public static string WEKA_GETMLP(weka.core.Instances insts)
{
string THEOUTPUT = " ";
try
{
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.functions.MultilayerPerceptron mlp = new weka.classifiers.functions.MultilayerPerceptron();
//SETTING PARAMETERS
weka.core.Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H 3");
mlp.buildClassifier(insts);
THEOUTPUT = mlp.ToString();
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
return(THEOUTPUT);
//new Program().Method3
}
public static void CalculateSuccessForAnn(weka.core.Instances originalInsts)
{
try
{
var form = Form.ActiveForm as Form1;
form.successPrcAnn.Text = "Training...";
form.successRtAnn.Text = "../" + testSize;
weka.core.Instances insts = originalInsts;
// Pre-process
insts = ConvertNominalToNumeric(insts);
insts = Normalize(insts);
// Classify
weka.classifiers.Classifier cl = new weka.classifiers.functions.MultilayerPerceptron();
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
double percentage = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
percentage = (double)numCorrect / (double)testSize * 100.0;
form.successRtAnn.Text = numCorrect + "/" + testSize;
form.successPrcAnn.Text = String.Format("{0:0.00}", percentage) + "%";
}
succesRates.Add(Classifier.ANN, percentage);
classifiers.Add(Classifier.ANN, cl);
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
MessageBox.Show(ex.ToString(), "Error for Neural Network", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
catch (Exception)
{
MessageBox.Show("Error for Neural Network", "Error for Neural Network", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public void trainMachineForEmotionUsingWeka(string wekaFile, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000)
{
//"C:\\Users\\Gulraiz\\Desktop\\Genereted2.arff" "MLP"
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(wekaFile));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.functions.MultilayerPerceptron cl;
cl = new weka.classifiers.functions.MultilayerPerceptron();
cl.setHiddenLayers(hiddelLayers.ToString());
cl.setLearningRate(learningRate);
cl.setMomentum(momentum);
cl.setNumDecimalPlaces(decimalPlaces);
cl.setTrainingTime(trainingTime);
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 = trainSize; i < insts.numInstances(); i++)
//{
// weka.core.Instance currentInst = insts.instance(i);
// 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();
}
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
String results = "";
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances data = new weka.core.Instances(buffReader); //source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
weka.classifiers.Classifier cl;
for (int i = 2; i <= data.numInstances(); i++)
{
//cl = new weka.classifiers.bayes.NaiveBayes();
//cl = new weka.classifiers.trees.J48();
//cl = new weka.classifiers.lazy.IB1();
cl = new weka.classifiers.functions.MultilayerPerceptron();
((weka.classifiers.functions.MultilayerPerceptron)cl).setHiddenLayers("12");
//cl = new weka.classifiers.trees.RandomForest();
weka.core.Instances subset = new weka.core.Instances(data, 0, i);
cl.buildClassifier(subset);
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(subset);
eval.crossValidateModel(cl, subset, subset.numInstances(), new java.util.Random(1));
results = results + eval.pctCorrect(); // For accuracy measurement
/* For Mathews Correlation Coefficient */
//double TP = eval.numTruePositives(1);
//double FP = eval.numFalsePositives(1);
//double TN = eval.numTrueNegatives(1);
//double FN = eval.numFalseNegatives(1);
//double correlationCoeff = ((TP*TN)-(FP*FN))/Math.Sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
//results = results + correlationCoeff;
if (i != data.numInstances())
{
results = results + ", ";
}
if (i == data.numInstances())
{
Debug.Log("Player: " + playerID + ", Num Maps: " + data.numInstances() + ", AUC: " + eval.areaUnderROC(1));
}
}
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
// Write values to file for a matlab read
// For accuracy
//StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldValidations_RandomForest.txt");
StreamWriter writer = new StreamWriter("DataForMatlab/" + playerID + "_LOOCrossFold_NeuralNet.txt");
//StreamWriter writer = new StreamWriter("DataForMatlab/"+playerID+"_CrossFoldCorrCoeff.txt"); // For mathews cc
writer.WriteLine(results);
writer.Close();
Debug.Log(playerID + " has been written to file");
}
