| 338 | // ------------------ |
| 339 | |
| 340 | void learn() |
| 341 | { |
| 342 | ILint i,j,b,g,r; |
| 343 | ILint radius,rad,alpha,step,delta,samplepixels; |
| 344 | ILubyte *p; |
| 345 | ILubyte *lim; |
| 346 | |
| 347 | alphadec = 30 + ((samplefac-1)/3); |
| 348 | p = thepicture; |
| 349 | lim = thepicture + lengthcount; |
| 350 | samplepixels = lengthcount/(3*samplefac); |
| 351 | delta = samplepixels/ncycles; |
| 352 | alpha = initalpha; |
| 353 | radius = initradius; |
| 354 | |
| 355 | rad = radius >> radiusbiasshift; |
| 356 | if (rad <= 1) rad = 0; |
| 357 | for (i=0; i<rad; i++) |
| 358 | radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad)); |
| 359 | |
| 360 | // beginning 1D learning: initial radius=rad |
| 361 | |
| 362 | if ((lengthcount%prime1) != 0) step = 3*prime1; |
| 363 | else { |
| 364 | if ((lengthcount%prime2) !=0) step = 3*prime2; |
| 365 | else { |
| 366 | if ((lengthcount%prime3) !=0) step = 3*prime3; |
| 367 | else step = 3*prime4; |
| 368 | } |
| 369 | } |
| 370 | |
| 371 | i = 0; |
| 372 | while (i < samplepixels) { |
| 373 | b = p[0] << netbiasshift; |
| 374 | g = p[1] << netbiasshift; |
| 375 | r = p[2] << netbiasshift; |
| 376 | j = contest(b,g,r); |
| 377 | |
| 378 | altersingle(alpha,j,b,g,r); |
| 379 | if (rad) alterneigh(rad,j,b,g,r); // alter neighbours |
| 380 | |
| 381 | p += step; |
| 382 | if (p >= lim) p -= lengthcount; |
| 383 | |
| 384 | i++; |
| 385 | if (i%delta == 0) { |
| 386 | alpha -= alpha / alphadec; |
| 387 | radius -= radius / radiusdec; |
| 388 | rad = radius >> radiusbiasshift; |
| 389 | if (rad <= 1) rad = 0; |
| 390 | for (j=0; j<rad; j++) |
| 391 | radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad)); |
| 392 | } |
| 393 | } |
| 394 | // finished 1D learning: final alpha=alpha/initalpha; |
| 395 | return; |
| 396 | } |
| 397 |
no test coverage detected