diff --git a/main.cpp b/main.cpp
index 7968f70..991231f 100644
--- a/main.cpp
+++ b/main.cpp
@@ -2,11 +2,18 @@
 
 #include "svm_context.h"
 
-
+void xor_test();
 int main(int argsc, char** args) {
+
+	xor_test();
+	getchar();
+	return 0;
+}
+void xor_test() {
 	// XOR test
-	
-	// Creating SVM model
+
+	// create the matrix of xor truth table
+	// inputs:
 	std::vector<std::vector<double>> attribute(4, std::vector<double>(2));
 	attribute[0][0] = 0.0;
 	attribute[0][1] = 0.0;
@@ -17,18 +24,23 @@ int main(int argsc, char** args) {
 	attribute[3][0] = 1.0;
 	attribute[3][1] = 1.0;
 
+	// outputs:
 	std::vector<int> label(4);
 	label[0] = 0;
 	label[1] = 1;
 	label[2] = 1;
 	label[3] = 0;
 
-	// initialize svm object with default settings
+	// initialize svm object with default settings (edit the svm_context::param to change any parameters)
+	// ideally those settings (especially the kernel parameters) should come from svm training using libsvm library
+
 	svm_context _svm;
 	if (_svm.init(attribute, label)) {
-			LIB_SVM::svm_model *model = _svm.generate_model();
 
+		// create svm model using default settings
+		LIB_SVM::svm_model *model = _svm.generate_model();
 
+		//testing the resulting model with testing node
 		std::vector<double>sample(2);
 		sample[0] = 1;
 		sample[1] = 0;
@@ -38,6 +50,11 @@ int main(int argsc, char** args) {
 
 		_svm.predict_probability(sample, prediction, probability);
 		_svm.predict(sample, prediction);
+
+		//display the results to standard output
+		// libsvm creates probability value for each label and assigns the prediction
+		// to the label with maximum probability
+
 		std::cout
 			<< "\n***********************************\n"
 			<< "prediction: " << prediction
@@ -46,8 +63,4 @@ int main(int argsc, char** args) {
 	}
 	else
 		std::cout << "error in initializing svm" << std::endl;
-	_svm.release();
-
-	getchar();
-	return 0;
 }
\ No newline at end of file