package io.sigpipe.sing.stat;

import java.io.BufferedReader;
import java.io.FileReader;
import java.util.ArrayList;
import java.util.List;

import org.apache.commons.math3.analysis.UnivariateFunction;

import org.ejml.simple.SimpleMatrix;

import de.tuhh.luethke.okde.model.SampleModel;

public class OnlineKDE implements UnivariateFunction {

    private RunningStatistics stats = new RunningStatistics();
    private SampleModel model;

    public static void main(String[] args) throws Exception {
        OnlineKDE test = new OnlineKDE();
    }

    public OnlineKDE() throws Exception {
        List<Double> temperatures = new ArrayList<>();
        BufferedReader br = new BufferedReader(
                new FileReader("temperatures.txt"));
        String line;
        while ((line = br.readLine()) != null) {
            double temp = Double.parseDouble(line);
            temperatures.add(temp);
            stats.put(temp);
        }
        br.close();

        System.out.println(stats);

        // disable the forgetting factor
        double forgettingFactor = 1;
        // set the compression threshold
        double compressionThreshold = 0.02;

        // sample model object used for sample distribution estimation
        SampleModel sampleDistribution = new SampleModel(
                forgettingFactor, compressionThreshold);

        double[][] c = { { 0 } };

        SimpleMatrix[] samples = new SimpleMatrix[temperatures.size()];
        for (int i = 0; i < temperatures.size(); ++i) {
            SimpleMatrix sample = new SimpleMatrix(
                    new double[][] {{ temperatures.get(i) }});
            samples[i] = sample;
        }

        /*
         * Now the sample model is updated using the generated sample data.
         */
        try {
            // Add three samples at once to initialize the sample model
            ArrayList<SimpleMatrix> initSamples = new ArrayList<SimpleMatrix>();
            initSamples.add(samples[0]);
            initSamples.add(samples[1]);
            initSamples.add(samples[2]);
            double[] w = { 1, 1, 1 };
            SimpleMatrix[] cov = { new SimpleMatrix(c), new SimpleMatrix(c),
                new SimpleMatrix(c) };
            sampleDistribution.updateDistribution(
                    initSamples.toArray(new SimpleMatrix[3]), cov, w);

            // Update the sample model with all generated samples one by one.
            for (int i = 3; i < temperatures.size(); i++) {
                SimpleMatrix pos = samples[i];
                sampleDistribution.updateDistribution(pos, new SimpleMatrix(c),
                        1d);
            }
        } catch (Exception e) {
            e.printStackTrace();
        }

        for (int i = 270; i < 320; ++i) {
                double[][] point = {{ (double) i }};
                SimpleMatrix pointVector = new SimpleMatrix(point);
                System.out.println(i + "\t" + sampleDistribution.evaluate(pointVector));
        }
    }

    public double value(double x) {
        return 0;
    }


}

@@ -1243,38 +1243,18 @@
     
-SampleModel sampleDistribution
+this.model
  = n
@@ -1268,33 +1268,16 @@
 leModel(
-%0A                
 forgetti
@@ -2187,34 +2187,26 @@
         
-sampleDistribution
+this.model
 .updateD
@@ -2491,34 +2491,26 @@
         
-sampleDistribution
+this.model
 .updateD
@@ -2879,26 +2879,18 @@
 %22 + 
-sampleDistribution
+this.model
 .eva

Use instance model