java中3d空间值链表匹配的神经网络
我试图将预先录制的 3d(float x, float y, float z) 链接列表与另一个 3d 链接列表(float x, float y, float z) 匹配。大约有 50 个预先录制的序列，每次都有 1000 个相同动作的示例，我正在尝试对其进行匹配。 链表的大小差异很大。 Java 中是否有特定的神经网络模式/库/框架/算法我可以用于此任务？
任何建议将不胜感激！ 等待您的回复）
I am trying to match a prerecorded 3d(float x, float y, float z) linked list to another 3d linked list(float x, float y, float z). There is about 50 prerecorded sequences with 1000 examples of the same move every time, against which I am trying to match through.
The size of linked lists highly vary.
Is there a specific neural network pattern/library/framework/algorhytm in Java I could use for this task ?
Any suggestions would be highly appreciated!
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如果您坚持使用神经网络...
简化您预先记录的输入
简化用户输入
创建用于图像识别的神经网络 为
用匹配的边界/约束进行实验
我可能只会生成一个大的 3d 走廊（想想宽的 3D 管，或连接的圆柱体的集合）代表我预先记录的点的极值，然后缩放用户输入点，然后检查用户输入点是否落在匹配的走廊内。
编辑 非常奇怪的是，问题本身被否决并关闭，几天后我的答案被否决了。实际上，由于主持人的集体思考失败，该问题很可能被关闭。也许对于一个带有+1“已接受”答案的封闭式问题看起来有点尴尬，所以这个答案随后被否决了（两次）。
根据我的经验，提出这样的问题的唯一方法是要么给予大笔赏金，要么以不会冒犯版主脆弱情感的方式重新措辞（大多数时候他们会做一个干得好，不要逾越...）。
If you insist on a neural network...
Simplify your prerecorded input
Simplify your user input
Create neural networks for image recognition
Experiment with your matching bounds/constraints
I would probably just generate a large 3d corridor (think wide 3d tube, or a collection of connected cylinders) that represented the extremes of my prerecorded points then scale the user input points then check if the user input points fall within the matching corridor.
EDIT Very odd that the question itself was down-voted and closed, then days later my answer was down-voted. Realistically, the question was likely closed due to a failure of group think on the part of the moderators. Maybe it then looked a little awkward to have a closed question with a +1 "Accepted" answer, so this answer was subsequently down-voted (twice).
From my experience, the only way to ask a question like this is to either slap a large bounty on it, or re-word it in a way that won't offend the fragile sensibilities of the moderators (most of the time they do a good job and don't overstep...).