/* -------------------------------------------------------------------------- */
/*                                                                            */
/*           D E C I S S I O N   T R E E   A P P L I C A T I O N              */
/*                                (R A N D O M)                               */
/*                                                                            */
/*                                Frans Coenen                                */
/*                                                                            */
/*                         Thursday 8th November 2007                         */
/*                    Updates: Wednesday 9 Februaruy 2010                     */
/*                                                                            */
/* -------------------------------------------------------------------------- */

// Dept Computer Science, University of Liverpool

//import lucsKDD_ARM.*;
import java.io.*;

// Decision tree application class using frequency of attribute occurance as the 
// "splitting criteria" (so not really random!).

class DecTreeRandomApp {

    // ------------------- FIELDS ------------------------

    // Mone

    // ---------------- CONSTRUCTORS ---------------------

    // None

    // ------------------ METHODS ------------------------

    /* MAIN */

    public static void main(String[] args) throws IOException {

		// Create instance of class decisionTree
        DecTreeRandom newDecisionTree = new DecTreeRandom(args);

		// Read data to be mined from file
		newDecisionTree.inputDataSet();

        // Output opportunity
		//newDecisionTree.outputDataArray();

		// Process Data
		double time1 = (double) System.currentTimeMillis();
        newDecisionTree.startClassification();
		newDecisionTree.outputDuration(time1,
                                         (double) System.currentTimeMillis());

		// Output
		//newDecisionTree.outputNumClasses();
		//newDecisionTree.outputTestDataArray();
		//newDecisionTree.outputDataArray();
		//newDecisionTree.outputDecTree();
		//newDecisionTree.outputRulesWithDefault();
        }
    }