private int[] CreateArrayOfPresentValues(bool actualizeInMatlab, string nameInMatlab = "")
{
// all input values to array, in chronological order (order of index)
var values = new List <int>();
foreach (var inp in _inputNodes)
{
// get value
int?value = null;
if (inp.GetType() == typeof(Variable))
{
var v = (Variable)inp;
if (v.Value.Value.HasValue)
{
value = v.Value.Value.Value + 1;
}
else
{
throw new ApplicationException("Variable's value is NULL!");
}
}
else if (inp.GetType() == typeof(BayesClassifierModule))
{
var b = (BayesClassifierModule)inp;
if (b.Result != null)
{
value = b.Result.MatlabIndex;
}
else
{
throw new ApplicationException("Bayesian Classifier Module has not result!");
}
}
// add to list and move to next input
if (value != null)
{
values.Add(value.Value);
}
}
if (values.Count != _inputNodes.Count)
{
throw new ApplicationException("Array of input values is not the same size as the number of inputs!");
}
var arrayOfValues = values.ToArray();
if (!actualizeInMatlab)
{
return(arrayOfValues);
}
if (nameInMatlab != "")
{
MatlabInterface.ArrayToMatlabVector(arrayOfValues, nameInMatlab, true);
}
else
{
throw new ApplicationException("Must specify a name to actualize array in Matlab!");
}
return(arrayOfValues);
}
/// <summary>
/// Associate current values with a particular category.
/// </summary>
public void AssociateWithCategory(int[] inputValues, ClassCategory c, bool plot)
{
MatlabInterface.ArrayToMatlabVector(inputValues, "evaluateTheseValues", true);
// MatlabInterface.Execute request
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".associateValuesWithCategory(evaluateTheseValues, " + c.MatlabIndex + ");");
// show us the result - comment out of not needed
if (plot)
{
MatlabInterface.Execute(ClassifierUniqueId + ".plotAllLikelihoods();");
}
}
/// <summary>
///
/// </summary>
/// <param name="inputVariable"></param>
/// <param name="priorProbabilities"></param>
public void SetCategoryPriories(PatternClassificationInput inputVariable, double[] priorProbabilities)
{
var matlabArrayName = "newPrioriesForInputNo_" + inputVariable.ClassifierMatlabIndex;
MatlabInterface.ArrayToMatlabVector(priorProbabilities, matlabArrayName, true);
if (priorProbabilities.Length != _classificationCategories.Count)
{
throw new ApplicationException(
"Number of priories in array does not match number of classification categories!");
}
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".setCategoryPrioriesForInput(" + inputVariable.ClassifierMatlabIndex +
", " + matlabArrayName + ");");
// plot resultant priories of BC
MatlabInterface.Execute("plot(" + ClassifierUniqueId + ".InputNodeClassifiers(" + inputVariable.ClassifierMatlabIndex +
").priories);");
}
/// <summary>
///
/// </summary>
/// <param name="inputValues"></param>
/// <returns></returns>
public ClassCategory GetDecision(int[] inputValues)
{
MatlabInterface.ArrayToMatlabVector(inputValues, "evaluateTheseValues", true);
// MatlabInterface.Execute request
return(GetDecisionFromMatlabObj());
}