public FeatureRegisteredEventArgs(DataStructureGroup datastructureGroup, int elementSize, double percentC, double percentR, double percentU, double percentD,
string classifiedDS, string currentDS, double predictedProbValue, string nextDS, int regCount)
{
this.InterfaceAndSetBagPropertyGroup = datastructureGroup;
this.ElementSizeAtTimeOfOperation = elementSize;
this.PercentC = percentC;
this.PercentR = percentR;
this.PercentU = percentU;
this.PercentD = percentD;
this.ClassifiedDataStructure = classifiedDS;
this.CurrentDataStructure = currentDS;
this.PredictionProbabilityValue = predictedProbValue;
this.NextPredictedDataStructure = nextDS;
this.RegisteredCountFromLastTransformation = regCount;
}
/// <summary>
/// Register feature to the CLassifier
/// Format:G,N,%C,%R,%U,%D
/// </summary>
/// <param name="datastructureGroup"></param>
/// <param name="elementSize">Number of elements in the data structure at the time of an operation, N</param>
/// <param name="percentC">%C</param>
/// <param name="percentR">%R</param>
/// <param name="percentU">%U</param>
/// <param name="percentD">%D</param>
public void RegisterFeature(DataStructureGroup datastructureGroup, int elementSize, double percentC, double percentR, double percentU, double percentD, CrudBasedCollection.C5DataStructure currentDS)
{
while (!isBusy)//this is to make sure that the next feature will be registered only after the prior one has been completely registered.
{
isBusy = true;
currentDataStructure = currentDS;
//1. Encode and normalize the feature values
double[] x = new double[10]; //10 input values of the ANN
double[] encodedNormInterfaceAndSetBagProperty = EncodeNormalizeDataStructureGroup(datastructureGroup); //X1 Feature (5-length array)
Array.Copy(encodedNormInterfaceAndSetBagProperty, 0, x, 0, 5); //copy to the first 5 arrays
x[5] = (elementSize - gaussMeanX2) / gaussStdvX2; //X2 Feature
x[6] = (percentC - gaussMeanX3) / gaussStdvX3; //X3 Feature
x[7] = (percentR - gaussMeanX4) / gaussStdvX4; //X4 Feature
x[8] = (percentU - gaussMeanX5) / gaussStdvX5; //X5 Feature
x[9] = (percentD - gaussMeanX6) / gaussStdvX6; //X6 Feature;
//2. Register to Classifier and compute the data structure
double[] computed_y = Classifier.ComputeOutputs(x);//9 output values of the ANN
//using winner-take-all method to evaluate y values
int maxComputedYIndex = MaxIndex(computed_y);
string computedDS = yValues[maxComputedYIndex];
ngram.Dequeue();
ngram.Enqueue(computedDS);
//3. Register the computed DS as an N-Gram sequence to the Predictor
Predictor.RegisterSequence(ngram.ToArray());
string[] ngramMinus1 = new string[NValue - 1];
Array.Copy(ngram.ToArray(), 1, ngramMinus1, 0, NValue - 1);
CrudBasedCollection.C5DataStructure predictedDS = GetC5DataStructure(Predictor.PredictNext(ngramMinus1));
isBusy = false;
registerCount++;
//For debugging or simulation purposes only, comment this line to improve performance
OnFeatureRegistered(new FeatureRegisteredEventArgs(datastructureGroup, elementSize, percentC, percentR, percentU, percentD, computedDS, currentDataStructure.ToString(), Predictor.PredictedProbabilty, predictedDS.ToString(), registerCount));//raise an event
bool reset = false;
DecisionMaker.MakeDecision(registerCount, currentDataStructure, predictedDS, Predictor.PredictedProbabilty, out reset);
if (reset)
{
registerCount = 0;
}
break;
}
}
private double[] EncodeNormalizeDataStructureGroup(DataStructureGroup datastructureGroup)
{
//encoding using 1-of-(N-1)
//ICollection,0
//ICollectionBag,1
//ICollectionSet,2
//IList,3
//IListBag,4
//IListSet,5
//THIS VALUES ARE STATIC AND SHOULD BE CHANGED ACCORDINGLY BASED ON THE THE VALUES FROM TRAINING DATASET
double[] icollection = new double[] { 1.0, 0.0, 0.0, 0.0, 0.0 };
double[] icollectionBag = new double[] { 0.0, 1.0, 0.0, 0.0, 0.0 };
double[] icollectionSet = new double[] { 0.0, 0.0, 1.0, 0.0, 0.0 };
double[] iList = new double[] { 0.0, 0.0, 0.0, 1.0, 0.0 };
double[] iListBag = new double[] { 0.0, 0.0, 0.0, 0.0, 1.0 };
double[] iListSet = new double[] { -1.0, -1.0, -1.0, -1.0, -1.0 };
if (datastructureGroup == DataStructureGroup.ICollection)
{
return(icollection);
}
else if (datastructureGroup == DataStructureGroup.ICollectionBag)
{
return(icollectionBag);
}
else if (datastructureGroup == DataStructureGroup.ICollectionSet)
{
return(icollectionSet);
}
else if (datastructureGroup == DataStructureGroup.IList)
{
return(iList);
}
else if (datastructureGroup == DataStructureGroup.IListBag)
{
return(iListBag);
}
else if (datastructureGroup == DataStructureGroup.IListSet)
{
return(iListSet);
}
else
{
return new double[] { 1.0, 1.0, 1.0, 1.0, 1.0 }
}; //unknown
}
private void SetDefaultDataStructure(DataStructureGroup interfaceAndSetBagProp)
{
C5DataStructure previousDS = base.CurrentDataStructure;
//this default setting is based on our prior research experiment
//please read our paper, "Predicting Data Structures for Energy Efficient Computing"
if (interfaceAndSetBagProp == DataStructureGroup.ICollection)
{
base.CurrentDataStructure = C5DataStructure.HashSet;
}
else if (interfaceAndSetBagProp == DataStructureGroup.ICollectionBag)
{
base.CurrentDataStructure = C5DataStructure.HashBag;
}
else if (interfaceAndSetBagProp == DataStructureGroup.ICollectionSet)
{
base.CurrentDataStructure = C5DataStructure.HashSet;
}
else if (interfaceAndSetBagProp == DataStructureGroup.IList)
{
base.CurrentDataStructure = C5DataStructure.HashedLinkedList;
}
else if (interfaceAndSetBagProp == DataStructureGroup.IListBag)
{
base.CurrentDataStructure = C5DataStructure.ArrayList;
}
else if (interfaceAndSetBagProp == DataStructureGroup.IListSet)
{
base.CurrentDataStructure = C5DataStructure.HashedLinkedList;
}
else
{
base.CurrentDataStructure = C5DataStructure.HashSet;
}
//This will make sure that the internal data structure is also changed with the same existing data in it
if (previousDS != base.CurrentDataStructure)
{
base.TransformTo(base.CurrentDataStructure);
}
}
