weka

I'm new in Weka, I'm triying to create new instances to be labeled with a previous trained MultilayerPerceptron , I did't know very much about how to create an instance so I got the first instance from my training data and then modified it by changing the atributes values: //Opening the model public boolean abrirModelo(String ruta) { try { clasificador = (MultilayerPerceptron) weka.core.SerializationHelper.read(ruta); return true; } catch (IOException e) { System.out.println("Fallo la lectura del archivo"); return false; } catch (ClassNotFoundException a) { System.out.println("Fallo el casting

I have trained a Decision Table ML model in Weka 3.8 Dekstop Version. I have saved the model accordingly in my assets folder and configured the wekaSTRIPPED.jar file as well. At this point, the build seems to be working fine on Android Studio. However, when I try to use the classification model in Java, I am getting some red highlights - errors - over my code which I am unable to autofix. I have visited some online guides here and visited this commonly used weka-android reference here and here . They follow the same structure as what I have been doing in my code (with different models), but

Overview I know that one can get the percentages of each prediction in a trained WEKA model through the GUI and command line options as conveniently explained and demonstrated in the documentation article " Making predictions " . Predictions I know that there are three ways documented to get these predictions: command line GUI Java code/using the WEKA API, which I was able to do in the answer to " Get risk predictions in WEKA using own Java code " this fourth one requires a generated WEKA .MODEL file I have a trained .MODEL file and now I want to classify new instances using this together with

I need to classify a datastream which comes from a sensor network consisting of 8 accelerometers. Each accelerometer gives me a X Y and Z value. Thus at each sample i have 8 x 3 = 24 acceleration values. I sample at about 30 hz and the performance time is about 0.5 seconds. At first i thought of using a Hidden Markov model for this but it seems that the WEKA toolkit does not provide such a thing. What is the WEKA equivalent for this? Thank you. EDIT: how to format data? I have collected data and now i want to use the HMMWeka for classification. On the website it states that Data instances must

I'm trying to retrieve classes from WEKA using MATLAB and WEKA API. All looks fine but classes are always 0. Any idea ?? My data set has 241 atributes, applying WEKA to this dataset I'm obtaining correct results. 1st train and test objects are created than classifier is build and classifyInstance performed. But this give wrong result train = [xtrain ytrain]; test = [xtest]; save ('train.txt','train','-ASCII'); save ('test.txt','test','-ASCII'); %## paths WEKA_HOME = 'C:\Program Files\Weka-3-7'; javaaddpath([WEKA_HOME '\weka.jar']); fName = 'train.txt'; %## read file loader = weka.core

In the case of Weka's Explorer, is there any way to get the class probabilities of the test instances as classified by a Naive Bayes' classifier? In Weka Explorer on the Classify tab, click on More options... and tick Output predictions . Then Start the training and testing and the result shows you the probabilities of assigning each class for each test instance. 来源： https://stackoverflow.com/questions/10868233/weka-how-to-get-the-probabilities-of-each-class-for-the-test-instances

I'm trying to filter a dataset using weka's java API. I've successfully filtered the attributes I want with a stringToWordVector filter in Weka's GUI but I can't seem to do the same in my java code. I copied and pasted the auto-generated filtering parameters and posted them into my code but am continuing to get errors. Currently, my code looks like this: Instances newInsts = new Instances(this.instances); StringToWordVector stringFilter = new StringToWordVector(); stringFilter.setOptions( weka.core.Utils.splitOptions("-R 1,2,3,4,8 -W 1000 -prune-rate -1.0 -N 0 -stemmer weka.core.stemmers