public void Classification_Train(double[,] train_docrule, int[] label, string algorithm)
{
string classmodelpath;
int attrSize = eclatlitems.Count;
int attrSizeTest = eclatlitems.Count;
// Specify the input variables
DecisionVariable[] variables = new DecisionVariable[attrSize];
for (int i = 0; i < attrSize; i++)
{
variables[i] = new DecisionVariable((i + 1).ToString(), DecisionVariableKind.Discrete);
}
if (algorithm == "Tree")
{
classmodelpath = algorithm + ".model";
//RandomForest tree2 = new RandomForest(2, variables);
DecisionTree tree = new DecisionTree(variables, 2);
C45Learning teacher = new C45Learning(tree);
var model = teacher.Learn(train_docrule.ToJagged(), label);
//save model
teacher.Save(Path.Combine("", classmodelpath));
}
if (algorithm == "SVM")
{
classmodelpath = algorithm + ".model";
var learn = new SequentialMinimalOptimization()
{
UseComplexityHeuristic = true,
UseKernelEstimation = false
};
SupportVectorMachine teacher = learn.Learn(train_docrule.ToJagged(), label);
//save model
teacher.Save(Path.Combine("", classmodelpath));
}
if (algorithm == "Logistic")
{
classmodelpath = algorithm + ".model";
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 1, // maximum number of iterations to perform
Regularization = 0
};
LogisticRegression teacher = learner.Learn(train_docrule.ToJagged(), label);
teacher.Save(Path.Combine("", classmodelpath));
}
if (algorithm == "GA")
{
weights_ga_matlab();
}
}
