Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>Your question might also be related to <a href="https://stackoverflow.com/q/10947366/1622937">this one</a></p> <p>This is kind of a hard problem. A possible way would be to implement the euclidian distance on your own, completely abandon <code>scipy</code> and make use of <a href="http://pypy.org/" rel="nofollow noreferrer">pypy</a>'s JIT compiler. But most likely this will not make you gane much. </p> <p><em>Personally, I would recommend you to write the routine in C.</em></p> <p>The problem is less the implementation but the way you approach this problem. You chose a brute force approach by calculating the euclidian distance for each distinct pair of points in each possible pair of the metric space subsets. This is computationally demanding:</p> <ul> <li>Assume you have 500 curves and each of them has 75 points. With the brute force approach you end up calculating the euclidean distance 500 * 499 * 75 * 75 = 1 403 437 500 times. It is not further surprising that this approach takes forever to run. </li> </ul> <p>I'm not an expert with this but I know that the Hausdorff distance is extensively used in image processing. I would suggest you to browse the literature for speed optimized algorithms. A starting point might be <a href="https://docs.google.com/viewer?url=http%3A%2F%2Fwww.vldb.org%2Fpvldb%2Fvol4%2Fp506-nutanong.pdf" rel="nofollow noreferrer">this</a>, or <a href="https://docs.google.com/viewer?url=http%3A%2F%2Fwww.cs.uu.nl%2Fdocs%2Fvakken%2Fgmspr%2FComparing%2520Images%2520Using%2520the%2520Hausdorff%2520Distance2.doc" rel="nofollow noreferrer">this</a> paper. Also, often mentioned in combination with the Hausdorff distance is the <a href="http://en.wikipedia.org/wiki/Voronoi_diagram" rel="nofollow noreferrer">Voroni diagram</a>.</p> <p>I hope these links might help you with this problem.</p>
    singulars
    1. This table or related slice is empty.
    1. POdistance matrix of curves in python
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USCommunity
    1. USjojo
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    1. COThe first linked paper is interesting, solves part of the problem. Now the other problem can be more or less formulated as this: given three sets A B C, if we know the Hausdorff distance between A and B and also the inner details obtained from such calculation, is it possible to find the Hausdorff distance from A to C with lesser computational complexity than it was to calculate for A and B if there are paths, considering some metric, from A to C that cross B ? Well, that was way longer than I expected when I started to write. I hope someone can make sense of it.
      singulars
      1. PO
      1. USmmgp
    2. COthank you for all your advices. I got the idea: "use a smarter algorithm". I hope I'll do it after using the current version for the paper deadline :)
      singulars
      1. PO
      1. USahmethungari
    3. CO@ahmethungari I'm sorry to not be able to help you more than that, but there is no easy solution for this problem. One idea for speeding up the computation I would have though: Instead of computing for a given point in Curve1 the euclid.dist. with all the points from Curve2, you could choose randomly a point from Curve2, compute the euclid.dist. (call it `r`) then for each other point from Curve2 you can compute the [manhatton distance](http://en.wikipedia.org/wiki/Manhattan_distance) (much faster to compute) and check if each dimension of the manhatton dist. is bigger than `r`, if so...
      singulars
      1. PO
      1. USjojo
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload