在Weka中对单个实例进行分类 [英] Classifying Single Instance in Weka

问题描述

我使用WEKA gui训练并创建了J48模型.我将模型文件保存到计算机上，现在我想用它来对Java代码中的单个实例进行分类.我想对属性"cluster"进行预测.我要做的是以下事情:

I trained and created a J48 model using WEKA gui. I saved the model file to 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 prediction for the attribute "cluster". What I do is 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's 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);
}

但是，我在 inst_co.setValue(latitude，lat); 行上得到了IndexArrayOutofBoundsException.我找不到发生此异常的原因.如果有人能指出我正确的方向，我将不胜感激.

However, I get an IndexArrayOutofBoundsException on the line inst_co.setValue(latitude, lat);. I couldn't find the reason for this exception. I will appreciate if someone could point me in the right direction.

推荐答案

您需要将inst_co添加到数据集(一个Instances对象).以下代码应该可以工作.

You need to add your inst_co to your data set, an Instances object. 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's 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;
    }
}

您从实例创建数据对象.将您的实例添加到该数据.之后，您可以在Instance中设置值.

You create data object from Instances. Add your instance to this data. After that you can set your values in Instance.

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

我建议从外部文件获取标头信息和实例值，或者仅创建一次此信息.

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

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