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 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
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 * are permitted provided that the following conditions are met:
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 *      contributors may be used to endorse or promote products derived from this
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package org.openimaj.ml.dataset;

import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.math.matrix.VectorFactory;

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.HashSet;
import java.util.Set;

import org.apache.log4j.Logger;
import org.openimaj.data.dataset.Dataset;
import org.openimaj.data.dataset.ListBackedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.data.dataset.MapBackedDataset;
import org.openimaj.experiment.annotations.DatasetDescription;

/**
 * A {@link Dataset} instance of the standard wine clustering experiment found here:
 * 
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 *
 */
@DatasetDescription(
                name = "Wine Data Set",
                description = "" +
                                "These data are the results of a chemical analysis of wines grown in the same region in " +
                                "Italy but derived from three different cultivars. The analysis determined the quantities " +
                                "of 13 constituents found in each of the three types of wines."+
                                ""+
                                "I think that the initial data set had around 30 variables, but for some reason I only have " +
                                "the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost" +
                                " it, and b.), I would not know which 13 variables are included in the set."+
                                ""+
                                "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it )"+
                                "1) Alcohol"+
                                "2) Malic acid"+
                                "3) Ash"+
                                "4) Alcalinity of ash"+
                                "5) Magnesium"+
                                "6) Total phenols"+
                                "7) Flavanoids"+
                                "8) Nonflavanoid phenols"+
                                "9) Proanthocyanins"+
                                "10)Color intensity"+
                                "11)Hue"+
                                "12)OD280/OD315 of diluted wines"+
                                "13)Proline"+
                                ""+
                                "In a classification context, this is a well posed problem with \"well behaved\" class structures." +
                                " A good data set for first testing of a new classifier, but not very challenging. ",
                creator = "Forina, M. et al, PARVUS - ",
                url = "http://archive.ics.uci.edu/ml/datasets/Wine",
                downloadUrls = {
                                "http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data"
                })
public class WineDataset extends MapBackedDataset<Integer, ListDataset<double[]>, double[]>{
        final static Logger logger = Logger.getLogger(WineDataset.class);
        
        /**
         * Loads the wine dataset, mean centres the dataset
         * @param clusters valid clusters, if empty all clusters are chosen
         */
        public WineDataset(Integer ... clusters) {
                this(true,clusters);
        }
        
        /**
         * Loads the wine dataset from wine.data
         * @param normalise whether to mean center the dataset
         * @param clusters valid clusters, if empty all clusters are chosen
         */
        public WineDataset(boolean normalise, Integer... clusters) {
                BufferedReader br = new BufferedReader(
                                new InputStreamReader(WineDataset.class.getResourceAsStream("wine.data")));
                String line = null;
                Vector mean = null;
                Set<Integer> clusterSet = null;
                if(clusters.length!=0){
                        clusterSet = new HashSet<Integer>();
                        clusterSet.addAll(Arrays.asList(clusters));
                }
                
                try {
                        while((line = br.readLine())!=null){
                                String[] parts = line.split(",");
                                int cluster = Integer.parseInt(parts[0].trim());
                                if(clusterSet!=null && !clusterSet.contains(cluster)) continue;
                                double[] data = new double[parts.length-1];
                                for (int i = 0; i < data.length; i++) {
                                        data[i] = Double.parseDouble(parts[i+1]);
                                }
                                
                                ListDataset<double[]> ds = this.get(cluster);
                                if(ds == null) this.put(cluster, ds = new ListBackedDataset<double[]>());
                                ds.add(data);
                                Vector copyArray = VectorFactory.getDefault().copyArray(data);
                                if(mean == null){
                                        mean = copyArray.clone();
                                }
                                else{
                                        mean.plusEquals(copyArray);
                                }
                        }
                        mean.scaleEquals(1./this.numInstances());
                        if(normalise) {
                                normalise(mean);
                        }
                } catch (Exception e) {
                        logger.error("Wine dataset failed to load",e);
                }
        }

        private void normalise(Vector mean) {
                for (double[] data : this) {
                        for (int i = 0; i < data.length; i++) {
                                data[i] -= mean.getElement(i);
                        }
                }
        }
}