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/*******************************************************************************
 * Copyright (c) 2015-2018 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.nd4j.linalg.dataset.api.iterator.fetcher;

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 transform fetcher
 *
 * @author Adam Gibson
 */
public abstract class BaseDataFetcher implements DataSetFetcher {

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

    /**
     * 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");

        INDArray inputs = createInputMatrix(examples.size());
        INDArray labels = createOutputMatrix(examples.size());
        for (int i = 0; i < examples.size(); i++) {
            inputs.putRow(i, examples.get(i).getFeatures());
            labels.putRow(i, examples.get(i).getLabels());
        }
        curr = new DataSet(inputs, labels);

    }

    @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;
    }


}