public static Instances RemoveClassAttribute(Instances origInstances)
{
var filter = new weka.filters.unsupervised.attribute.Remove();
filter.setOptions(weka.core.Utils.splitOptions(string.Format("-R {0}", origInstances.classIndex() + 1)));
filter.setInputFormat(origInstances);
Instances newInstances = weka.filters.Filter.useFilter(origInstances, filter);
return(newInstances);
}
//uses a 'Remove' filter
public static Instances useRemoveFilter(Instances data, int[] topIGFeatures, bool invert)
{
weka.filters.unsupervised.attribute.Remove filter = new weka.filters.unsupervised.attribute.Remove();
filter.setAttributeIndicesArray(topIGFeatures);
filter.setInvertSelection(invert);
filter.setInputFormat(data);
Instances newData = Filter.useFilter(data, filter);
return(newData);
}
//public object Clone() {
// MemoryStream ms = new MemoryStream(500000000);
// System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bf =
// new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(null,
// new System.Runtime.Serialization.StreamingContext(System.Runtime.Serialization.StreamingContextStates.Clone));
// bf.Serialize(ms, this);
// ms.Seek(0, SeekOrigin.Begin);
// object obj = bf.Deserialize(ms);
// ms.Close();
// return obj;
//}
public void train(bool LoadingFromFile)
{
if (!LoadingFromFile) {
DisplayMessage("Begin training on Efigi galaxies...");
Console.Write("Begin training on Efigi galaxies...");
}
else {
DisplayImage(0);
DisplayMessage("Load from file...");
Console.Write("Load from file...");
frV = new GeneralMatrix(ReadFVMatrix(0));
fgV = new GeneralMatrix(ReadFVMatrix(1));
fbV = new GeneralMatrix(ReadFVMatrix(2));
}
weka.classifiers.trees.M5P tree = new weka.classifiers.trees.M5P();
String[] options = new String[1];
weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource(OutputDir + "Results/" + "resultsGalaxy.arff");
data = source.getDataSet();
if (data == null) {
DisplayMessage("Cannot load from file.");
throw new Exception("Arff File not valid");
}
data.setClassIndex(0);
tree.buildClassifier(data);
StreamWriter output = new StreamWriter(OutputDir + "Results/" + "classification.txt");
rmse = 0.0;
int classifiedCount = 0;
weka.filters.unsupervised.attribute.Remove rm = new weka.filters.unsupervised.attribute.Remove();
rm.setInputFormat(data);
fc = new FilteredClassifier();
fc.setFilter(rm);
fc.setClassifier(tree);
for (int i = 0; i < data.numInstances(); i++) {
int classPrediction = (int)Math.Round(fc.classifyInstance(data.instance(i)));
if (classPrediction < -6) {
classPrediction = -6;
}
else if (classPrediction > 11) {
classPrediction = 11;
}
int actualClass = (int)Math.Round(data.instance(i).classValue());
int error = Math.Abs(classPrediction - actualClass);
rmse += error * error;
classifiedCount++;
output.WriteLine("\n" + classPrediction + ", " + error);
if (i % 10 == 0 && !LoadingFromFile)
DisplayImage(i);
}
rmse = Math.Sqrt(rmse / classifiedCount);
output.WriteLine("\nRMSE: " + rmse);
DisplayMessage("RMSE: " + rmse);
output.Flush();
output.Close();
output.Dispose();
readyToClassify = true;
Console.WriteLine("Finished training on Efigi galaxies; RMSE: " + rmse.ToString());
}
