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Currently, large graph spaces are nowhere near as fast as continuous spaces.
Examples:
## Distance between random points
- Euclidean 2D: 4.88 µs
- 10 x 10 grid graph: 4.7 µs
- Manhattan Stree…
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I compared the distance errors in simulation using a cummulative histogram on the errors generated in simulation:
 {
// Matlab code
// distance(data_vector,1)=norm(s_pos(cent,:,part)-Data(data_vec…
nvyin updated
8 years ago
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Useful distance measures include but are not limited to:
- [x] (minimum) distance https://shapely.readthedocs.io/en/stable/manual.html#object.distance
- [x] Hausdorff distance https://shapely.read…
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```
What steps will reproduce the problem?
1. Follow readme.txt and run the example FastDtwTest with Euclidean and
Manhattan distances
2. Warping distances are identical
3. Try different data sets
W…
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This package works really well and it's super fast. I would like to request a feature that could enhance its utility in a common setup for 2D particle simulations: periodic boundary conditions (PBC), …
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Hi,
In `kaolin/kaolin/metrics/pointcloud.py` it says that the sided_distance() method computes
```
For every point in p1 find the indices and euclidean distances of the closest point in p2.
```
…
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```
What steps will reproduce the problem?
1. Follow readme.txt and run the example FastDtwTest with Euclidean and
Manhattan distances
2. Warping distances are identical
3. Try different data sets
W…
-
```
What steps will reproduce the problem?
1. Follow readme.txt and run the example FastDtwTest with Euclidean and
Manhattan distances
2. Warping distances are identical
3. Try different data sets
W…