karimyoussef91 / moa Goto Github PK
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Automatically exported from code.google.com/p/moa
Purpose of code changes on this branch:
Dynamic arff file format
Original issue reported on code.google.com by [email protected]
on 21 Oct 2011 at 11:04
moa.streams.clustering.FileStream
line 191 reads:
numAttsOption = new IntOption("numAtts", 'a',"", removeAttributes.length+1);
should be:
numAttsOption = new IntOption("numAtts", 'a',"", instances.numAttributes() -
removeAttributes.length);
Leads to incorrect functioning of clustering GUI.
Original issue reported on code.google.com by [email protected]
on 23 Nov 2011 at 4:06
What steps will reproduce the problem?
1. Connect to "Iris" collection in Mongo using Java API. Get DBCursor object
that points to whole learning set.
2.Initialize dataSet with "schema" from first Mongo document from the
collection. Treat every Attribute as nominal (use DBCursor.distinct()) or
string type.
3. Declare NaiveBayes and initialize it by prepareForUse().
4. Convert BSONs to Instance using BSON.toMap() and SparseInstance(double
weight, double[] atrValues) constructor with given attributes values that match
their value index in dataSet. Use setDataset() to give reference for Attributes.
5. Train NaiveBayes by NaiveBayes.trainOnInstanceImp() or
NaiveBayes.trainOnInstance().
6. Print out NaiveBayes.getVotesForInstance(inst).
What is the expected output? What do you see instead?
There should be probabilities for 3 Iris classes that appears non-Random and
greater than numeric 0.
Instead I get values that differ although are 'very small' (i.e. E-15) for
every class value, Basic(...)Evalutator gives values of 0 for correctly
classified and the same for incorrectly classified.
What version of the product are you using? On what operating system?
Linux Ubuntu 12.04.5 / MOA 2014.04a
Original issue reported on code.google.com by p.s.gliniecki
on 2 Sep 2014 at 1:54
What steps will reproduce the problem?
1. The problem is always present, but hidden from user.
What is the expected output? What do you see instead?
Budget is expected to be respected.
What version of the product are you using? On what operating system?
MOA 2012.08 Ubuntu 10.10
Please provide any additional information below.
To correct, just put "this.costLabeling++;" on line 196 of
ActiveClassifier.java.
Original issue reported on code.google.com by [email protected]
on 1 Nov 2012 at 1:50
Purpose of code changes on this branch:
Classifiers that can deal with dynamic addition, deletion and modification of
attributes in the stream
Original issue reported on code.google.com by [email protected]
on 21 Oct 2011 at 11:03
Reproducing problem:
GaussianEstimator estimator = new GaussianEstimator();
estimator.addObservation(7.0, 2.0);
estimator.addObservation(9.0, 2.0);
System.out.println(estimator.getMean()); // prints 7.5, expected 8.0
System.out.println(estimator.getVariance()); // prints 1.0, expected 1.333
MOA Version: 3a28638d4f55 May 2, 2012
Patch attached.
I am pretty sure the variance calculation in addObservation(GaussianEstimator)
is also wrong, but I don't know how to fix it.
Original issue reported on code.google.com by [email protected]
on 14 Nov 2012 at 9:38
Attachments:
Purpose of code changes on this branch:
Re-use of the same stream in different tasks using the observer pattern.
Original issue reported on code.google.com by [email protected]
on 21 Oct 2011 at 11:06
"For the system to automatically take care of option handling, the options
need to be public members of the class, that extend the moa.options.Option
type."
Line 115 of AbstractOptionHandler.java:
>>> if (fName.endsWith("Option")) {
should be (one of the possible solutions):
>>> if (fType.getName().endsWith("Option")) {
Original issue reported on code.google.com by [email protected]
on 16 Oct 2012 at 6:50
Purpose of code changes on this branch:
Use of generics in instances to allow several different examples to feed MOA
Learner<E>
AbstractClassifier extends Learner<Instance>
Original issue reported on code.google.com by [email protected]
on 21 Oct 2011 at 10:52
What steps will reproduce the problem?
Create a filtered stream with more than one filter where the filters are order
dependent. (i.e. one filter depends on another)
FilterA depends on FilterB
FilteredStream -f FilterA,FilterB
What is the expected output?
FilterA is applied to the stream first, followed by FilterB.
What do you see instead?
FilterB will be executed first, and fail.
What version of the product are you using?
moa-20111003
On what operating system?
Windows 7
Original issue reported on code.google.com by [email protected]
on 23 Nov 2011 at 3:58
Budget condition should be tested with future value.
Look at the additional code "+ 1" in the line below.
costNow = (this.costLabeling - this.numInstancesInitOption.getValue() + 1) /
((double) this.iterationControl - this.numInstancesInitOption.getValue());
Corrected in clone:
https://code.google.com/r/dpsabc-mostly-active-learning-mods/
Original issue reported on code.google.com by [email protected]
on 8 Dec 2012 at 10:52
Version: 2012.09-SNAPSHOT
RandomRBFGeneratorEvents generates Instance objects correctly. The problem
occurs when they are generated via noise. In that case
weka.core.Instance.toString() throws exception, which is annoying because is
not possible to use WriteStreamToARFFFile for example.
Here is an example:
import moa.streams.clustering.RandomRBFGeneratorEvents;
import weka.core.Instance;
public class InstanceGenerator {
private RandomRBFGeneratorEvents randomRBFGeneratorEvents;
public InstanceGenerator() {
randomRBFGeneratorEvents = new RandomRBFGeneratorEvents();
//randomRBFGeneratorEvents.noiseLevelOption.setValue(0);
randomRBFGeneratorEvents.prepareForUse();
}
public Instance next() {
return randomRBFGeneratorEvents.nextInstance();
}
public static void main(String[] args) {
InstanceGenerator generator = new InstanceGenerator();
for (int i = 0; i < 10; i++) {
Instance instance = generator.next();
System.out.println(instance.toString());
}
}
}
Original issue reported on code.google.com by [email protected]
on 28 Feb 2013 at 4:44
What steps will reproduce the problem?
1. Create an instance of SingleClassifierDrift from Java code
2. Set its CLI String to "-d DDM -l (bayes.NaiveBayes)"
3. Perform a prequential in a dataset with at least 40k instances
What is the expected output? What do you see instead?
When debug messages are suppressed by a dummy output stream, instead of
filtering with 'grep' or just showing them,
algorithm runs 3X faster.
What version of the product are you using? On what operating system?
MOA 2012.08 from Maven
Please provide any additional information below.
The messages are about the drift detection levels: N, W and D.
They could be suppressed by an CLI option.
Original issue reported on code.google.com by [email protected]
on 14 Nov 2013 at 5:48
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