protected void bprop(float target) {
      assert (target != missing_real_value);
      if (params.loss != Loss.MeanSquare) throw new UnsupportedOperationException("Regression is only implemented for MeanSquare error.");
      final int row = 0;
      // Computing partial derivative: dE/dnet = dE/dy * dy/dnet = dE/dy * 1
      final float g = target - _a.get(row); //for MSE -dMSE/dy = target-y
      float m = momentum();
      float r = _minfo.adaDelta() ? 0 : rate(_minfo.get_processed_total()) * (1f - m);
      bprop(row, g, r, m);
    }
protected void bprop ( float target ) { assert ( target != missing_real_value ) ; if ( params . loss != Loss . MeanSquare ) throw new UnsupportedOperationException ( "Regression is only implemented for MeanSquare error." ) ; final int row = 0 ; final float g = target - _a . get ( row ) ; float m = momentum ( ) ; float r = _minfo . adaDelta ( ) ? 0 : rate ( _minfo . get_processed_total ( ) ) * ( 1f - m ) ; bprop ( row , g , r , m ) ; }
Backpropagation for regression
@param target floating-point target value