public List <double> testSMOUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel);
java.util.ArrayList attributeList = new java.util.ArrayList();
foreach (string attribute in attributeArray)
{
weka.core.Attribute newAttribute = new weka.core.Attribute(attribute);
attributeList.Add(newAttribute);
}
attributeList.add(classHeaderName);
weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0);
data.setClassIndex(data.numAttributes() - 1);
// Set instance's values for the attributes
weka.core.Instance inst_co = new DenseInstance(data.numAttributes());
for (int i = 0; i < data.numAttributes() - 1; i++)
{
inst_co.setValue(i, Math.Round(dataValues.ElementAt(i), 5));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
weka.core.Instance currentInst = data.get(0);
int j = 0;
//foreach (float value in dataValues)
//{
// // double roundedValue = Math.Round(value);
// //var rounded = Math.Floor(value * 100) / 100;
// if (array.ElementAt(j) != value)
// {
// System.Console.WriteLine("Masla occur");
// }
// j++;
//}
//double predictedClass = cl.classifyInstance(data.get(0));
weka.classifiers.functions.SMO clRead = new weka.classifiers.functions.SMO();
try
{
java.io.File path = new java.io.File("/models/");
clRead = loadSMOModel(modelName, path);
}
catch (Exception e)
{
//string p1 = Assembly.GetExecutingAssembly().Location;
string ClassifierName = Path.GetFileName(Path.GetFileName(modelName));
string Path1 = HostingEnvironment.MapPath(@"~//libs//models//" + ClassifierName);
//string Path1 = HostingEnvironment.MapPath(@"~//libs//models//FusionCustomized.model");
clRead = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(modelName);
}
// weka.classifiers.functions.SMO clRead = loadSMOModel(modelName, path);
clRead.setBatchSize("100");
clRead.setCalibrator(new weka.classifiers.functions.Logistic());
clRead.setKernel(new weka.classifiers.functions.supportVector.PolyKernel());
clRead.setEpsilon(1.02E-12);
clRead.setC(1.0);
clRead.setDebug(false);
clRead.setChecksTurnedOff(false);
clRead.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER));
double classValue = clRead.classifyInstance(data.get(0));
double[] predictionDistribution = clRead.distributionForInstance(data.get(0));
//for (int predictionDistributionIndex = 0;
// predictionDistributionIndex < predictionDistribution.Count();
// predictionDistributionIndex++)
//{
// string classValueString1 = classLabel.get(predictionDistributionIndex).ToString();
// double prob= predictionDistribution[predictionDistributionIndex]*100;
// System.Console.WriteLine(classValueString1 + ":" + prob);
//}
List <double> prediction = new List <double>();
prediction.Add(classValue);
//prediction.AddRange(predictionDistribution);
return(prediction);
}
public void trainSMOUsingWeka(string wekaFile, string modelName)
{
try
{
weka.core.converters.CSVLoader csvLoader = new weka.core.converters.CSVLoader();
csvLoader.setSource(new java.io.File(wekaFile));
weka.core.Instances insts = csvLoader.getDataSet();
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(wekaFile));
insts.setClassIndex(insts.numAttributes() - 1);
cl = new weka.classifiers.functions.SMO();
cl.setBatchSize("100");
cl.setCalibrator(new weka.classifiers.functions.Logistic());
cl.setKernel(new weka.classifiers.functions.supportVector.PolyKernel());
cl.setEpsilon(1.02E-12);
cl.setC(1.0);
cl.setDebug(false);
cl.setChecksTurnedOff(false);
cl.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER));
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
// weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
//myRandom.setInputFormat(insts);
// insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
java.io.File path = new java.io.File("/models/");
cl.buildClassifier(train);
saveModel(cl, modelName, path);
#region test whole set
int numCorrect = 0;
for (int i = 0; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
if (i == 12)
{
array = new List <float>();
foreach (float value in currentInst.toDoubleArray())
{
array.Add(value);
}
}
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
#endregion
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}