package ca.pfv.spmf.test;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.io.UnsupportedEncodingException;
import java.net.URL;
import java.util.ArrayList;
import java.util.Scanner;

import ca.pfv.spmf.algorithms.sequenceprediction.ipredict.database.Item;
import ca.pfv.spmf.algorithms.sequenceprediction.ipredict.database.Sequence;
import ca.pfv.spmf.algorithms.sequenceprediction.ipredict.database.SequenceDatabase;
import ca.pfv.spmf.algorithms.sequenceprediction.ipredict.database.SequenceStatsGenerator;
import ca.pfv.spmf.algorithms.sequenceprediction.ipredict.predictor.CPT.CPTPlus.CPTPlusPredictor;

/**
 * Example of how to use the CPT+ sequence prediction model in the source code.
 * Copyright 2015.
 */
public class MainTestCPTPlus {

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

		// Load the set of training sequences
		String inputPath = fileToPath("DATA20170214.txt");
		SequenceDatabase trainingSet = new SequenceDatabase();
		trainingSet.loadFileSPMFFormat(inputPath, Integer.MAX_VALUE, 0, Integer.MAX_VALUE);

		// Print the training sequences to the console
		System.out.println("--- Training sequences ---");
		for(Sequence sequence : trainingSet.getSequences()) {
			System.out.println(sequence.toString());
		}
		System.out.println();

		// Print statistics about the training sequences
		SequenceStatsGenerator.prinStats(trainingSet, " training sequences ");

		// The following line is to set optional parameters for the prediction model.
		// We want to
		// activate the CCF and CBS strategies which generally improves its performance (see paper)
		String optionalParameters = "CCF:true CBS:true CCFmin:1 CCFmax:6 CCFsup:2 splitMethod:0 minPredictionRatio:1.0 noiseRatio:1.0";
		// 			List<List<Item>> itemsets = finder.findFrequentItemsets(trainingSequences, parameters.paramInt("CCFmin"), parameters.paramInt("CCFmax"), parameters.paramInt("CCFsup"));

		// Train the prediction model
		CPTPlusPredictor predictionModel = new CPTPlusPredictor("CPT+", optionalParameters);
		predictionModel.Train(trainingSet.getSequences());

		// Now we will make a prediction.
		// We want to predict what would occur after the sequence <1, 3>.
		// We first create the sequence

        FileReader fr = new FileReader("D:\\Test.txt");
        BufferedReader br = new BufferedReader(fr);
        String line,Teststring;
        String[] TestArray= new String[500000];
        String[][] MainTestArray = new String[30000][8];
        ArrayList myList = new ArrayList();
        int Q=0;
        int SequenceT=0,member=0;
        while((line = br.readLine())!=null)
        {
           //br.readLine()是指讀取txt檔的每一行資料,把讀到的資料存到line
           //再將line丟給Teststring去儲存
             Teststring = line;

          //因為我這個test檔的資料格式是-->一行有N個字串，用空白隔開，
          //tempstring.split("\\s") 會依照空白鍵來切割,剛好切三個,所以這邊我的tempArray的大小才會宣告3
             TestArray = Teststring.split("\\s");

             //這邊就是按照順序,一行一行的儲存到動態陣列裡面
              for(Q=0;Q< TestArray.length;Q++)
              {
            	  if(TestArray[Q].equals(-2))
            	  {
            		  SequenceT = SequenceT + 1;
            		  member = 0;
            	  }
                  else if(!TestArray[Q].equals(-1))
                  {
                	  MainTestArray[SequenceT][member] = TestArray[Q];
                	  member = member+1;
                  }
              }
        }



		int i;
        int Test;
        int r=0;
        for(r=0;r<MainTestArray[SequenceT].length;r++)
        {
        	String OutputTest[] = new String[30000] ;
        	for (i = 1; i <= 30000; i++)
        		{
				Scanner cin = new Scanner(MainTestArray[r].length);
				Test = 2;
				while(Test != 1)
					{
					Sequence sequence = new Sequence(0);
					int n = cin.nextInt();
					for(int x = 0 ; x<n ; x++)
					{
						sequence.addItem(new Item(cin.nextInt()));
					}
					Sequence thePredicition = predictionModel.Predict(sequence);
					//  System.out.println("Prediction" + thePredicition);
					String X = String.valueOf(thePredicition);
					Test = 1;
					OutputTest[i-1] = X.substring(9,11);
				}
				//  System.out.println(OutputTest[i-1]);
				//break;

        		}

        	//  \r\n next line symbol
        	FileWriter fw = new FileWriter("D:\\OutputTest.txt");
        	for(int count=1 ; count <= 30000 ; count++)
        	{
        		fw.write(OutputTest[i-1] + "\r\n");
        	}
        	fw.close();
        }

	}

	public static String fileToPath(String filename) throws UnsupportedEncodingException{
		URL url = MainTestCPTPlus.class.getResource(filename);
		 return java.net.URLDecoder.decode(url.getPath(),"UTF-8");
	}
}