<div dir="ltr">Hello,<br><br><ul><li>I have uploaded my draft on GSOC 2016 site. Please review it. <br></li></ul><div><br></div><br>I have few doubts regarding the implementation of trees. Currently, I have mentioned 5 trees which would be implemented in the project time - <span id="docs-internal-guid-5da1c2ae-8c2f-4b69-c2b7-df935478bbf2"><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Vantage point trees,k-means trees, random projection trees, Bregman ball trees, segment trees..
Among these, I have coded vantage point trees based on a paper. You can find the code on my github. </span></span><div><span><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent"><br></span></span></div><div><span><span style="font-size:14.6667px;font-family:Arial;color:rgb(0,0,0);vertical-align:baseline;white-space:pre-wrap;background-color:transparent">Please review the other trees, whether it would be fine to implement them. Please tell if you have some tree type in mind which I have missed. Also, I was
</span></span></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Fri, Mar 11, 2016 at 8:26 PM, Ryan Curtin <span dir="ltr"><<a href="mailto:ryan@ratml.org" target="_blank">ryan@ratml.org</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><span class="">On Fri, Mar 11, 2016 at 06:00:28PM +0530, Parijat Dewangan wrote:<br>
> Hi,<br>
><br>
> I went through various research papers to have a better understanding of<br>
> the dual tree algorithms and various trees. Currently, I am focusing on<br>
> vantage point trees, by referring the following papers.<br>
><br>
</span>> - "IMPROVING DUAL-TREE ALGORITHMS"<br>
> <<a href="http://www.ratml.org/pub/pdf/2015improving.pdf" rel="noreferrer" target="_blank">http://www.ratml.org/pub/pdf/2015improving.pdf</a>> Thesis by Ryan Curtin .<br>
> - "Data Structures and Algorithms for Nearest Neighbor Search in<br>
<span class="">> General Metric Spaces by Peter N. Yianilos*"<br>
><br>
> I am completely comfortable with the MLPack API after going through the<br>
> above thesis of Ryan Curtin. So, I was thinking of coding vantage point<br>
> trees. What do you suggest ?<br>
<br>
</span>Sure, vantage point trees would be interesting. Note that vantage point<br>
trees are actually the same as metric trees, so we'll have to provide<br>
some documentation somewhere indicating that they are the same thing.<br>
<span class=""><br>
> Should I provide you with the pseudo code of Vantage Point Trees? Or should<br>
> I try fixing some issues? I was thinking of taking up issue #275.<br>
> <a href="https://github.com/mlpack/mlpack/issues/275" rel="noreferrer" target="_blank">https://github.com/mlpack/mlpack/issues/275</a>.<br>
<br>
</span>If you can do #275 without breaking any of the tests, please feel free<br>
and I'd be happy to merge in the improvement. It will be a significant<br>
refactoring.<br>
<br>
Another possibility from there would be to implement a leaf size<br>
parameter for cover trees, that would cause the tree building process to<br>
terminate when the number of points in a node was small enough. But I<br>
think that would be a lot more difficult and maybe we can save that for<br>
another day... :)<br>
<br>
Thanks,<br>
<br>
Ryan<br>
<span class="HOEnZb"><font color="#888888"><br>
--<br>
Ryan Curtin | "Like, with jetpacks?"<br>
<a href="mailto:ryan@ratml.org">ryan@ratml.org</a> | - Scott<br>
</font></span></blockquote></div><br></div>