本文整理了Java中weka.core.Instances类的一些代码示例，展示了Instances类的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Instances类的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
[英]Class for handling an ordered set of weighted instances.
Typical usage:
import weka.core.converters.ConverterUtils.DataSource;
...
// Read all the instances in the file (ARFF, CSV, XRFF, ...)
DataSource source = new DataSource(filename);
Instances instances = source.getDataSet();
// Make the last attribute be the class
instances.setClassIndex(instances.numAttributes() - 1);
// Print header and instances.
System.out.println("\nDataset:\n");
System.out.println(instances);
...All methods that change a set of instances are safe, ie. a change of a set of instances does not affect any other sets of instances. All methods that change a datasets's attribute information clone the dataset before it is changed.
[中]类，用于处理一组有序的加权实例。
典型用法：
import weka.core.converters.ConverterUtils.DataSource;
...
// Read all the instances in the file (ARFF, CSV, XRFF, ...)
DataSource source = new DataSource(filename);
Instances instances = source.getDataSet();
// Make the last attribute be the class
instances.setClassIndex(instances.numAttributes() - 1);
// Print header and instances.
System.out.println("\nDataset:\n");
System.out.println(instances);
...所有更改实例集的方法都是安全的，即更改实例集不会影响任何其他实例集。所有更改数据集属性信息的方法都会在更改数据集之前克隆该数据集。
代码示例来源：origin: Waikato/meka
/**
* Stack two Instances together row-wise.
*/
public static final Instances combineInstances(Instances D1, Instances D2) {
Instances D = new Instances(D1);
for(int i = 0; i < D2.numInstances(); i++) {
D.add(D2.instance(i));
}
return D;
}代码示例来源：origin: stackoverflow.com
System.out.println("--------------------------");
double[] instanceValue1 = new double[dataRaw.numAttributes()];
instanceValue1[0] = dataRaw.attribute(0).addStringValue("This is a string!");
instanceValue1[1] = 0;
dataRaw.add(new DenseInstance(1.0, instanceValue1));
System.out.println("--------------------------");
double[] instanceValue2 = new double[dataRaw.numAttributes()];
instanceValue2[0] = dataRaw.attribute(0).addStringValue("This is second string!");
instanceValue2[1] = 1;
dataRaw.add(new DenseInstance(1.0, instanceValue2));代码示例来源：origin: stackoverflow.com
Instances dataUnlabeled = new Instances("TestInstances", atts, 0);
dataUnlabeled.add(newInst);
dataUnlabeled.setClassIndex(dataUnlabeled.numAttributes() - 1);
double classif = ibk.classifyInstance(dataUnlabeled.firstInstance());代码示例来源：origin: stackoverflow.com
//load model
String rootPath="/some/where/";
Classifier cls = (Classifier) weka.core.SerializationHelper.read(rootPath+"tree.model");
//predict instance class values
Instances originalTrain= //load or create Instances to predict
//which instance to predict class value
int s1=0;
//perform your prediction
double value=cls.classifyInstance(originalTrain.instance(s1));
//get the name of the class value
String prediction=originalTrain.classAttribute().value((int)value);
System.out.println("The predicted value of instance "+
Integer.toString(s1)+
": "+prediction);代码示例来源：origin: stackoverflow.com
DataSource source = new DataSource(new File("mycsvinputfile"));
System.out.println(source.getStructure());
Instances data = source.getDataSet();
// setting class attribute if the data format does not provide this information
// For example, the XRFF format saves the class attribute information as well
if (data.classIndex() == -1)
data.setClassIndex(data.numAttributes() - 1);
//initialize svm classifier
LibSVM svm = new LibSVM();
svm.buildClassifier(data);代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Returns a random number generator. The initial seed of the random number
* generator depends on the given seed and the hash code of a string
* representation of a instances chosen based on the given seed.
*
* @param seed the given seed
* @return the random number generator
*/
public Random getRandomNumberGenerator(long seed) {
Random r = new Random(seed);
r.setSeed(instance(r.nextInt(numInstances())).toStringNoWeight().hashCode()
+ seed);
return r;
}代码示例来源：origin: sc.fiji/Trainable_Segmentation
/**
* bag class for getting the result of the loaded classifier
*/
private static class LoadedClassifier {
private AbstractClassifier newClassifier = null;
private Instances newHeader = null;
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Add missing values to a dataset.
*
* @param data the instances to add missing values to
* @param level the level of missing values to add (if positive, this is the
* probability that a value will be set to missing, if negative all
* but one value will be set to missing (not yet implemented))
* @param predictorMissing if true, predictor attributes will be modified
*/
protected void addMissing(Instances data, int level, boolean predictorMissing) {
Random random = new Random(1);
for (int i = 0; i < data.numInstances(); i++) {
Instance current = data.instance(i);
for (int j = 0; j < data.numAttributes(); j++) {
if (predictorMissing) {
if (random.nextInt(100) < level) {
current.setMissing(j);
}
}
}
}
}代码示例来源：origin: nz.ac.waikato.cms.weka/meka
@Override
public void buildClassifier(Instances Train) throws Exception {
testCapabilities(Train);
this.m_NumClasses = Train.classIndex();
int indices[] = MLUtils.gen_indices(m_NumClasses);
MLUtils.randomize(indices,new Random(m_S));
if(getDebug()) System.out.print(":- Chain (");
root = new Tink(indices,0,Train);
if (getDebug()) System.out.println(" ) -:");
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Shuffles the instances in the set so that they are ordered randomly.
*
* @param random a random number generator
*/
public void randomize(Random random) {
for (int j = numInstances() - 1; j > 0; j--) {
swap(j, random.nextInt(j + 1));
}
}代码示例来源：origin: nz.ac.waikato.cms.weka/rotationForest
/**
* Adds random instances to the dataset.
*
* @param dataset the dataset
* @param numInstances the number of instances
* @param random a random number generator
*/
protected void addRandomInstances( Instances dataset, int numInstances,
Random random ) {
int n = dataset.numAttributes();
double [] v = new double[ n ];
for( int i = 0; i < numInstances; i++ ) {
for( int j = 0; j < n; j++ ) {
Attribute att = dataset.attribute( j );
if( att.isNumeric() ) {
v[ j ] = random.nextDouble();
}
else if ( att.isNominal() ) {
v[ j ] = random.nextInt( att.numValues() );
}
}
dataset.add( new DenseInstance( 1, v ) );
}
}代码示例来源：origin: nz.ac.waikato.cms.weka/predictiveApriori
/**
* generates a class association rule out of a given premise. It randomly
* chooses a class label as consequence.
*
* @param itemArray the (randomly constructed) premise of the class
* association rule
* @return a class association rule stored in a RuleItem
*/
public final RuleItem addCons(int[] itemArray) {
ItemSet premise = new ItemSet(itemArray);
int[] cons = new int[itemArray.length];
for (int i = 0; i < itemArray.length; i++) {
cons[i] = -1;
}
cons[m_instances.classIndex()] = m_randNum.nextInt((m_instances
.attribute(m_instances.classIndex())).numValues());
ItemSet consequence = new ItemSet(cons);
RuleItem current = new RuleItem();
current.m_premise = premise;
current.m_consequence = consequence;
return current;
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Initializes the format for the dataset produced. Must be called before the
* generateExample or generateExamples methods are used. Re-initializes the
* random number generator with the given seed.
*
* @return the format for the dataset
* @throws Exception if the generating of the format failed
* @see #getSeed()
*/
@Override
public Instances defineDataFormat() throws Exception {
ArrayList<Attribute> atts;
m_Random = new Random(getSeed());
m_NoiseRandom = new Random(getSeed());
// number of examples is the same as given per option
setNumExamplesAct(getNumExamples());
// initialize dataset format
atts = new ArrayList<Attribute>();
atts.add(new Attribute("x"));
atts.add(new Attribute("y"));
m_DatasetFormat = new Instances(getRelationNameToUse(), atts, 0);
return m_DatasetFormat;
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
newInst = new DenseInstance(model.numAttributes());
newInst.setDataset(model);
for (int i = 0, j = 0; i < model.numAttributes(); i++) {
if (model.attribute(i).isNumeric()) {
if (j >= m_Elements.length)
throw new Exception("Datatypes are not compatible.");
newInst.setValue(i, m_Elements[j++]);
if (model.attribute(i).isNominal()) {
int newVal = (int)
(random.nextDouble() * (double) (model.attribute(i).numValues()));
if (newVal == (int) model.attribute(i).numValues())
newVal -= 1;
newInst.setValue(i, newVal);代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
double value = random.nextDouble();
if (format.attribute(i).isNumeric()) {
attributes[i] = value;
} else {
if (format.attribute(i).isNominal()) {
attributes[i] = (value > 0.5) ? 1.0 : 0.0;
} else {
example = new DenseInstance(1.0, attributes);
example.setDataset(format);
example.setClassMissing();代码示例来源：origin: Waikato/weka-trunk
/**
* Generate an example of the dataset.
*
* @param format the dataset format
* @param randomG the random number generator
* @param stdDev the standard deviation to use
* @param center the centers
* @param cName the class value
* @return the instance generated examples one by one is not possible, because
* voting is chosen
*/
private Instance generateInstance(Instances format, Random randomG,
double stdDev, double[] center, String cName) {
Instance example;
int numAtts = getNumAttributes();
if (getClassFlag()) {
numAtts++;
}
double[] data = new double[numAtts];
for (int i = 0; i < getNumAttributes(); i++) {
data[i] = randomG.nextGaussian() * stdDev + center[i];
}
if (getClassFlag()) {
data[format.classIndex()] = format.classAttribute().indexOfValue(cName);
}
example = new DenseInstance(1.0, data);
example.setDataset(format);
return example;
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Updates the perturbed values for the plots when the jitter value is changed
*/
private void updatePturb() {
double xj = 0;
double yj = 0;
for (int j = 0; j < m_plots.size(); j++) {
PlotData2D temp_plot = (m_plots.get(j));
for (int i = 0; i < temp_plot.m_plotInstances.numInstances(); i++) {
if (temp_plot.m_plotInstances.instance(i).isMissing(m_xIndex)
|| temp_plot.m_plotInstances.instance(i).isMissing(m_yIndex)) {
} else {
if (m_JitterVal > 0) {
xj = m_JRand.nextGaussian();
yj = m_JRand.nextGaussian();
}
temp_plot.m_pointLookup[i][2] =
pturbX(temp_plot.m_pointLookup[i][0], xj);
temp_plot.m_pointLookup[i][3] =
pturbY(temp_plot.m_pointLookup[i][1], yj);
}
}
}
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
private Instance makeOutputInstance(Instances output, Instance source) {
double[] newVals = new double[output.numAttributes()];
for (int i = 0; i < newVals.length; i++) {
newVals[i] = Utils.missingValue();
}
for (int i = 0; i < source.numAttributes(); i++) {
if (!source.isMissing(i)) {
Attribute s = source.attribute(i);
int outputIndex = output.attribute(s.name()).index();
if (s.isNumeric()) {
newVals[outputIndex] = source.value(s);
} else if (s.isString()) {
String sVal = source.stringValue(s);
newVals[outputIndex] = output.attribute(outputIndex).addStringValue(
sVal);
} else if (s.isRelationValued()) {
Instances rVal = source.relationalValue(s);
newVals[outputIndex] = output.attribute(outputIndex)
.addRelation(rVal);
} else if (s.isNominal()) {
String nomVal = source.stringValue(s);
newVals[outputIndex] = output.attribute(outputIndex).indexOfValue(
nomVal);
}
}
}
Instance newInst = new DenseInstance(source.weight(), newVals);
newInst.setDataset(output);
return newInst;
}代码示例来源：origin: net.sf.meka/meka
/**
* Transforms the instance in the prediction process before given to the internal multi-label
* or multi-target classifier. The instance is passed having the original set of labels, these
* must be replaced with the transformed labels (attributes) so that the internla classifier
* can predict them.
*
* @param x The instance to transform. Consists of features and labels.
* @return The transformed instance. Consists of features and transformed labels.
*/
@Override
public Instance transformInstance(Instance x) throws Exception{
Instances tmpInst = new Instances(x.dataset());
tmpInst.delete();
tmpInst.add(x);
Instances features = this.extractPart(tmpInst, false);
Instances labels = new Instances(this.m_PatternInstances);
labels.add(new DenseInstance(labels.numAttributes()));
Instances result = Instances.mergeInstances(labels, features);
result.setClassIndex(labels.numAttributes());
return result.instance(0);
}代码示例来源：origin: nz.ac.waikato.cms.weka/meka
@Override
public void buildClassifier(Instances D) throws Exception {
testCapabilities(D);
int L = D.classIndex();
if(getDebug()) System.out.print("Creating "+L+" models ("+m_Classifier.getClass().getName()+"): ");
m_MultiClassifiers = AbstractClassifier.makeCopies(m_Classifier,L);
m_Templates = new Instances[L];
for(int j = 0; j < L; j++) {
//Select only class attribute 'j'
m_Templates[j] = MLUtils.keepAttributesAt(new Instances(D),new int[]{j},L);
m_Templates[j].setClassIndex(0);
//Build the classifier for that class
m_MultiClassifiers[j].buildClassifier(m_Templates[j]);
if(getDebug()) System.out.print(" " + (m_Templates[j].classAttribute().name()));
m_Templates[j] = new Instances(m_Templates[j], 0);
}
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