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 com.heatonresearch.aifh.error.ErrorCalculation

    /**
     * {@inheritDoc}
     */
    @Override
    public double calculateScore(final MLMethod algo) {
        ErrorCalculation ec = this.errorCalc.create();


        final RegressionAlgorithm ralgo = (RegressionAlgorithm) algo;
        final Genome genome = (Genome)ralgo;


        if( genome.size()>this.maxLength ) {
            return Double.POSITIVE_INFINITY;
        }


        // evaulate
        ec.clear();
        for (final BasicData pair : this.trainingData) {
            final double[] output = ralgo.computeRegression(pair.getInput());
            ec.updateError(output, pair.getIdeal(), 1.0);
        }


        return ec.calculate();
    }

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Examples of com.heatonresearch.aifh.error.ErrorCalculation


    @Test
    public void testGeneral() {
        final List<BasicData> training = BasicData.convertArrays(TEST_INPUT, TEST_IDEAL);
        final ScoreRegressionData score = new ScoreRegressionData(training);
        final ErrorCalculation ec = new ErrorCalculationSSE();
        score.setErrorCalc(ec);
        assertEquals(ec, score.getErrorCalc());
    }

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Examples of com.heatonresearch.aifh.error.ErrorCalculation

     */
    public void process() {


        final NumberFormat nf = NumberFormat.getInstance();


        final ErrorCalculation calcESS = new ErrorCalculationSSE();
        final ErrorCalculation calcMSE = new ErrorCalculationMSE();
        final ErrorCalculation calcRMS = new ErrorCalculationRMS();


        final DataHolder smallErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.1);
        final DataHolder mediumErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.5);
        final DataHolder largeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 1.0);
        final DataHolder hugeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 10.0);

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Examples of com.heatonresearch.aifh.error.ErrorCalculation

    /**
     * {@inheritDoc}
     */
    @Override
    public double calculateScore(final MLMethod algo) {
        ErrorCalculation ec = this.errorCalc.create();


        final RegressionAlgorithm ralgo = (RegressionAlgorithm) algo;
        // evaulate
        ec.clear();
        for (final BasicData pair : this.trainingData) {
            final double[] output = ralgo.computeRegression(pair.getInput());
            ec.updateError(output, pair.getIdeal(), 1.0);
        }


        return ec.calculate();
    }

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Examples of org.encog.engine.util.ErrorCalculation

   * @param data
   *            The training set.
   * @return The error percentage.
   */
  public double calculateError(final EngineIndexableSet data) {
    final ErrorCalculation errorCalculation = new ErrorCalculation();


    final double[] actual = new double[this.outputCount];
    final EngineData pair = BasicEngineData.createPair(data.getInputSize(),
        data.getIdealSize());


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

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Examples of org.encog.engine.util.ErrorCalculation


  /**
   * @return The error from the last evaluation.
   */
  public double getError() {
    ErrorCalculation ec = new ErrorCalculation();
    double result = 0;
    for (int i = 0; i < this.errors.length; i++) {
      result += this.errors[i];
    }
    return result/(this.errors.length*this.flat.getOutputCount());

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Examples of org.encog.mathutil.error.ErrorCalculation

   * {@inheritDoc}
   */
  @Override
  public final 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

   * @param data
   *            The training set.
   * @return The error percentage.
   */
  public final double calculateError(final MLDataSet data) {
    final ErrorCalculation errorCalculation = new ErrorCalculation();


    final double[] actual = new double[this.outputCount];
    final MLDataPair pair = BasicMLDataPair.createPair(data.getInputSize(),
        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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Examples of org.encog.mathutil.error.ErrorCalculation


    if (this.mustInit) {
      initWeight();
    }


    final ErrorCalculation error = new ErrorCalculation();


    for (final MLDataPair pair : this.training) {
      final MLData out = this.network.computeInstar(pair.getInput());


      final int j = EngineArray.indexOfLargest(out.getData());
      for (int i = 0; i < this.network.getOutstarCount(); i++) {
        final double delta = this.learningRate
            * (pair.getIdeal().getData(i) - this.network
                .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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Examples of org.encog.mathutil.error.ErrorCalculation

   }


  public static double calculateRegressionError(MLRegression method,
      MLDataSet data) {
    
    final ErrorCalculation errorCalculation = new ErrorCalculation();
    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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