Transformed data access

[ayirpil]

On standard mnist prediction output(char 7 actually) tranformed frame output, what do the 6.0, 9.0 match? How can I get to the DenseTemnsor itself? My hunch is whatever correcponds to 7 in that DenseTensor will have non zero output.

val data = transformed_frame.get.dataset
data: Seq[ml.combust.mleap.runtime.frame.Row] = List(Row(0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,36.0,94.0,1...
scala> println(data)
List(Row(0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,0.0,43.0,154.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.0,1.0,154.0,253.0,90.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,139.0,253.0,190.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,11.0,190.0,253.0,70.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,35.0,241.0,225.0,160.0,108.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,81.0,240.0,253.0,253.0,119.0,25.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,45.0,186.0,253.0,253.0,150.0,27.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,16.0,93.0,252.0,253.0,187.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,249.0,253.0,249.0,64.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SparseTensor(ArraySeq(List(152), List(153), List(154), List(155), List(156), List(157), List(158), List(159), List(160), List(161), List(162), List(163), List(176), List(177), List(178), List(179), List(180), List(181), List(182), List(183), List(184), List(185), List(186), List(187), List(188), List(189), List(190), List(191), List(203), List(204), List(205), List(206), List(207), List(208), List(209), List(210), List(211), List(212), List(213), List(214), List(215), List(216), List(217), List(218), List(231), List(232), List(233), List(234), List(235), List(236), List(237), List(238), List(239), List(240), List(241), List(260), List(261), List(262), List(263), List(264), List(265), List(266), List(268), List(269), List(289), List(290), List(291), List(292), List(293), List(319), List(320), List(321), List(322), List(347), List(348), List(349), List(350), List(376), List(377), List(378), List(379), List(380), List(381), List(405), List(406), List(407), List(408), List(409), List(410), List(434), List(435), List(436), List(437), List(438), List(439), List(463), List(464), List(465), List(466), List(467), List(493), List(494), List(495), List(496), List(518), List(519), List(520), List(521), List(522), List(523), List(524), List(544), List(545), List(546), List(547), List(548), List(549), List(550), List(551), List(570), List(571), List(572), List(573), List(574), List(575), List(576), List(577), List(578), List(596), List(597), List(598), List(599), List(600), List(601), List(602), List(603), List(604), List(605), List(622), List(623), List(624), List(625), List(626), List(627), List(628), List(629), List(630), List(631), List(648), List(649), List(650), List(651), List(652), List(653), List(654), List(655), List(656), List(657), List(676), List(677), List(678), List(679), List(680), List(681), List(682), List(683)),[D@59e55c63,List(784)),6.0,SparseTensor(ArraySeq(List(152), List(153), List(154), List(155), List(156), List(157), List(158), List(159), List(160), List(161), List(162), List(163), List(176), List(177), List(178), List(179), List(180), List(181), List(182), List(183), List(184), List(185), List(186), List(187), List(188), List(189), List(190), List(191), List(203), List(204), List(205), List(206), List(207), List(208), List(209), List(210), List(211), List(212), List(213), List(214), List(215), List(216), List(217), List(218), List(231), List(232), List(233), List(234), List(235), List(236), List(237), List(238), List(239), List(240), List(241), List(260), List(261), List(262), List(263), List(264), List(265), List(266), List(268), List(269), List(289), List(290), List(291), List(292), List(293), List(319), List(320), List(321), List(322), List(347), List(348), List(349), List(350), List(376), List(377), List(378), List(379), List(380), List(381), List(405), List(406), List(407), List(408), List(409), List(410), List(434), List(435), List(436), List(437), List(438), List(439), List(463), List(464), List(465), List(466), List(467), List(493), List(494), List(495), List(496), List(518), List(519), List(520), List(521), List(522), List(523), List(524), List(544), List(545), List(546), List(547), List(548), List(549), List(550), List(551), List(570), List(571), List(572), List(573), List(574), List(575), List(576), List(577), List(578), List(596), List(597), List(598), List(599), List(600), List(601), List(602), List(603), List(604), List(605), List(622), List(623), List(624), List(625), List(626), List(627), List(628), List(629), List(630), List(631), List(648), List(649), List(650), List(651), List(652), List(653), List(654), List(655), List(656), List(657), List(676), List(677), List(678), List(679), List(680), List(681), List(682), List(683)),[D@58c8b3b6,List(784)),DenseTensor([D@a84e375,List(10)),DenseTensor([D@76fa0548,List(10)),DenseTensor([D@58f49b0,List(10)),9.0))

data(0).getRaw(787)
res60: Any = SparseTensor(ArraySeq(List(152), List(153), List(154), List(155), List(156), List(157), List(158), List(159), List(160), List(161), List(162), List(163), List(176), List(177), List(178), List(179), List(180), List(181), List(182), List(183), List(184), List(185), List(186), List(187), List(188), List(189), List(190), List(191), List(203), List(204), List(205), List(206), List(207), List(208), List(209), List(210), List(211), List(212), List(213), List(214), List(215), List(216), List(217), List(218), List(231), List(232), List(233), List(234), List(235), List(236), List(237), List(238), List(239), List(240), List(241), List(260), List(261), List(262), List(263), List(264), List(265), List(266), List(268), List(269), List(289), List(290), List(291), List(292), List(293), Lis...
scala> data(0).getRaw(786)
res61: Any = 6.0

scala> data(0).getRaw(790)
res62: Any = DenseTensor([D@58f49b0,List(10))