machine learning - Weka - How to use classifier in Java -


i have created model in weka explorer, , saved .model file

first, load saved model java code

classifier cls = null;     try {         cls = (classifier) weka.core.serializationhelper.read("model.model");     } catch (exception e1) {         e1.printstacktrace();     }

then, read instance want classify, .arff file

 bufferedreader reader = new bufferedreader(new filereader(file));  arffreader arff = new arffreader(reader);  instances data = arff.getdata();

the file, contains 1 instance. value of class attribute '?'. code below, try make classification of instance.

data.setclassindex(data.numattributes()-1);             try {                  double value=cls.classifyinstance(data.firstinstance());                  string prediction=data.classattribute().value((int)value);                   system.out.println("the predicted value of instance "+                                     integer.tostring(s1)+                                     ": "+prediction);              } catch (exception e) {                 e.printstacktrace();             }

is right way;

that's correct sample of code wanted do. useful method is:

yourclassifier.distributionforinstance(yourinstance);

that returns double[] probabilities of instance every class label. it's useful not-so-clear problems, class membership fuzzy concept.


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