@Override
    public void revert(INDArray array, INDArray maskArray, DistributionStats stats) {
    """
	Denormalize a data array
	
	@param array the data to denormalize
	@param stats statistics of the data population
    """
        if (array.rank() <= 2) {
            array.muliRowVector(filteredStd(stats));
            array.addiRowVector(stats.getMean());
        } else {
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(array, filteredStd(stats), array, 1));
            Nd4j.getExecutioner().execAndReturn(new BroadcastAddOp(array, stats.getMean(), array, 1));
        }

        if (maskArray != null) {
            DataSetUtil.setMaskedValuesToZero(array, maskArray);
        }
    }
@Override
    public void revert(INDArray array, INDArray maskArray, DistributionStats stats) {
        if (array.rank() <= 2) {
            array.muliRowVector(filteredStd(stats));
            array.addiRowVector(stats.getMean());
        } else {
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(array, filteredStd(stats), array, 1));
            Nd4j.getExecutioner().execAndReturn(new BroadcastAddOp(array, stats.getMean(), array, 1));
        }

        if (maskArray != null) {
            DataSetUtil.setMaskedValuesToZero(array, maskArray);
        }
    }
@ Override public void revert ( INDArray array , INDArray maskArray , DistributionStats stats ) { if ( array . rank ( ) <= 2 ) { array . muliRowVector ( filteredStd ( stats ) ) ; array . addiRowVector ( stats . getMean ( ) ) ; } else { Nd4j . getExecutioner ( ) . execAndReturn ( new BroadcastMulOp ( array , filteredStd ( stats ) , array , 1 ) ) ; Nd4j . getExecutioner ( ) . execAndReturn ( new BroadcastAddOp ( array , stats . getMean ( ) , array , 1 ) ) ; } if ( maskArray != null ) { DataSetUtil . setMaskedValuesToZero ( array , maskArray ) ; } }
Denormalize a data array

@param array the data to denormalize
@param stats statistics of the data population
Denormalize a data array