@Override
    public void revert(INDArray array, INDArray maskArray, MinMaxStats stats) {
    """
	Denormalize a data array
	
	@param array the data to denormalize
	@param stats statistics of the data population
    """
        // Subtract target range minimum value
        array.subi(minRange);
        // Scale by target range
        array.divi(maxRange - minRange);

        if (array.rank() <= 2) {
            array.muliRowVector(stats.getRange());
            array.addiRowVector(stats.getLower());
        } else {
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(array, stats.getRange(), array, 1));
            Nd4j.getExecutioner().execAndReturn(new BroadcastAddOp(array, stats.getLower(), array, 1));
        }

        if (maskArray != null) {
            DataSetUtil.setMaskedValuesToZero(array, maskArray);
        }
    }
@Override
    public void revert(INDArray array, INDArray maskArray, MinMaxStats stats) {
        // Subtract target range minimum value
        array.subi(minRange);
        // Scale by target range
        array.divi(maxRange - minRange);

        if (array.rank() <= 2) {
            array.muliRowVector(stats.getRange());
            array.addiRowVector(stats.getLower());
        } else {
            Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(array, stats.getRange(), array, 1));
            Nd4j.getExecutioner().execAndReturn(new BroadcastAddOp(array, stats.getLower(), array, 1));
        }

        if (maskArray != null) {
            DataSetUtil.setMaskedValuesToZero(array, maskArray);
        }
    }
@ Override public void revert ( INDArray array , INDArray maskArray , MinMaxStats stats ) { // Subtract target range minimum value array . subi ( minRange ) ; // Scale by target range array . divi ( maxRange - minRange ) ; if ( array . rank ( ) <= 2 ) { array . muliRowVector ( stats . getRange ( ) ) ; array . addiRowVector ( stats . getLower ( ) ) ; } else { Nd4j . getExecutioner ( ) . execAndReturn ( new BroadcastMulOp ( array , stats . getRange ( ) , array , 1 ) ) ; Nd4j . getExecutioner ( ) . execAndReturn ( new BroadcastAddOp ( array , stats . getLower ( ) , 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