package geniusweb.bagga.dicehaggler;

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.LineNumberReader;
import java.util.stream.Collectors;

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ResourceUtils;

public class Dl4jUtils {

	public DataNormalization getTrainedNormalization(String fileName) throws IOException, InterruptedException {
		final int numLinesToSkip = 1;
		final char delimiter = ',';
		//final File excelFile = new File(fileName);
//		ClassLoader classLoader = getClass().getClassLoader();
//		//InputStream resourceFile = classLoader.getResourceAsStream(fileName);
//		//Properties config = new Properties();
//		//config.load(resourceFile);
//		//String resourceFileName = config.getProperty("name");
//		//String resourceFileName = classLoader.getResource(fileName).getFile();
//		String resourceFileName;
//		try (InputStream inputStream = getClass().getResourceAsStream("/"+fileName);
//			    BufferedReader reader = new BufferedReader(new InputStreamReader(inputStream))) {
//			    resourceFileName = reader.lines()
//			      .collect(Collectors.joining(System.lineSeparator()));
//			}
//		final File excelFile;
////		if(resourceFileName == null) {
////			System.out.println("inside the if consition:");
////			excelFile = ResourceUtils.getFile(fileName);
////		}
////		else
////		{
////			System.out.println("inside else condition");
//			excelFile = new File(resourceFileName);
////		}
		
		String completeFile;
		InputStream aa = Thread.currentThread().getContextClassLoader().getResourceAsStream(fileName);
		BufferedReader br = new BufferedReader(new InputStreamReader(aa));
		completeFile = br.lines().collect(Collectors.joining(System.lineSeparator()));
//		final File excelFile;
//
//		excelFile = new File(completeFile);
		File excelFile = File.createTempFile("tempfile", ".csv");
		 BufferedWriter bw = new BufferedWriter(new FileWriter(excelFile));
	    bw.write(completeFile);
	    bw.close();

		final RecordReader recordReader = new CSVRecordReader(numLinesToSkip, delimiter);
		recordReader.initialize(new FileSplit(excelFile));
		System.out.println(recordReader.getLabels());
		final int batchSize = getRowCountFromExcel(excelFile);
		final RecordReaderDataSetIterator iterator = new RecordReaderDataSetIterator(recordReader, batchSize - 1);
		iterator.setCollectMetaData(true); // Instruct the iterator to collect metadata, and store
		// it in the DataSet objects
		final DataSet allData = iterator.next();

		final DataNormalization normalizer = new NormalizerStandardize();
		normalizer.fit(allData); // Collect the statistics (mean/stdev) from the training data. This
		// does not modify the input data
		// Apply normalization to the test data. This is using statistics calculated from the
		// *training* set
		return normalizer;
		// allData.shuffle(123)
	}

	private int getRowCountFromExcel(File file) throws IOException {
		int linenumber = 0;
		if (file.exists()) {
			final FileReader fr = new FileReader(file);
			final LineNumberReader lnr = new LineNumberReader(fr);

			while (lnr.readLine() != null) {
				linenumber++;
			}
	//		System.out.println("Total number of lines : " + linenumber);
			lnr.close();

		} else {
			System.out.println("File does not exist!");
		}
		return linenumber;
	}

	public static INDArray getInput(double time, int totalNumberOfPossibleBids, int numberOfIssues, int givenNumberOfbids,
			double recentlyReceivedBidUtility, double opponentBestBidUtility, double averageOpponentUtility,
			double standardDevUtility) {
		final INDArray features = Nd4j.zeros(8);
		features.putScalar(new int[] { 0 }, time); 
		features.putScalar(new int[] { 1 }, totalNumberOfPossibleBids);
		features.putScalar(new int[] { 2 }, numberOfIssues); 
		features.putScalar(new int[] { 3 }, givenNumberOfbids);
		features.putScalar(new int[] { 4 }, recentlyReceivedBidUtility); 
		features.putScalar(new int[] { 5 }, opponentBestBidUtility);
		features.putScalar(new int[] { 6 }, averageOpponentUtility); 
		features.putScalar(new int[] { 7 }, standardDevUtility); 
		return features;
	}

}