package org.greenstone.mars;

import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

import weka.core.neighboursearch.LinearNNSearch;
import weka.core.neighboursearch.NearestNeighbourSearch;
//import weka.core.neighboursearch.KDTree;

// Based on StackOverflow:
//   https://stackoverflow.com/questions/31350506/how-to-calculate-the-nearest-neighbors-using-weka-from-the-command-line

public class WekaFindInstanceKNN 
{

    public static Instances loadDataset(String input_filename) 
    {
	Instances instances = null;

	try {
	    DataSource source = new DataSource(input_filename);
	    instances = source.getDataSet();
	}
	catch (Exception e) {
	    e.printStackTrace();
	}
	
	return instances;
    }

    public static NearestNeighbourSearch initKNN(Instances instances) 
    {
	LinearNNSearch knn = new LinearNNSearch(instances);
	
	return knn;
    }

    public static void findNearestKNN(NearestNeighbourSearch knn, Instance sample_instance, int k_nearest)
    {
	try {
	    Instances nearest_instances= knn.kNearestNeighbours(sample_instance, k_nearest);
	    
	    //cycle through the instances and printout the nearestneighbors
	    
	    System.out.println("\n" + sample_instance);
	    for(int i =0; i<k_nearest; i++) {
		System.out.println("\t" + nearest_instances.instance(i));		 
	    }	
	}
	catch (Exception e) {
	    e.printStackTrace();
	}
	    
    }


    public static void main(String[] args)
    {	
	if (args.length != 2) {
	    System.err.println("Usage: k-nearest-num file.{arff,csv}");
	    System.exit(1);
	}

	String k_nearest_str     = args[0];
	String input_filename = args[1];
	
	int k_nearest = Integer.parseInt(k_nearest_str);
	 
	System.out.println("Weka Command Line Find Nearest " + k_nearest_str
			   + " Neighbors for each Instance in "  + input_filename);

	Instances instances = loadDataset(input_filename);
	NearestNeighbourSearch knn = initKNN(instances);

	/*
	DataSource source = new DataSource(input_filename);
	Instances instances = source.getDataSet();
	
	LinearNNSearch knn = new LinearNNSearch(instances);
	//KDTree knn = new KDTree(instances);
	*/

	//cycle through the dataset and get instances for the nearestneighbors
	
	int num_instances = instances.numInstances();
	for(int j=0; j<num_instances; j++) {
	    //Instances nearestInstances= knn.kNearestNeighbours(instances.instance(j), k_nearest);
	    
	    Instance sample_instance = instances.instance(j);
	    findNearestKNN(knn,sample_instance,k_nearest);
	}	 
    }
}