source: anac2020/TheDiceHaggler/src/main/java/geniusweb/bagga/dicehaggler/Dl4jUtils.java@ 43

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;
        }

}