/* *****************************************************************************
 * Copyright (c) 2020 Konduit K.K.
 * Copyright (c) 2015-2019 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://www.apache.org/licenses/LICENSE-2.0.
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.deeplearning4j.examples.quickstart.modeling.feedforward.classification;
import java.util.Date;
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.deeplearning4j.datasets.iterator.AsyncShieldDataSetIterator;
import org.deeplearning4j.examples.utils.DownloaderUtility;
import org.deeplearning4j.examples.utils.PlotUtil;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.DropoutLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.io.File;
import java.util.concurrent.TimeUnit;

/**
 * "Linear" Data Classification Example
 * <p>
 * Based on the data from Jason Baldridge:
 * https://github.com/jasonbaldridge/try-tf/tree/master/simdata
 *
 * @author Josh Patterson
 * @author Alex Black (added plots)
 */
@SuppressWarnings("DuplicatedCode")
public class scs {

    public static boolean visualize = true;
    public static String dataLocalPath;

    public static void main(String[] args) throws Exception {
        int seed = 123;
        double learningRate = 0.01;
        int batchSize = 25;
        int nEpochs = 20;

        int numInputs = 7;
        int numOutputs = 6;
        int numHiddenNodes = 20;

        //dataLocalPath = ''
        //Load the training data:
        RecordReader rr = new CSVRecordReader();
        rr.initialize(new FileSplit(new File( "scs_TRAIN.csv")));
        DataSetIterator trainIter = new RecordReaderDataSetIterator(rr, batchSize, 7,6);
        DataSetIterator trainI = new AsyncShieldDataSetIterator(trainIter);

        //Load the test/evaluation data:
        RecordReader rrTest = new CSVRecordReader();
        rrTest.initialize(new FileSplit(new File("scs_TEST.csv")));
        DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest, batchSize, 7,6);
        DataSetIterator testI = new AsyncShieldDataSetIterator(testIter);

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .weightInit(WeightInit.XAVIER)
            .updater(new Nesterovs(learningRate, 0.9))
            .list()
            .layer(new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes)
                .activation(Activation.RELU)
                .dropOut(0.75)
                .build())
            //.layer(new DropoutLayer(0.5))
            .layer(new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
                .activation(Activation.SOFTMAX)
                .nIn(numHiddenNodes).nOut(numOutputs).build())
            .build();
        Date date_start = new Date();
        System.out.println(date_start.toString());
        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(100));  //Print score every 100 parameter updates

        model.fit(trainI, nEpochs);


        System.out.println("Evaluate model....");
        Evaluation eval = new Evaluation(numOutputs);
        while (testI.hasNext()) {
            DataSet t = testI.next();
            INDArray features = t.getFeatures();
            INDArray labels = t.getLabels();
            INDArray predicted = model.output(features, false);
            eval.eval(labels, predicted);
        }
        //An alternate way to do the above loop
        //Evaluation evalResults = model.evaluate(testIter);

        //Print the evaluation statistics
        System.out.println(eval.stats());
        Date date_end = new Date();
        System.out.println("\n****************Example finished********************");
        System.out.println("\n******Time start******\n");
        System.out.println(date_start.toString());
        System.out.println("\n******Time end******\n");
        System.out.println(date_end.toString());
        //Training is complete. Code that follows is for plotting the data & predictions only

    }

}