Examples of RegressionAlgorithm

Examples of com.heatonresearch.aifh.learning.RegressionAlgorithm

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     */
    @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.learning.RegressionAlgorithm

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    /**
     * {@inheritDoc}
     */
    @Override
    public double calculateScore(final MachineLearningAlgorithm algo) {
        final RegressionAlgorithm ralgo = (RegressionAlgorithm) algo;
        // evaulate
        errorCalc.clear();
        for (final BasicData pair : this.trainingData) {
            final double[] output = ralgo.computeRegression(pair.getInput());
            errorCalc.updateError(output, pair.getIdeal(), 1.0);
        }

        return errorCalc.calculate();
    }
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Examples of com.heatonresearch.aifh.learning.RegressionAlgorithm

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    @Override
    public double calculateScore(final MLMethod algo) {
        int incorrectCount = 0;
        int totalCount = 0;

        final RegressionAlgorithm alg = (RegressionAlgorithm) algo;

        for (final BasicData aTrainingData : this.trainingData) {
            totalCount++;
            boolean predictSurvive = alg.computeRegression(aTrainingData.getInput())[0] > 0.5;
            boolean idealSurvive = aTrainingData.getIdeal()[0] > 0.5;

            if (predictSurvive == idealSurvive) {
                incorrectCount++;
            }
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Examples of com.heatonresearch.aifh.learning.RegressionAlgorithm

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     */
    @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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