package io.sigpipe.sing.stat;

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

import org.ejml.simple.SimpleMatrix;

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

public class OnlineKDE {


    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) {
            temperatures.add(Double.parseDouble(line));
        }
        System.out.println("Samples: " + temperatures.size());

        // disable the forgetting factor
        double forgettingFactor = 1;
        // set the compression threshold
        double compressionThreshold = 0.02;
        // number of samples to genereate
        int noOfSamples = 1000;

        // 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 < noOfSamples; 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));
        }
    }
}

@@ -246,17 +246,16 @@
 eKDE %7B%0A%0A
-%0A
     publ
@@ -921,82 +921,8 @@
 .02;
-%0A        // number of samples to genereate%0A        int noOfSamples = 1000;
 %0A%0A  
@@ -1047,16 +1047,33 @@
 leModel(
+%0A                
 forgetti
@@ -1081,32 +1081,16 @@
 gFactor,
-%0A               
  compres
@@ -2208,19 +2208,27 @@
 i %3C 
-noOfSamples
+temperatures.size()
 ; i+

Use the entire test dataset