void Learn()
{
int i, j, b, g, r;
int radius, rad, alpha, step, delta, samplepixels;
int pix, lim;
if (lengthcount < minpicturebytes)
samplefac = 1;
alphadec = 30 + ((samplefac - 1) / 3);
var 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].r & 0xff) << netbiasshift;
g = (p [pix].g & 0xff) << netbiasshift;
r = (p [pix].b & 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));
}
}
}