Package weka.core

Examples of weka.core.WekaException

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      } catch (Exception ex) {
      }
    }
   
    if (noRule)
      throw new WekaException("No attributes found to work with!");
  }
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    else if ( estimator.equals("pace4") ) paceEstimator = pace4Estimator;
    else if ( estimator.equals("pace6") ) paceEstimator = pace6Estimator;
    else if ( estimator.equals("aic") ) paceEstimator = aicEstimator;
    else if ( estimator.equals("bic") ) paceEstimator = bicEstimator;
    else if ( estimator.equals("ric") ) paceEstimator = ricEstimator;
    else throw new WekaException("unknown estimator " + estimator +
         " for -E option" );

    String string = Utils.getOption('S', options);
    if( ! string.equals("") ) olscThreshold = Double.parseDouble( string );
   
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    Object x = instanceToArray(instance);
    double v;
    double[] result = new double[instance.numClasses()];
    if (m_ProbabilityEstimates) {
      if (m_SVMType != SVMTYPE_L2_LR) {
        throw new WekaException("probability estimation is currently only " +
            "supported for L2-regularized logistic regression");
      }

      int[] labels = (int[])invokeMethod(m_Model, "getLabels", null, null);
      double[] prob_estimates = new double[instance.numClasses()];
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    if (!isFirstBatchDone()) {
      getActualClassifier();
      if (!getSerializedClassifierFile().isDirectory()) {
  // same dataset format?
  if ((m_SerializedHeader != null) && (!m_SerializedHeader.equalHeaders(instances)))
    throw new WekaException(
        "Training header of classifier and filter dataset don't match:\n"
        + m_SerializedHeader.equalHeaders(instances));
      }
      else {
  m_ActualClassifier.buildClassifier(instances);
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      } catch (Exception ex) {
      }
    }
   
    if (noRule)
      throw new WekaException("No attributes found to work with!");
  }
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    else if ( estimator.equals("pace4") ) paceEstimator = pace4Estimator;
    else if ( estimator.equals("pace6") ) paceEstimator = pace6Estimator;
    else if ( estimator.equals("aic") ) paceEstimator = aicEstimator;
    else if ( estimator.equals("bic") ) paceEstimator = bicEstimator;
    else if ( estimator.equals("ric") ) paceEstimator = ricEstimator;
    else throw new WekaException("unknown estimator " + estimator +
         " for -E option" );

    String string = Utils.getOption('S', options);
    if( ! string.equals("") ) olscThreshold = Double.parseDouble( string );
   
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      } catch (Exception ex) {
      }
    }
   
    if (noRule)
      throw new WekaException("No attributes found to work with!");
  }
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      } catch (Exception ex) {
      }
    }
   
    if (noRule)
      throw new WekaException("No attributes found to work with!");
  }
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      m_Document.remove(0, m_Document.getLength());
     
      // add new content
      content = ScriptUtils.load(file);
      if (content == null)
  throw new WekaException("Error reading content of file '" + file + "'!");
      m_Document.insertString(0, content, null);
     
      m_Modified = false;
      m_Filename = file;
      result     = true;
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    }
    else {
      // process options
      tmpStr = Utils.getOption('s', args);
      if (tmpStr.length() == 0)
        throw new WekaException("No script supplied!");
      else
  scriptFile = new File(tmpStr);
      script.setOptions(args);
     
      // remove empty elements from array
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Related Classes of weka.core.WekaException

  • weka.classifiers.evaluation.output.prediction.AbstractOutput
  • weka.classifiers.functions.LibLINEAR
  • weka.classifiers.functions.PaceRegression
  • weka.classifiers.rules.OneR
  • weka.experiment.ExplicitTestsetResultProducer
  • weka.filters.supervised.attribute.AddClassification
  • weka.filters.unsupervised.attribute.AddCluster
  • weka.gui.scripting.Script
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