Examples of org.encog.EncogError

org.encog.EncogError
General error class for Encog. All Encog errors should extend from this class. Doing this ensures that they will be caught as Encog errors. This also ensures that any subclasses will be logged.

   */
  @Override
  public final void process() {


    if (this.hidden.size() == 0) {
      throw new EncogError(
          "To calculate the optimal hidden size, at least "
              + "one hidden layer must be defined.");
    }


    this.hiddenCounts = new int[this.hidden.size()];


    // set the best network
    this.bestNetwork = null;


    // set to minimums
    int i = 0;
    for (final HiddenLayerParams parm : this.hidden) {
      this.hiddenCounts[i++] = parm.getMin();
    }


    // make sure hidden layer 1 has at least one neuron
    if (this.hiddenCounts[0] == 0) {
      throw new EncogError(
          "To calculate the optimal hidden size, at least "
              + "one neuron must be the minimum for the first hidden layer.");
    }


    super.process();

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      }


      csv.close();
      is.close();
    } catch (final IOException e) {
      throw new EncogError(e);
    }
  }

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          ReflectionUtil.classMap.put(c.getSimpleName(), c);
        }
      }
      is.close();
    } catch (final IOException e) {
      throw new EncogError(e);
    } catch (final ClassNotFoundException e) {
      throw new EncogError("Unknown class: " + currentClass);
    }


  }

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          ReflectionUtil.classMap.put(c.getSimpleName(), c);
        }
      }
      is.close();
    } catch (final IOException e) {
      throw new EncogError(e);
    } catch (final ClassNotFoundException e) {
      throw new EncogError("Unknown class: " + currentClass);
    }
  }

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   * {@inheritDoc}
   */
  @Override
  public final int classify(final MLData input) {
    if (this.model == null) {
      throw new EncogError(
          "Can't use the SVM yet, it has not been trained, " 
          + "and no model exists.");
    }


    final svm_node[] formattedInput = makeSparse(input);

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   */
  @Override
  public final MLData compute(final MLData input) {


    if (this.model == null) {
      throw new EncogError(
          "Can't use the SVM yet, it has not been trained, "
          + "and no model exists.");
    }


    final MLData result = new BasicMLData(1);

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          .toByteArray()); // E
      ois = new ObjectInputStream(bin); // F
      // return the new object
      return ois.readObject(); // G
    } catch (final Exception e) {
      throw new EncogError(e);
    } finally {
      try {
        if (oos != null) {
          oos.close();
        }
        if (ois != null) {
          ois.close();
        }
      } catch (final Exception e) {
        throw new EncogError(e);
      }
    }
  }

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      out.close();
      outFile.close();


    } catch(IOException ex) {
      throw new EncogError(ex);
    }
    
  }

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      final CSVFormat format = new CSVFormat(CSVFormat
          .getDecimalCharacter(), delim);
      this.reader = new BufferedReader(new FileReader(filename));
      begin(headers, format);
    } catch (final IOException e) {
      throw new EncogError(e);
    }
  }

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      final CSVFormat format) {
    try {
      this.reader = new BufferedReader(new FileReader(filename));
      begin(headers, format);
    } catch (final IOException e) {
      throw new EncogError(e);
    }
  }

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org.encog.app.analyst.csv.normalize.AnalystNormalizeCSV

org.encog.app.analyst.script.prop.PropertyConstraints

org.encog.engine.network.activation.ActivationCompetitive

org.encog.examples.neural.image.ImageNeuralNetwork

org.encog.mathutil.Equilateral

org.encog.mathutil.randomize.FanInRandomizer

org.encog.mathutil.randomize.NguyenWidrowRandomizer

org.encog.ml.data.auto.AutoFloatDataSet

org.encog.ml.data.auto.AutoFloatDataSet$AutoFloatIterator

org.encog.ml.data.basic.BasicMLDataSet$BasicMLIterator

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