private void Actualize()
{
// create a corresponding BayesClassifierModule object in MATLAB
var noOfCategories = _classificationCategories.Count;
var cmd = ClassifierUniqueId + " = BayesClassifierModule(" + noOfCategories + ");";
MatlabInterface.Execute(cmd);
// generate a Bayesian Classifier for each 'Pattern Classification Input' object.
foreach (var pci in InputNodes)
{
var minVal = 0;
var maxVal = 0;
if (pci.GetType() == typeof(Variable))
{
var v = (Variable)pci;
minVal = v.Value.MinimumAllowableValue;
maxVal = v.Value.MaximumAllowableValue;
}
else if (pci.GetType() == typeof(BayesClassifierModule))
{
var b = (BayesClassifierModule)pci;
minVal = 1;
maxVal = b.ClassificationCategories.Count;
}
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".newInputNode(" + minVal + ", " + maxVal + ");");
}
// lock BayesClassifierModule
cmd = ClassifierUniqueId + " = " + ClassifierUniqueId + ".lock();";
MatlabInterface.Execute(cmd);
}
public void ReadStatusFlagsAndUpdateResult()
{
// set status flags
if (MatlabInterface.MatlabVariableToInteger(ClassifierUniqueId + ".WaitingForTrainingResponse", false).Equals(0))
{
_waitingForTrainingResponse = false;
}
else
{
_waitingForTrainingResponse = true;
}
// update/fetch the result
if (MatlabInterface.MatlabVariableToInteger(ClassifierUniqueId + ".ObjectFound", false).Equals(0))
{
Result = false;
}
else
{
Result = true;
}
// inform all
if (NewResultAvailable != null)
{
NewResultAvailable(this, new EventArgs());
}
}
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>
///
/// </summary>
/// <param name="inputVariable"></param>
/// <param name="inputVariableValue"></param>
/// <returns></returns>
public ClassCategory GetDecision(PatternClassificationInput inputVariable, int inputVariableValue)
{
// MatlabInterface.Execute request
MatlabInterface.Execute("inputClassifierDecision = " + ClassifierUniqueId + ".InputNodeClassifiers(" + inputVariable.ClassifierMatlabIndex + ").getDecision(" + inputVariableValue + ");");
// fetch value and return
var intResult = MatlabInterface.MatlabVariableToInteger("inputClassifierDecision", true);
var c = _classificationCategories[intResult - 1];
return(c);
}
/// <summary>
///
/// </summary>
/// <param name="inputVariable"></param>
/// <param name="inputVariableValue"></param>
/// <param name="c"></param>
/// <param name="plot"></param>
public void AssociateWithCategory(PatternClassificationInput inputVariable, int inputVariableValue,
ClassCategory c, bool plot)
{
// associate input value with cateogiry, for the specified variable
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".InputNodeClassifiers(" + inputVariable.ClassifierMatlabIndex + ").createAssociation(" + inputVariableValue + ", " + c.MatlabIndex + ");");
// plot
if (plot)
{
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".InputNodeClassifiers(" + inputVariable.ClassifierMatlabIndex + ").plotLikelihoods();");
}
}
/// <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();");
}
}
private void CaptureReferenceImage(bool cropImage)
{
if (cropImage)
{
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".captureRefImgAndCrop();");
}
else
{
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".captureRefImg();");
}
}
private ClassCategory GetDecisionFromMatlabObj()
{
// execute request
Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".getDecision(evaluateTheseValues);");
Execute("Decision = " + ClassifierUniqueId + ".Decision;");
// get result
//var categoryNumber = Cognition.MatlabVariableToInteger(ClassifierUniqueId + ".Decision", false)
var categoryNumber = MatlabInterface.MatlabVariableToInteger("Decision", false);
var cat = _classificationCategories[categoryNumber - 1];
return(cat);
}
/// <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>
/// Associate current values with a particular category.
/// </summary>
public void AssociateWithCategory(ClassCategory c, bool plot)
{
if (c == null)
{
throw new ApplicationException("No Category Selected.");
}
// create array of values in MATLAB
CreateArrayOfPresentValues(true, "evaluateTheseValues");
// 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();");
}
}
static void Main(string[] args)
{
MatlabInterface.InitMainWindow();
int n = 0;
while (!MatlabInterface.IsSettingReady())
{
Thread.Sleep(100);
Console.WriteLine("Waiting." + (n++));
}
var s = MatlabInterface.GetSetting();
Console.WriteLine(s.str);
Console.WriteLine("Finished");
Console.ReadKey();
}
public void ProvideTrainingResponse(bool objectDetected)
{
if (!_waitingForTrainingResponse)
{
throw new ApplicationException("MATLAB SURFDetector object is not expecting a training response!");
}
var v = 0;
// get int representative
if (objectDetected)
{
v = 1;
}
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".trainClassifier(" + v + ");");
// reset flag
_waitingForTrainingResponse = false;
}
/// <summary>
///
/// </summary>
/// <param name="inputValues"></param>
/// <returns></returns>
public ClassCategory GetDecision(int[] inputValues)
{
MatlabInterface.ArrayToMatlabVector(inputValues, "evaluateTheseValues", true);
// MatlabInterface.Execute request
return(GetDecisionFromMatlabObj());
}
// DO THIS LATER
//public SurfFeaturesDetector SurfFeature
/// <summary>
/// New Avinsor Pattern Classification object to manage all pattern classification components in the project.
/// </summary>
public Cognition()
{
MatlabInterface.InitializeMatlab();
BayesClassifierModules = new BayesClassifierModuleList(MatlabInterface.MatlabServer);
}
public void CaptureCompareTrain()
{
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".captureCompareTrain();");
}
public Matlab()
{
MatlabInterface.InitializeMatlab();
Server = MatlabInterface.MatlabServer;
InitializeComponent();
}
/// <summary>
///
/// </summary>
private void ActualizeInMatlab()
{
MatlabInterface.Execute(ClassifierUniqueId + " = " + ClassifierUniqueId + ".SURFDetector(0);");
}
