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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.fetchers;

import org.deeplearning4j.datasets.iterator.DataSetFetcher;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.util.FeatureUtil;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.List;

/**
 * A base class for assisting with creation of matrices
 * with the data applyTransformToDestination fetcher
 * @author Adam Gibson
 *
 */
public abstract class BaseDataFetcher implements DataSetFetcher {

    /**
     * 
     */
    private static final long serialVersionUID = -859588773699432365L;
    protected int cursor = 0;
    protected int numOutcomes = -1;
    protected int inputColumns = -1;
    protected DataSet curr;
    protected int totalExamples;
    protected static final Logger log = LoggerFactory.getLogger(BaseDataFetcher.class);

    /**
     * Creates a feature vector
     * @param numRows the number of examples
     * @return a feature vector
     */
    protected INDArray createInputMatrix(int numRows) {
        return Nd4j.create(numRows, inputColumns);
    }

    /**
     * Creates an output label matrix
     * @param outcomeLabel the outcome label to use
     * @return a binary vector where 1 is transform to the
     * index specified by outcomeLabel
     */
    protected INDArray createOutputVector(int outcomeLabel) {
        return FeatureUtil.toOutcomeVector(outcomeLabel, numOutcomes);
    }

    protected INDArray createOutputMatrix(int numRows) {
        return Nd4j.create(numRows, numOutcomes);
    }

    /**
     * Initializes this data transform fetcher from the passed in datasets
     * @param examples the examples to use
     */
    protected void initializeCurrFromList(List examples) {

        if (examples.isEmpty())
            log.warn("Warning: empty dataset from the fetcher");
        curr = null;
        INDArray inputs = createInputMatrix(examples.size());
        INDArray labels = createOutputMatrix(examples.size());
        for (int i = 0; i < examples.size(); i++) {
            INDArray data = examples.get(i).getFeatureMatrix();
            INDArray label = examples.get(i).getLabels();
            inputs.putRow(i, data);
            labels.putRow(i, label);
        }
        curr = new DataSet(inputs, labels);
        examples.clear();

    }

    /**
     * Sets a list of label names to the curr dataset
     */
    public void setLabelNames(List names) {
        curr.setLabelNames(names);
    }

    public String getLabelName(int i) {
        return curr.getLabelNames().get(i);
    }

    @Override
    public boolean hasMore() {
        return cursor < totalExamples;
    }

    @Override
    public DataSet next() {
        return curr;
    }

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

    @Override
    public int inputColumns() {
        return inputColumns;
    }

    @Override
    public int totalExamples() {
        return totalExamples;
    }

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

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



}