本文整理了Java中libsvm.svm.svm_predict_probability()方法的一些代码示例，展示了svm.svm_predict_probability()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。svm.svm_predict_probability()方法的具体详情如下：
包路径：libsvm.svm
类名称：svm
方法名：svm_predict_probability

svm.svm_predict_probability介绍

暂无

代码示例

代码示例来源：origin: datumbox/datumbox-framework

svm.svm_predict_probability(model, xSVM, prob_estimates);

代码示例来源：origin: org.apache.ctakes/ctakes-coreference

public double predict(svm_node[] vec, TreebankNode path){
    double[] probs = new double[2];
    svm.svm_predict_probability(svmCls, vec, probs);
    return probs[clsIndex];
  }
}

代码示例来源：origin: apache/ctakes

public double predict(svm_node[] vec, TreebankNode path){
    double[] probs = new double[2];
    svm.svm_predict_probability(svmCls, vec, probs);
    return probs[clsIndex];
  }
}

代码示例来源：origin: org.apache.ctakes/ctakes-coreference

private double calcAnaphoricity (JCas aJCas, Markable m) {
  svm_node[] nodes = createAnaphoricityVector(m, aJCas);
  double[] prob = new double[2];
  svm.svm_predict_probability(anaph_model, nodes, prob);
  int[] labels = new int[2];
  svm.svm_get_labels(anaph_model, labels);
  int anaph_idx = labels[0]==1 ? 0 : 1;
  return prob[anaph_idx];
}

代码示例来源：origin: apache/ctakes

private double calcAnaphoricity (JCas aJCas, Markable m) {
  svm_node[] nodes = createAnaphoricityVector(m, aJCas);
  double[] prob = new double[2];
  svm.svm_predict_probability(anaph_model, nodes, prob);
  int[] labels = new int[2];
  svm.svm_get_labels(anaph_model, labels);
  int anaph_idx = labels[0]==1 ? 0 : 1;
  return prob[anaph_idx];
}

代码示例来源：origin: ch.epfl.bbp.nlp/bluima_jsre

svm.svm_predict_probability(model, x, probs);

代码示例来源：origin: org.cleartk/cleartk-ml-libsvm

@Override
public Map<OUTCOME_TYPE, Double> score(List<Feature> features) throws CleartkProcessingException {
 FeatureVector featureVector = this.featuresEncoder.encodeAll(features);
 double[] decisionValues = new double[this.model.nr_class];
 libsvm.svm.svm_predict_probability(this.model, convertToLIBSVM(featureVector), decisionValues);
 Map<OUTCOME_TYPE, Double> results = Maps.newHashMap();
 for (int i = 0; i < this.model.nr_class; ++i) {
  int intLabel = this.model.label[i];
  OUTCOME_TYPE outcome = this.outcomeEncoder.decode(this.decodePrediction(intLabel));
  results.put(outcome, decisionValues[i]);
 }
 return results;
}

代码示例来源：origin: education-service/speech-mfcc

public double classifyInstance(Observation observation, svm_model model) {
    List<Double> features = observation.getFeatures();

    svm_node[] nodes = new svm_node[observation.getFeatures().size()];
    for (int i = 0; i < features.size(); i++) {
      svm_node node = new svm_node();
      node.index = i + 1;
      node.value = features.get(i);
      nodes[i] = node;
    }

    int[] labels = new int[TOTAL_CLASSES];
    svm.svm_get_labels(model, labels);

    double[] prob_estimates = new double[TOTAL_CLASSES];
    return svm.svm_predict_probability(model, nodes, prob_estimates);
  }
}

代码示例来源：origin: ClearTK/cleartk

@Override
public Map<OUTCOME_TYPE, Double> score(List<Feature> features) throws CleartkProcessingException {
 FeatureVector featureVector = this.featuresEncoder.encodeAll(features);
 double[] decisionValues = new double[this.model.nr_class];
 libsvm.svm.svm_predict_probability(this.model, convertToLIBSVM(featureVector), decisionValues);
 Map<OUTCOME_TYPE, Double> results = Maps.newHashMap();
 for (int i = 0; i < this.model.nr_class; ++i) {
  int intLabel = this.model.label[i];
  OUTCOME_TYPE outcome = this.outcomeEncoder.decode(this.decodePrediction(intLabel));
  results.put(outcome, decisionValues[i]);
 }
 return results;
}

代码示例来源：origin: jzy3d/jzy3d-api

if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC))
  v = svm.svm_predict_probability(model,x.get(i),prob_estimates);

代码示例来源：origin: org.maochen.nlp/CoreNLP-NLP

@Override
public Map<String, Double> predict(Tuple predict) {
  double[] feats = predict.vector.getVector();
  svm_node[] svmfeats = new svm_node[feats.length];
  for (int i = 0; i < feats.length; i++) {
    svm_node svmfeatI = new svm_node();
    svmfeatI.index = i;
    svmfeatI.value = feats[i];
    svmfeats[i] = svmfeatI;
  }
  int totalSize = labelIndexer.getLabelSize();
  int[] labels = new int[totalSize];
  svm.svm_get_labels(model, labels);
  double[] probs = new double[totalSize];
  svm.svm_predict_probability(model, svmfeats, probs);
  Map<String, Double> result = new HashMap<>();
  for (int i = 0; i < labels.length; i++) {
    result.put(labelIndexer.getLabel(labels[i]), probs[i]);
  }
  return result;
}

代码示例来源：origin: ch.epfl.bbp.nlp/bluima_jsre

if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC))
  v = svm.svm_predict_probability(model,x,prob_estimates);
  output.writeBytes(v+" ");
  for(int j=0;j<nr_class;j++)

代码示例来源：origin: dkpro/dkpro-tc

if (predict_probability == 1
    && (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.NU_SVC)) {
  v = svm.svm_predict_probability(model, x, prob_estimates);
  output.writeBytes(v + " ");
  for (int j = 0; j < nr_class; j++)

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-libsvm

if (predict_probability == 1
    && (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.NU_SVC)) {
  v = svm.svm_predict_probability(model, x, prob_estimates);
  output.writeBytes(v + " ");
  for (int j = 0; j < nr_class; j++)

代码示例来源：origin: com.facebook.thirdparty/libsvm

target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);

代码示例来源：origin: tw.edu.ntu.csie/libsvm

target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);

代码示例来源：origin: eu.fbk.utils/utils-svm

@Override
LabelledVector doPredict(final boolean withProbabilities, final Vector vector) {
  final svm_node[] nodes = encodeVector(this.dictionary, vector);
  if (withProbabilities) {
    final int numLabels = getParameters().getNumLabels();
    final double[] p = new double[numLabels];
    final int label = (int) svm.svm_predict_probability(this.model, nodes, p);
    final float[] probabilities = new float[numLabels];
    for (int i = 0; i < p.length; ++i) {
      final int labelIndex = this.model.label[i];
      probabilities[labelIndex] = (float) p[i];
    }
    return vector.label(label, probabilities);
  } else {
    final int label = (int) svm.svm_predict(this.model, nodes);
    return vector.label(label);
  }
}

代码示例来源：origin: DigitalPebble/TextClassification

svm.svm_predict_probability(model, svm_nodes, scores);
return scores;

代码示例来源：origin: jdmp/java-data-mining-package

svm.svm_predict_probability(model, x, prediction);
int[] label = new int[svm.svm_get_nr_class(model)];
svm.svm_get_labels(model, label);

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

if (m_ProbabilityEstimates
    && ((m_SVMType == SVMTYPE_C_SVC) || (m_SVMType == SVMTYPE_NU_SVC))) {
 v = svm.svm_predict_probability(m_Model, x, prob_estimates);