Examples of org.encog.mathutil.error.ErrorCalculation.calculate()

Class org.encog.mathutil.error.ErrorCalculation

Examples of org.encog.mathutil.error.ErrorCalculation.calculate()

org.encog.mathutil.error.ErrorCalculation.calculate()
Returns the root mean square error for a complete training set. @return The current error for the neural network.

      errorCalculation.updateError(output.getData(), pair.getIdeal()
          .getData(),pair.getSignificance());
    }


    // set the global error
    setError(errorCalculation.calculate());
  }


  /**
   * {@inheritDoc}
   */

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    for (int i = 0; i < data.getRecordCount(); i++) {
      data.getRecord(i, pair);
      compute(pair.getInputArray(), actual);
      errorCalculation.updateError(actual, pair.getIdealArray(), pair.getSignificance());
    }
    return errorCalculation.calculate();
  }


  /**
   * Clear any connection limits.
   */

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      final MLData out2 = this.network.computeOutstar(out);
      error.updateError(out2.getData(), pair.getIdeal().getData(), pair.getSignificance());
    }


    setError(error.calculate());
  }


  /**
   * {@inheritDoc}
   */

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    for (final MLDataPair pair : data) {
      final MLData actual = method.compute(pair.getInput());
      errorCalculation.updateError(actual.getData(), pair.getIdeal()
          .getData(),pair.getSignificance());
    }
    return errorCalculation.calculate();
  }


  public static void saveCSV(File targetFile, CSVFormat format, MLDataSet set) {
    try {
      FileWriter outFile = new FileWriter(targetFile);

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    for (final MLDataPair pair : data) {
      final MLData actual = method.compute(pair.getInput());
      errorCalculation.updateError(actual.getData(), pair.getIdeal()
          .getData(),pair.getSignificance());
    }
    return errorCalculation.calculate();
  }
}

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      for (int i = 0; i < prob.l; i++) {
        final double ideal = prob.y[i];
        final double actual = target[i];
        error.updateError(actual, ideal);
      }
      return error.calculate();
    } else {
      for (int i = 0; i < prob.l; i++) {
        if (target[i] == prob.y[i]) {
          ++totalCorrect;
        }

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    for (int i = 0; i < data.getRecordCount(); i++) {
      data.getRecord(i, pair);
      compute(pair.getInputArray(), actual);
      errorCalculation.updateError(actual, pair.getIdealArray(), pair.getSignificance());
    }
    return errorCalculation.calculate();
  }


  /**
   * Clear any connection limits.
   */

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      final MLData out2 = this.network.computeOutstar(out);
      error.updateError(out2.getData(), pair.getIdeal().getData(), pair.getSignificance());
    }


    setError(error.calculate());
  }


  /**
   * {@inheritDoc}
   */

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        calculateNeuronGradient(neuron);
      }
    }


    // Set the overall error.
    setError(errorCalc.calculate());
    
    // Learn for all data.
    learn();    
  }

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    if( lastLearn>0 ) {
      learn();
    }


    // Set the overall error.
    setError(errorCalc.calculate());
    
  }
  
  /**
   * Learn for the entire network.

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