Weka Two-time development tutorials (Eclipse+weka)

Weka Two-time development Tutorials (Eclipse+weka)

 

the full name of Weka is the Waikato Intelligent Analytics Environment (Waikato Environment for knowledge analysis), a free, non-commercial, open-source machine learning and data mining software based on the Java environment.

after installing the environment, Weka's download address: http://www.cs.waikato.ac.nz/ml/weka/developer Verstion

Create a new Java project, add a jar package, under the Weka installation directory (Weka.jar and Weka-src.jar)

The following is the test code, the data set used by the weka-3-7 comes with

Package test;

import Java.io.File;

import Weka.classifiers.CheckClassifier;

import Weka.classifiers.Classifier;

import weka.classifiers.Evaluation;

import weka.classifiers.evaluation.*;

import weka.core.Instance;

import weka.core.Instances;

import weka.core.converters.*;

 

Public class Testweka {

 

    /**

* @param args

     */

Public static void Main (string[] args) {

Instances ins = null;

Classifier cfs = null;

      

try {

//Read into the Training test sample

File File = new file ("C:\\Program Files\\weka-3-7\\data\\contact-lenses.arff");

Arffloader loader = new Arffloader ();

loader.setfile (file);

ins = Loader.getdataset ();

Ins.setclassindex (Ins.numattributes ()-1);

//Initialize classifier

cfs = (Classifier) class.forname ("Weka.classifiers.bayes.NaiveBayes"). newinstance ();

//Use training samples for classification

cfs.buildclassifier (INS);

//Use test samples to test the learning effect of the classifier

Instance Testinst;

Evaluation testingevaluation = new Evaluation (ins);

int length = Ins.numinstances ();

for (int i = 0; i < length; i++) {

testinst = ins.instance (i);

testingevaluation.evaluatemodelonceandrecordprediction (CFS, testinst);

           }

//Print classification results

System.out.println ("The correct rate of classification" + (1-testingevaluation.errorrate ()));

} catch (Exception e) {

e.printstacktrace ();

       }

    }

}

All normal, the output is: classification of the correct rate 0.9583333333333334