DiagnosingDouglasPouchObliteration.java

package org.deeplearning4j.eunice;


import org.apache.commons.math3.random.MersenneTwister;
import org.apache.commons.math3.random.RandomGenerator;
import org.deeplearning4j.datasets.fetchers.BaseDataFetcher;
import org.deeplearning4j.datasets.vectorizer.ImageVectorizer;
import org.deeplearning4j.datasets.vectorizer.Vectorizer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.util.ArrayUtil;
import org.deeplearning4j.datasets.vectorizer.ImageVectorizer;
import org.deeplearning4j.datasets.vectorizer.Vectorizer;
import org.deeplearning4j.distributions.Distributions;
import org.deeplearning4j.nn.WeightInit;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.nd4j.linalg.api.activation.Activations;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.deeplearning4j.datasets.fetchers.*;


import java.io.File;
import java.util.ArrayList;
import java.util.List;

/**
 *
 * @ (#) DiagnosingDouglasPouchObliteration.java 2015-02-17
 *
 * Copyright (c) 2015 Keiron-Teilo O'Shea
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public Licence as
 * published by the Free Software Foundation, either version 3 of the
 * License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with this program. If not, see <http://www.gnu.org/licenses/>.
 *
 */


public class DiagnosingDouglasPouchObliteration  extends BaseDataFetcher {

    // Initalising the logger interface, the main user entry point.
//    private static Logger log = LoggerFactory.getLogger(DiagnosingDouglasPouchObliteration.class);

    public static void main(String args[]) {


}


    @Override
    public void fetch(int i) {

        // Temporary solution, a GUI would be awesome for this.
        File rootPath = new File ("/home/momo/dissertation/data set/videos of positive sliding sign from reproducibility study");
        List<String> fileLabels = new ArrayList<String>();
//
//        RandomGenerator numberGenerator = new MersenneTwister(123);
//
//        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
//                .iterations(42)
//                .weightInit(WeightInit.DISTRIBUTION).dist(Distributions.normal(numberGenerator,1e-3)).constrainGradientToUnitNorm(false)
//                .lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).activationFunction(Activations.tanh())
//                .learningRate(1e-3f).nIn(4).nOut(3).build();

        if(!rootPath.exists()) {
            System.out.println("The files do not exist!");
        }

        for(File file : rootPath.listFiles()) {
            if(file.isDirectory())
                fileLabels.add(file.getName());
        }


        File rootDirectory = new File(fileLabels.toString()); // This that's wrong too.


        for (String s : fileLabels) {
            System.out.println(s);
        }

            List<DataSet> dataFilesVectorised = new ArrayList<>();
            for(File f : rootDirectory.listFiles()) { //rootDirectory = root path on line 59.
                if(f.isFile()) {
                    System.out.println(f.toString());
                    Vectorizer vectorizer = new ImageVectorizer(
                            f, // Image - the input image to convert
                            fileLabels.size(), // The number of labels
                            fileLabels.indexOf(f.getParentFile().getName())); // The label of these images
                    DataSet data = vectorizer.vectorize();
                    dataFilesVectorised.add(data);
                }
                initializeCurrFromList(dataFilesVectorised);
            }
            System.out.println(dataFilesVectorised.toString());
        }
    }
}