本文整理汇总了Java中weka.core.Instances.setClassIndex方法的典型用法代码示例。如果您正苦于以下问题:Java Instances.setClassIndex方法的具体用法?Java Instances.setClassIndex怎么用?Java Instances.setClassIndex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.core.Instances
的用法示例。
在下文中一共展示了Instances.setClassIndex方法的15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: buildAssociate
import weka.core.Instances; //导入方法依赖的package包/类
public static String buildAssociate() throws Exception {
InstanceQuery query = new InstanceQuery();
query.setUsername("root");
query.setPassword("cs6310");
query.setDatabaseURL("jdbc:mysql://localhost/system?#characterEncoding=UTF-8");
query.setQuery("select * from courses_sessions;");
// You can declare that your data set is sparse
// query.setSparseData(true);
Instances data = query.retrieveInstances();
data.setClassIndex(data.numAttributes() - 1);
final NumericToNominal filter = new NumericToNominal();
filter.setInputFormat(data);
data = Filter.useFilter(data, filter);
if (data.size() > 0) {
// build associator
Apriori apriori = new Apriori();
apriori.setClassIndex(data.classIndex());
apriori.buildAssociations(data);
return String.valueOf(apriori);
} else {
return "Not enough data provided";
}
}
示例2: getDist
import weka.core.Instances; //导入方法依赖的package包/类
/**
* <p>To get the distribution of inTrace and outTrace instance in given dataset in <b>path</b>.</p>
* @param ins Instances of each project
* @throws Exception
*/
public static void getDist(String path) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr-1);
int numIns = ins.numInstances();
int intrace = 0;
int outtrace = 0;
for(int i=0; i<numIns; i++){
if(ins.get(i).stringValue(ins.attribute(ins.classIndex())).equals("InTrace")){
intrace++;
}else{
outtrace++;
}
}
System.out.printf("[ %-30s ] inTrace:%4d, outTrace:%4d.\n", path, intrace, outtrace);
}
示例3: getEvalResultbySMOTE
import weka.core.Instances; //导入方法依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>SMOTE</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbySMOTE(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
SMOTE smote = new SMOTE();
smote.setInputFormat(ins);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
FilteredClassifier fc = new FilteredClassifier();
fc.setClassifier(j48);
fc.setFilter(smote);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(fc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
示例4: main
import weka.core.Instances; //导入方法依赖的package包/类
/**
* Run a test case of the backend.
* Instantiates the command pattern, loads data into instance, and runs some tests.
*
* @param args
*/
public static void main(String[] args) {
int maxThreads = 8;
//Dispatcher dispatch = new Dispatcher(maxThreads);
NoUserParameterDispatcherBuilder handler = new NoUserParameterDispatcherBuilder();
//load some data
String path = "src/test/resources/data/cpu.arff";
Instances data = null;
try {
data = read_data(path);
} catch (IOException e) {
System.out.println("Error opening file: " + path +", exiting.");
System.out.println("Error: " + e);
System.out.println(System.getProperty("user.dir"));
System.exit(0);
}
//designate the last column as the data's 'class'
data.setClassIndex(data.numAttributes() - 1);
//give data to the WekaTaskManager
handler.dispatcher.setData(data);
handler.dispatcher.setTimeLimit(1);
handler.dispatcher.launch();
}
示例5: defineDataset
import weka.core.Instances; //导入方法依赖的package包/类
/**
* Defines the structure of a Weka table
*
* @param features
* Holds all features including a label, if training data is
* created.
* @param datasetName
* Holds the dataset´s name
* @return returns the empty created dataset
*/
private Instances defineDataset(FeatureVectorDataSet features, String datasetName) {
ArrayList<weka.core.Attribute> attributes = new ArrayList<weka.core.Attribute>();
// create features per attributes of the FeatureVectorDataSet
for (Iterator<Attribute> attrIterator = features.getSchema().get().iterator(); attrIterator.hasNext();) {
Attribute attr = attrIterator.next();
if (!attr.equals(FeatureVectorDataSet.ATTRIBUTE_LABEL)) {
weka.core.Attribute attribute = new weka.core.Attribute(attr.getIdentifier());
attributes.add(attribute);
}
}
// Treat the label as a special case, which is always at the last
// position of the dataset.
ArrayList<String> labels = new ArrayList<String>();
labels.add("0");
labels.add("1");
weka.core.Attribute cls = new weka.core.Attribute("class", labels);
attributes.add(cls);
Instances dataset = new Instances(datasetName, attributes, 0);
dataset.setClassIndex(attributes.size() - 1);
return dataset;
}
示例6: main
import weka.core.Instances; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception{
String databasePath = "data/features.arff";
// Load the data in arff format
Instances data = new Instances(new BufferedReader(new FileReader(databasePath)));
// Set class the last attribute as class
data.setClassIndex(data.numAttributes() - 1);
// Build a basic decision tree model
String[] options = new String[]{};
J48 model = new J48();
model.setOptions(options);
model.buildClassifier(data);
// Output decision tree
System.out.println("Decision tree model:\n"+model);
// Output source code implementing the decision tree
System.out.println("Source code:\n"+model.toSource("ActivityRecognitionEngine"));
// Check accuracy of model using 10-fold cross-validation
Evaluation eval = new Evaluation(data);
eval.crossValidateModel(model, data, 10, new Random(1), new String[] {});
System.out.println("Model performance:\n"+eval.toSummaryString());
String[] activities = new String[]{"Walk", "Walk", "Walk", "Run", "Walk", "Run", "Run", "Sit", "Sit", "Sit"};
DiscreteLowPass dlpFilter = new DiscreteLowPass(3);
for(String str : activities){
System.out.println(str +" -> "+ dlpFilter.filter(str));
}
}
开发者ID:PacktPublishing,项目名称:Machine-Learning-End-to-Endguide-for-Java-developers,代码行数:35,代码来源:ActivityRecognition.java
示例7: testCOMT2
import weka.core.Instances; //导入方法依赖的package包/类
public static void testCOMT2() throws Exception{
BestConf bestconf = new BestConf();
Instances trainingSet = DataIOFile.loadDataFromArffFile("data/trainingBestConf0.arff");
trainingSet.setClassIndex(trainingSet.numAttributes()-1);
Instances samplePoints = LHSInitializer.getMultiDimContinuous(bestconf.getAttributes(), InitialSampleSetSize, false);
samplePoints.insertAttributeAt(trainingSet.classAttribute(), samplePoints.numAttributes());
samplePoints.setClassIndex(samplePoints.numAttributes()-1);
COMT2 comt = new COMT2(samplePoints, COMT2Iteration);
comt.buildClassifier(trainingSet);
Evaluation eval = new Evaluation(trainingSet);
eval.evaluateModel(comt, trainingSet);
System.err.println(eval.toSummaryString());
Instance best = comt.getInstanceWithPossibleMaxY(samplePoints.firstInstance());
Instances bestInstances = new Instances(trainingSet,2);
bestInstances.add(best);
DataIOFile.saveDataToXrffFile("data/trainingBestConf_COMT2.arff", bestInstances);
//now we output the training set with the class value updated as the predicted value
Instances output = new Instances(trainingSet, trainingSet.numInstances());
Enumeration<Instance> enu = trainingSet.enumerateInstances();
while(enu.hasMoreElements()){
Instance ins = enu.nextElement();
double[] values = ins.toDoubleArray();
values[values.length-1] = comt.classifyInstance(ins);
output.add(ins.copy(values));
}
DataIOFile.saveDataToXrffFile("data/trainingBestConf0_predict.xrff", output);
}
示例8: Main
import weka.core.Instances; //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();
}
}
示例9: BookDecisionTree
import weka.core.Instances; //导入方法依赖的package包/类
public BookDecisionTree(String fileName) {
try {
BufferedReader reader = new BufferedReader(new FileReader(fileName));
trainingData = new Instances(reader);
trainingData.setClassIndex(trainingData.numAttributes() - 1);
} catch (IOException ex) {
ex.printStackTrace();
}
}
示例10: getIns
import weka.core.Instances; //导入方法依赖的package包/类
/***
* <p>To Merge the datasets in path array and save the total dataset in dirpath.
* </p>
* @param path String array of arff file
* @throws Exception
*/
public static void getIns(String[] path, String dirpath) throws Exception{
/** Create a empty dataset total*/
Instances total = new Instances("total3500", getStandAttrs(), 1);
total.setClassIndex(total.numAttributes() - 1);
int len = path.length;
Instances[] temp = new Instances[len];
for(int i=0; i<path.length; i++){
temp[i] = DataSource.read(path[i]);
temp[i].setClassIndex(temp[i].numAttributes() - 1);
total.addAll(temp[i]);
System.out.println("adding " + path[i] + " " + temp[i].numInstances());
// System.out.println("data" + total.numInstances() + "\n");
}
String totalName = dirpath+"total3500" + String.valueOf(System.currentTimeMillis()) + ".arff";
DataSink.write(totalName,
total);
System.out.println("Writing the data into [" + totalName + "] successfully.\n");
}
示例11: getEvalResultbyNo
import weka.core.Instances; //导入方法依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Only use C4.5 to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyNo(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/** classifiers setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(j48, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
示例12: constructInstances
import weka.core.Instances; //导入方法依赖的package包/类
public static Instances constructInstances(List<Pair<String, Attribute>> attributesList, List<Observation> observations) {
List<Pair<String, Attribute>> attributes = attributesList
.stream()
.filter(pair -> !pair.getValue().isString())
.collect(Collectors.toList());
Instances trainingSet = new Instances(
"Standard set",
(ArrayList<Attribute>) attributes
.stream()
.map(pair -> pair.getValue())
.collect(Collectors.toList()),
observations.size());
for(int i = 0; i < observations.size(); i ++) {
Instance instance = new DenseInstance(attributes.size());
for(Pair<String, Attribute> attributePair : attributes) {
updateInstance(attributePair, instance, observations, i);
}
trainingSet.add(instance);
}
trainingSet.setClassIndex(attributes.stream().map(pair -> pair.getKey()).collect(Collectors.toList()).indexOf(Fields.GENDER.name()));
return trainingSet;
}
示例13: getEvalResultbyCost
import weka.core.Instances; //导入方法依赖的package包/类
/***
* <p>To get 10-fold cross validation in one single arff in <b>path</b></p>
* <p>Use C4.5 and <b>Cost-sensitive learning</b> to classify the dataset.</p>
* @param path dataset path
* @throws Exception
*/
public static void getEvalResultbyCost(String path, int index) throws Exception{
Instances ins = DataSource.read(path);
int numAttr = ins.numAttributes();
ins.setClassIndex(numAttr - 1);
/**Classifier setting*/
J48 j48 = new J48();
// j48.setConfidenceFactor(0.4f);
j48.buildClassifier(ins);
CostSensitiveClassifier csc = new CostSensitiveClassifier();
csc.setClassifier(j48);
csc.setCostMatrix(new CostMatrix(new BufferedReader(new FileReader("files/costm"))));
Evaluation eval = new Evaluation(ins);
eval.crossValidateModel(csc, ins, 10, new Random(1));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(0), eval.recall(0), eval.fMeasure(0));
// System.out.printf(" %4.3f %4.3f %4.3f", eval.precision(1), eval.recall(1), eval.fMeasure(1));
// System.out.printf(" %4.3f \n\n", (1-eval.errorRate()));
results[index][0] = eval.precision(0);
results[index][1] = eval.recall(0);
results[index][2] = eval.fMeasure(0);
results[index][3] = eval.precision(1);
results[index][4] = eval.recall(1);
results[index][5] = eval.fMeasure(1);
results[index][6] = 1-eval.errorRate();
}
示例14: train
import weka.core.Instances; //导入方法依赖的package包/类
/**
* This function only train the model with the trainSet as it is.
* In other words, no feature removal will done here.
*
* @param trainSet
* @throws Exception
*/
public void train(Instances trainSet) throws Exception {
trainSet.setClassIndex(trainSet.numAttributes() - 1);
// set classifier: use linear SVM only
String[] options = weka.core.Utils.splitOptions("-K 0");
String classifierName = "weka.classifiers.functions.LibSVM";
this.m_Classifier = Classifier.forName(classifierName, options);
// get probability instead of explicit prediction
LibSVM libsvm = (LibSVM) this.m_Classifier;
libsvm.setProbabilityEstimates(true);
// build model
this.m_Classifier.buildClassifier(trainSet);
}
示例15: createInstances
import weka.core.Instances; //导入方法依赖的package包/类
/**
* Creates a basic model.
*
* @return the model
*/
private Instances createInstances() {
ArrayList<Attribute> attributes = new ArrayList<>();
attributes.add(new Attribute("text", (ArrayList<String>) null));
attributes.add(new Attribute("@@[email protected]@", CLASSES));
Instances instances = new Instances("instances", attributes, 0);
instances.setClassIndex(instances.numAttributes() - 1);
return instances;
}