Example usage for weka.classifiers.functions GaussianProcesses GaussianProcesses

Introduction

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

Prototype

GaussianProcesses

Source Link

Usage

From source file:gr.ntua.ece.cslab.panic.core.models.GaussianCurves.java

License:Apache License

public GaussianCurves() {
    super();
    this.classifier = new GaussianProcesses();
}

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From source file:org.datahack.forecast.BayExample.java

public static void main(String[] args) {
    try {//from   ww w. ja  v  a  2  s . c  o  m
        // path to the Australian wine data included with the time series forecasting
        // package

        File output = File.createTempFile("tempWineData", "arff");
        output.deleteOnExit();
        String dataFileName = "sample-data/wine.arff";
        URL resource = BayExample.class.getResource("/" + dataFileName);
        FileUtils.copyURLToFile(resource, output);

        String pathToWineData = output.getPath();

        // load the wine data
        Instances wine = new Instances(new BufferedReader(new FileReader(pathToWineData)));

        InstanceQuery q = new InstanceQuery();

        // new forecaster
        WekaForecaster forecaster = new WekaForecaster();

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

        // default underlying classifier is SMOreg (SVM) - we'll use
        // gaussian processes for regression instead
        forecaster.setBaseForecaster(new GaussianProcesses());

        forecaster.getTSLagMaker().setTimeStampField("Date"); // date time stamp
        forecaster.getTSLagMaker().setMinLag(1);
        forecaster.getTSLagMaker().setMaxLag(12); // monthly data

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

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

        // build the model
        forecaster.buildForecaster(wine, System.out);

        // prime the forecaster with enough recent historical data
        // to cover up to the maximum lag. In our case, we could just supply
        // the 12 most recent historical instances, as this covers our maximum
        // lag period
        forecaster.primeForecaster(wine);

        // forecast for 12 units (months) beyond the end of the
        // training data
        List<List<NumericPrediction>> forecast = forecaster.forecast(12, System.out);

        // output the predictions. Outer list is over the steps; inner list is over
        // the targets
        for (int i = 0; i < 12; i++) {
            List<NumericPrediction> predsAtStep = forecast.get(i);
            for (int j = 0; j < 2; j++) {
                NumericPrediction predForTarget = predsAtStep.get(j);
                System.out.print("" + predForTarget.predicted() + " ");
            }
            System.out.println();
        }

        // we can continue to use the trained forecaster for further forecasting
        // by priming with the most recent historical data (as it becomes available).
        // At some stage it becomes prudent to re-build the model using current
        // historical data.

    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

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From source file:org.datahack.forecast.TimeSeriesExample.java

public static void main(String[] args) {
    try {/*w w w . java2  s  . c  om*/
        // path to the Australian wine data included with the time series forecasting
        // package

        File output = File.createTempFile("tempWineData", "arff");
        output.deleteOnExit();
        String dataFileName = "sample-data/parking344.arff";
        URL resource = TimeSeriesExample.class.getResource("/" + dataFileName);
        FileUtils.copyURLToFile(resource, output);

        String pathToWineData = output.getPath();

        // load the wine data
        Instances wine = new Instances(new BufferedReader(new FileReader(pathToWineData)));

        // new forecaster
        WekaForecaster forecaster = new WekaForecaster();

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

        // default underlying classifier is SMOreg (SVM) - we'll use
        // gaussian processes for regression instead
        forecaster.setBaseForecaster(new GaussianProcesses());

        forecaster.getTSLagMaker().setTimeStampField("eventTime"); // date time stamp
        forecaster.getTSLagMaker().setMinLag(1);
        forecaster.getTSLagMaker().setMaxLag(12); // monthly data

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

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

        // build the model
        forecaster.buildForecaster(wine, System.out);

        // prime the forecaster with enough recent historical data
        // to cover up to the maximum lag. In our case, we could just supply
        // the 12 most recent historical instances, as this covers our maximum
        // lag period
        forecaster.primeForecaster(wine);

        // forecast for 12 units (months) beyond the end of the
        // training data
        List<List<NumericPrediction>> forecast = forecaster.forecast(12, System.out);

        // output the predictions. Outer list is over the steps; inner list is over
        // the targets
        for (int i = 0; i < 12; i++) {
            List<NumericPrediction> predsAtStep = forecast.get(i);
            for (int j = 0; j < 2; j++) {
                NumericPrediction predForTarget = predsAtStep.get(j);
                System.out.print("" + predForTarget.predicted() + " ");
            }
            System.out.println();
        }

        // we can continue to use the trained forecaster for further forecasting
        // by priming with the most recent historical data (as it becomes available).
        // At some stage it becomes prudent to re-build the model using current
        // historical data.

    } catch (Exception ex) {
        ex.printStackTrace();
    }
}

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