Could you please help with the problem?

Thanks in advance.
Tomasz

When using the ‘maxsamples’ option for the sdparzen classifier, how are the training samples chosen? Is this completely random?

Best regards,
Davy

When showing image data using the sdimage function, one can set the label transparency to “very transparent” using the GUI. Is it also possible to set this property automatically in the code itself?

Cheers,
Davy

Line 204 in fixedcc.m function :
par - w.data{4};

Should it be replaced by ?
par = w.data{4};

Thank you for your help.

>> sdtree(ds)
??? Error using ==> perclass_mex
Tree training: Internal error

Error in ==>
C:\Documents\Nauka\ResearchEnv\PerClass\perClass_Demo\perclass\sdtree.p>sdtree at
181

I am using:
perClass Demo 3.1.2 (22-Dec-2011), Copyright (C) 2007-2011, PR Sys Design, All rights reserved

Regards.

I had the previous version of PRTools and tried to install the newer version (4.2.1) instead.

However, I was not able to find the mapping.p and mapping.m file with the newer version. I remember having it in the previous version.

The ‘Contents.m’ file in the newer version mentions the file. I downloaded the zip file containing the codes twice and tried. Is there any step of the installation that I am missing?

Thanks!

I have 2 questions regarding ROC analysis in perClass:

(1) When I use sddecide to create an operating point using the weighting-based option (‘w’), is there a way to retrieve the actual operating point (e.g. to mark it in the ROC curve)?

(2) [Probably this is very simple, but I cannot seem to find it] When doing the ROC analysis, how can I choose the operating point that minimizes the overall classification error (not the operating point that minimizes the per-class classification errors)?

Cheers,
Davy

NumOfClassifiers=5;

for clf=1:NumOfClassifiers

Wsvm{clf}=svc();
Wsvmtrn{clf}=Atrn*Wsvm{clf};
str_Wsvmtrn=struct(Wsvmtrn{clf});
SupportVectorIndices=Wsvmtrn{clf}.data{5};
Wsvmtrn{clf}.data{2}.data=Usom(SupportVectorIndices,:); % include U here ????
end

From the workspace I saw that the data field of the struct str_Wsvmtrn is a 1x5 cell : The Wsvmtrn.data{5} field is a 42x1 vector which represent the support vector indices and Wsvmtrn.data{3} field is a 43x1 vector which represent the weights (but why 43x1 and not 42x1 ?).As for the fields Wsvmtrn.data{1} (1x2 vector) and Wsvmtrn.data{2} ( a 42 by 2 data with 2 classes: [21 21]) ,what exactly do they represent? I thought that the Wsvmtrn.data{2} could be those Trainset’s objects that are pointed by the 42
support vector indices(as they have the same dimensionality) but the values aren’t the same!Please help me!I have no idea!

From the 100 weights of the membership matrix , I take the 42 which corresponds to the 42 support vector indices and now I want to update the struct str_Wsvmtrn with them.But I don’t know how.

Any response will be appreciated.
Thanks a lot.

1) I have changed the branch that handles the “not uniquely defined ROC”. I have replaced the find (linear search on the whole vector) to a smaller loop, exploiting the fact that the thresholds are sorted.

The code is much faster now. With 10000 objects of which 86288 are unique, these are the timings on my computer:
- original 15 secs
- modified 0.33 secs

2) I have added a threshold to the end of threshold vector in order to be of the same size of f (errors).
Assume you have 10 thresholds, currently the tenth threshold thr(10) corresponds to objects having “posterior probability” GREATER EQUAL (right?) than that threshold (the number is stored in f(10)). Since this is the last threshold, the ≥ is satisfied only by one object and as strict equality.

f(11) is about all object reject by the classifier. 
Adding this line:

You will a have a u(11) greater than u(10). This threshold will be strictly greater than any posterior probability generated, so all object are rejected at this threshold (in accordance with f(11))

the modified file is attached. Let me know what you think about.

Bye,
Giuseppe