int nr_class=svm.svm_get_nr_class(model); double[] prob_estimates=null;
int nr_class = svm.svm_get_nr_class(model); double[] prob_estimates = null;
int nr_class=svm.svm_get_nr_class(model); int[] labels=new int[nr_class]; double[] prob_estimates=null;
int nr_class = svm.svm_get_nr_class(model); double[] prob_estimates = null;
param.svm_type == svm_parameter.NU_SVC)) double[] prob_estimates= new double[svm_get_nr_class(submodel)]; for(j=begin;j<end;j++) target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
param.svm_type == svm_parameter.NU_SVC)) double[] prob_estimates= new double[svm_get_nr_class(submodel)]; for(j=begin;j<end;j++) target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
param.svm_type == svm_parameter.NU_SVC)) double[] prob_estimates= new double[svm_get_nr_class(submodel)]; for(j=begin;j<end;j++) target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
int nr_class = svm.svm_get_nr_class(model); double[] prediction = new double[nr_class]; return output; } else { int nr_class = svm.svm_get_nr_class(model); double[] prediction = new double[nr_class]; int[] label = new int[svm.svm_get_nr_class(model)]; svm.svm_get_labels(model, label); Matrix output = Matrix.Factory.zeros(1, MathUtil.max(label) + 1);
public void svm_predict_with_kbestlist(svm_model model, svm_node[] x, KBestList kBestList) throws MaltChainedException { int i; final int nr_class = svm.svm_get_nr_class(model); final double[] dec_values = new double[nr_class*(nr_class-1)/2]; svm.svm_predict_values(model, x, dec_values);
public void svm_predict_with_kbestlist(svm_model model, svm_node[] x, KBestList kBestList) throws MaltChainedException { int i; final int nr_class = svm.svm_get_nr_class(model); final double[] dec_values = new double[nr_class*(nr_class-1)/2]; svm.svm_predict_values(model, x, dec_values);
public final double[] classify(Document document) throws Exception { int svm_type = svm.svm_get_svm_type(model); int nr_class = svm.svm_get_nr_class(model); int[] labels = new int[nr_class]; svm.svm_get_labels(model, labels);
double[] buf = new double[svm.svm_get_nr_class(model)]; for(int i = 0; i < prob.l && iter.valid(); iter.advance(), i++) { V vec = relation.get(iter);