/*******************************************************************************
 * Copyright (c) 2010 Haifeng Li
 *   
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *  
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *******************************************************************************/
package smile.demo.projection;

import java.awt.BorderLayout;
import java.awt.FlowLayout;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;

import javax.swing.BorderFactory;
import javax.swing.JButton;
import javax.swing.JComboBox;
import javax.swing.JComponent;
import javax.swing.JLabel;
import javax.swing.JOptionPane;
import javax.swing.JPanel;

import smile.classification.FLD;
import smile.data.AttributeDataset;
import smile.data.NominalAttribute;
import smile.data.parser.DelimitedTextParser;
import smile.plot.Palette;
import smile.plot.PlotCanvas;
import smile.math.Math;

@SuppressWarnings("serial")
public class LDADemo extends JPanel implements Runnable, ActionListener {

    private static final String[] datasetName = {
        "IRIS", "Pen Digits"
    };

    private static final String[] datasource = {
        "classification/iris.txt",
        "pendigits.txt"
    };

    static AttributeDataset[] dataset = new AttributeDataset[datasetName.length];
    static int datasetIndex = 0;

    JPanel optionPane;
    JComponent canvas;
    private JButton startButton;
    private JComboBox datasetBox;
    char pointLegend = '.';

    /**
     * Constructor.
     */
    public LDADemo() {
        startButton = new JButton("Start");
        startButton.setActionCommand("startButton");
        startButton.addActionListener(this);

        datasetBox = new JComboBox<>();
        for (int i = 0; i < datasetName.length; i++) {
            datasetBox.addItem(datasetName[i]);
        }
        datasetBox.setSelectedIndex(0);
        datasetBox.setActionCommand("datasetBox");
        datasetBox.addActionListener(this);

        optionPane = new JPanel(new FlowLayout(FlowLayout.LEFT));
        optionPane.setBorder(BorderFactory.createRaisedBevelBorder());
        optionPane.add(startButton);
        optionPane.add(new JLabel("Dataset:"));
        optionPane.add(datasetBox);

        setLayout(new BorderLayout());
        add(optionPane, BorderLayout.NORTH);
    }

    /**
     * Execute the projection algorithm and return a swing JComponent representing
     * the clusters.
     */
    public JComponent learn() {
        double[][] data = dataset[datasetIndex].toArray(new double[dataset[datasetIndex].size()][]);

        String[] names = dataset[datasetIndex].toArray(new String[dataset[datasetIndex].size()]);
        if (names[0] == null) {
            names = null;
        }
        
        int[] label = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
        int min = Math.min(label);
        for (int i = 0; i < label.length; i++) {
            label[i] -= min;
        }

        long clock = System.currentTimeMillis();
        FLD lda = new FLD(data, label, Math.unique(label).length > 3 ? 3 : 2);
        System.out.format("Learn LDA from %d samples in %dms\n", data.length, System.currentTimeMillis()-clock);

        double[][] y = lda.project(data);

        PlotCanvas plot = new PlotCanvas(Math.colMin(y), Math.colMax(y));
        if (names != null) {
            plot.points(y, names);
        } else if (dataset[datasetIndex].responseAttribute() != null) {
            int[] labels = dataset[datasetIndex].toArray(new int[dataset[datasetIndex].size()]);
            for (int i = 0; i < y.length; i++) {
                plot.point(pointLegend, Palette.COLORS[labels[i]], y[i]);
            }
        } else {
            plot.points(y, pointLegend);
        }

        plot.setTitle("Linear Discriminant Analysis");
        return plot;
    }

    @Override
    public void run() {
        startButton.setEnabled(false);
        datasetBox.setEnabled(false);

        JComponent plot = learn();
        if (plot != null) {
            if (canvas != null)
                remove(canvas);
            canvas = plot;
            add(plot, BorderLayout.CENTER);
        }
        validate();

        startButton.setEnabled(true);
        datasetBox.setEnabled(true);
    }

    @Override
    public void actionPerformed(ActionEvent e) {
        if ("startButton".equals(e.getActionCommand())) {
            datasetIndex = datasetBox.getSelectedIndex();

            if (dataset[datasetIndex] == null) {
                DelimitedTextParser parser = new DelimitedTextParser();
                parser.setDelimiter("[\t]+");
                if (datasetIndex == 0) {
                    parser.setColumnNames(true);
                }
                if (datasetIndex == 0) {
                    parser.setResponseIndex(new NominalAttribute("class"), 4);
                }
                if (datasetIndex == 1) {
                    parser.setResponseIndex(new NominalAttribute("class"), 16);
                }

                try {
                    dataset[datasetIndex] = parser.parse(datasetName[datasetIndex], smile.data.parser.IOUtils.getTestDataFile(datasource[datasetIndex]));
                } catch (Exception ex) {
                    JOptionPane.showMessageDialog(null, "Failed to load dataset.", "ERROR", JOptionPane.ERROR_MESSAGE);
                    System.out.println(ex);
                }
            }

            if (dataset[datasetIndex].size() < 500) {
                pointLegend = 'o';
            } else {
                pointLegend = '.';
            }

            Thread thread = new Thread(this);
            thread.start();
        }
    }

    @Override
    public String toString() {
        return "Linear Discriminant Analysis";
    }
}