Rank: 3709 based on 10 downloads (last 30 days) and 1 file submitted

Joerg Bretschneider

E-mail: Contact Author
Lat/Long: 50.999496, 13.40744

Personal Profile:: Forest worker, Mathematician, Ph.D. in Earthquake engineering, from Saxony
Professional Interests:: earthquake engineering, automotive, sensors

Files
Watch Lists

Files Posted by Joerg					1 - 1 of 1
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
04 Jun 2009		LISTMATCH unique adaptive STRMATCH Author: Joerg Bretschneider	char, cellstr, match, strings	10	0	4.0 4.0 \| 1 rating

Comments and Ratings by Joerg		View all 1 - 4 of 4
Updated	File	Comments	Rating
18 Jun 2009	DOTNOT - Dot Notation Implements a replacement interface to GET/SET for graphic/gui options. Author: Peter Rydesäter	This seems to be a fine idea, I wonder why nobody has commented so far. I guess the problem is portability of code using this class, the class dotnot must always be delivered, dependencies evolve which restrict usage. Seems to require deep understanding of the handle graphics package, so this may be another obstacle. Did The Mathworks relate to this so far? Maybe you should contact them directly
18 Jun 2009	AddRecursivePath Adds the directory specified and all subdirectories to the path. Author: Michael Robbins	I was not able to run this on Matlab 6.1, so Release 5.2 seems to be an error. Matlab 6.5 has regexp, but one which doesn#t support 3 argins. Nevertheless the mfile is very simplistic, all subdirectories are used, but only the first level of a possible tree, and certain directories like @ or private or ., .. are included.
16 Jun 2009	LISTMATCH unique adaptive STRMATCH Author: Joerg Bretschneider
17 Dec 2008	units.m Converts physical unit1 into unit2. Author: Henning Ressing	useful & correct for simple purposes, but not a systematic approach. SI prefixes are incomplete and not handled properly (ym for µm is puzzling, as it has its use for yocto (1e-24)). The code could be written far more compact (1000 lines reduced to 200 using simple concepts)

Comments and Ratings on Joerg's Files			1 - 1 of 1
Updated	File	Comment by	Comments	Rating
16 Jun 2009	LISTMATCH unique adaptive STRMATCH Author: Joerg Bretschneider	Bretschneider, Joerg

Top Tags Applied by Joerg	1 - 4 of 4
cellstr, char, match, strings

Files Tagged by Joerg					1 - 1 of 1
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
04 Jun 2009		LISTMATCH unique adaptive STRMATCH Author: Joerg Bretschneider	char, cellstr, match, strings	10	0	4.0 4.0 \| 1 rating

Currently Watching
Type	Description
File	IPDM: Inter-Point Distance Matrix
File	LISTMATCH

Files Matching Joerg's Watch List				View all 1 - 2 of 2
Updated	File	Tags	Downloads (last 30 days)	Comments	Rating
04 Jun 2009	LISTMATCH unique adaptive STRMATCH Author: Joerg Bretschneider	char, cellstr, match, strings	10	0	4.0 4.0 \| 1 rating
21 Apr 2008	IPDM: Inter-Point Distance Matrix An efficient and accurate Inter-Point Distance Matrix Author: John D'Errico	nearest neighbor, euclidean, distance, interpoint, metric	158	6	4.0 4.0 \| 3 ratings

Comments and Ratings Matching Joerg's Watch List			1-5 of 7
Date	File	Comment by	Comments	Rating
12 Aug 2009	IPDM: Inter-Point Distance Matrix An efficient and accurate Inter-Point Distance Matrix	Wolfgang Schwanghart	Thanks for this great contribution! Would it, however, be possible that ipdm (in case only data1 is provided) only returns the row and column indices and distances so that they are arranged in the order (distance(rowix,colix)): (2,1), (3,1), ..., (n,1), (3,2), ..., (n,2), ..., (n,n–1)). That is the way pdist (part of the statistics toolbox) returns the distance matrix in a vector to be more memory efficient. pdist, however, does not support distance subsets. So far, I use this workaround in my code. % remove distances were d.columnindex = d.rowindex I = d.columnindex==d.rowindex; d.columnindex(I) = []; d.rowindex(I) = []; d.distance(I) = []; % remove double entry of pairs [dtemp, m] = unique(sort([d.rowindex d.columnindex],2),'rows'); d.rowindex = dtemp(:,1); d.columnindex = dtemp(:,2); d.distance = d.distance(m); This reduces the size of d by more than a half. But it doesn't avoid the generation of the huge struct returned by ipdm, which is a real memory bottleneck for me. Would it be possible to calculate this more efficient output within ipdm? So far I don't see a straightforward way, but perhaps you do? Best regards, Wolfgang	Comment only
16 Jun 2009	LISTMATCH unique adaptive STRMATCH	Joerg Bretschneider		4
20 Apr 2008	IPDM: Inter-Point Distance Matrix An efficient and accurate Inter-Point Distance Matrix	John D'Errico	Yes, for small problems, the naive code can be more efficient. This tool has many options however, and just the task of parsing the possibilities takes a significant amount of time. So for a tiny problem, almost all the work is in the problem overhead. Note that for these small problems, the time required for ipdm is almost constant, roughly 0.0025 seconds. For example, the following test shows the times required in a simple test. I computed the distances from each of 100 points to a set of N points (N is the first column) in 2 dimensions. [N',ipdmtimes,looptimes] ans = 2 0.0025589 0.0006432 10 0.0033113 0.00076637 50 0.0051397 0.0038982 100 0.0071307 0.0077328 500 0.02467 0.037465 1000 0.04776 0.07536 2000 0.092433 0.15087 Since the minimum overhead on my CPU to run ipdm is roughly 0.0025 seconds, the simple loop will be faster for small problems. As you can see, the loop is slower for the larger problems, anything larger than 100x100 in this case, at least on my CPU. Other tests show that the break-even point was indeed roughly 10,000 total distances to compute for a simple, full distance matrix. What ipdm provides is the ability to generate a wide variety of distance matrices, especially when you only need some limited subset of the complete set of distances. If you only want a subset of the complete set of distances, but that complete set of distances would be too large to compute in the memory that you have available, then you may have no choice but to use a tool like this. As far as the issue of memory goes, I get no memory errors for the problem size indicated. This just means that I've got more memory available to my system. However, ipdm also allows you to control the size of the chunks it uses, so Jim could have avoided that problem. The 'ChunkSize' parameter is available as a (currently undocumented) option in ipdm. It was not listed in the help, as I thought it would be of little common utility. Use of the 'ChunkSize' property is currently shown in the examples in the demo file. I'll add a description of 'ChunkSize' to the help for those with limited memory.	Comment only
20 Apr 2008	IPDM: Inter-Point Distance Matrix An efficient and accurate Inter-Point Distance Matrix	Jim Stevens	I am very confused. This should be efficient routine, and I replaced my own very naive (not vectorized, loop-in-loop approach) distance calculation with IPDM call, and my code slowed down essentially. Little play with tic-toc timer gave me surprising results, I calculated distance from one point to several points. With only 10 points naive approach was 7.5 times faster, with 100 points 2.5 times, and around 500 points they were equal. On the other hand, while calculating whole point matrix (distance from all points to other points) of point set, IPDM will give out of memory error already with 1400 two dimensional points. With naive approach, calcuation of 1300 points takes appr 10 times longer than with IPDM, but on the other hand, naive approach can handle far larger point sets. So, as there seems to be some cases when this implementation is inferior to naive approach, I dont understand what was the talk about efficiency.	2
05 Mar 2008	IPDM: Inter-Point Distance Matrix An efficient and accurate Inter-Point Distance Matrix	Urs (us) Schwarz	since the cyclomatic complexity of this submission is already 106 (!!) i refrain from any more redundancy and simply add - good stuff, john: help/examples/computational engine us	5

MATLAB Central Terms of Use

NOTICE: Any content you submit to MATLAB Central, including personal information, is not subject to the protections which may be afforded information collected under other sections of The MathWorks, Inc. Web site. You are entirely responsible for all content that you upload, post, e-mail, transmit or otherwise make available via MATLAB Central. The MathWorks does not control the content posted by visitors to MATLAB Central and, does not guarantee the accuracy, integrity, or quality of such content. Under no circumstances will The MathWorks be liable in any way for any content not authored by The MathWorks, or any loss or damage of any kind incurred as a result of the use of any content posted, e-mailed, transmitted or otherwise made available via MATLAB Central. Read the complete Terms prior to use.