yes I read too quickly :) anyway thanks for your help Younes On Sun, Mar 6, 2011 at 12:51 PM, Younes Fadakar <yfa.st...@ymail.com> wrote:
> To Nicolas, > > question: > > >> What happens if B = {A + noisy points} (false positive)? > > answer: > You probably missed the second part of my previous email, where > Card(B)>Card(A) with noise: > I copied here, see: > --------------------------------------------------------------------------- > #-----the realistic implementation----- > N = 100 # > A.x = rand(N) #set A.x > A.y = rand(N) #set A.y: coordinate pairs > B.x = shake(A.x,10%) #slightly repositions points = > noisy positions > > B.y = shake(A.y,10%) # randomly with 10% move > B.x = B.x+rand(N/10) #adds extra 10% rand points = > extra noisy points > B.y = B.y+rand(N/10) #Card(B)=1.1*Card(A) > > M = PositionAccuracy(A,B) # > > Score = M/N*100 #my score=normalized based on N > #N=Card(A) > --------------------------------------------------------------------------- > the computed score is: > score = M(=#concordances)/N(=Card(A))*100 > which seems to be right answer. Back to the first example, if A=B the score > will be 100%.[correct] > applying your scoring method if A=B then the score is smaller than 1. > [incorrect]! > Anyway, I'm happy you have found your satisfactory answer. > > To Duane: > Thanks for your message. Do you have any information about existing > statistically best random generator? > I appreciate your replies. > > To All: > Dear everybody, > Is there any more robust/strong/reliable/high performance random generator > satisfying statistically and being computing friendly? How can we evaluate > the randomness of such generators then? > > To myself: > Should double check the literature for concerns in randomness. > > Best Regards, > . > Younes > yfa.st...@ymail.com > http://alghalandis.com > ------------------------------ > > > > ------------------------------ > *From:* Nicolas Maisonneuve <n.maisonne...@gmail.com> > *To:* Younes Fadakar <yfa.st...@ymail.com> > *Cc:* Ask Geostatisticians <ai-geostats@jrc.it> > *Sent:* Sun, 6 March, 2011 7:25:38 PM > > *Subject:* Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set > of points with different cardinalities > > > > In your example Card(A Union B) is always = Card(A) =N and that's an > issue. > > What happens if B = {A + noisy points} (false positive)? > According to your calcul the score will be 1.0... and that's not right. > > Actually I think the answer is actually trivial. > (but I didn't think to formulate the problem in algebra terms) > > score = Card(A Intersection B)/Card(A Union B) > score = # corcordances/ (#discordances+#concordances) > score = # corcordances/ (# omissions (=Card(elements in A not included in > B))+ # false positives(=Card(elements in B not included in > A))+#concordances) > > Best, > Nicolas > > > On Sun, Mar 6, 2011 at 3:33 AM, Younes Fadakar <yfa.st...@ymail.com>wrote: > >> Dear Nicolas, >> >> Hope this can help you. >> >> Let have a look at my implementation: >> >> #-----the simplest implementation----- >> N = 100 #number of ref points=Crad(A) >> A.x = rand(N) #set A.x >> A.y = rand(N) #set A.y: coordinate pairs >> B.X = A.x[:-10] #set B = sampling >> B.Y = A.y[:-10] # has 10 points less than A >> # Card(B)-Card(A)=-10 >> M = PositionAccuracy(A,B) #as you defined=#concordances >> >> Score = M/N*100 #my score=normalized based on N >> # N=Card(A) >> >> So the Score will be always in [0,1], here is 0.9 or 90.00%. >> >> and >> >> #-----the realistic implementation----- >> N = 100 # >> A.x = rand(N) #set A.x >> A.y = rand(N) #set A.y: coordinate pairs >> B.x = shake(A.x,10%) #slightly repositions points >> B.y = shake(A.y,10%) # randomly with 10% move >> B.x = B.x+rand(N/10) #adds extra 10% rand points >> B.y = B.y+rand(N/10) #Card(B)=1.1*Card(A) >> >> M = PositionAccuracy(A,B) # >> >> Score = M/N*100 #my score=normalized based on N >> #N=Card(A) >> >> Again the Score will be always in [0,1]. >> This is what I used to generate the previously sent figures. >> >> >> Best Regards, >> >> Younes >> yfa.st...@ymail.com >> http://alghalandis.com >> ------------------------------ >> >> >> >> ------------------------------ >> *From:* Nicolas Maisonneuve <n.maisonne...@gmail.com> >> *To:* Younes Fadakar <yfa.st...@ymail.com> >> *Cc:* Ask Geostatisticians <ai-geostats@jrc.it> >> *Sent:* Wed, 2 March, 2011 6:27:48 PM >> *Subject:* Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set >> of points with different cardinalities >> >> Thanks for your support Younges >> >> my idea was inspired and adapted from the Kendall correlation coefficient >> (http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient >> ) but with the pb of cardinality. >> >> - number of concordances (accurate observations) >> - number of discordances(omission + false positive) >> and do a sum and then a normalisation to get something like 1.0 = max >> corcordance max 0.0 = max discordance. >> but I am not sure how to normalize: >> - the range of concordance [0, Card(A)] is smaller than the >> discordance [0, Card(A+B)] so anormalisation should be something like >> (2Card(A)+Card(B)) but I am not sure about that , and I am not sure >> the whole idea is right.. >> >> How did you normalize in your calcul? >> >> >> >> >> On Wed, Mar 2, 2011 at 5:50 AM, Younes Fadakar <yfa.st...@ymail.com> >> wrote: >> > Dear Nicolas, >> > >> > This is not the answer to your question but a try to implement your idea >> and >> > to have an experience with it. >> > Please see the attached, the output. >> > It seems the total score provided by the method is very dependent to the >> > 'r', the radius of search for neighbors around each ref point (A). >> > However, being able to define the right 'r', the score seems a realistic >> > measure of accuracy to me. >> > Of course, this is just a practical understanding hoping the community >> could >> > provide the statistical references. >> > Anyway, I liked the idea. >> > >> > Best Regards, >> > . >> > Younes >> > yfa.st...@ymail.com >> > http://alghalandis.com >> > ________________________________ >> > >> > >> > ________________________________ >> > From: Nicolas Maisonneuve <n.maisonne...@gmail.com> >> > To: ai-geostats@jrc.it >> > Sent: Mon, 28 February, 2011 6:21:49 PM >> > Subject: AI-GEOSTATS: Estimation of the position accuracy of 2 set of >> points >> > with different cardinalities >> > >> > Hi everyone, >> > >> > A simple question: >> > I have 1 set of 2D location points A that I use as reference. >> > I have another set of location points B generated by observations. >> > >> > Is there any standard method/measure to estimate a kind of position >> > accuracy error knowing that >> > - A and B dont have the same cardinality of elements e.g. B could have >> > more points than A? >> > - a point in A should be associated to only one point in B. >> > >> > For the moment I created my own error measure using 3 estimations. >> > for a given accuracy rate (<20 meters) I compute: >> > - O: number of omissions (when there is no observation in B closed >> > enough of a point in A) , >> > - FP: number of false positive (when a B point has been observed but >> > not closed to a A point - or already taken from another >> > observation) >> > - M: number of matching (when a B point is closed enought of a A point) >> > and then I aggregate the result = M- (O+FP) to get an indicator.. >> > >> > I am pretty sure there are other more traditional ways to do that. >> > >> > Thanks in advance >> > -NM >> > + >> > + To post a message to the list, send it to >> ai-geost...@jrc.ec.europa.eu >> > + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no >> subject >> > and "unsubscribe ai-geostats" in the message body. 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