/// <summary>
/// Create weka file of wrinkles and distances
/// </summary>
public void createWekaFileDistanceAndWrinkleBased(List <EmotionDataModel> emotionsDistances)
{
List <string> features = new List <string>();
foreach (EmotionDataModel obj in emotionsDistances)
{
string wekaData = obj.LEB1_CPLE1
+ "," + obj.LEB2_CPLE2
+ "," + obj.REB1_CPRE1
+ "," + obj.REB2_CPRE2
+ "," + obj.OPEN_LE1
+ "," + obj.OPEN_LE2
+ "," + obj.OPEN_RE1
+ "," + obj.OPEN_RE2
+ "," + obj.EXP_MO
+ "," + obj.OPEN_MO1
+ "," + obj.OPEN_MO2
+ "," + obj.OPEN_MO3
+ "," + obj.NS_LPLIP
+ "," + obj.NS_RPLIP
+ "," + obj.betweenEyesWrinkles
+ "," + obj.noseWrinkles
+ "," + obj.Label;
features.Add(wekaData);
}
// string[] features = { "1,4.5,34,apple", "2,56,3.4,banana", "3.5,6.5,34,orange" };
string[] lOD = { "LEB1_CPLE1 numeric", "LEB2_CPLE2 numeric", "REB1_CPRE1 numeric", "REB2_CPRE2 numeric", "OPEN_LE1 numeric", "OPEN_LE2 numeric", "OPEN_RE1 numeric", "OPEN_RE2 numeric", "EXP_MO numeric", "OPEN_MO1 numeric", "OPEN_MO2 numeric", "OPEN_MO3 numeric", "NS_LPLIP numeric", "NS_RPLIP numeric", "betweenEyesWrinkles numeric", "noseWrinkles numeric" };
List <string> attributes = lOD.ToList <string>();
string classHeader = "label {anger, smile, sad, surprise, neutral, fear, disgust}";
attributes.Add(classHeader);
MLMain.getInstance().createWekaFile("%% weka file for methodology2 emotion", "Emotion", attributes, features, (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Arff));
}
/// <summary>
/// Train machiene based on wrinkles and distances
/// </summary>
/// <param name="emotionDistances">Distances and wrinkles features to train emotion</param>
public void trainSystemForEmotionDistanceAndWrinkleBased(List <EmotionTrainingMLModel> emotionDistances)
{
db.saveToDbDistanceAndWrinkleBased(EDRMLUtil.getInstance().ConvertToDataModels(emotionDistances));
List <EmotionDataModel> emotionsDistances = db.getDataFromDatabaseDistanceAndWrinkleBased();
createWekaFileDistanceAndWrinkleBased(emotionsDistances);
MLMain.getInstance().trainMachineForEmotionUsingWeka((string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Arff), (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Model));
}
/// <summary>
/// Get calculated emotion after applying machiene learning algorithm based on wrinkles and distances
/// </summary>
/// <param name="emotionDistances">Distances and wrinkles features to test emotion probilities</param>
/// <returns>Returns a array probilities of emotion </returns>
public List <double> getCalculatedEmotionsProbilitiesDistanceAndWrinkleBased(EmotionTrainingMLModel emotionDistances)
{
string[] attributeArray = { "LEB1_CPLE1", "LEB2_CPLE2", "REB1_CPRE1", "REB2_CPRE2", "OPEN_LE1", "OPEN_LE2", "OPEN_RE1", "OPEN_RE2", "EXP_MO", "OPEN_MO1", "OPEN_MO2", "OPEN_MO3", "NS_LPLIP", "NS_RPLIP", "betweenEyesWrinkles", "noseWrinkles" };
string[] classNames = { "anger", "smile", "sad", "surprise", "neutral", "fear", "disgust" };
double[] dataValues = { emotionDistances.LEB1_CPLE1, emotionDistances.LEB2_CPLE2, emotionDistances.REB1_CPRE1, emotionDistances.REB2_CPRE2, emotionDistances.OPEN_LE1, emotionDistances.OPEN_LE2, emotionDistances.OPEN_RE1, emotionDistances.OPEN_RE2, emotionDistances.EXP_MO, emotionDistances.OPEN_MO1, emotionDistances.OPEN_MO2, emotionDistances.OPEN_MO3, emotionDistances.NS_LPLIP, emotionDistances.NS_RPLIP, emotionDistances.betweenEyesWrinkles, emotionDistances.noseWrinkles };
string classHeader = "label";
string defaultclass = "neutral";
return(MLMain.getInstance().testMLPPredictionsUsingWeka(attributeArray, classNames, dataValues, classHeader, defaultclass, (string)ConstantsLoader.getInstance().getValue(EnumConstant.emotionMethod2Model)));
}