Classification of a single instance in Weka

Question

I trained and created a J48 model using WEKA gui. I saved the model file on my computer, and now I would like to use it to classify a single instance in my Java code. I would like to get a forecast for the attribute "cluster". I do the following:

public void classify(double lat, double lon, double co) { // Create attributes to be used with classifiers Attribute latitude = new Attribute("latitude"); Attribute longitude = new Attribute("longitude"); Attribute carbonmonoxide = new Attribute("co"); // Create instances for each pollutant with attribute values latitude, longitude and pollutant itself inst_co = new DenseInstance(4); // Set instance values for the attributes "latitude", "longitude", and "pollutant concentration" inst_co.setValue(latitude, lat); inst_co.setValue(longitude, lon); inst_co.setValue(carbonmonoxide, co); inst_co.setMissing(cluster); Classifier cls_co = (Classifier) weka.core.SerializationHelper.read("/CO_J48Model.model");//load classifier from file // Test the model double result = cls_co.classifyInstance(inst_co); } 

However, I get an IndexArrayOutofBoundsException in the string inst_co.setValue(latitude, lat); . I could not find the reason for this exception. I would appreciate it if someone could point me in the right direction.

Answer 1

You need to add inst_co to your dataset, an instance of the object. The following code should work.

 import java.util.ArrayList; import weka.classifiers.Classifier; import weka.core.Attribute; import weka.core.DenseInstance; import weka.core.Instance; import weka.core.Instances; public class QuestionInstanceClassifiy { public static void main(String[] args) { QuestionInstanceClassifiy q = new QuestionInstanceClassifiy(); double result = q.classify(1.0d, 1, 1); System.out.println(result); } private Instance inst_co; public double classify(double lat, double lon, double co) { // Create attributes to be used with classifiers // Test the model double result = -1; try { ArrayList<Attribute> attributeList = new ArrayList<Attribute>(2); Attribute latitude = new Attribute("latitude"); Attribute longitude = new Attribute("longitude"); Attribute carbonmonoxide = new Attribute("co"); ArrayList<String> classVal = new ArrayList<String>(); classVal.add("ClassA"); classVal.add("ClassB"); attributeList.add(latitude); attributeList.add(longitude); attributeList.add(carbonmonoxide); attributeList.add(new Attribute("@@class@@",classVal)); Instances data = new Instances("TestInstances",attributeList,0); // Create instances for each pollutant with attribute values latitude, // longitude and pollutant itself inst_co = new DenseInstance(data.numAttributes()); data.add(inst_co); // Set instance values for the attributes "latitude", "longitude", and // "pollutant concentration" inst_co.setValue(latitude, lat); inst_co.setValue(longitude, lon); inst_co.setValue(carbonmonoxide, co); // inst_co.setMissing(cluster); // load classifier from file Classifier cls_co = (Classifier) weka.core.SerializationHelper .read("/CO_J48Model.model"); result = cls_co.classifyInstance(inst_co); } catch (Exception e) { // TODO Auto-generated catch block e.printStackTrace(); } return result; } } 

You create a data object from instances. Add your instance to this data. After that, you can set your values ​​in the instance.

 Instances data = new Instances("TestInstances",attributeList,0); inst_co = new DenseInstance(data.numAttributes()); data.add(inst_co); 

I suggest getting header information and instance values ​​from an external file or creating this information only once.

Answer 2

Actually, I tried in my situation to call the instance.setDataSet () method, and not the addInstance method. So the code should be inst_co.setDataSet (data).