Jacques CHEVALIER | 17 Nov 21:11 2014
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Xmx Resolved

Thanks to Michael Hall and Elbe Frank for their answers. In fact on an Apple computer, it’s not easy to change Info.plist in  weka-3-7-11-oracle-jvm.dmg. But finally I  successfully used  the following process

1- With Pacifist.app you can extract weka-3-7-11-oracle-jvm.app on the desktop

2- By pressing the ctrl key while clicking  weka-3-7-11-oracle-jvm.app in the Finder, you can display the Contents folder

3- By clicking on Info.plist in this folder, Xcode is active and you can change the memory.max  in JVMOPtions Item0. 

I choosed 4g and in fact, in weka, Help>System Info gives 3959MB.

A few years ago, when Apple supported Java, there was JavaTools.app which worked directly on weka.app but you don’t find it no more in Mac Apple 10.10 (Yosemite).

Excuse  my bad english as I am french speaking.


Jacques CHEVALIER









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echalom | 17 Nov 11:30 2014
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How do I use Weka's MLP output prediction model in Matlab?

Hi,
I'm trying to do prediction in Matlab using the output of Weka's single
layer MLP.
In my case I have a single layer with 100 nodes and 200 features.
I'm running Weka 3.7.10, and the options for
weka.classifiers.functions.MultilayerPerceptron is
-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a

Node 0 is Linear in my case, not Sigmoid.
Node 1-100 are all Sigmoid. I didn't use the -C or -I option, so by default
Weka normalizes the data.

When I try to compute the predicted value in Matlab, I don't get the same
value as on Weka.
In the following code:
featvals is my feature vector (stored as 200 rows)
featweightsall is the 200 feature weights from each of the 100 nodes that
Weka provides.
nodeweights is the 100 nodeweights that Weka provides.
nodethresh is the 101 thresholds that Weka provides (rows 2-101 contain the
threshold for Nodes 1-100, and row 1 contains the threshold for Node 0).

    featvalsnorm = interp1([min(featvals) max(featvals)],[-1
1],featvals,'linear');
    featvalsnorm2 = (featvals -
min(featvals))/(max(featvals)-min(featvals));
    for j = 1:100
        featweights = featweightsall(  ((j-1)*200+1):(j*200));
        x = sum(featvalsnorm.*featweights) + nodethresh(j+1);
        nodenorm(j) = 1/(1+exp(-x));
    end
    predvalnorm = sum(nodenorm.*nodeweights)+nodethresh(1);
    predval = interp1([-1 1],[min(featvals)
max(featvals)],predvalnorm,'linear');

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Roni Shouval | 17 Nov 18:56 2014
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variable importance ADT

Hello, 

is there a way to calculate the conditional variable importance for alternating decision trees? 


I know it is possible for random forests (http://www.biomedcentral.com/1471-2105/9/307)
, however not sure if it can be done for ADTs. If not possible, is their a way to determine variable importance in ADT, apart from the boosting iteration on which they were added?
Thanks
Roni 
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Leonardo Lion | 17 Nov 18:18 2014
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Probability Distribution

Hi,

In Weka how to determine probability distribution for the attributes before and after classification?

Thanks.
Leo

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long zhao | 17 Nov 16:05 2014
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How to detect which attribute is no use for classification?

Dear,
If I have 10 attribute and do a classification, how can I know which attribute is almost no use in the model? Just like if I add a new attribute and put same value 0 for all instances, I wish I can find it out automatically. 

Kind regards,
Long
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double d s | 17 Nov 14:20 2014
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attribute selection

Dear all, 

I have this dataset [(attributes: E, F, G, H, A, B, C and D) and (Class: Results)]

E F G H A B C D Results
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 7
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 7
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 7
-1 -2 -3 -4 1 2 3 4 7
-1 -2 -3 -4 1 2 3 4 7
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 7
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 6
-1 -2 -3 -4 1 2 3 4 7

I applied attribute selection using AttributeSelectedClassifier with base classifier LinearRegression. Weka results showed the selected attribute is "G". 

That led me to ask this question: 

a-What is the standard technique that used in Weka (i.e. distance) to perform attribute selection? Or why I had this result?

b- Is this technique considered can also be applied even with with other cases. For instance, having  numeric attributes, nominal class and utilizing AttributeSelectedClassifier with base classifier "NaiveBayse"?
To make it clear, if attribute selection in the first case (a) was based on distance, does that mean the distance can be used to select the attributes in case b?



Thanks.

Best Regards,
Sandler
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Roni Shouval | 16 Nov 18:00 2014
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Alternating decision trees

Hello,
Would appreciate your help in understanding how are attributes with missing values handled in the alternating decision tree algorithm?
Thx
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lovish | 16 Nov 16:03 2014
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One Class SVM in Weka 3.6.11

I am trying to use one class SVM. The training file contains all the
instances belonging to class "-1" but the testing file contains attributes
belonging to both "-1" and "1" . But when I try to run the classification
process , it says that "Training and testing files are not compatible.

The links to my training and testing file are :

Training : http://pastebin.com/WCwcAJNw
Testing : http://pastebin.com/iTxexNKR

Please help me to resolve the issue. I have tried to replace {1,-1} class in
test to {Nan} ( weka 3.6.11) but the issue remains the same.

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Martin | 16 Nov 16:02 2014
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Re: Filter values numeric to nominal

I meant using mix mode (string-number) might affect negatively on the filter. Thus, I suggest using terms like "Group A".

Good luck.
Martin

On Nov 16, 2014 10:33 PM, "littledevils326 [via WEKA]" <[hidden email]> wrote:
I have an attribute with numeric value from 0 - 99. I want to convert these values to nominal ie instead of having 99 distinct values, i want to have 0-16, 17-25, 26-30, 30-34 and so on...
I tried using the unsupervised filter 'NumericToNominal' and setting the attributeIndices to first-16, 17-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-last
However, when I apply this filter to my attribute, I get a message saying "Problem filtering instances: invalid range list first-16"
What am I doing wrong? Am I using the correct filtering option?
I'm new to Weka, can you tell me what I'm doing wrong?

If you reply to this email, your message will be added to the discussion below:
http://weka.8497.n7.nabble.com/Filter-values-numeric-to-nominal-tp32687.html
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NAML

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Martin | 16 Nov 15:53 2014
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Re: Filter values numeric to nominal

Try not to use the word "first" because it is cinsidered as string (not nominal).

On Nov 16, 2014 10:33 PM, "littledevils326 [via WEKA]" <[hidden email]> wrote:
I have an attribute with numeric value from 0 - 99. I want to convert these values to nominal ie instead of having 99 distinct values, i want to have 0-16, 17-25, 26-30, 30-34 and so on...
I tried using the unsupervised filter 'NumericToNominal' and setting the attributeIndices to first-16, 17-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-last
However, when I apply this filter to my attribute, I get a message saying "Problem filtering instances: invalid range list first-16"
What am I doing wrong? Am I using the correct filtering option?
I'm new to Weka, can you tell me what I'm doing wrong?

If you reply to this email, your message will be added to the discussion below:
http://weka.8497.n7.nabble.com/Filter-values-numeric-to-nominal-tp32687.html
To unsubscribe from WEKA, click here.
NAML

View this message in context: Re: Filter values numeric to nominal
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Jacques CHEVALIER | 15 Nov 07:44 2014
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Xmx command

I work under  JavaForOSX2014_001 (java 1.6.0_65-b14-462) in Mac OS 10.10 Yosemite. How can I extend the memory for weka-3-6-11-apple-jvm ?

Jacques CHEVALIER






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Gmane