public static double TestModel(ModelLR model, DataLR testData)
{
double acc = 0;
try
{
acc = (double)PredictiveAccuracy(testData.IndepVar, testData.DepVar, model.Betas) / 100.0; // percent of data cases correctly predicted in the test data set.
}
catch (Exception ex)
{
Console.WriteLine("Fatal in TestModel: " + ex.Message);
}
return(acc);
}
//Le fichier doit contenir pour chaque lignes les valeurs des indépendants suivie de la dépendante
public static ModelLR ComputeModel(DataLR datas)
{
ModelLR model = new ModelLR();
try
{
int maxIterations = 25;
double epsilon = 0.01; // stop if all new beta values change less than epsilon (algorithm has converged?)
double jumpFactor = 1000.0; // stop if any new beta jumps too much (algorithm spinning out of control?)
model.Betas = ComputeBestBeta(datas.IndepVar, datas.DepVar, maxIterations, epsilon, jumpFactor); // computing the beta parameters is synonymous with 'training'
}
catch (Exception ex)
{
Console.WriteLine("Fatal in ComputeBestBeta: " + ex.Message);
}
return(model);
}
