ocr.java

VHDL制作的ann的code

				.showInputDialog("Please enter a letter you would like to assign this sample to.");
		if (letter == null) {
			return;
		}

		if (letter.length() > 1) {
			JOptionPane.showMessageDialog(this,
					"Please enter only a single letter.", "Error",
					JOptionPane.ERROR_MESSAGE);
			return;
		}

		this.entry.downSample();
		final SampleData sampleData = (SampleData) this.sample.getData()
				.clone();
		sampleData.setLetter(letter.charAt(0));

		for (i = 0; i < this.letterListModel.size(); i++) {
			final Comparable str = (Comparable) this.letterListModel
					.getElementAt(i);
			if (str.equals(letter)) {
				JOptionPane.showMessageDialog(this,
						"That letter is already defined, delete it first!",
						"Error", JOptionPane.ERROR_MESSAGE);
				return;
			}

			if (str.compareTo(sampleData) > 0) {
				this.letterListModel.add(i, sampleData);
				return;
			}
		}
		this.letterListModel.add(this.letterListModel.size(), sampleData);
		this.letters.setSelectedIndex(i);
		this.entry.clear();
		this.sample.repaint();

	}

	/**
	 * Called to clear the image.
	 * 
	 * @param event
	 *            The event
	 */
	void clear_actionPerformed(final java.awt.event.ActionEvent event) {
		this.entry.clear();
		this.sample.getData().clear();
		this.sample.repaint();

	}

	/**
	 * Called when the del button is pressed.
	 * 
	 * @param event
	 *            The event.
	 */
	void del_actionPerformed(final java.awt.event.ActionEvent event) {
		final int i = this.letters.getSelectedIndex();

		if (i == -1) {
			JOptionPane.showMessageDialog(this,
					"Please select a letter to delete.", "Error",
					JOptionPane.ERROR_MESSAGE);
			return;
		}

		this.letterListModel.remove(i);
	}

	/**
	 * Called to downsample the image.
	 * 
	 * @param event
	 *            The event
	 */
	void downSample_actionPerformed(final java.awt.event.ActionEvent event) {
		this.entry.downSample();

	}

	/**
	 * Called when a letter is selected from the list box.
	 * 
	 * @param event
	 *            The event
	 */
	void letters_valueChanged(final javax.swing.event.ListSelectionEvent event) {
		if (this.letters.getSelectedIndex() == -1) {
			return;
		}
		final SampleData selected = (SampleData) this.letterListModel
				.getElementAt(this.letters.getSelectedIndex());
		this.sample.setData((SampleData) selected.clone());
		this.sample.repaint();
		this.entry.clear();

	}

	/**
	 * Called when the load button is pressed.
	 * 
	 * @param event
	 *            The event
	 */
	void load_actionPerformed(final java.awt.event.ActionEvent event) {
		try {
			FileReader f;// the actual file stream
			BufferedReader r;// used to read the file line by line

			f = new FileReader(new File("./sample.dat"));
			r = new BufferedReader(f);
			String line;
			int i = 0;

			this.letterListModel.clear();

			while ((line = r.readLine()) != null) {
				final SampleData ds = new SampleData(line.charAt(0),
						OCR.DOWNSAMPLE_WIDTH, OCR.DOWNSAMPLE_HEIGHT);
				this.letterListModel.add(i++, ds);
				int idx = 2;
				for (int y = 0; y < ds.getHeight(); y++) {
					for (int x = 0; x < ds.getWidth(); x++) {
						ds.setData(x, y, line.charAt(idx++) == '1');
					}
				}
			}

			r.close();
			f.close();
			clear_actionPerformed(null);
			JOptionPane.showMessageDialog(this, "Loaded from 'sample.dat'.",
					"Training", JOptionPane.PLAIN_MESSAGE);

		} catch (final Exception e) {
			e.printStackTrace();
			JOptionPane.showMessageDialog(this, "Error: " + e, "Training",
					JOptionPane.ERROR_MESSAGE);
		}

	}

	/**
	 * Used to map neurons to actual letters.
	 * 
	 * @return The current mapping between neurons and letters as an array.
	 */
	char[] mapNeurons() {
		final char map[] = new char[this.letterListModel.size()];

		for (int i = 0; i < map.length; i++) {
			map[i] = '?';
		}
		for (int i = 0; i < this.letterListModel.size(); i++) {
			final NeuralData input = new BasicNeuralData(5 * 7);
			int idx = 0;
			final SampleData ds = (SampleData) this.letterListModel
					.getElementAt(i);
			for (int y = 0; y < ds.getHeight(); y++) {
				for (int x = 0; x < ds.getWidth(); x++) {
					input.setData(idx++, ds.getData(x, y) ? .5 : -.5);
				}
			}
			
			final int best = this.net.winner(input);
			map[best] = ds.getLetter();
		}
		return map;
	}

	/**
	 * Called when the recognize button is pressed.
	 * 
	 * @param event
	 *            The event.
	 */
	void recognize_actionPerformed(final java.awt.event.ActionEvent event) {
		if (this.net == null) {
			JOptionPane.showMessageDialog(this, "I need to be trained first!",
					"Error", JOptionPane.ERROR_MESSAGE);
			return;
		}
		this.entry.downSample();

		final NeuralData input = new BasicNeuralData(5 * 7);
		int idx = 0;
		final SampleData ds = this.sample.getData();
		for (int y = 0; y < ds.getHeight(); y++) {
			for (int x = 0; x < ds.getWidth(); x++) {
				input.setData(idx++, ds.getData(x, y) ? .5 : -.5);
			}
		}

		final int best = this.net.winner(input);
		final char map[] = mapNeurons();
		JOptionPane
				.showMessageDialog(this, "  " + map[best] + "   (Neuron #"
						+ best + " fired)", "That Letter Is",
						JOptionPane.PLAIN_MESSAGE);
		clear_actionPerformed(null);

	}

	/**
	 * Run method for the background training thread.
	 */
	public void run() {
		try {
			final int inputNeuron = OCR.DOWNSAMPLE_HEIGHT
					* OCR.DOWNSAMPLE_WIDTH;
			final int outputNeuron = this.letterListModel.size();			

			NeuralDataSet trainingSet = new BasicNeuralDataSet();
			for (int t = 0; t < this.letterListModel.size(); t++) {
				NeuralData item = new BasicNeuralData(inputNeuron);
				int idx = 0;
				final SampleData ds = (SampleData) this.letterListModel
						.getElementAt(t);
				for (int y = 0; y < ds.getHeight(); y++) {
					for (int x = 0; x < ds.getWidth(); x++) {
						item.setData(idx++,ds.getData(x, y) ? .5 : -.5);
					}
				}
				
				trainingSet.add(new BasicNeuralDataPair(item,null));
			}
			
			this.net = new BasicNetwork();
			this.net.addLayer(new SOMLayer(inputNeuron,NormalizationType.MULTIPLICATIVE));
			this.net.addLayer(new BasicLayer(outputNeuron));	
			this.net.reset();

			final TrainSelfOrganizingMap train = new TrainSelfOrganizingMap(
					this.net, trainingSet,LearningMethod.SUBTRACTIVE,0.5);
			int tries = 1;

			do {
				train.iteration();
				update(tries++, train.getTotalError(), train.getBestError());
			} while ((train.getTotalError() > MAX_ERROR) && !this.halt);

			this.halt = true;
			update(tries, train.getTotalError(), train.getBestError());

		} catch (final Exception e) {
			e.printStackTrace();
			JOptionPane.showMessageDialog(this, "Error: " + e, "Training",
					JOptionPane.ERROR_MESSAGE);
		}

	}

	/**
	 * Called when the save button is clicked.
	 * 
	 * @param event
	 *            The event
	 */
	void save_actionPerformed(final java.awt.event.ActionEvent event) {
		try {
			OutputStream os;// the actual file stream
			PrintStream ps;// used to read the file line by line

			os = new FileOutputStream("./sample.dat", false);
			ps = new PrintStream(os);

			for (int i = 0; i < this.letterListModel.size(); i++) {
				final SampleData ds = (SampleData) this.letterListModel
						.elementAt(i);
				ps.print(ds.getLetter() + ":");
				for (int y = 0; y < ds.getHeight(); y++) {
					for (int x = 0; x < ds.getWidth(); x++) {
						ps.print(ds.getData(x, y) ? "1" : "0");
					}
				}
				ps.println("");
			}

			ps.close();
			os.close();
			clear_actionPerformed(null);
			JOptionPane.showMessageDialog(this, "Saved to 'sample.dat'.",
					"Training", JOptionPane.PLAIN_MESSAGE);

		} catch (final Exception e) {
			e.printStackTrace();
			JOptionPane.showMessageDialog(this, "Error: " + e, "Training",
					JOptionPane.ERROR_MESSAGE);
		}

	}

	/**
	 * Called when the train button is pressed.
	 * 
	 * @param event
	 *            The event.
	 */
	void train_actionPerformed(final java.awt.event.ActionEvent event) {
		if (this.trainThread == null) {
			this.train.setText("Stop Training");
			this.train.repaint();
			this.trainThread = new Thread(this);
			this.trainThread.start();
		} else {
			this.halt = true;
		}
	}

	/**
	 * Called to update the stats, from the neural network.
	 * 
	 * @param trial
	 *            How many tries.
	 * @param error
	 *            The current error.
	 * @param best
	 *            The best error.
	 */
	public void update(final int retry, final double totalError,
			final double bestError) {

		if (this.halt) {
			this.trainThread = null;
			this.train.setText("Begin Training");
			JOptionPane.showMessageDialog(this, "Training has completed.",
					"Training", JOptionPane.PLAIN_MESSAGE);
		}
		final UpdateStats stats = new UpdateStats();
		stats._tries = retry;
		stats._lastError = totalError;
		stats._bestError = bestError;
		try {
			SwingUtilities.invokeAndWait(stats);
		} catch (final Exception e) {
			JOptionPane.showMessageDialog(this, "Error: " + e, "Training",
					JOptionPane.ERROR_MESSAGE);
		}
	}

}