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package org.deeplearning4j.nn.modelimport.keras.preprocessing.sequence;

import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;

import static org.junit.jupiter.api.Assertions.assertEquals;

public class TimeSeriesGeneratorTest extends BaseDL4JTest {

    @Test
    public void tsGeneratorTest() throws InvalidKerasConfigurationException {
        INDArray data = Nd4j.create(50, 10);
        INDArray targets = Nd4j.create(50, 10);


        int length = 10;
        int samplingRate = 2;
        int stride = 1;
        int startIndex = 0;
        int endIndex = 49;
        int batchSize = 1;

        boolean shuffle = false;
        boolean reverse = false;

        TimeSeriesGenerator gen = new TimeSeriesGenerator(data, targets, length,
                samplingRate, stride, startIndex, endIndex, shuffle, reverse, batchSize);

        assertEquals(length, gen.getLength());
        assertEquals(startIndex + length, gen.getStartIndex());
        assertEquals(reverse, gen.isReverse());
        assertEquals(shuffle, gen.isShuffle());
        assertEquals(endIndex, gen.getEndIndex());
        assertEquals(batchSize, gen.getBatchSize());
        assertEquals(samplingRate, gen.getSamplingRate());
        assertEquals(stride, gen.getStride());

        Pair<INDArray, INDArray> next = gen.next(0);
    }
}
