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

Class org.encog.mathutil.error.ErrorCalculation

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

org.encog.mathutil.error.ErrorCalculation.updateError()
Update the error with single values. @param actual The actual value. @param ideal The ideal value.

          this.network.addWeight(0, i, currentAdaline,
              this.learningRate * diff * input);
        }
      }


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


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

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        data.getIdealSize());


    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();
  }


  /**

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                .getWeightsInstarToOutstar().get(j, i));
        this.network.getWeightsInstarToOutstar().add(j, i, delta);
      }


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


    setError(error.calculate());
  }

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    if( method instanceof MLContext )
      ((MLContext)method).clearContext();


    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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    }


    // calculate error
    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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    if ((param.svm_type == svm_parameter.EPSILON_SVR)
        || (param.svm_type == svm_parameter.NU_SVR)) {
      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]) {

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    ErrorCalculation error = new ErrorCalculation();
    
    for(int i=0;i<ideal.length;i++)
    {
      error.updateError(actual_good[i], ideal[i], 1.0);
    }
    TestCase.assertEquals(0.0,error.calculateRMS());
    
    error.reset();

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    error.reset();
    
    for(int i=0;i<ideal.length;i++)
    {
      error.updateError(actual_bad[i], ideal[i], 1.0);
    }
    TestCase.assertEquals(250,(int)(error.calculateRMS()*1000));
    
  }
}

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    for (final MLDataPair pair : this.training) {
      EngineArray.fill(derivative, 0);
      final MLData networkOutput = this.network.compute(pair.getInput());


      e = pair.getIdeal().getData(outputNeuron) - networkOutput.getData(outputNeuron);      
      error.updateError(networkOutput.getData(outputNeuron), pair.getIdeal().getData(outputNeuron));
      
      int currentWeight = 0;
      
      // loop over the output weights
      int outputFeedCount  = network.getLayerTotalNeuronCount(network.getLayerCount()-2);

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        data.getIdealSize());


    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();
  }


  /**

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