java生成动态gif格式与png格式的验证码(代码3)

import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.BufferedOutputStream;
import java.io.ByteArrayOutputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStream;

/**
 * Class AnimatedGifEncoder - Encodes a GIF file consisting of one or more
 * frames.
 * 
 * <pre>
 * Example:
 *    AnimatedGifEncoder e = new AnimatedGifEncoder();
 *    e.start(outputFileName);
 *    e.setDelay(1000);   // 1 frame per sec
 *    e.addFrame(image1);
 *    e.addFrame(image2);
 *    e.finish();
 * </pre>
 * 
 * No copyright asserted on the source code of this class. May be used for any
 * purpose, however, refer to the Unisys LZW patent for restrictions on use of
 * the associated Encoder class. Please forward any corrections to questions at
 * fmsware.com.
 * 
 * @author wzztestin
 * 
 */
public class GifEncoder {
	protected int width; // image size
	protected int height;
	protected Color transparent = null; // transparent color if given
	protected int transIndex; // transparent index in color table
	protected int repeat = -1; // no repeat
	protected int delay = 0; // frame delay (hundredths)
	protected boolean started = false; // ready to output frames
	protected OutputStream out;
	protected BufferedImage image; // current frame
	protected byte[] pixels; // BGR byte array from frame
	protected byte[] indexedPixels; // converted frame indexed to palette
	protected int colorDepth; // number of bit planes
	protected byte[] colorTab; // RGB palette
	protected boolean[] usedEntry = new boolean[256]; // active palette entries
	protected int palSize = 7; // color table size (bits-1)
	protected int dispose = -1; // disposal code (-1 = use default)
	protected boolean closeStream = false; // close stream when finished
	protected boolean firstFrame = true;
	protected boolean sizeSet = false; // if false, get size from first frame
	protected int sample = 10; // default sample interval for quantizer

	/**
	 * Sets the delay time between each frame, or changes it for subsequent
	 * frames (applies to last frame added).
	 * 
	 * @param ms
	 *            int delay time in milliseconds
	 */
	public void setDelay(int ms) {
		delay = Math.round(ms / 10.0f);
	}

	/**
	 * Sets the GIF frame disposal code for the last added frame and any
	 * subsequent frames. Default is 0 if no transparent color has been set,
	 * otherwise 2.
	 * 
	 * @param code
	 *            int disposal code.
	 */
	public void setDispose(int code) {
		if (code >= 0) {
			dispose = code;
		}
	}

	/**
	 * Sets the number of times the set of GIF frames should be played. Default
	 * is 1; 0 means play indefinitely. Must be invoked before the first image
	 * is added.
	 * 
	 * @param iter
	 *            int number of iterations.
	 * @return
	 */
	public void setRepeat(int iter) {
		if (iter >= 0) {
			repeat = iter;
		}
	}

	/**
	 * Sets the transparent color for the last added frame and any subsequent
	 * frames. Since all colors are subject to modification in the quantization
	 * process, the color in the final palette for each frame closest to the
	 * given color becomes the transparent color for that frame. May be set to
	 * null to indicate no transparent color.
	 * 
	 * @param c
	 *            Color to be treated as transparent on display.
	 */
	public void setTransparent(Color c) {
		transparent = c;
	}

	/**
	 * Adds next GIF frame. The frame is not written immediately, but is
	 * actually deferred until the next frame is received so that timing data
	 * can be inserted. Invoking <code>finish()</code> flushes all frames. If
	 * <code>setSize</code> was not invoked, the size of the first image is used
	 * for all subsequent frames.
	 * 
	 * @param im
	 *            BufferedImage containing frame to write.
	 * @return true if successful.
	 */
	public boolean addFrame(BufferedImage im) {
		if ((im == null) || !started) {
			return false;
		}
		boolean ok = true;
		try {
			if (!sizeSet) {
				// use first frame's size
				setSize(im.getWidth(), im.getHeight());
			}
			image = im;
			getImagePixels(); // convert to correct format if necessary
			analyzePixels(); // build color table & map pixels
			if (firstFrame) {
				writeLSD(); // logical screen descriptior
				writePalette(); // global color table
				if (repeat >= 0) {
					// use NS app extension to indicate reps
					writeNetscapeExt();
				}
			}
			writeGraphicCtrlExt(); // write graphic control extension
			writeImageDesc(); // image descriptor
			if (!firstFrame) {
				writePalette(); // local color table
			}
			writePixels(); // encode and write pixel data
			firstFrame = false;
		} catch (IOException e) {
			ok = false;
		}

		return ok;
	}

	// added by alvaro
	public boolean outFlush() {
		boolean ok = true;
		try {
			out.flush();
			return ok;
		} catch (IOException e) {
			ok = false;
		}

		return ok;
	}

	public byte[] getFrameByteArray() {
		return ((ByteArrayOutputStream) out).toByteArray();
	}

	/**
	 * Flushes any pending data and closes output file. If writing to an
	 * OutputStream, the stream is not closed.
	 */
	public boolean finish() {
		if (!started)
			return false;
		boolean ok = true;
		started = false;
		try {
			out.write(0x3b); // gif trailer
			out.flush();
			if (closeStream) {
				out.close();
			}
		} catch (IOException e) {
			ok = false;
		}

		return ok;
	}

	public void reset() {
		// reset for subsequent use
		transIndex = 0;
		out = null;
		image = null;
		pixels = null;
		indexedPixels = null;
		colorTab = null;
		closeStream = false;
		firstFrame = true;
	}

	/**
	 * Sets frame rate in frames per second. Equivalent to
	 * <code>setDelay(1000/fps)</code>.
	 * 
	 * @param fps
	 *            float frame rate (frames per second)
	 */
	public void setFrameRate(float fps) {
		if (fps != 0f) {
			delay = Math.round(100f / fps);
		}
	}

	/**
	 * Sets quality of color quantization (conversion of images to the maximum
	 * 256 colors allowed by the GIF specification). Lower values (minimum = 1)
	 * produce better colors, but slow processing significantly. 10 is the
	 * default, and produces good color mapping at reasonable speeds. Values
	 * greater than 20 do not yield significant improvements in speed.
	 * 
	 * @param quality
	 *            int greater than 0.
	 * @return
	 */
	public void setQuality(int quality) {
		if (quality < 1)
			quality = 1;
		sample = quality;
	}

	/**
	 * Sets the GIF frame size. The default size is the size of the first frame
	 * added if this method is not invoked.
	 * 
	 * @param w
	 *            int frame width.
	 * @param h
	 *            int frame width.
	 */
	public void setSize(int w, int h) {
		if (started && !firstFrame)
			return;
		width = w;
		height = h;
		if (width < 1)
			width = 320;
		if (height < 1)
			height = 240;
		sizeSet = true;
	}

	/**
	 * Initiates GIF file creation on the given stream. The stream is not closed
	 * automatically.
	 * 
	 * @param os
	 *            OutputStream on which GIF images are written.
	 * @return false if initial write failed.
	 */
	public boolean start(OutputStream os) {
		if (os == null)
			return false;
		boolean ok = true;
		closeStream = false;
		out = os;
		try {
			writeString("GIF89a"); // header
		} catch (IOException e) {
			ok = false;
		}
		return started = ok;
	}

	/**
	 * Initiates writing of a GIF file with the specified name.
	 * 
	 * @param file
	 *            String containing output file name.
	 * @return false if open or initial write failed.
	 */
	public boolean start(String file) {
		boolean ok = true;
		try {
			out = new BufferedOutputStream(new FileOutputStream(file));
			ok = start(out);
			closeStream = true;
		} catch (IOException e) {
			ok = false;
		}
		return started = ok;
	}

	/**
	 * Analyzes image colors and creates color map.
	 */
	protected void analyzePixels() {
		int len = pixels.length;
		int nPix = len / 3;
		indexedPixels = new byte[nPix];
		Quant nq = new Quant(pixels, len, sample);
		// initialize quantizer
		colorTab = nq.process(); // create reduced palette
		// convert map from BGR to RGB
		for (int i = 0; i < colorTab.length; i += 3) {
			byte temp = colorTab[i];
			colorTab[i] = colorTab[i + 2];
			colorTab[i + 2] = temp;
			usedEntry[i / 3] = false;
		}
		// map image pixels to new palette
		int k = 0;
		for (int i = 0; i < nPix; i++) {
			int index = nq.map(pixels[k++] & 0xff, pixels[k++] & 0xff,
					pixels[k++] & 0xff);
			usedEntry[index] = true;
			indexedPixels[i] = (byte) index;
		}
		pixels = null;
		colorDepth = 8;
		palSize = 7;
		// get closest match to transparent color if specified
		if (transparent != null) {
			transIndex = findClosest(transparent);
		}
	}

	/**
	 * Returns index of palette color closest to c
	 * 
	 */
	protected int findClosest(Color c) {
		if (colorTab == null)
			return -1;
		int r = c.getRed();
		int g = c.getGreen();
		int b = c.getBlue();
		int minpos = 0;
		int dmin = 256 * 256 * 256;
		int len = colorTab.length;
		for (int i = 0; i < len;) {
			int dr = r - (colorTab[i++] & 0xff);
			int dg = g - (colorTab[i++] & 0xff);
			int db = b - (colorTab[i] & 0xff);
			int d = dr * dr + dg * dg + db * db;
			int index = i / 3;
			if (usedEntry[index] && (d < dmin)) {
				dmin = d;
				minpos = index;
			}
			i++;
		}
		return minpos;
	}

	/**
	 * Extracts image pixels into byte array "pixels"
	 */
	protected void getImagePixels() {
		int w = image.getWidth();
		int h = image.getHeight();
		int type = image.getType();
		if ((w != width) || (h != height)
				|| (type != BufferedImage.TYPE_3BYTE_BGR)) {
			// create new image with right size/format
			BufferedImage temp = new BufferedImage(width, height,
					BufferedImage.TYPE_3BYTE_BGR);
			Graphics2D g = temp.createGraphics();
			g.drawImage(image, 0, 0, null);
			image = temp;
		}
		pixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
	}

	/**
	 * Writes Graphic Control Extension
	 */
	protected void writeGraphicCtrlExt() throws IOException {
		out.write(0x21); // extension introducer
		out.write(0xf9); // GCE label
		out.write(4); // data block size
		int transp, disp;
		if (transparent == null) {
			transp = 0;
			disp = 0; // dispose = no action
		} else {
			transp = 1;
			disp = 2; // force clear if using transparent color
		}
		if (dispose >= 0) {
			disp = dispose & 7; // user override
		}
		disp <<= 2;

		// packed fields
		out.write(0 | // 1:3 reserved
				disp | // 4:6 disposal
				0 | // 7 user input - 0 = none
				transp); // 8 transparency flag

		writeShort(delay); // delay x 1/100 sec
		out.write(transIndex); // transparent color index
		out.write(0); // block terminator
	}

	/**
	 * Writes Image Descriptor
	 */
	protected void writeImageDesc() throws IOException {
		out.write(0x2c); // image separator
		writeShort(0); // image position x,y = 0,0
		writeShort(0);
		writeShort(width); // image size
		writeShort(height);
		// packed fields
		if (firstFrame) {
			// no LCT - GCT is used for first (or only) frame
			out.write(0);
		} else {
			// specify normal LCT
			out.write(0x80 | // 1 local color table 1=yes
					0 | // 2 interlace - 0=no
					0 | // 3 sorted - 0=no
					0 | // 4-5 reserved
					palSize); // 6-8 size of color table
		}
	}

	/**
	 * Writes Logical Screen Descriptor
	 */
	protected void writeLSD() throws IOException {
		// logical screen size
		writeShort(width);
		writeShort(height);
		// packed fields
		out.write((0x80 | // 1 : global color table flag = 1 (gct used)
		0x70 | // 2-4 : color resolution = 7
		0x00 | // 5 : gct sort flag = 0
		palSize)); // 6-8 : gct size

		out.write(0); // background color index
		out.write(0); // pixel aspect ratio - assume 1:1
	}

	/**
	 * Writes Netscape application extension to define repeat count.
	 */
	protected void writeNetscapeExt() throws IOException {
		out.write(0x21); // extension introducer
		out.write(0xff); // app extension label
		out.write(11); // block size
		writeString("NETSCAPE" + "2.0"); // app id + auth code
		out.write(3); // sub-block size
		out.write(1); // loop sub-block id
		writeShort(repeat); // loop count (extra iterations, 0=repeat forever)
		out.write(0); // block terminator
	}

	/**
	 * Writes color table
	 */
	protected void writePalette() throws IOException {
		out.write(colorTab, 0, colorTab.length);
		int n = (3 * 256) - colorTab.length;
		for (int i = 0; i < n; i++) {
			out.write(0);
		}
	}

	/**
	 * Encodes and writes pixel data
	 */
	protected void writePixels() throws IOException {
		Encoder encoder = new Encoder(width, height, indexedPixels, colorDepth);
		encoder.encode(out);
	}

	/**
	 * Write 16-bit value to output stream, LSB first
	 */
	protected void writeShort(int value) throws IOException {
		out.write(value & 0xff);
		out.write((value >> 8) & 0xff);
	}

	/**
	 * Writes string to output stream
	 */
	protected void writeString(String s) throws IOException {
		for (int i = 0; i < s.length(); i++) {
			out.write((byte) s.charAt(i));
		}
	}
}

 

/**
 * 
 * @author wzztestin
 *
 */
public class Quant {
	protected static final int netsize = 256; /* number of colours used */

	/* four primes near 500 - assume no image has a length so large */
	/* that it is divisible by all four primes */
	protected static final int prime1 = 499;
	protected static final int prime2 = 491;
	protected static final int prime3 = 487;
	protected static final int prime4 = 503;

	protected static final int minpicturebytes = (3 * prime4);
	/* minimum size for input image */

	/*
	 * Program Skeleton ---------------- [select samplefac in range 1..30] [read
	 * image from input file] pic = (unsigned char*) malloc(3*width*height);
	 * initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write
	 * output image header, using writecolourmap(f)] inxbuild(); write output
	 * image using inxsearch(b,g,r)
	 */

	/*
	 * Network Definitions -------------------
	 */

	protected static final int maxnetpos = (netsize - 1);
	protected static final int netbiasshift = 4; /* bias for colour values */
	protected static final int ncycles = 100; /* no. of learning cycles */

	/* defs for freq and bias */
	protected static final int intbiasshift = 16; /* bias for fractions */
	protected static final int intbias = (((int) 1) << intbiasshift);
	protected static final int gammashift = 10; /* gamma = 1024 */
	protected static final int gamma = (((int) 1) << gammashift);
	protected static final int betashift = 10;
	protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */
	protected static final int betagamma = (intbias << (gammashift - betashift));

	/* defs for decreasing radius factor */
	protected static final int initrad = (netsize >> 3); /*
														 * for 256 cols, radius
														 * starts
														 */
	protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
	protected static final int radiusbias = (((int) 1) << radiusbiasshift);
	protected static final int initradius = (initrad * radiusbias); /*
																	 * and
																	 * decreases
																	 * by a
																	 */
	protected static final int radiusdec = 30; /* factor of 1/30 each cycle */

	/* defs for decreasing alpha factor */
	protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
	protected static final int initalpha = (((int) 1) << alphabiasshift);

	protected int alphadec; /* biased by 10 bits */

	/* radbias and alpharadbias used for radpower calculation */
	protected static final int radbiasshift = 8;
	protected static final int radbias = (((int) 1) << radbiasshift);
	protected static final int alpharadbshift = (alphabiasshift + radbiasshift);
	protected static final int alpharadbias = (((int) 1) << alpharadbshift);

	/*
	 * Types and Global Variables --------------------------
	 */

	protected byte[] thepicture; /* the input image itself */
	protected int lengthcount; /* lengthcount = H*W*3 */

	protected int samplefac; /* sampling factor 1..30 */

	// typedef int pixel[4]; /* BGRc */
	protected int[][] network; /* the network itself - [netsize][4] */

	protected int[] netindex = new int[256];
	/* for network lookup - really 256 */

	protected int[] bias = new int[netsize];
	/* bias and freq arrays for learning */
	protected int[] freq = new int[netsize];
	protected int[] radpower = new int[initrad];

	/* radpower for precomputation */

	/*
	 * Initialise network in range (0,0,0) to (255,255,255) and set parameters
	 * -----------------------------------------------------------------------
	 */
	public Quant(byte[] thepic, int len, int sample) {

		int i;
		int[] p;

		thepicture = thepic;
		lengthcount = len;
		samplefac = sample;

		network = new int[netsize][];
		for (i = 0; i < netsize; i++) {
			network[i] = new int[4];
			p = network[i];
			p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
			freq[i] = intbias / netsize; /* 1/netsize */
			bias[i] = 0;
		}
	}

	public byte[] colorMap() {
		byte[] map = new byte[3 * netsize];
		int[] index = new int[netsize];
		for (int i = 0; i < netsize; i++)
			index[network[i][3]] = i;
		int k = 0;
		for (int i = 0; i < netsize; i++) {
			int j = index[i];
			map[k++] = (byte) (network[j][0]);
			map[k++] = (byte) (network[j][1]);
			map[k++] = (byte) (network[j][2]);
		}
		return map;
	}

	/*
	 * Insertion sort of network and building of netindex[0..255] (to do after
	 * unbias)
	 * ------------------------------------------------------------------
	 * -------------
	 */
	public void inxbuild() {

		int i, j, smallpos, smallval;
		int[] p;
		int[] q;
		int previouscol, startpos;

		previouscol = 0;
		startpos = 0;
		for (i = 0; i < netsize; i++) {
			p = network[i];
			smallpos = i;
			smallval = p[1]; /* index on g */
			/* find smallest in i..netsize-1 */
			for (j = i + 1; j < netsize; j++) {
				q = network[j];
				if (q[1] < smallval) { /* index on g */
					smallpos = j;
					smallval = q[1]; /* index on g */
				}
			}
			q = network[smallpos];
			/* swap p (i) and q (smallpos) entries */
			if (i != smallpos) {
				j = q[0];
				q[0] = p[0];
				p[0] = j;
				j = q[1];
				q[1] = p[1];
				p[1] = j;
				j = q[2];
				q[2] = p[2];
				p[2] = j;
				j = q[3];
				q[3] = p[3];
				p[3] = j;
			}
			/* smallval entry is now in position i */
			if (smallval != previouscol) {
				netindex[previouscol] = (startpos + i) >> 1;
				for (j = previouscol + 1; j < smallval; j++)
					netindex[j] = i;
				previouscol = smallval;
				startpos = i;
			}
		}
		netindex[previouscol] = (startpos + maxnetpos) >> 1;
		for (j = previouscol + 1; j < 256; j++)
			netindex[j] = maxnetpos; /* really 256 */
	}

	/*
	 * Main Learning Loop ------------------
	 */
	public void learn() {

		int i, j, b, g, r;
		int radius, rad, alpha, step, delta, samplepixels;
		byte[] p;
		int pix, lim;

		if (lengthcount < minpicturebytes)
			samplefac = 1;
		alphadec = 30 + ((samplefac - 1) / 3);
		p = thepicture;
		pix = 0;
		lim = lengthcount;
		samplepixels = lengthcount / (3 * samplefac);
		delta = samplepixels / ncycles;
		alpha = initalpha;
		radius = initradius;

		rad = radius >> radiusbiasshift;
		if (rad <= 1)
			rad = 0;
		for (i = 0; i < rad; i++)
			radpower[i] = alpha
					* (((rad * rad - i * i) * radbias) / (rad * rad));

		// fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);

		if (lengthcount < minpicturebytes)
			step = 3;
		else if ((lengthcount % prime1) != 0)
			step = 3 * prime1;
		else {
			if ((lengthcount % prime2) != 0)
				step = 3 * prime2;
			else {
				if ((lengthcount % prime3) != 0)
					step = 3 * prime3;
				else
					step = 3 * prime4;
			}
		}

		i = 0;
		while (i < samplepixels) {
			b = (p[pix + 0] & 0xff) << netbiasshift;
			g = (p[pix + 1] & 0xff) << netbiasshift;
			r = (p[pix + 2] & 0xff) << netbiasshift;
			j = contest(b, g, r);

			altersingle(alpha, j, b, g, r);
			if (rad != 0)
				alterneigh(rad, j, b, g, r); /* alter neighbours */

			pix += step;
			if (pix >= lim)
				pix -= lengthcount;

			i++;
			if (delta == 0)
				delta = 1;
			if (i % delta == 0) {
				alpha -= alpha / alphadec;
				radius -= radius / radiusdec;
				rad = radius >> radiusbiasshift;
				if (rad <= 1)
					rad = 0;
				for (j = 0; j < rad; j++)
					radpower[j] = alpha
							* (((rad * rad - j * j) * radbias) / (rad * rad));
			}
		}
		// fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
	}

	/*
	 * Search for BGR values 0..255 (after net is unbiased) and return colour
	 * index
	 * --------------------------------------------------------------------
	 * --------
	 */
	public int map(int b, int g, int r) {

		int i, j, dist, a, bestd;
		int[] p;
		int best;

		bestd = 1000; /* biggest possible dist is 256*3 */
		best = -1;
		i = netindex[g]; /* index on g */
		j = i - 1; /* start at netindex[g] and work outwards */

		while ((i < netsize) || (j >= 0)) {
			if (i < netsize) {
				p = network[i];
				dist = p[1] - g; /* inx key */
				if (dist >= bestd)
					i = netsize; /* stop iter */
				else {
					i++;
					if (dist < 0)
						dist = -dist;
					a = p[0] - b;
					if (a < 0)
						a = -a;
					dist += a;
					if (dist < bestd) {
						a = p[2] - r;
						if (a < 0)
							a = -a;
						dist += a;
						if (dist < bestd) {
							bestd = dist;
							best = p[3];
						}
					}
				}
			}
			if (j >= 0) {
				p = network[j];
				dist = g - p[1]; /* inx key - reverse dif */
				if (dist >= bestd)
					j = -1; /* stop iter */
				else {
					j--;
					if (dist < 0)
						dist = -dist;
					a = p[0] - b;
					if (a < 0)
						a = -a;
					dist += a;
					if (dist < bestd) {
						a = p[2] - r;
						if (a < 0)
							a = -a;
						dist += a;
						if (dist < bestd) {
							bestd = dist;
							best = p[3];
						}
					}
				}
			}
		}
		return (best);
	}

	public byte[] process() {
		learn();
		unbiasnet();
		inxbuild();
		return colorMap();
	}

	/*
	 * Unbias network to give byte values 0..255 and record position i to
	 * prepare for sort
	 * ----------------------------------------------------------
	 * -------------------------
	 */
	@SuppressWarnings("unused")
	public void unbiasnet() {

		int i, j;

		for (i = 0; i < netsize; i++) {
			network[i][0] >>= netbiasshift;
			network[i][1] >>= netbiasshift;
			network[i][2] >>= netbiasshift;
			network[i][3] = i; /* record colour no */
		}
	}

	/*
	 * Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in
	 * radpower[|i-j|]
	 * ----------------------------------------------------------
	 * -----------------------
	 */
	protected void alterneigh(int rad, int i, int b, int g, int r) {

		int j, k, lo, hi, a, m;
		int[] p;

		lo = i - rad;
		if (lo < -1)
			lo = -1;
		hi = i + rad;
		if (hi > netsize)
			hi = netsize;

		j = i + 1;
		k = i - 1;
		m = 1;
		while ((j < hi) || (k > lo)) {
			a = radpower[m++];
			if (j < hi) {
				p = network[j++];
				try {
					p[0] -= (a * (p[0] - b)) / alpharadbias;
					p[1] -= (a * (p[1] - g)) / alpharadbias;
					p[2] -= (a * (p[2] - r)) / alpharadbias;
				} catch (Exception e) {
				} // prevents 1.3 miscompilation
			}
			if (k > lo) {
				p = network[k--];
				try {
					p[0] -= (a * (p[0] - b)) / alpharadbias;
					p[1] -= (a * (p[1] - g)) / alpharadbias;
					p[2] -= (a * (p[2] - r)) / alpharadbias;
				} catch (Exception e) {
				}
			}
		}
	}

	/*
	 * Move neuron i towards biased (b,g,r) by factor alpha
	 * ----------------------------------------------------
	 */
	protected void altersingle(int alpha, int i, int b, int g, int r) {

		/* alter hit neuron */
		int[] n = network[i];
		n[0] -= (alpha * (n[0] - b)) / initalpha;
		n[1] -= (alpha * (n[1] - g)) / initalpha;
		n[2] -= (alpha * (n[2] - r)) / initalpha;
	}

	/*
	 * Search for biased BGR values ----------------------------
	 */
	protected int contest(int b, int g, int r) {

		/* finds closest neuron (min dist) and updates freq */
		/* finds best neuron (min dist-bias) and returns position */
		/*
		 * for frequently chosen neurons, freq[i] is high and bias[i] is
		 * negative
		 */
		/* bias[i] = gamma*((1/netsize)-freq[i]) */

		int i, dist, a, biasdist, betafreq;
		int bestpos, bestbiaspos, bestd, bestbiasd;
		int[] n;

		bestd = ~(((int) 1) << 31);
		bestbiasd = bestd;
		bestpos = -1;
		bestbiaspos = bestpos;

		for (i = 0; i < netsize; i++) {
			n = network[i];
			dist = n[0] - b;
			if (dist < 0)
				dist = -dist;
			a = n[1] - g;
			if (a < 0)
				a = -a;
			dist += a;
			a = n[2] - r;
			if (a < 0)
				a = -a;
			dist += a;
			if (dist < bestd) {
				bestd = dist;
				bestpos = i;
			}
			biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
			if (biasdist < bestbiasd) {
				bestbiasd = biasdist;
				bestbiaspos = i;
			}
			betafreq = (freq[i] >> betashift);
			freq[i] -= betafreq;
			bias[i] += (betafreq << gammashift);
		}
		freq[bestpos] += beta;
		bias[bestpos] -= betagamma;
		return (bestbiaspos);
	}
}