Prediccion.PrecidePasoNodo.java Source code

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Here is the source code for Prediccion.PrecidePasoNodo.java

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/*
 * Copyright (C) 2014 antares
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package Prediccion;

import Entorno.Configuracion.Config;
import Entorno.Depuracion.Debug;
import SincronizarFusionTables.conectarFusionTables;

import com.google.api.services.fusiontables.model.Sqlresponse;

import java.text.ParseException;
import java.util.ArrayList;
import java.util.List;
import java.util.logging.Level;
import java.util.logging.Logger;

import weka.core.Instances;
import weka.classifiers.evaluation.NumericPrediction;
import weka.classifiers.functions.SMOreg;
import weka.classifiers.timeseries.WekaForecaster;
import weka.classifiers.timeseries.core.TSLagMaker;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.experiment.InstanceQuery;

/**
 * Clase encargada de predecir los pasos de nodos por horas
 *
 * @author antares
 */
public class PrecidePasoNodo {

    private static Config _c = new Config();
    private static Debug _d = new Debug();
    private conectarFusionTables cFT = new conectarFusionTables();
    private final String TABLAID = _c.get("ft.PASOSPORHORAS.ID");
    private String fecha;
    private String nodo;

    public Instances pasos = null;
    WekaForecaster forecaster = null;

    public PrecidePasoNodo(String fecha, String nodo) {
        this.fecha = fecha;
        this.nodo = nodo;

        forecaster = new WekaForecaster();

    }

    void GenerarModeloPrediccion() {
        try {
            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);
        }

    }

    Instances cargarDatos(int hora) throws ParseException {
        //Declaramos los atributos de las instancias
        Attribute a0 = new Attribute("Intervalo", "yyyy-MM-dd HH:mm:ss");
        Attribute a1 = new Attribute("Total");

        ArrayList<Attribute> c = new ArrayList<>();
        c.add(a0);
        c.add(a1);

        //Creamos el conjunto de instancias
        Instances instances = new Instances(nodo, c, 1000);

        //Instanciamos conexion con FT
        cFT = new conectarFusionTables();
        Sqlresponse r = cFT.select(TABLAID, "Intervalo, Total",
                "idNodo = " + nodo + " and Intervalo ENDS WITH '00:00:00'",
                "ORDER BY \'Intervalo\' DESC LIMIT 10000");

        for (List<Object> a : r.getRows()) {
            Instance i = new DenseInstance(2);

            String s0 = (String) a.get(0);
            String s1 = (String) a.get(1);

            System.err.println(s0 + " ->" + s1);

            i.setValue(instances.attribute(0), instances.attribute(0).parseDate(s0));
            i.setValue(instances.attribute(1), Integer.parseInt(s1));

            instances.add(i);
        }

        instances.sort(0);

        return instances;
    }

}