public void EvaluateIncrementalBatches(int batchSize)
{
//Randomize Filter
Randomize randomizeFilter = new Randomize();
randomizeFilter.setInputFormat(this.data);
//RemoveRange Filter
//number of classes
int numClasses = this.data.attribute(this.data.numAttributes() - 1).numValues();
Instances[] classInstances = new Instances[numClasses];
for (int i = 1; (i <= numClasses); i++)
{
//RemoveWithValues Filter
RemoveWithValues removeValuesFilter = new RemoveWithValues();
removeValuesFilter.setInputFormat(this.data);
// removeValuesFilter.set_AttributeIndex("last");
// removeValuesFilter.
removeValuesFilter.set_MatchMissingValues(false);
removeValuesFilter.set_NominalIndices("1-1");
classInstances[i] = Filter.useFilter(this.data, removeValuesFilter);
}
}
public void EvaluateIncrementalExamples()
{
//Calculate the number of increments for the training data based on the increment size
int numberIncrements = (int)Math.Ceiling((double)(this.numExamples * (DEFAULT_FOLDS - 1) / DEFAULT_FOLDS) / (double)TRAINING_INCRMENETS);
this.trainingSizeMatrix = new double[this.classCount, DEFAULT_FOLDS, numberIncrements];
for (int i = 0; (i < this.classCount); i++)
for (int j = 0; (j < DEFAULT_FOLDS); j++)
for (int k = 0; (k < numberIncrements); k++)
this.trainingSizeMatrix[i, j, k] = 0.0;
//Randomize the data
Randomize randomizeFilter = new Randomize();
randomizeFilter.setInputFormat(this.data);
Instances randomData = Filter.useFilter(this.data, randomizeFilter);
//Run incremental training data for each fold and store the results for each activity
for (int i = 1; (i <= DEFAULT_FOLDS); i++)
{
//Training folds filter
RemoveFolds trainingFoldsFilter = new RemoveFolds();
trainingFoldsFilter.set_NumFolds(DEFAULT_FOLDS);
trainingFoldsFilter.inputFormat(randomData);
trainingFoldsFilter.set_InvertSelection(true);
trainingFoldsFilter.set_Fold(i);
Instances alltraining = Filter.useFilter(randomData, trainingFoldsFilter);
RemoveFolds testFoldsFilter = new RemoveFolds();
testFoldsFilter.set_NumFolds(DEFAULT_FOLDS);
testFoldsFilter.inputFormat(randomData);
testFoldsFilter.set_InvertSelection(false);
testFoldsFilter.set_Fold(i);
Instances test = Filter.useFilter(randomData, testFoldsFilter);
for (int j = 1; (j <= numberIncrements); j++)
{
//Range Filter
RemoveRange rangeFilter = new RemoveRange();
rangeFilter.setInputFormat(alltraining);
int first = 1;
int last = j * TRAINING_INCRMENETS;
if (last > (alltraining.m_Instances.size()))
last = alltraining.m_Instances.size();
string range = first.ToString() + "-" + last.ToString();
rangeFilter.set_InstancesIndices(range);
rangeFilter.set_InvertSelection(true);
Instances training = Filter.useFilter(alltraining, rangeFilter);
//ready for training and testing
J48 tree = new J48(); // new instance of tree
tree.set_MinNumObj(10);
tree.set_ConfidenceFactor((float)0.25);
tree.buildClassifier(training); // build classifier
Evaluation eval = new Evaluation(training);
eval.evaluateModel(tree, test);
//store the results for each activity
for (int k = 0; (k < this.classCount); k++)
{
double tpRate = eval.truePositiveRate(k);
trainingSizeMatrix[k, i - 1, j - 1] = +eval.truePositiveRate(k);
}
}
}
TextWriter tw = new StreamWriter("evaluation.txt");
for (int i = 0; (i < this.classCount); i++)
{
string line = randomData.attribute(this.data.numAttributes() - 1).value_Renamed(i);
for (int k = 0; (k < numberIncrements); k++)
{
double percentage = 0.0;
for (int j = 0; (j < DEFAULT_FOLDS); j++)
percentage += this.trainingSizeMatrix[i, j, k];
percentage /= DEFAULT_FOLDS;
percentage *= 100;
line += "," + percentage.ToString("0.00");
}
tw.WriteLine(line);
}
tw.Close();
for (int i = 0; (i < this.classCount); i++)
{
string activity = randomData.attribute(this.data.numAttributes() - 1).value_Renamed(i);
tw = new StreamWriter("evaluation-" + activity + ".txt");
for (int j = 0; (j < DEFAULT_FOLDS); j++)
{
string line = j.ToString();
for (int k = 0; (k < numberIncrements); k++)
{
double percentage = this.trainingSizeMatrix[i, j, k];
percentage /= DEFAULT_FOLDS;
percentage *= 100;
line += "\t" + percentage.ToString("0.00");
}
tw.WriteLine(line);
}
tw.Close();
}
}
