java-ml-support — Java ML support mailinglist

[Java-ML-support] good luck TT MD

[TT MD <how...@ya...>]

 news   http://www.weinhaus-paus.de/tb/xgogcwxnfvwknv.htm    TT MD

 

Re: [Java-ML-support] KMedoids

[Thomas A. <ta...@br...>]

Hi Josh,

Sorry for the very  much delayed response.

If this is still an issue, can you provide a minimal dataset that 
illustrates the problem and share it so I can reproduce?

thanks,
Thomas
On 1/25/2013 1:32 PM, Hailpern, Joshua wrote:
> Kmeans works fine. However, when I try to run Kmedoids (same input) I get a NoSuchElementException.
>
>
> Exception in thread "main" java.util.NoSuchElementException
>
> at java.util.HashMap$HashIterator.nextEntry(HashMap.java:796)
>
> at java.util.HashMap$KeyIterator.next(HashMap.java:828)
>
> at net.sf.javaml.clustering.KMedoids.recalculateMedoids(KMedoids.java:150)
>
> at net.sf.javaml.clustering.KMedoids.cluster(KMedoids.java:86)
>
> at _06.cluster.KmeansModeling.<init>(KmeansModeling.java:71)
>
> at Debug.main(Debug.java:69)
>
> Please advise if possible, considering it works correctly on kmeans.
>
> -Josh
>
> ----------------------------------------------------
> Joshua Hailpern
> Research Scientist, HP Labs
>
> Homepage: www.hailpern.com/joshua/
>
>
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Re: [Java-ML-support] JavaML and Dynamic Time Warping (DTW) confusion

[<je...@hi...>]

Hi Thomas,

thank you for your email.

I've testet another java library called fastdtw
(http://code.google.com/p/fastdtw/) and on some non-representative test
examples it brought me the same results. Though I used the

---
DTW.getWarpDistBetween(TimeSeries tsI, TimeSeries tsJ);
---

for my following work.

Best regards,
Jens

Am 04.04.2013 15:54, schrieb Thomas Abeel:
> Hi Jens,
> 
> Sorry for the delay in my response.
> 
> If you want the classical dtw distance, I'd guess
> 
> DTW.getWarpDistBetween is the one you want to use.
> 
> Using that DTW is at your own risk. I didn't implement the DTW metrics
> myself and I am not yet 100% convinced the implementation is correct. If
> you could provide more insights, I'd be grateful. If you know about
> better implementations I'd be happy to include those, but it seems this
> is one of the very few available.
> 
> cheers,
> Thomas
> 
> On 3/1/2013 6:04 PM, je...@hi... wrote:
>> Hi,
>>
>> I need an implementation of calculating the DTW (dynamic time warping)
>> distance of timeseries patterns in order to compare and classify them.
>> At this time, the patterns are double[] arrays which is no problem since
>> I can make them to timeseries objects.
>>
>> In JavaML I found two implementations of DTW now I'm quite confused. The
>> two variations in JavaML package are:
>>
>> -- 
>> DTW.getWarpDistBetween(TimeSeries tsI, TimeSeries tsJ);
>> -- 
>>
>> and
>>
>> -- 
>> DTWSimilarity dtw = new DTWSimilarity();
>> dtw.measure(Instance i1, Instance i2);
>> -- 
>>
>> I looked into source code and the two functions getWarpDistBetween() and
>> measure() look quite different. In measure() there is a hint that it
>> maps the dtw distance with f(x)= 1 - (x / (1 + x)) to a similarity
>> measure. But when I apply this formula to the output of
>> getWarpDistBetween() it does not provide the same solution.
>>
>> Could you tell me, whats the difference of these two functions apart
>> from comversation to a similarity measure in measure()? And which
>> approach should I choose, if I want a classical dtw distance for two
>> timeseries?
>>
>> Thank you in advance!
>>
>> Best regards,
>> Jens
>>
>> ------------------------------------------------------------------------------
>>
>> Everyone hates slow websites. So do we.
>> Make your web apps faster with AppDynamics
>> Download AppDynamics Lite for free today:
>> http://p.sf.net/sfu/appdyn_d2d_feb
>> _______________________________________________
>> Java-ml-support mailing list
>> Jav...@li...
>> https://lists.sourceforge.net/lists/listinfo/java-ml-support
> 

Re: [Java-ML-support] JavaML and Dynamic Time Warping (DTW) confusion

[Thomas A. <ta...@br...>]

Hi Jens,

Sorry for the delay in my response.

If you want the classical dtw distance, I'd guess

DTW.getWarpDistBetween is the one you want to use.

Using that DTW is at your own risk. I didn't implement the DTW metrics myself and I am not yet 100% convinced the implementation is correct. If you could provide more insights, I'd be grateful. If you know about better implementations I'd be happy to include those, but it seems this is one of the very few available.

cheers,
Thomas

On 3/1/2013 6:04 PM, je...@hi... wrote:
> Hi,
>
> I need an implementation of calculating the DTW (dynamic time warping)
> distance of timeseries patterns in order to compare and classify them.
> At this time, the patterns are double[] arrays which is no problem since
> I can make them to timeseries objects.
>
> In JavaML I found two implementations of DTW now I'm quite confused. The
> two variations in JavaML package are:
>
> --
> DTW.getWarpDistBetween(TimeSeries tsI, TimeSeries tsJ);
> --
>
> and
>
> --
> DTWSimilarity dtw = new DTWSimilarity();
> dtw.measure(Instance i1, Instance i2);
> --
>
> I looked into source code and the two functions getWarpDistBetween() and
> measure() look quite different. In measure() there is a hint that it
> maps the dtw distance with f(x)= 1 - (x / (1 + x)) to a similarity
> measure. But when I apply this formula to the output of
> getWarpDistBetween() it does not provide the same solution.
>
> Could you tell me, whats the difference of these two functions apart
> from comversation to a similarity measure in measure()? And which
> approach should I choose, if I want a classical dtw distance for two
> timeseries?
>
> Thank you in advance!
>
> Best regards,
> Jens
>
> ------------------------------------------------------------------------------
> Everyone hates slow websites. So do we.
> Make your web apps faster with AppDynamics
> Download AppDynamics Lite for free today:
> http://p.sf.net/sfu/appdyn_d2d_feb
> _______________________________________________
> Java-ml-support mailing list
> Jav...@li...
> https://lists.sourceforge.net/lists/listinfo/java-ml-support

[Java-ML-support] JavaML and Dynamic Time Warping (DTW) confusion

[<je...@hi...>]

Hi,

I need an implementation of calculating the DTW (dynamic time warping)
distance of timeseries patterns in order to compare and classify them.
At this time, the patterns are double[] arrays which is no problem since
I can make them to timeseries objects.

In JavaML I found two implementations of DTW now I'm quite confused. The
two variations in JavaML package are:

--
DTW.getWarpDistBetween(TimeSeries tsI, TimeSeries tsJ);
--

and

--
DTWSimilarity dtw = new DTWSimilarity();
dtw.measure(Instance i1, Instance i2);
--

I looked into source code and the two functions getWarpDistBetween() and
measure() look quite different. In measure() there is a hint that it
maps the dtw distance with f(x)= 1 - (x / (1 + x)) to a similarity
measure. But when I apply this formula to the output of
getWarpDistBetween() it does not provide the same solution.

Could you tell me, whats the difference of these two functions apart
from comversation to a similarity measure in measure()? And which
approach should I choose, if I want a classical dtw distance for two
timeseries?

Thank you in advance!

Best regards,
Jens

[Java-ML-support] (no subject)

[TT MD <how...@ya...>]

         http://www.personalevent.es/bv/phrg4/l2dfgww495g312jpq4spjbj2&ulf2        TT MD

[Java-ML-support] KMedoids

[Hailpern, J. <jos...@hp...>]

Kmeans works fine. However, when I try to run Kmedoids (same input) I get a NoSuchElementException.


Exception in thread "main" java.util.NoSuchElementException

at java.util.HashMap$HashIterator.nextEntry(HashMap.java:796)

at java.util.HashMap$KeyIterator.next(HashMap.java:828)

at net.sf.javaml.clustering.KMedoids.recalculateMedoids(KMedoids.java:150)

at net.sf.javaml.clustering.KMedoids.cluster(KMedoids.java:86)

at _06.cluster.KmeansModeling.<init>(KmeansModeling.java:71)

at Debug.main(Debug.java:69)

Please advise if possible, considering it works correctly on kmeans.

-Josh

----------------------------------------------------
Joshua Hailpern
Research Scientist, HP Labs

Homepage: www.hailpern.com/joshua/

Re: [Java-ML-support] NullPointerException during NaiveBayesClassifier.buildClassifier()

[Tom D. <tom...@gm...>]

Thanks for the reply. The way in which I'm constructing training data is
the following:

  private Dataset buildTrainingDataset(Object[][] data, int
sampleIntervalWidth, int futureDays) throws StorageException{
    Log.logMessage(LogLevel.DEBUG,
"Engine.buildDataset(",data,sampleIntervalWidth,")");

    // The data set we are building up.
    Dataset ds = new DefaultDataset();

    // Create all samples
    for(int i = 0; i + sampleIntervalWidth <= data.length - futureDays;
i++){
      // Add the sample domain data
      DenseInstance di = new DenseInstance(buildSampleDoubleArr(data, i,
sampleIntervalWidth));

      // Get the "current" and future price for this sample
      double original =
Double.valueOf(data[i+sampleIntervalWidth-1][data[0].length-1].toString());
      double future =
Double.valueOf(data[i+sampleIntervalWidth+futureDays-1][data[0].length-1].toString());

      // Find the relative growth or decline
      Double classification = (future - original) / original;

      // Round the growth or decline to the nearest hundredth
      classification = ((int) ((classification * 100.0)+0.5)) / 100.0;

      // Convert to percentage
      classification = classification * 100;

      // Use the percentage growth
      di.setClassValue(classification);

      // Add the sample to the data set
      ds.add(di);
    }

    return ds;
  }

Summary: I am indeed setting a class value for each Instance in the
training Dataset, and the values are of type Double. Please let me know if
you need to see anything else.

Thanks,

Tom



On Mon, Nov 12, 2012 at 11:16 AM, Thomas Abeel <th...@ab...> wrote:

>  Hi Tom,
>
> Did you set class labels in the method buildTrainingDataset?
>
> Training data needs class labels.
>
> You'll need to provide a fully contained working code sample to get more
> help. We can't help you if the two methods that create the objects that are
> responsible for the exception are not provided.
>
> cheers,
> Thomas
>
>  On 11/11/2012 4:29 PM, Tom Deering wrote:
>
> I am new to Java-ML, so I apologize if the answer to this is trivial:
>
>  I'm trying to construct and use a Classifier in the following way:
>
>          // Construct an instance of the selected classifier
>         Classifier c = constructClassifier(classifier); // This returns a
> NaiveBayesClassifier
>         ...
>         // Build a training Dataset for the symbol using the historical
> data
>         Dataset trainingData = buildTrainingDataset(dataArr,
> sampleIntervalWidth, futureDays);
>         ...
>         // Train the classifier
>         c.buildClassifier(trainingData); // NullPointerException happens
> here
>         ...
>         // Now, classify against the latest sample
>         DenseInstance latestSample = new
> DenseInstance(buildSampleDoubleArr(dataArr, dataArr.length -
> sampleIntervalWidth - 1, sampleIntervalWidth));
>         ...
>         // Store the result
>         results.put(s, (Double)c.classify(latestSample));
>
>  Each time I try this, I seem to be getting a NullPointerException from
> the innards of AbstractBayesianClassifier with the following stack trace:
>
>  Exception in thread "AWT-EventQueue-0" java.lang.NullPointerException
>  at
> net.sf.javaml.classification.bayes.AbstractBayesianClassifier.calculateClassFreqs(AbstractBayesianClassifier.java:207)
>  at
> net.sf.javaml.classification.bayes.AbstractBayesianClassifier.buildClassifier(AbstractBayesianClassifier.java:103)
>  at
> net.sf.javaml.classification.bayes.NaiveBayesClassifier.buildClassifier(NaiveBayesClassifier.java:42)
>  at tdeering.iastate.cs572.ainvestor.engine.Engine.rank(Engine.java:106)
>
>  Can anyone tell me what I am doing wrong? Am I missing a step where I
> tell my Classfier about all possible classes?
>
>  Thanks,
>
>  Tom
>
>
> ------------------------------------------------------------------------------
> Everyone hates slow websites. So do we.
> Make your web apps faster with AppDynamics
> Download AppDynamics Lite for free today:http://p.sf.net/sfu/appdyn_d2d_nov
>
>
>
> _______________________________________________
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>
>
>

Re: [Java-ML-support] NullPointerException during NaiveBayesClassifier.buildClassifier()

[Thomas A. <th...@ab...>]

Hi Tom,

Did you set class labels in the method buildTrainingDataset?

Training data needs class labels.

You'll need to provide a fully contained working code sample to get more 
help. We can't help you if the two methods that create the objects that 
are responsible for the exception are not provided.

cheers,
Thomas

On 11/11/2012 4:29 PM, Tom Deering wrote:
> I am new to Java-ML, so I apologize if the answer to this is trivial:
>
> I'm trying to construct and use a Classifier in the following way:
>
>         // Construct an instance of the selected classifier
>         Classifier c = constructClassifier(classifier); // This 
> returns a NaiveBayesClassifier
>         ...
>         // Build a training Dataset for the symbol using the 
> historical data
>         Dataset trainingData = buildTrainingDataset(dataArr, 
> sampleIntervalWidth, futureDays);
>         ...
>         // Train the classifier
> c.buildClassifier(trainingData); // NullPointerException happens here
>         ...
>         // Now, classify against the latest sample
>         DenseInstance latestSample = new 
> DenseInstance(buildSampleDoubleArr(dataArr, dataArr.length - 
> sampleIntervalWidth - 1, sampleIntervalWidth));
>         ...
>         // Store the result
>         results.put(s, (Double)c.classify(latestSample));
>
> Each time I try this, I seem to be getting a NullPointerException from 
> the innards of AbstractBayesianClassifier with the following stack trace:
>
> Exception in thread "AWT-EventQueue-0" java.lang.NullPointerException
> at 
> net.sf.javaml.classification.bayes.AbstractBayesianClassifier.calculateClassFreqs(AbstractBayesianClassifier.java:207)
> at 
> net.sf.javaml.classification.bayes.AbstractBayesianClassifier.buildClassifier(AbstractBayesianClassifier.java:103)
> at 
> net.sf.javaml.classification.bayes.NaiveBayesClassifier.buildClassifier(NaiveBayesClassifier.java:42)
> at tdeering.iastate.cs572.ainvestor.engine.Engine.rank(Engine.java:106)
>
> Can anyone tell me what I am doing wrong? Am I missing a step where I 
> tell my Classfier about all possible classes?
>
> Thanks,
>
> Tom
>
>
> ------------------------------------------------------------------------------
> Everyone hates slow websites. So do we.
> Make your web apps faster with AppDynamics
> Download AppDynamics Lite for free today:
> http://p.sf.net/sfu/appdyn_d2d_nov
>
>
> _______________________________________________
> Java-ml-support mailing list
> Jav...@li...
> https://lists.sourceforge.net/lists/listinfo/java-ml-support

[Java-ML-support] NullPointerException during NaiveBayesClassifier.buildClassifier()

[Tom D. <tom...@gm...>]

I am new to Java-ML, so I apologize if the answer to this is trivial:

I'm trying to construct and use a Classifier in the following way:

        // Construct an instance of the selected classifier
        Classifier c = constructClassifier(classifier); // This returns a
NaiveBayesClassifier
        ...
        // Build a training Dataset for the symbol using the historical data
        Dataset trainingData = buildTrainingDataset(dataArr,
sampleIntervalWidth, futureDays);
        ...
        // Train the classifier
        c.buildClassifier(trainingData); // NullPointerException happens
here
        ...
        // Now, classify against the latest sample
        DenseInstance latestSample = new
DenseInstance(buildSampleDoubleArr(dataArr, dataArr.length -
sampleIntervalWidth - 1, sampleIntervalWidth));
        ...
        // Store the result
        results.put(s, (Double)c.classify(latestSample));

Each time I try this, I seem to be getting a NullPointerException from the
innards of AbstractBayesianClassifier with the following stack trace:

Exception in thread "AWT-EventQueue-0" java.lang.NullPointerException
at
net.sf.javaml.classification.bayes.AbstractBayesianClassifier.calculateClassFreqs(AbstractBayesianClassifier.java:207)
at
net.sf.javaml.classification.bayes.AbstractBayesianClassifier.buildClassifier(AbstractBayesianClassifier.java:103)
at
net.sf.javaml.classification.bayes.NaiveBayesClassifier.buildClassifier(NaiveBayesClassifier.java:42)
at tdeering.iastate.cs572.ainvestor.engine.Engine.rank(Engine.java:106)

Can anyone tell me what I am doing wrong? Am I missing a step where I tell
my Classfier about all possible classes?

Thanks,

Tom

Re: [Java-ML-support] Read MCL result from SparseMatrix representation

[Chris C. <chr...@ya...>]

Hi Thomas,
The code in net.sf.javaml.clustering.mcl.MCL.java does the trick, provided I fill the matrix entirely (else my triangular superior matrix does not favour random walks ;-))

Thanks a lot !
Chris


________________________________
 From: Thomas Abeel <th...@ab...>
To: Chris Csdev <chr...@ya...>; jav...@li... 
Sent: Thursday, August 23, 2012 6:09 PM
Subject: Re: [Java-ML-support] Read MCL result from SparseMatrix representation
 

Hi Chris,

I think your best bet is to go into the SparseMatrix code or into
    the MCL clustering class code.

net.sf.javaml.clustering.mcl.MCL.java has a method that extracts the
    clusters from the sparse matrix. You can check how it's done there
    and that should give you a good idea on how to do it yourself.

cheers,
Thomas



On 8/22/2012 5:47 PM, Chris Csdev wrote:

Dear all,
>
>
>
>I am investigating java-ml for its Markov CLustering implementation.
>In my current project, I have a in-memory model of 2D adjency matrix (a two-dimensional array of the double primitive type : double[][]).
>
>
>Note that this matrix is sparse in the sense that it is triangular superior (I am using a symetric distance, hence I don't fill the inferior triangle of my 2D matrix).
>
>
>Fortunately, I can convert it into a SparseMatrix using the associated constructor and then run the MCL algorithm.
>
>
>The code fragment from my program is as follows.
>
>
>double[][] adjencyMatrix;
>
>
>// omitted operations to fill adjencyMatrix
>
>
>SparseMatrix smatrix = new SparseMatrix(adjencyMatrix);
>MarkovClustering mcl = new MarkovClustering();
>double maxResidual = 0.001;
>double gammaExp = 2.0;
>double loopGain = 0.;
>double zeroMax = 0.001;
>SparseMatrix matrix = mcl.run(smatrix, maxResidual, gammaExp, loopGain, zeroMax);
>
>
>From this point, I understand that "matrix" helds a "sparse matrix with row-major format" of my results, that is, a compressed version of the traditional 2D matrix resulting from MCL. I am able to read the MCL results from a traditional 2D data structure, yet I don't know to grasp how to unveil clusters from the resulting SparseMatrix.
>
>
>I have been unable to find the proper documentation to understand the SparseMatrix data structure used in java-ml. Can you point me to the paper or documentation that you may have used to create the data structure ? If no, is it possible to convert a SparseMatrix into a double[][] (which I suspect to be performance killing) ?
>
>
>Thanks you in advance for any help !
>
>
>Best Regards,
>
>
>Chris
>
>
>------------------------------------------------------------------------------
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and 
threat landscape has changed and how IT managers can respond. Discussions 
will include endpoint security, mobile security and the latest in malware 
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
>
>
>_______________________________________________
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Re: [Java-ML-support] Read MCL result from SparseMatrix representation

[Thomas A. <th...@ab...>]

Hi Chris,

I think your best bet is to go into the SparseMatrix code or into the 
MCL clustering class code.

net.sf.javaml.clustering.mcl.MCL.java has a method that extracts the 
clusters from the sparse matrix. You can check how it's done there and 
that should give you a good idea on how to do it yourself.

cheers,
Thomas



On 8/22/2012 5:47 PM, Chris Csdev wrote:
> Dear all,
>
> I am investigating java-ml for its Markov CLustering implementation.
> In my current project, I have a in-memory model of 2D adjency matrix 
> (a two-dimensional array of the double primitive type : double[][]).
>
> Note that this matrix is sparse in the sense that it is triangular 
> superior (I am using a symetric distance, hence I don't fill the 
> inferior triangle of my 2D matrix).
>
> Fortunately, I can convert it into a SparseMatrix using the associated 
> constructor and then run the MCL algorithm.
>
> The code fragment from my program is as follows.
>
> double[][] adjencyMatrix;
>
> // omitted operations to fill adjencyMatrix
>
> SparseMatrix smatrix = new SparseMatrix(adjencyMatrix);
> MarkovClustering mcl = new MarkovClustering();
> double maxResidual = 0.001;
> double gammaExp = 2.0;
> double loopGain = 0.;
> double zeroMax = 0.001;
> SparseMatrix matrix = mcl.run(smatrix, maxResidual, gammaExp, 
> loopGain, zeroMax);
>
> From this point, I understand that "matrix" helds a "sparse matrix 
> with row-major format" of my results, that is, a compressed version of 
> the traditional 2D matrix resulting from MCL. I am able to read the 
> MCL results from a traditional 2D data structure, yet I don't know to 
> grasp how to unveil clusters from the resulting SparseMatrix.
>
> I have been unable to find the proper documentation to understand the 
> SparseMatrix data structure used in java-ml. Can you point me to the 
> paper or documentation that you may have used to create the data 
> structure ? If no, is it possible to convert a SparseMatrix into a 
> double[][] (which I suspect to be performance killing) ?
>
> Thanks you in advance for any help !
>
> Best Regards,
>
> Chris
>
>
> ------------------------------------------------------------------------------
> Live Security Virtual Conference
> Exclusive live event will cover all the ways today's security and
> threat landscape has changed and how IT managers can respond. Discussions
> will include endpoint security, mobile security and the latest in malware
> threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
>
>
> _______________________________________________
> Java-ml-support mailing list
> Jav...@li...
> https://lists.sourceforge.net/lists/listinfo/java-ml-support

[Java-ML-support] Read MCL result from SparseMatrix representation

[Chris C. <chr...@ya...>]

Dear all,


I am investigating java-ml for its Markov CLustering implementation.
In my current project, I have a in-memory model of 2D adjency matrix (a two-dimensional array of the double primitive type : double[][]).

Note that this matrix is sparse in the sense that it is triangular superior (I am using a symetric distance, hence I don't fill the inferior triangle of my 2D matrix).

Fortunately, I can convert it into a SparseMatrix using the associated constructor and then run the MCL algorithm.

The code fragment from my program is as follows.

double[][] adjencyMatrix;

// omitted operations to fill adjencyMatrix

SparseMatrix smatrix = new SparseMatrix(adjencyMatrix);
MarkovClustering mcl = new MarkovClustering();
double maxResidual = 0.001;
double gammaExp = 2.0;
double loopGain = 0.;
double zeroMax = 0.001;
SparseMatrix matrix = mcl.run(smatrix, maxResidual, gammaExp, loopGain, zeroMax);

From this point, I understand that "matrix" helds a "sparse matrix with row-major format" of my results, that is, a compressed version of the traditional 2D matrix resulting from MCL. I am able to read the MCL results from a traditional 2D data structure, yet I don't know to grasp how to unveil clusters from the resulting SparseMatrix.

I have been unable to find the proper documentation to understand the SparseMatrix data structure used in java-ml. Can you point me to the paper or documentation that you may have used to create the data structure ? If no, is it possible to convert a SparseMatrix into a double[][] (which I suspect to be performance killing) ?

Thanks you in advance for any help !

Best Regards,

Chris

Re: [Java-ML-support] Java ML library , question

[Thomas A. <th...@ab...>]

Java-ML does not allow direct interaction with a DB. You would have to 
write the code to extract data from the DB and convert it in the typical 
Dataset and Instance structure that Java-ML requires.

cheers,
Thomas

On 3/4/2012 7:30 PM, Mask Ar wrote:
> To the Java ML developer team,
>
> First of all I would like to thank you for sharing this great library
> with the rest of the world , currently I am developing a small
> application for academical purposes in which I need to use some of your
> clustering methods , the library has worked perfectly when working with
> files , but I am afraid that the information I will use is stored in a
> Database, so far i have not found anyway of connecting to a DB through
> your api ,nor a way to fill a Dataset structure with a resultset , array
> , list , etc. thus, I would certainly appreciate if you could provide me
> some guidance with this part, forgive me if the question is silly but
> i'm definitely not a computer expert.
>
> Best Regards,
>
> Mask Ar,
> Msc. Candidate
> National Cheng Chi University
> Taipei , ROC, Taiwan
>
>
> ------------------------------------------------------------------------------
> Virtualization&  Cloud Management Using Capacity Planning
> Cloud computing makes use of virtualization - but cloud computing
> also focuses on allowing computing to be delivered as a service.
> http://www.accelacomm.com/jaw/sfnl/114/51521223/
>
>
>
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[Java-ML-support] Java ML library , question

[Mask Ar <rug...@ya...>]

To the Java ML developer team, 

First of all I would like to thank you for sharing this great library with the rest of the world , currently I am developing a small application for academical purposes in which I need to use some of your clustering methods , the library has worked perfectly when working with files , but I am afraid that the information I will use is stored in a Database, so far i have not found anyway of connecting to a DB through your api ,nor a way to fill a Dataset structure with a resultset , array , list , etc. thus, I would certainly appreciate if you could provide me some guidance with this part, forgive me if the question is silly but i'm definitely not a computer expert.

Best Regards,

Mask Ar,
Msc. Candidate
National Cheng Chi University
Taipei , ROC, Taiwan

[Java-ML-support] LibSVM.getWeights()

[TT MD <how...@ya...>]

Dear Sirs,

I'm using Java-ml for my project which is a multi-class classification problem.

I successfully trained my data using Linear (kernel_type = svm_parameter.LINEAR) LibSVM, but I want to get the weights for each class
- which, as I understand, the parameters for my hypothesis.
I used the method LibSVM.getWeights().


As I have 20 classes and 400 features, I expected to get a double array (double[]) of the size 400x20 = 8000, but only got 400 instead.

I did many Google searches and get to know that LibSVM use one-against-one approach, but still I think there may be more weights than 400.
So I don't quite sure if this is a mistake or not. May be LibSVM discribed "weights" something I haven't understood, or maybe what I received
was only the weights for the 1st class, ... so I'm writing this and hope that someone could explain it to me, or give me some hints / links/ documents
for the problem.

I thank that someone in advance, and wish you all the best for the wonderful careers you are making.

A beginner of machine learning.

P.S: I also noticed the rawDecisionValues() method, but not quite had any idea what it means when you get a double array of these values.
I read some papers on SVM, but can't tell what-is-what or which is which from the things in the papers and the things in the API. 

So if you please, enlight me about this too.

Sorry for my smattering and the time it took you for reading this.

Re: [Java-ML-support] wavelet support in java-ml

[Thomas A. <th...@ab...>]

On 12/8/2011 5:48 AM, hafid bbbbbbbbb wrote:
> Hello everybody,
> I an new to this mailing list . I wonder if there is a support for Haar
> wavelet in this library to get the wavelet coefficients of a sound
> signal before training a classifier.

I don't think there is wavelet support at the moment.
  	

> Is it possible to save a classifier after a long traning session ? any
> help is welcome in advance !

You can serialize any classifier and store it to a file.

cheers,
Thomas

> Best regards!
> Hafid
>
>
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[Java-ML-support] wavelet support in java-ml

[hafid b. <ch...@ya...>]

Hello everybody,
I an new to this mailing list .  I wonder if there is a support for  Haar wavelet in this library to get the wavelet coefficients of a sound signal before training a classifier.
Is it possible to save a classifier after a long traning session ? any help is welcome in advance !
Best regards!
Hafid

Re: [Java-ML-support] Creating a sparse Instance

[Thomas A. <th...@ab...>]

Make sure you have the statement

import net.sf.javaml.core.Instance
and  not
import weka.core.SparseInstance

cheers,
Thomas

On 4/20/2011 11:03 AM, Naveen Somasundaram wrote:
> Hello everyone!
>
> I followed the tutorial listed in the website to create a sparseInstance.
> Instance instanceVar = (Instance)new SparseInstance(10);
>
> I am getting an exception:
>           weka.core.SparseInstance cannot be cast to
> net.sf.javaml.core.Instance
>
> Thanks in advance.
>
>
>
>
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[Java-ML-support] Creating a sparse Instance

[Naveen S. <nav...@gm...>]

Hello everyone!

I followed the tutorial listed in the website to create a sparseInstance.
Instance instanceVar = (Instance)new SparseInstance(10);

I am getting an exception:
         weka.core.SparseInstance cannot be cast to 
net.sf.javaml.core.Instance

Thanks in advance.

[Java-ml-support] test support

[Thomas A. <tho...@gm...>]