Examples of weka.classifiers.evaluation.output.prediction.AbstractOutput.printFooter()

weka.classifiers.evaluation.output.prediction.AbstractOutput.printFooter()
Prints the footer to the buffer. This will also store the generated output in a file if an output file was specified. @throws Exception if check fails

      evaluateModel(copiedClassifier, test, forPredictionsPrinting);
    }
    m_NumFolds = numFolds;


    if (classificationOutput != null)
      classificationOutput.printFooter();
  }


  /**
   * Performs a (stratified if class is nominal) cross-validation
   * for a classifier on a set of instances.

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      m_Log.statusMessage("Evaluating on training data. Processed "
              +jj+" instances...");
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText && classificationOutput.generatesOutput()) {
    outBuff.append("\n");
        } 
        outBuff.append("=== Evaluation on training set ===\n");
        break;

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        classificationOutput.printClassification(current, test.instance(jj), jj);
      }
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        if (inst.attribute(classIndex).isNominal()) {
    outBuff.append("=== Stratified cross-validation ===\n");

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      m_Log.statusMessage("Evaluating on test split. Processed "
              +jj+" instances...");
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        outBuff.append("=== Evaluation on test split ===\n");
        break;

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          +jj+" instances...");
    }
        }


        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        outBuff.append("=== Evaluation on test set ===\n");
        break;

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          +jj+" instances...");
    }
        }


        if (outputPredictionsText)
    classificationOutput.printFooter();
              if (outputPredictionsText && classificationOutput.generatesOutput()) {
                outBuff.append("\n");
              } 
      
              if (outputSummary) {

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      evaluateModel(copiedClassifier, test, forPredictionsPrinting);
    }
    m_NumFolds = numFolds;


    if (classificationOutput != null)
      classificationOutput.printFooter();
  }


  /**
   * Performs a (stratified if class is nominal) cross-validation
   * for a classifier on a set of instances.

View Full Code Here

      m_Log.statusMessage("Evaluating on training data. Processed "
              +jj+" instances...");
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText && classificationOutput.generatesOutput()) {
    outBuff.append("\n");
        } 
        outBuff.append("=== Evaluation on training set ===\n");
        break;

View Full Code Here

        classificationOutput.printClassification(current, test.instance(jj), jj);
      }
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        if (inst.attribute(classIndex).isNominal()) {
    outBuff.append("=== Stratified cross-validation ===\n");

View Full Code Here

      m_Log.statusMessage("Evaluating on test split. Processed "
              +jj+" instances...");
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();
        if (outputPredictionsText) {
    outBuff.append("\n");
        } 
        outBuff.append("=== Evaluation on test split ===\n");
        break;

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