本文整理汇总了Java中weka.classifiers.functions.SMO.buildClassifier方法的典型用法代码示例。如果您正苦于以下问题：Java SMO.buildClassifier方法的具体用法？Java SMO.buildClassifier怎么用？Java SMO.buildClassifier使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.functions.SMO的用法示例。
在下文中一共展示了SMO.buildClassifier方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: Main
import weka.classifiers.functions.SMO; //导入方法依赖的package包/类
public Main() {
try {
BufferedReader datafile;
datafile = readDataFile("camping.txt");
Instances data = new Instances(datafile);
data.setClassIndex(data.numAttributes() - 1);
Instances trainingData = new Instances(data, 0, 14);
Instances testingData = new Instances(data, 14, 5);
Evaluation evaluation = new Evaluation(trainingData);
SMO smo = new SMO();
smo.buildClassifier(data);
evaluation.evaluateModel(smo, testingData);
System.out.println(evaluation.toSummaryString());
// Test instance
Instance instance = new DenseInstance(3);
instance.setValue(data.attribute("age"), 78);
instance.setValue(data.attribute("income"), 125700);
instance.setValue(data.attribute("camps"), 1);
instance.setDataset(data);
System.out.println("The instance: " + instance);
System.out.println(smo.classifyInstance(instance));
} catch (Exception ex) {
ex.printStackTrace();
}
}
开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:30，代码来源:Main-SVG.java
示例2: trainClassifier
import weka.classifiers.functions.SMO; //导入方法依赖的package包/类
/**
* Train the classifier with the given dataset
* @param trainingData The dataset to be given for classification
* @throws ClassifierException If classification fails for some reason
*/
public void trainClassifier(Instances trainingData,String pathToSaveModel,
boolean crossValidate) throws ClassifierException {
log.info("Training the classifier with "+trainingData.numInstances()+" instances");
SMO smoClassifier = new SMO();
trainingData.setClass(trainingData.attribute("@@[email protected]@"));
try {
String[] options = Utils.splitOptions("-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers" +
".functions.supportVector.PolyKernel -C 250007 -E 1.0\"");
smoClassifier.setOptions(options);
smoClassifier.buildClassifier(trainingData);
classifier = smoClassifier;
if(crossValidate){
crossValidate(trainingData);
}
saveModel(new File(pathToSaveModel,"latest.model").getAbsolutePath());
log.info("Model built and saved");
} catch (Exception e) {
log.error("Training classifier failed.",e);
throw new ClassifierException("Classification failed.",e);
}
}
示例3: classifySMO
import weka.classifiers.functions.SMO; //导入方法依赖的package包/类
public SMO classifySMO(Instances data) throws Exception {
SMO smo = new SMO();
smo.buildClassifier(data);
return smo;
}
示例4: computeWeights
import weka.classifiers.functions.SMO; //导入方法依赖的package包/类
String computeWeights(String path) throws Exception {
weightList = new ArrayList<Double>();
defaultWeight = 100 / noofAttributes;
BufferedReader br = null;
StringReader sr = new StringReader(path); // wrap your String
br = new BufferedReader(sr);
// br = new BufferedReader(new FileReader(path));
Instances data = new Instances(br);
if (data.classIndex() == -1)
data.setClassIndex(data.numAttributes() - 1);
// Initialize the SMO classifier
SMO smo = new SMO();
smo.buildClassifier(data);
Evaluation eval = new Evaluation(data);
// Get the weights in sparse representation
double[][][] sparseWeights = smo.sparseWeights();
int[][][] sparseIndices = smo.sparseIndices();
// Transform the weights into non-sparse representation
double[] weights = new double[10];
for (int i = 0; i < sparseIndices[0][1].length; i++) {
weights[sparseIndices[0][1][i]] = sparseWeights[0][1][i];
}
// Output the weights
for (int i = 0; i < data.numAttributes(); i++) {
// Skip the class
if (i != data.classIndex()) {
weightList.add((Double) weights[i]);
svm_weights.put(String.valueOf(weights[i]), (String) data
.attribute(i).name());
}
}
getOldWeights();
return scaleWeights();
}