Package org.encog.mathutil.error

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

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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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      final MLData input = pair.getInput();
      final MLData ideal = pair.getIdeal();
      final MLData actual = this.network.compute(input);
      final double sig = pair.getSignificance();

      errorCalc.updateError(actual.getData(), ideal.getData(), sig);

      for (int i = 0; i < this.network.getOutputCount(); i++) {
        final double diff = (ideal.getData(i) - actual.getData(i))
            * sig;
        final FreeformNeuron neuron = this.network.getOutputLayer()
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      final MLData input = pair.getInput();
      final MLData ideal = pair.getIdeal();
      final MLData actual = this.network.compute(input);
      final double sig = pair.getSignificance();

      errorCalc.updateError(actual.getData(), ideal.getData(), sig);

      for (int i = 0; i < this.network.getOutputCount(); i++) {
        final double diff = (ideal.getData(i) - actual.getData(i))
            * sig;
        final FreeformNeuron neuron = this.network.getOutputLayer()
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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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    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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    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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    }

    // 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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          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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    ErrorCalculation result = new ErrorCalculation();
   
    for (int i = 0; i < this.trainingLength; i++) {
      this.indexableTraining.getRecord(i, this.pair);
      final MLData actual = this.network.compute(this.pair.getInput());
      result.updateError(actual.getData(), this.pair.getIdeal().getData(),pair.getSignificance());
    }   
   
    return result.calculateESS();
  }
 
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