public List <double> testMLPUsingWeka(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, dataValues.ElementAt(i));
}
inst_co.setValue(classHeaderName, defaultclass);
data.add(inst_co);
java.io.File path = new java.io.File("/models/");
weka.classifiers.functions.MultilayerPerceptron clRead = loadModel(modelName, path);
clRead.setHiddenLayers(hiddelLayers.ToString());
clRead.setLearningRate(learningRate);
clRead.setMomentum(momentum);
clRead.setNumDecimalPlaces(decimalPlaces);
clRead.setTrainingTime(trainingTime);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(data);
data = weka.filters.Filter.useFilter(data, myRandom);
double classValue = clRead.classifyInstance(data.get(0));
double[] predictionDistribution = clRead.distributionForInstance(data.get(0));
List <double> predictionDistributions = new List <double>();
for (int predictionDistributionIndex = 0;
predictionDistributionIndex < predictionDistribution.Count();
predictionDistributionIndex++)
{
string classValueString1 = classLabel.get(predictionDistributionIndex).ToString();
double prob = predictionDistribution[predictionDistributionIndex] * 100;
predictionDistributions.Add(prob);
}
List <double> prediction = new List <double>();
prediction.Add(classValue);
prediction.AddRange(predictionDistributions);
return(prediction);
}
