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/*-
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://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.
 *
 */

package org.deeplearning4j.datasets.iterator;

import lombok.Getter;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;

import java.util.List;

/**
 * A wrapper for a dataset to sample from.
 * This will randomly sample from the given dataset.
 * @author Adam GIbson
 */
public class SamplingDataSetIterator implements DataSetIterator {

    /**
     * 
     */
    private static final long serialVersionUID = -2700563801361726914L;
    private DataSet sampleFrom;
    private int batchSize;
    private int totalNumberSamples;
    private int numTimesSampled;
    @Getter
    private DataSetPreProcessor preProcessor;

    /**
     *
     * @param sampleFrom the dataset to sample from
     * @param batchSize the batch size to sample
     * @param totalNumberSamples the sample size
     */
    public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples) {
        super();
        this.sampleFrom = sampleFrom;
        this.batchSize = batchSize;
        this.totalNumberSamples = totalNumberSamples;
    }

    @Override
    public boolean hasNext() {
        return numTimesSampled < totalNumberSamples;
    }

    @Override
    public DataSet next() {
        DataSet ret = sampleFrom.sample(batchSize);
        numTimesSampled += batchSize;
        return ret;
    }

    @Override
    public void remove() {
        throw new UnsupportedOperationException();
    }

    @Override
    public int totalExamples() {
        return totalNumberSamples * batchSize;
    }

    @Override
    public int inputColumns() {
        return sampleFrom.numInputs();
    }

    @Override
    public int totalOutcomes() {
        return sampleFrom.numOutcomes();
    }

    @Override
    public boolean resetSupported() {
        return true;
    }

    @Override
    public boolean asyncSupported() {
        return true;
    }

    @Override
    public void reset() {
        numTimesSampled = 0;
    }

    @Override
    public int batch() {
        return batchSize;
    }

    @Override
    public int cursor() {
        return numTimesSampled;
    }

    @Override
    public int numExamples() {
        return sampleFrom.numExamples();
    }

    @Override
    public void setPreProcessor(DataSetPreProcessor preProcessor) {
        this.preProcessor = (DataSetPreProcessor) preProcessor;
    }

    @Override
    public List getLabels() {
        return null;
    }


    @Override
    public DataSet next(int num) {
        DataSet ret = sampleFrom.sample(num);
        numTimesSampled++;
        return ret;
    }



}