weka.core.Utils类的使用及代码示例

x33g5p2x  于2022-02-01 转载在 其他  
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本文整理了Java中weka.core.Utils类的一些代码示例，展示了Utils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。Utils类的具体详情如下：
包路径：weka.core.Utils
类名称：Utils

Utils介绍

[英]Class implementing some simple utility methods.
[中]类实现一些简单的实用程序方法。

代码示例

代码示例来源：origin: nz.ac.waikato.cms.weka/meka

@Override
public void setOptions(String[] options) throws Exception {
 String    tmpStr;
 
 
 tmpStr = Utils.getOption('S', options);
 if (tmpStr.length() > 0)
  m_S = Integer.parseInt(tmpStr);
 else
  m_S = 0;
 super.setOptions(options);
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

@Override
public void setOptions(String[] options) throws Exception {
 m_encodeMissingAsZero = Utils.getFlag('M', options);
 Utils.checkForRemainingOptions(options);
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * returns the current DOM document as string array.
 * 
 * @return the document as string array
 * @throws Exception if anything goes wrong initializing the parsing
 */
public String[] toArray() throws Exception {
 return Utils.splitOptions(toCommandLine());
}

代码示例来源：origin: nz.ac.waikato.cms.weka/conjunctiveRule

/**
 * Prints this antecedent
 * 
 * @return a textual description of this antecedent
 */
@Override
public String toString() {
 String symbol = Utils.eq(value, 0.0) ? " <= " : " > ";
 return (att.name() + symbol + Utils.doubleToString(splitPoint, 6));
}

代码示例来源：origin: nz.ac.waikato.cms.weka/meka

@Override
public void setOptions(String[] options) throws Exception {
  m_Is = (Utils.getOptionPos("Is",options) >= 0) ? Integer.parseInt(Utils.getOption("Is", options)) : m_Is;
  m_Iy = (Utils.getOptionPos("Iy",options) >= 0) ? Integer.parseInt(Utils.getOption("Iy", options)) : m_Iy;
  m_Payoff = (Utils.getOptionPos('P',options) >= 0) ? Integer.parseInt(Utils.getOption('P', options)) : m_Payoff;
  super.setOptions(options);
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Gets the current settings of KDtree.
 * 
 * @return         an array of strings suitable for passing to setOptions
 */
public String[] getOptions() {
 Vector<String>    result = new Vector<String>();
 
 Collections.addAll(result, super.getOptions());
 
 result.add("-C");
 result.add(
 (m_TreeConstructor.getClass().getName() + " " +
  Utils.joinOptions(m_TreeConstructor.getOptions())).trim());
 return result.toArray(new String[result.size()]);
}

代码示例来源：origin: net.sf.meka.thirdparty/mulan

knn = new Instances(lnn.kNearestNeighbours(instance, numOfNeighbors));
} catch (Exception ex) {
  Logger.getLogger(MLkNN.class.getName()).log(Level.SEVERE, null, ex);
    double value = Double.parseDouble(train.attribute(labelIndices[i]).value(
        (int) knn.instance(k).value(labelIndices[i])));
    if (Utils.eq(value, 1.0)) {
      aces++;
    predictions[i] = false;
  } else {
    Random rnd = new Random();
    predictions[i] = (rnd.nextInt(2) == 1) ? true : false;

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

Instances trainData = new Instances(instances);
trainData.deleteWithMissingClass();
Instances trainDataCopy = new Instances(trainData); // Just in case base classifier is sensitive to order of data.
m_BestPerformance = -99;
m_NumAttributes = trainData.numAttributes();
Random random = new Random(m_Seed);
trainData.randomize(random);
m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();
if (m_CVParams.size() == 0) {
  m_Classifier.buildClassifier(trainDataCopy);
  m_BestClassifierOptions = m_InitOptions;
  return;
if (trainData.classAttribute().isNominal()) {
 trainData.stratify(m_NumFolds);
for (int i = 0; i < m_CVParams.size(); i++) {
 Utils.getOption(((CVParameter)m_CVParams.elementAt(i)).m_ParamChar,
     m_ClassifierOptions);
m_Classifier.buildClassifier(trainDataCopy);

代码示例来源：origin: nz.ac.waikato.cms.weka/conjunctiveRule

Instances data = new Instances(instances);
data.deleteWithMissingClass();
if (data.numInstances() < m_Folds) {
 throw new Exception("Not enough data for REP.");
m_ClassAttribute = data.classAttribute();
if (m_ClassAttribute.isNominal()) {
 m_NumClasses = m_ClassAttribute.numValues();
} else {
 m_NumClasses = 1;
m_Cnsqt = new double[m_NumClasses];
m_Targets = new ArrayList<double[][]>();
m_Random = new Random(m_Seed);
if (m_ClassAttribute.isNominal()) {
 Utils.normalize(m_Cnsqt);
 if (Utils.gr(Utils.sum(m_DefDstr), 0)) {
  Utils.normalize(m_DefDstr);

代码示例来源：origin: nz.ac.waikato.cms.weka/meka

testCapabilities(D);
r = new Random(m_S);
int L = D.classIndex(); 
int N = D.numInstances();
int d = D.numAttributes()-L;
  double w = payoff(h,new Instances(D));
  if (getDebug()) System.out.print("h_{t="+0+"} & "+Arrays.toString(s)); //+"; w = "+w);
  if (getDebug()) System.out.print("& "+Utils.doubleToString(payoff(h,new Instances(D),1),8,2));
  if (getDebug()) System.out.print("& "+Utils.doubleToString(payoff(h,new Instances(D),2),8,2));
  if (getDebug()) System.out.println("& "+Utils.doubleToString(payoff(h,new Instances(D),5),8,2));
      h = h_;
      if (getDebug()) System.out.print("h_{t="+t+"} & "+Arrays.toString(s)); //+"; w = "+w);
      if (getDebug()) System.out.print("& "+Utils.doubleToString(payoff(h_,new Instances(D),1),8,2));
      if (getDebug()) System.out.print("& "+Utils.doubleToString(payoff(h_,new Instances(D),2),8,2));
      if (getDebug()) System.out.println("& "+Utils.doubleToString(payoff(h_,new Instances(D),5),8,2));

代码示例来源：origin: Waikato/weka-trunk

atts = new double[m_DatasetFormat.numAttributes()];
x = rand.nextDouble();
if (Utils.eq(x, 0)) {
 y = getAmplitude();
} else {
y = y + getAmplitude() * m_NoiseRandom.nextGaussian() * getNoiseRate()
result = new DenseInstance(1.0, atts);
result.setDataset(m_DatasetFormat);

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/** Display a representation of this estimator */
@Override
public String toString() {
 if (m_Covariance == null) {
  calculateCovariance();
 }
 String result = "NN Conditional Estimator. " + m_CondValues.size()
  + " data points.  Mean = " + Utils.doubleToString(m_ValueMean, 4, 2)
  + "  Conditional mean = " + Utils.doubleToString(m_CondMean, 4, 2);
 result += "  Covariance Matrix: \n" + m_Covariance;
 return result;
}

代码示例来源：origin: Waikato/weka-trunk

result = new Instances(getOutputFormat());
for (i = 0; i < instances.numInstances(); i++) {
 inst = instances.instance(i);
 values = inst.toDoubleArray();
  if (!m_Cols.isInRange(n) || !instances.attribute(n).isNumeric()
   || inst.isMissing(n)) {
   continue;
  if (instances.attribute(n).type() == Attribute.DATE) {
   value = inst.stringValue(n);
  } else {
   value = Utils.doubleToString(inst.value(n), MAX_DECIMALS);
  int index = result.attribute(n).indexOfValue(value);
  if (index == -1) {
   values[n] = Utils.missingValue();;
  } else {
   values[n] = index;
  newInst = new SparseInstance(inst.weight(), values);
 } else {
  newInst = new DenseInstance(inst.weight(), values);

代码示例来源：origin: nz.ac.waikato.cms.weka/multiInstanceLearning

Instance before = data.instance(pos);
if ((int) before.classValue() == 0) {
 m_NoiseM[pos] = null;
 m_NoiseV[pos] = null;
  int index = Utils.minIndex(dists);
  pred[(int) m_Class[index]]++;
  dists[index] = Double.POSITIVE_INFINITY;
 int clas = Utils.maxIndex(pred);
 if ((int) before.classValue() != clas) {
  noises_relationInsts.add(datum);
relationValue = noises.attribute(1).addRelation(noises_relationInsts);
noises.setValue(0, before.value(0));
noises.setValue(1, relationValue);
after.setValue(2, before.classValue());
if (Utils.gr(noises.relationalValue(1).sumOfWeights(), 0)) {
 for (int i = 0; i < m_Dimension; i++) {
  m_NoiseM[pos][i] = noises.relationalValue(1).meanOrMode(i);
  m_NoiseV[pos][i] = noises.relationalValue(1).variance(i);
  if (Utils.eq(m_NoiseV[pos][i], 0.0)) {
   m_NoiseV[pos][i] = m_ZERO;

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

int newAttIndex = m_outputStructure.numAttributes() - 1;
 if (m_voteLabels && Utils.sum(labelVotes) > 0) {
  int maxIndex = Utils.maxIndex(labelVotes);
  label = m_matchRules.get(maxIndex).getLabel();
 double[] vals = new double[m_outputStructure.numAttributes()];
 for (int i = 0; i < inputI.numAttributes(); i++) {
  if (!inputI.attribute(i).isString()) {
   vals[i] = inputI.value(i);
  } else {
   if (!batch) {
    vals[i] = 0;
    String v = inputI.stringValue(i);
    m_outputStructure.attribute(i).setStringValue(v);
   } else {
    String v = inputI.stringValue(i);
    vals[i] = m_outputStructure.attribute(i).addStringValue(v);
  if (m_hasLabels) {
   if (!getConsumeNonMatching()) {
    vals[newAttIndex] = Utils.missingValue();
   } else {
    return null;
 result = new DenseInstance(1.0, vals);
 result.setDataset(m_outputStructure);

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

@Override
public void updateFinished() {
 if (m_canopies == null || m_canopies.numInstances() == 0) {
  return;
  double[] finalCenter = new double[m_canopies.numAttributes()];
  for (int j = 0; j < m_canopies.numAttributes(); j++) {
   if (m_canopies.attribute(j).isNumeric()) {
    if (numMissingForNumerics[j] == density[0]) {
     finalCenter[j] = Utils.missingValue();
    } else {
     finalCenter[j] = centerSums[j][0]
      / (density[0] - numMissingForNumerics[j]);
   } else if (m_canopies.attribute(j).isNominal()) {
    int mode = Utils.maxIndex(centerSums[j]);
    if (mode == centerSums[j].length - 1) {
     finalCenter[j] = Utils.missingValue();
    } else {
     finalCenter[j] = mode;
  Instance finalCenterInst = m_canopies.instance(i) instanceof SparseInstance ? new SparseInstance(
   1.0, finalCenter) : new DenseInstance(1.0, finalCenter);
  m_canopies.set(i, finalCenterInst);
  m_canopies.instance(i).setWeight(density[0]);
  densities[i] = density[0];

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

public void testsqrt() {
 m_Filter = getFilter("sqrt(A)");
 Instances result = useFilter();
 assertEquals(m_Instances.numAttributes(), result.numAttributes());
 assertEquals(m_Instances.numInstances(), result.numInstances());
 // check equality
 boolean equal = true;
 for (int i = 0; i < result.numInstances(); i++) {
  if (m_Instances.instance(i) instanceof SparseInstance)
   continue;
  if (!Utils.eq(
     Math.sqrt(m_Instances.instance(i).value(m_AttIndex)), 
     result.instance(i).value(m_AttIndex))) {
   equal = false;
   break;
  }
 }
 if (!equal)
  fail("Filter produces different result)!");
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

public void testTypical() {
 Instances result = useFilter();
 // Number of attributes and instances shouldn't change
 assertEquals(m_Instances.numAttributes(), result.numAttributes());
 assertEquals(m_Instances.numInstances(),  result.numInstances());
 // Check conversion is OK
 for (int j = 0; j < result.numAttributes(); j++) {
  if (result.attribute(j).isNumeric()) {
 double mean = result.meanOrMode(j);
 assertTrue("Mean should be 0", Utils.eq(mean, 0));
 double stdDev = Math.sqrt(result.variance(j));
 assertTrue("StdDev should be 1 (or 0)", 
     Utils.eq(stdDev, 0) || Utils.eq(stdDev, 1));
  }
 }
}

代码示例来源：origin: net.sf.meka.thirdparty/mulan

/**
 * Computing Prior and PriorN Probabilities for each class of the training
 * set
 */
private void ComputePrior() {
  for (int i = 0; i < numLabels; i++) {
    int temp_Ci = 0;
    for (int j = 0; j < train.numInstances(); j++) {
      double value = Double.parseDouble(train.attribute(labelIndices[i]).value(
          (int) train.instance(j).value(labelIndices[i])));
      if (Utils.eq(value, 1.0)) {
        temp_Ci++;
      }
    }
    PriorProbabilities[i] = (smooth + temp_Ci) / (smooth * 2 + train.numInstances());
    PriorNProbabilities[i] = 1 - PriorProbabilities[i];
  }
}

代码示例来源：origin: nz.ac.waikato.cms.weka/meka

@Override
public double[] distributionForInstance(Instance x) throws Exception {
  int L = x.classIndex();
  //if there is only one class (as for e.g. in some hier. mtds) predict it
  //if(L == 1) return new double[]{1.0};
  Instance x_sl = convertInstance(x,L);                            // the sl instance
  x_sl.setDataset(m_InstancesTemplate);							// where y in {comb_1,comb_2,...,comb_k}
  double w[] = m_Classifier.distributionForInstance(x_sl);		// w[j] = p(y_j) for each j = 1,...,L
  int max_j  = Utils.maxIndex(w);									// j of max w[j]
  //int max_j = (int)m_Classifier.classifyInstance(x_sl);			// where comb_i is selected
  String y_max = m_InstancesTemplate.classAttribute().value(max_j);									// comb_i e.g. "0+3+0+0+1+2+0+0"
  double y[] = Arrays.copyOf(MLUtils.toDoubleArray(MLUtils.decodeValue(y_max)),L*2);					// "0+3+0+0+1+2+0+0" -> [0.0,3.0,0.0,...,0.0]
  HashMap<Double,Double> votes[] = new HashMap[L];
  for(int j = 0; j < L; j++) {
    votes[j] = new HashMap<Double,Double>();
  }
  for(int i = 0; i < w.length; i++) {
    double y_i[] = MLUtils.toDoubleArray(MLUtils.decodeValue(m_InstancesTemplate.classAttribute().value(i)));
    for(int j = 0; j < y_i.length; j++) {
      votes[j].put(y_i[j] , votes[j].containsKey(y_i[j]) ? votes[j].get(y_i[j]) + w[i] : w[i]);
    }
  }
  // some confidence information
  for(int j = 0; j < L; j++) {
    y[j+L] = votes[j].size() > 0 ? Collections.max(votes[j].values()) : 0.0;
  }
  return y;
}

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