Examples of ErrorCalculation

com.heatonresearch.aifh.error.ErrorCalculation
An error calculation metric calculates the difference between two vector sets. One vector set will be the ideal expected output from a Machine Learning Algorithm. The other vector set is the actual output. Training a Machine Learning Algorithm typically involves minimizing this error.
Error calculation metrics are very similar to distance metrics. However, an error calculation metric operates over a set of vectors, whereas a distance metric operates over just two vectors.
org.encog.engine.util.ErrorCalculation
Calculate the error of a neural network. Encog currently supports three error calculation modes. See ErrorCalculationMode for more info.
org.encog.mathutil.error.ErrorCalculation
Calculate the error of a neural network. Encog currently supports three error calculation modes. See ErrorCalculationMode for more info.

Examples of org.encog.mathutil.error.ErrorCalculation

   */
  private double evaluate(final svm_parameter param, final svm_problem prob,
      final double[] target) {
    int totalCorrect = 0;


    final ErrorCalculation error = new ErrorCalculation();


    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]) {
          ++totalCorrect;
        }

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Examples of org.encog.mathutil.error.ErrorCalculation


public class CalculateRegressionError {
  
  public static double calculateError(final MLRegression method, 
      final MLDataSet data) {
    final ErrorCalculation errorCalculation = new ErrorCalculation();
    
    // clear context
    if( method instanceof MLContext ) {
      ((MLContext)method).clearContext();
    }


    // 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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Examples of org.encog.mathutil.error.ErrorCalculation

   * {@inheritDoc}
   */
  @Override
  public void iteration() {


    final ErrorCalculation errorCalculation = new ErrorCalculation();


    for (final MLDataPair pair : this.training) {
      // calculate the error
      final MLData output = this.network.compute(pair.getInput());


      for (int currentAdaline = 0; currentAdaline < output.size(); currentAdaline++) {
        final double diff = pair.getIdeal().getData(currentAdaline)
            - output.getData(currentAdaline);


        // weights
        for (int i = 0; i <= this.network.getInputCount(); i++) {
          final double input;


          if (i == this.network.getInputCount()) {
            input = 1.0;
          } else {
            input = pair.getInput().getData(i);
          }


          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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Examples of org.encog.mathutil.error.ErrorCalculation

  
  /**
   * @return The SSE error with the current weights.
   */
  private double calculateError() {
    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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