Searching in Weka with Processing

Posted by bryan

Further to the last Weka example, I used the same CSV data file for neighbourhood search. By pressing the mouse button, it generated a random sequence of numbers between 1 to 4. The program used the sequence as an instance to match against the database from the CSV data file. The closet match will be shown together with the distance between the test case (random) and the closet match from the database.

A sample screenshot

 
Source codes

import weka.core.converters.CSVLoader;
import weka.core.Instances;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.neighboursearch.LinearNNSearch;
import java.util.Enumeration;
import java.io.File;
 
Instances data;
String csv;
LinearNNSearch lnn;
boolean search;
int idx;
float dist;
String testCase;
String matchCase;
String distance;
 
void setup() {
  size(500, 500);
  csv = "Testing.csv";
  try {
    loadData();
    buildModel();
  } 
  catch (Exception e) {
    e.printStackTrace();
  }
  search = false;
  idx = -1;
  dist = 0.0;
  testCase = "";
  matchCase = "";
  distance = "";
  fill(255);
}
 
void draw() {
  background(0);
  if (search) {
    text(testCase, 100, 100);
    text(matchCase, 100, 150);
    text(distance, 100, 200);
  }
}
 
void loadData() throws Exception {
  // load external CSV data file, without header row.
  CSVLoader loader = new CSVLoader();
  loader.setNoHeaderRowPresent(true);
  loader.setSource(new File(dataPath(csv)));
  data = loader.getDataSet();
  data.setClassIndex(0);
 
  println("Attributes : " + data.numAttributes());
  println("Instances : " + data.numInstances());
  println("Name : " + data.classAttribute().toString());
 
  Enumeration all = data.enumerateInstances();
  while (all.hasMoreElements()) {
    Instance single = (Instance) all.nextElement();
    println("Instance : " + (int) single.classValue() + ": " + single.toString());
  }
}
 
void buildModel() throws Exception {
  // Build linear search model.
  lnn = new LinearNNSearch(data);
  println("Model built ...");
}
 
void test() throws Exception {
  // Construct a test case and do a linear searching.
  double [] val = new double[data.numAttributes()];
  val[0] = 0;
  testCase  = "Test case:  ";
  matchCase = "Match case: ";
  distance  = "Distance:   ";
  for (int i=1; i<val.length; i++) {
    val[i] = floor(random(4))+1;
    testCase += (nf((float)val[i]) + ",");
  }
  testCase = testCase.substring(0, testCase.length()-1);
  DenseInstance x = new DenseInstance(1.0, val);
  x.setDataset(data);
  Instance c = lnn.nearestNeighbour(x);
  double [] tmp = lnn.getDistances();
  dist = (float) tmp[0];
  idx = (int) c.classValue();
  matchCase += data.instance(idx).toString();
  distance += nf(dist);
  saveFrame("weka####.png");
}
 
void mousePressed() {
  try {
    test();
  } 
  catch (Exception e) {
    e.printStackTrace();
  }
  search = true;
}