Sorry for the double post.
Okay, I'm in the home stretch here. I'm just trying to figure out how
to use a WHERE command or a histogram to select only the data that lies
below a given line specified in y=mx+b form.
Thanks for all ya'll's help.
- Rob
rdellsy@gmail.com wrote:
> Interesting. I found that if I take the 4th power of the y variable, I
> get a few decent clusters. I was planning on doing a linear fit of
> those good clusters, and then using that to find the trailing data. Add
> in a small histogram to find the lower x bound of what I want and it
> should work. Your idea also sounds promissing. I'm getting close.
> Hopefully, I'll have something that'll work by Tuesday, and I'll let
> you all tear it to pieces...err find more efficient ways of doing it.
> =)
> Thanks,
> - Rob
>
>
> JD Smith wrote:
>> On Fri, 11 Aug 2006 10:22:35 -0700, kuyper wrote:
>>
>>> rdellsy@gmail.com wrote:
>>>> I'm working on doing a cluster tree and getting say the lower-right
>>>> cluster and the one or two nearest neighbors (sp?). I may still be
>>>> loosing some data though. Another possibilty would be compressing the
>>>> data, say, by half, and see if that helps.
>>>> Thanks,
>>>> Rob
>>>
>>> IDL> help,data
>>> DATA FLOAT = Array[2, 681]
>>>
>>> If all of the dimensions of your data have the same physical meaning,
>>> then
>>> you don't need to do anything to your data. However, I got the
>>> following
>>> results:
>>>
>>> IDL> print,stddev(data[0,*]),stddev(data[1,*])
>>> 2748.5689 1.7135388
>>>
>>> Which implies to me that your x and y coordinates probably have
>>> drastically
>>> different meanings, so they need to be scaled to have a meaningful
>>> distance
>>> measurement. The simplest way is to base the scale factors on the
>>> standard deviations:
>>>
>>> IDL> scaled = data
>>> IDL> scaled[0,*] /= stddev(data[0,*])
>>> IDL> scaled[1,*] /= stddev(data[1,*])
>>>
>>> I recommend, since you're analyzing many different but comparable
>>> datasets, to use a single scaling factor on each axis for all the
>>> datasets; otherwise it will be difficult to compare your results
>>> between one dataset and another.
>>>
>>> IDL> pairdistance = DISTANCE_MEASURE(scaled)
>>> IDL> clusters =
>>> CLUSTER_TREE(pairdistance,linkdistance,LINKAGE=0,data=scaled )
>>>
>>> I'm surprised by the fact that I haven't been able to locate an IDL
>>> function or procedure for taking the output from CLUSTER_TREE and using
>>> it to determine cluster membership at the point
>>> when there are N clusters left, so I wrote my own:
>>>
>>> FUNCTION cluster_member, clusters
>>> dims = SIZE(clusters, /DIMENSIONS)
>>> num = dims[1] + 1
>>> membership = INTARR(num, num-1)
>>> work = indgen(num)
>>> FOR i=0, num-2 DO BEGIN
>>> newclust = WHERE (work eq clusters[0,i] OR work EQ
>>> clusters[1,i])
>>> work[newclust] = num+i
>>> membership[0,i] = work
>>> ENDFOR
>>>
>>> RETURN, membership
>>> END
>>>
>>> There's probably a more efficient way of handling that loop.
>>
>> Very cool! I'll have to remember this one. If you only care about n
>> remaining clusters, you can simplify somewhat to:
>>
>> function cluster_member, clusters,n
>> dims = SIZE(clusters, /DIMENSIONS)
>> num = dims[1] + 1L
>> n>=1
>> work = lindgen(num)
>> for i=0L, num-1L-n do $
>> work[where(work eq clusters[0,i] OR work eq clusters[1,i])]= num+i
>> return, work
>> end
>>
>> JD
|
comp.lang.idl-pvwave archive
Members
Search
Help
Login
Home
