputs = numberOfOutputs;
this.Whiten = whiten;
}
public void runPCA() //DataTable mydt, List<string> numvars, string method, string subtitle)
{
if (_dt == null || _final_N == 0)
{
_subtitle += " *No data rows*";
_final_N = 0;
}
else
{
////from http://accord-framework.net/docs/html/T_Accord_Statistics_Analysis_PrincipalComponentAnalysis.htm
PrincipalComponentMethod mymethod = new PrincipalComponentMethod();
if (method == "Center")
{
mymethod = PrincipalComponentMethod.Center;
}
else if (method == "Standardize")
{
mymethod = PrincipalComponentMethod.Standardize;
}
var pca = new PrincipalComponentAnalysis()
{
Method = mymethod,
Whiten = true
};
// Learn the PCA projection using passing the cov matrix
MultivariateLinearRegression transform = pca.Learn(_data);
// Now, we can transform data as usual
double[][] out1 = pca.Transform(_data);
_pca = pca;
buildPCAtable();
}
}
public InternalPrincipalComponentAnalysis(PrincipalComponentMethod method = PrincipalComponentMethod.Center, bool whiten = false, int numberOfOutputs = 0) :
base(method, whiten, numberOfOutputs)
{
}
public PrincipalComponentAnalyzer(PrincipalComponentMethod method = PrincipalComponentMethod.Svd)
{
Method = method;
}
