svm.svm_predict_probability(model, xSVM, prob_estimates);
public double predict(svm_node[] vec, TreebankNode path){ double[] probs = new double[2]; svm.svm_predict_probability(svmCls, vec, probs); return probs[clsIndex]; } }
public double predict(svm_node[] vec, TreebankNode path){ double[] probs = new double[2]; svm.svm_predict_probability(svmCls, vec, probs); return probs[clsIndex]; } }
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]; }
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]; }
svm.svm_predict_probability(model, x, probs);
@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; }
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); } }
@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; }
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);
@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; }
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++)
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++)
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++)
target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
@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); } }
svm.svm_predict_probability(model, svm_nodes, scores); return scores;
svm.svm_predict_probability(model, x, prediction); int[] label = new int[svm.svm_get_nr_class(model)]; svm.svm_get_labels(model, label);
if (m_ProbabilityEstimates && ((m_SVMType == SVMTYPE_C_SVC) || (m_SVMType == SVMTYPE_NU_SVC))) { v = svm.svm_predict_probability(m_Model, x, prob_estimates);