Example usage for weka.classifiers.functions SMOreg SMOreg

Introduction

In this page you can find the example usage for weka.classifiers.functions SMOreg SMOreg.

Prototype

SMOreg

Source Link

Usage

From source file:bi_project2.Classify.java

public Classify() {
    saver = new CSVSaver();
    smo = new SMOreg(); // create a new SMO classifier-create
}

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From source file:crystalball.quant.QuantWekaSMOreg.java

License:Open Source License

public void forecast(BarData barData, int startBarId, int endBarId) {
    makeDataSet(barData, startBarId, endBarId);
    try {//from w ww . j  av a  2  s.c o m
        if (isLastBarOfHalfHour(barData, endBarId) || forecaster == null) {
            // new forecaster
            forecaster = new WekaForecaster();

            // set the targets we want to forecast. This method calls
            // setFieldsToLag() on the lag maker object for us
            forecaster.setFieldsToForecast("close");

            // set the underlying classifier as SMOreg (SVM)
            forecaster.setBaseForecaster(new SMOreg());

            forecaster.getTSLagMaker().setTimeStampField("id"); // date time stamp
            forecaster.getTSLagMaker().setMinLag(1);
            forecaster.getTSLagMaker().setMaxLag(78);

            // add a month of the year indicator field
            forecaster.getTSLagMaker().setAddMonthOfYear(false);

            // add a quarter of the year indicator field
            forecaster.getTSLagMaker().setAddQuarterOfYear(false);

            // build the model
            forecaster.buildForecaster(instances);
        }

        // prime the forecaster with enough recent historical data
        // to cover up to the maximum lag. 
        forecaster.primeForecaster(instances);

        // forecast for 6 units beyond the end of the training data
        List<List<NumericPrediction>> forecast = forecaster.forecast(predictSteps, System.out);

        // output the predictions. Outer list is over the steps; inner list is over
        // the targets
        for (int i = 0; i < predictSteps; i++) {
            List<NumericPrediction> predsAtStep = forecast.get(i);
            for (int j = 0; j < 1; j++) {
                NumericPrediction predForTarget = predsAtStep.get(j);
                predicted[i] = predForTarget.predicted();
            }
        }
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

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From source file:Prediccion.PrecidePasoNodo.java

License:Open Source License

void GenerarModeloPrediccion() {
    try {/*w w  w  .j a v a 2 s . c o  m*/
        if (pasos == null) {
            try {
                pasos = cargarDatos(0);
            } catch (ParseException ex) {
                Logger.getLogger(PrecidePasoNodo.class.getName()).log(Level.SEVERE, null, ex);
            }
        }

        //Defimimos el atributo que queremos predecir
        forecaster.setFieldsToForecast("Total");

        //Definimos el mtodo de prediccin a emplear. En este caso, regresin lineal porque 
        //en el artculo es el que mejor ha funcionado
        forecaster.setBaseForecaster(new SMOreg());

        //Defimimos el atributo que "marca" el tiempo y su peridiocidad
        forecaster.getTSLagMaker().setTimeStampField("Intervalo");
        forecaster.getTSLagMaker().setMinLag(1);
        forecaster.getTSLagMaker().setMaxLag(24);
        forecaster.getTSLagMaker().setPeriodicity(TSLagMaker.Periodicity.HOURLY);

        forecaster.buildForecaster(pasos, System.out);

    } catch (Exception ex) {
        Logger.getLogger(PrecidePasoNodo.class.getName()).log(Level.SEVERE, null, ex);
    }

}

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