可视化高维场箭头?
我有一个很大的元组列表 (a, b),其中 a 和 b 都是来自同一空间的 9 维向量。这本质上是对系统的状态和一些转换进行编码。我想将这些元组描述的字段可视化为从 a->b 指向的箭头,无论是 2D 还是 3D。然而,我的问题之一是,这不是一个表现良好的矢量场(不连续),但我有理由相信它可能可以很好地布局,即使在二维中也是如此。
有谁知道可以执行此操作的工具箱(用于 matlab/python)或程序?这可能首先涉及对 a 和 b 进行某种降维,然后绘制从一个点到另一个点的小箭头。
感谢您的帮助!
I have a big list of tuples (a, b), where both a and b are 9-dimensional vectors from the same space. This essentially encodes states of a system and some transitions. I would like to visualize the field described by these tuples, as arrows pointing from a->b, either in 2D or 3D. One of my problems however is that this is not a well-behaved vector field (not continuous) but I have reasons to believe that it can probably be laid out nicely, even in 2D.
Does anyone know of a toolbox (for matlab/python) or program that can do this? This would presumably first involve some kind of dimensionality reduction on a and b and then plot little arrows from one point to another.
Thank you for your help!
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我不确定这是否能回答您的问题,但您可能想看看 重复图。如果这就是您所追求的,那么您将不需要任何额外的 Matlab 工具箱。
I'm not 100% sure if this answers your question or not, but you may want to look at Recurrence Plots. If this is what you're after, then you wont need any additional Matlab toolboxes.
好吧,事实证明 MATLAB 可以做到这一点,但它不是很漂亮。
它基本上可以归结为进行 PCA,然后使用 quiver 函数进行绘图:
我的矩阵 X 包含奇数行中的高维节点的起点和偶数行中的终点。然后:
缺点是我找不到给边缘着色的好方法,所以如果我只是简单地做,我会得到一个巨大的混乱。啊,好吧,现在已经足够了!
Okay, turns out MATLAB can do this but it's not very pretty.
It basically boils down to doing PCA, and then using the quiver function to do the plotting:
My matrix X here contains starting points of my high dimensional nodes in odd rows, and ending points in even rows. Then:
The downside is that I can't find a good way to color the edges, so I get a huge mess if I just do it trivially. Ah well, good enough for now!
这是降维算法的 Matlab 工具箱。我没有使用过它,但我已经使用过降维,并且似乎流形图表/局部坐标算法能够提取低维表示。
代尔夫特理工大学昏暗。红色的。工具箱
Here's a Matlab toolbox of dimension reduction algorithms. I haven't worked with it, but I have worked with dimension reduction, and it seems like a manifold charting/local coordinates algorithm would be able to extract a low-dimensional representation.
TU Delft Dim. Red. Toolbox