Genetic Optimisation of SLM Holograms , run by a ITOM plataform for high performance interferometrie
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 | # Schritt 0 : Einstellung und definition aller Parameter, Classen und funktionen für die optimitation
# Durch das Kalibrierungsprogramm erstellte Matrix ,
# mit Schwerpunkte der gitter und breite des messberreich um den SP
matrix_sp_messbreite= [[615.3756038647343, 865.2252415458937, 18], [615.6591619762352, 824.7116948092557, 15], [615.8033088235294, 783.1850490196078, 16], [615.8986810551559, 742.068345323741, 17], [615.4225609756097, 701.3396341463415, 18], [615.7853907134768, 660.3737259343148, 19], [615.7416619559073, 619.2589033352176, 19], [615.9786301369863, 577.841095890411, 19], [616.2704333516182, 536.8869994514537, 19], [616.7010928961748, 495.87158469945354, 18], [616.6470911086718, 454.7365532381998, 19], [616.8727858293075, 413.8217928073, 19], [616.4873903508771, 373.2998903508772, -1], [616.3637339055794, 331.94957081545067, 19], [616.3268625393495, 291.1579223504722, 20], [616.9497409326425, 248.89844559585492, 19], [618.158024691358, 205.01086419753085, 20], [618.9689880304679, 162.04406964091405, 19], [618.8880846325167, 121.37193763919822, 19], [618.6595622119816, 80.90783410138249, 18], [569.8849332485696, 864.9796567069294, 6], [570.1013767209012, 824.6182728410513, -1], [570.436004784689, 782.9413875598086, 16], [570.696261682243, 741.6571261682243, 18], [570.7545977011495, 700.7919540229885, 18], [570.7955555555556, 659.7716666666666, 19], [570.7969009407858, 618.7183176535694, 19], [570.8208791208791, 577.895054945055, 19], [571.188689217759, 536.8292811839324, 20], [571.3521505376344, 495.41182795698927, 20], [571.4215686274509, 454.3278867102396, 20], [571.2629609834313, 413.29396044895776, 20], [570.9492332099418, 372.246430460074, 20], [570.8037037037037, 331.44867724867726, 20], [570.9392, 290.65226666666666, 19], [570.988770053476, 249.55347593582889, 20], [571.3678220382825, 206.15623383341955, 20], [572.5376344086021, 162.3783922171019, 21], [573.4187192118227, 120.60864805692393, 19], [573.3159955257271, 80.3579418344519, 19], [524.0891803278688, 864.3940983606558, -1], [524.6053268765133, 823.7602905569007, -1], [524.4666254635353, 782.4202719406675, -1], [524.5879602571596, 741.583869082408, 18], [524.9230769230769, 700.5075757575758, 18], [525.1335963923337, 659.5, 19], [525.1531728665208, 618.3490153172867, 19], [525.4741707449701, 577.516041326808, 19], [525.7800851970181, 536.3184238551651, 20], [525.9858416360776, 495.00157315154695, 20], [526.3817427385892, 453.38641078838174, 20], [525.7843137254902, 412.3518812930578, 20], [525.3870967741935, 371.7059139784946, 19], [525.3951871657754, 330.9374331550802, 19], [525.3709591944886, 290.1494435612083, 20], [525.3016877637131, 249.01476793248946, 20], [525.5937984496124, 206.57364341085272, 20], [526.2912474849095, 163.07997987927564, 21], [527.2956243329776, 120.534151547492, 20], [527.4642058165548, 80.07606263982103, 19], [479.1939840392879, 863.8354818907305, 8], [478.9385687143762, 822.8809373020899, -1], [479.0852994555354, 782.0810647307925, 18], [479.3661075766339, 741.5164835164835, 19], [479.1430219146482, 700.0732410611304, 19], [479.30056179775283, 658.9775280898876, 18], [479.2433774834437, 618.1280353200883, 19], [479.27371273712737, 577.3289972899729, 19], [479.65711252653927, 536.2266454352441, 20], [480.0068493150685, 494.9067439409905, 19], [480.28850102669406, 452.3752566735113, 20], [479.95299145299145, 411.2986111111111, 20], [479.92841765339074, 370.79278794402586, 19], [479.8868126001068, 330.6097170315003, 19], [479.9957582184517, 289.84623541887595, 19], [479.75819451907574, 248.57872111767867, 19], [479.7102657634184, 206.62480458572173, 19], [480.0815407703852, 164.0015007503752, 21], [481.1975116640746, 120.85225505443235, 20], [481.1991223258365, 79.51508502468458, 19], [432.77059569074777, 863.5836501901141, 9], [433.3181008902077, 822.7341246290802, 8], [433.7618203309693, 781.7021276595744, 17], [433.52389380530974, 740.7705014749263, 18], [433.59450171821305, 699.4054982817869, -1], [433.731884057971, 658.5189520624303, 19], [433.7505617977528, 617.5544943820224, 19], [433.72095608671486, 576.8010005558643, 19], [433.62615803814714, 535.9051771117166, 19], [433.87293729372936, 494.93949394939494, 18], [434.3100414078675, 452.13923395445136, 20], [433.9570815450644, 411.0402360515021, 20], [434.33988316516195, 370.5326606479023, 20], [434.3816503800217, 329.8061889250814, 8], [434.1232076473712, 289.1147105682422, 20], [434.0784421283598, 247.73230938014262, 19], [434.04421949920084, 206.62067128396376, 19], [434.3600620796689, 164.36368339368858, 20], [434.78607983623334, 120.2906857727738, 20], [434.9400665926748, 79.21032186459489, 19], [387.26809314033983, 863.3612334801762, 18], [387.2650221378874, 822.5173940543959, -1], [387.5045153521975, 781.2450331125827, 6], [387.8211009174312, 740.1095183486239, 19], [387.94379521424594, 699.0946021146354, 19], [387.8568281938326, 658.0616740088105, 19], [388.2665553700612, 617.0940456316082, 19], [388.1651634723788, 576.272266065389, 19], [388.54471101417664, 535.7889858233369, 19], [388.25823591923483, 494.58979808714133, 18], [388.19838056680163, 451.62854251012146, 20], [388.5298826040555, 410.7241195304162, 19], [388.26954620010935, 369.901585565883, 19], [388.3962678375412, 329.3781558726674, 20], [388.15587918015103, 288.6084142394822, 19], [388.4327077747989, 247.30723860589814, 20], [388.5550755939525, 206.43196544276458, 19], [388.39858012170384, 164.16683569979716, 21], [388.6238670694864, 120.09264853977845, 21], [388.95235487404165, 78.46440306681271, 19], [341.37120211360633, 862.9009247027741, -1], [341.39403758911214, 821.7109526895658, -1], [341.81669585522474, 780.6497373029772, -1], [341.94076655052265, 739.7090592334495, 18], [342.0402722631878, 698.7702779353375, 18], [342.0060840707965, 657.6216814159292, 19], [341.7494419642857, 616.6897321428571, 19], [342.22434497816596, 575.9918122270742, 18], [342.2217391304348, 535.2983695652174, 20], [341.9388739946381, 493.857908847185, 19], [341.93985355648533, 451.3342050209205, 20], [342.56902002107483, 409.949947312961, 19], [342.5172786177106, 369.3768898488121, 19], [342.6225619399051, 328.8671586715867, 19], [342.74297827239, 288.1600423953365, 20], [342.7968421052632, 247.07368421052632, 20], [342.8684070324987, 205.92488012786362, 19], [342.51947368421054, 163.6357894736842, 19], [342.5246406570842, 120.17967145790554, 21], [342.89473684210526, 78.42317916002126, 20], [295.0700416088766, 862.5561719833564, -1], [295.078431372549, 821.5287792536369, -1], [295.47435897435895, 780.310606060606, 19], [295.2836363636364, 739.150303030303, -1], [295.44607566346696, 697.9785431959345, 18], [295.67690557451647, 656.8577929465301, 18], [295.8887640449438, 616.5775280898877, 19], [295.8791507893304, 575.6374523679913, 19], [296.0512682137075, 534.5402050728549, 19], [296.0793901156677, 493.2397476340694, 20], [295.8712606837607, 451.19070512820514, 20], [296.372654155496, 409.56300268096516, 19], [296.69586243954865, 368.7587318645889, 19], [296.97047772410093, 328.50670960815887, 20], [297.0693703308431, 287.6654215581644, 19], [296.80021482277124, 247.16326530612244, 12], [296.9405204460966, 205.29580456718003, 20], [296.79501525941, 163.03458799593082, 21], [296.7662141779789, 120.24484665661136, 21], [296.7735341581495, 78.43141473910704, 19], [249.15107913669064, 862.1164159581426, -1], [249.52715070164734, 821.3770591824283, 8], [249.14873035066506, 779.8917775090689, 13], [249.1837223219629, 738.7450628366248, 18], [249.4012702078522, 697.8487297921478, 18], [249.69006176305447, 656.7259966311061, 19], [249.57756696428572, 616.1194196428571, 19], [249.6, 574.9606557377049, 19], [249.54778809393773, 534.2151829601311, 19], [249.96209016393442, 492.5983606557377, 20], [249.72471324296143, 451.1475495307612, 20], [250.06210191082803, 409.6656050955414, 20], [250.55448201825013, 368.64573268921094, 19], [250.86608122941823, 328.1322722283205, 19], [250.80519480519482, 287.44534632034635, 19], [250.86787426744806, 246.39318060735215, 19], [250.97360248447205, 204.83695652173913, 20], [250.94008056394765, 162.3585095669688, 21], [250.47058823529412, 119.75577731092437, 19], [250.51942522618415, 78.38797232570516, 19], [203.7084917617237, 861.8795944233207, 6], [203.87061668681983, 820.6106408706166, -1], [204.13656114214774, 779.1800124146492, 6], [204.30251071647274, 738.4170238824249, 18], [204.25678119349004, 697.4406268836649, 18], [204.34635879218473, 656.2125518058023, 19], [204.3450292397661, 615.2216374269005, 19], [204.43651753325273, 574.3821039903265, 10], [204.06578947368422, 533.8323798627002, 19], [204.06291390728478, 492.4635761589404, 19], [204.38711423930698, 451.06767731456415, 20], [204.49267498643516, 409.55561584373305, 20], [204.75635359116023, 368.59889502762434, 19], [204.8064343163539, 327.8134048257373, 19], [205.06412382531786, 286.92371475953564, 19], [205.4340909090909, 245.57329545454544, 12], [205.64309031556039, 204.6088139281828, 19], [205.0021052631579, 161.89736842105262, 19], [204.23214285714286, 119.07773109243698, 20], [204.52730192719486, 77.57012847965738, 20]]
#...............................................................Parameter..................................................
periode, orientations =5,180 #Parameter Hologram
generationen_anzahl=100 #anzah der generationen
cooling_param=0.95 #cooling von 0 bis 0.99
uberlebende=75 #survivors von 0 bis 200
mutation_rate=0.9 # mutation rate von 0 bis 1
gen_one=0 #Chromosomen der Erste generation
#falls gen_one=0=test mit nur eine Chromosom kombination
chrom0,chrom1,chrom2,chrom3,chrom4,chrom5=0,0.5,0,0,0,0
#fall gen_one=1= zufaellige erstellte chromosome im bereich min/max
a0min,a0max=-0.5,1.0
a1min,a1max=-1.0,1.0
a2min,a2max=-1.0,1.0
a3min,a3max=-1.0,1.0
a4min,a4max=-0.5,0.5
a5min,a5max=-0.2,0.2
#------ Für Protokolierung & Auswertung sind wichtige Ausgabe erförderlich,
# wie z.b Bilder und optimisierte Chromosome. Hier befinden sich die wichtigste
# mögliche Ausgabe mit ihrem True/False Schalter
#...............................................................Am Anfang der Optimierung..........................................
prnt0=0 #plot Bild des Hintergrunds und für der Normierung der Intensitaetsverteilung
prnt1=0 #Ausgabe der Hintergrunds intesnsitaets liste
prnt2=0 #Ausgabe der Brutto & Netto intensitaeten bei der Normierung
prnt3=0 #Ausgabe er Koeffizienten der Normierung
# .................................................................. Nach jede Generation..........................................
prnt4=0 #Ausgabe Chromosome Aller individuen
prnt5=0 #Ausgabe Kennlinie Aller individuen
prnt6=0 #Plot Bild jede neue generation
plt_gen=[0,99] #Plot Bild bestimmte Generationen
prnt7=0 #Ausgabe der individuelle Brutto.intensitäten
prnt8=0 #Ausgabe gesamt Brutto & Netto intensitaet
prnt9=0 #Ausgabe der individuelle Netto.intensitaeten
#................................................................. Am Ende der Optimierung..................................................
prnt10=0 #Ausgabe liste mit alle die Gesammt Brutto & Netto aller individuen in jede generation
prnt11=1 #Ausgabe Wert Brutto & Netto intensität bestes individuum jeder Gen
prnt12=1 #Ausgabe Chromosome Beste Ergebnis
prnt13=1 #Ausgabe Wert Netto intensitaet
# Referenz: "Bruto"= Intensität direckt aus Kamera Aufnahme
# "Netto"= Hintergrund intensitaet abgezogen von "Brutto" ,
# und mit normalkoefient dividiert
#...............................................................Classen & Functions..................................................
# Simple straight forward Genetic Optimization based on a fixed
# Chromosome Length. The genes are just numbers (integers)
#
# Please see the example sample_geneticOptimization.py for a
# very simple example
# and sample_geneticOptimization.py for nearly as simple example using
# inheritance.
# T. Haist, November 2012
#
import random # for random numbers
import copy # for the deep copy
gGenDebug =False # set to True for some debugging
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
class Individual:
"""
Implements one individual in the population
"""
# ----------------------------------------
# ctor
# maxlength gives the chromosome length
def __init__(self, maxlength):
# ------ The class variables --------------------------------
self.mChromosome = [] # the chromosome itself (a list of integers)
self.mChromosomeMin = [] # Minimum value of integers of the chromosome
self.mChromosomeMax = [] # Maximum value of integers of the chromosome
self.mChromosomeSize = maxlength # The length of the chromosome (numbers)
for x in range(maxlength):
self.mChromosome.append(0) # Default Value = 0
self.mChromosomeMin.append(0) # Default Value Range: 0 ... 255
self.mChromosomeMax.append(255)
def setValue(self,Chromosome,value):
self.mChromosome[Chromosome]=value
# ----------------------------------------
# Mutate the Chromosome at position "position"
# If delta = True then strength will give the CHANGE compared to the fully
# allowed range of values. 0.5 means e.g. that the change will be allowed
# to be in maximually 0.5 of the whole range of values.
def mutate(self, position, strength, delta):
bereich = self.mChromosomeMax[position] - self.mChromosomeMin[position]
if delta:
bereich *= strength
change =(bereich * 2*(random.random()-0.5)*strength)
self.mChromosome[position] += change
else: # completely random change
change = (bereich * random.random() * strength)
self.mChromosome[position] = change + self.mChromosomeMin[position] +bereich/2
# Check the boundaries and correct if necessary
if self.mChromosome[position] < self.mChromosomeMin[position]:
self.mChromosome[position] = self.mChromosomeMin[position]
if self.mChromosome[position] > self.mChromosomeMax[position]:
self.mChromosome[position] = self.mChromosomeMax[position]
# ----------------------------------------
# Define the minimum and maximum value of the Chromosome-entry/Gene at
# position "position"
def setMinMax(self, position, mini, maxi):
self.mChromosomeMin[position] = mini;
self.mChromosomeMax[position] = maxi;
# ----------------------------------------
# Print out the whole Chromosome
def show(self):
print(self.mChromosome)
# ----------------------------------------
# Set the entry/gene "number" of the Chromosome to "number"
def erase(self, number):
for t in range(self.mChromosomeSize):
self.mChromosome[t] = number
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
class Population:
"""
Implements a population (consiting of several Individuals)
"""
# ----------------------------------------
# ctor
def __init__(self):
# ------ The class variables --------------------------------
self.mIndividual = [] # the list of individuals
self.mPopulationSize = 0 # The size of the population
# ----------------------------------------
# Append one individual to the population
def append(self, indi):
self.mIndividual.append(indi)
self.mPopulationSize +=1
# ---------------------------------------
# one of the individuals (which) with strength and Delta
def mutate (self, which, strength, delta):
for u in range(self.mIndividual[which].mChromosomeSize):
self.mIndividual[which].mutate(u, strength, delta)
# ----------------------------------------
# show all individuals of the population
def show(self):
for t in range(self.mPopulationSize):
self.mIndividual[t].show()
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
class GeneticOptimization:
"""
Implements simple Genetic Optimization
"""
# ----------------------------------------
def __init__(self, population, coolingParameter):
# ------ The class variables --------------------------------
self.mTime = 0 # Time stamp
self.mPopulation = population# The population of individuals
self.mGenerations = 0 # The current generation number
self.mCoolingParameter = coolingParameter # 0 -> linear cooling, 1 -> exp.
self.mMutationRate =mutation_rate # Cooling parameter for cooling
self.mDeltaMutation = True # False -> Random Mutation, True -> Delta
self.mEvaluations = [] # Array of the Evaluations
self.mDeltaModulation = True # True -> Delta when doing a mutation
self.mBestMerrit = 0 # Best Merrit Value (Individual[0] after optimiz.
self.mBestIndividual = 0 # Best Found Solution after optimization
# ----------------------------------------
# Evaluate the fitness of Individual with number index.
# THIS IS JUST AN EXAMPLE (optimization of sum of para1, para2 and para3
def evaluateIndividual(self, index): # Example
para1 = self.mPopulation.mIndividual[index].mChromosome[0]
para2 = self.mPopulation.mIndividual[index].mChromosome[1]
para3 = self.mPopulation.mIndividual[index].mChromosome[2]
return para1 + para2 + para3
# ----------------------------------------
# Evaluate the fitness of the whole population and save the result in
# the array mEvaluations. Beware: The array stores them as Tuples with
# (fitness, number). This is necessary to be later able to sort the array
# easily. ( A version using dictionaries lead to problems)
def evaluateFitness(self): # Defualt Implementierung
self.mEvaluations = [] # set the start array to empty
for t in range(self.mPopulation.mPopulationSize): # go through all Individuals
self.mEvaluations.append((self.evaluateIndividual(t), t))
# ----------------------------------------
# Exponential or Linear Decrease of the
def cooling(self, type):
self.mTime += 1; # Increase the time stamp
if type == 0:
self.mMutationRate *= self.mCoolingParameter # exponential decrease
else:
self.mMutationRate -= self.mCoolingParameter # linear decrease
# ----------------------------------------
# Mutate all but the best "survivors" individuals
def mutateAll(self,survivors):
for t in range(self.mPopulation.mPopulationSize-survivors):
self.mPopulation.mutate(t+survivors, self.mMutationRate/4, self.mDeltaMutation)
# ----------------------------------------
# Genetic selection based on the fitness.
# MIGHT BE OVERRIDDEN !
# Currently, just the best "survivors" Individuals will survive to the
# next generation and might get descendents
def selection(self, survivors):
# Step 1: Sort the array of Evaluations based on Fitness
self.mEvaluations = sorted(self.mEvaluations)
# Step 2: Put the best "survivors" Individuals to the array sortiert
sortiert = []
for t in range(self.mPopulation.mPopulationSize):
number = self.mEvaluations[t][1]
sortiert.append(self.mPopulation.mIndividual[number])
# Step 3: Copy (beware: Deepcopy is mandatory !) the best Individuals
# to the front of the Population
for t in range(self.mPopulation.mPopulationSize):
self.mPopulation.mIndividual[t] = copy.deepcopy(sortiert[t])
if gGenDebug:
print(self.mEvaluations[t], \
self.mPopulation.mIndividual[t].mChromosome[0], \
self.mPopulation.mIndividual[t].mChromosome[1], \
self.mPopulation.mIndividual[t].mChromosome[2])
if gGenDebug:
print("best ",self.mEvaluations[0], \
self.mPopulation.mIndividual[0].mChromosome[0], \
self.mPopulation.mIndividual[0].mChromosome[1], \
self.mPopulation.mIndividual[0].mChromosome[2])
self.mBestMerrit = self.mEvaluations[0][0]
self.mBestIndividual = copy.deepcopy(self.mPopulation.mIndividual[0])
# ----------------------------------------
# Create the descendents of the survivors.
# Mating as well as asexual reproduction is possible
# the prop. of mating is given bei propCrossover (0...1)
def mating(self, survivors, propCrossover):
children = self.mPopulation.mPopulationSize - survivors
for t in range(children):
mama = int(random.random()*survivors)
papa = int(random.random()*survivors)
child = survivors + t
if(random.random() < propCrossover):
self.crossover(mama,papa,child)
else:
self.mPopulation.mIndividual[child] = \
copy.deepcopy(self.mPopulation.mIndividual[papa])
# ----------------------------------------
# Crossover between two parents (mama, papa) to create the child
# The crossoverposition is randomly chosen.
def crossover(self, mama, papa, child):
size = self.mPopulation.mIndividual[0].mChromosomeSize
pos = int(random.random() * size)
for t in range(pos):
self.mPopulation.mIndividual[child].mChromosome[t] = \
self.mPopulation.mIndividual[papa].mChromosome[t]
for t in range(size-pos):
self.mPopulation.mIndividual[child].mChromosome[pos + t] = \
self.mPopulation.mIndividual[mama].mChromosome[t+pos]
# ----------------------------------------
# Show the whole population (all individuals)
def show(self):
for t in range(self.mPopulation.mPopulationSize):
self.mPopulation.mIndividual[t].show()
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
#-------------------------------------------------Program-----------------------------------------------
# Schritt 1 : Ausführung des Programms
import time #Für die Kamera Aufnahme
# Schritt 1.1 : Kamara aufnahme und messung der Hintergrundsintensitaet
cam = dataIO('Vistek')
cam.setParam("exposure",0.08)
cam.startDevice()
cam. acquire()
time.sleep(0.3)
img = dataObject()
cam.getVal(img)
if (prnt0):
plot(img)
cam.stopDevice()
hintergrundliste = []
for o in range(200):
sume=0
for w in range (matrix_sp_messbreite[o][2]*2+1):
for q in range (matrix_sp_messbreite[o][2]*2+1):
pixel=img[int(matrix_sp_messbreite[o][0]-matrix_sp_messbreite[o][2]+q), int(matrix_sp_messbreite[o][1]-matrix_sp_messbreite[o][2]+w)]
sume+=pixel
total=(-sume/((2*matrix_sp_messbreite[o][2]+1)**2))
hintergrundliste.append(total)
if(prnt1):
print("hintergrundsliste",hintergrundliste)
del cam
# Schritt 1.2 : Ausrechnug einer Kennlinie für alle Gitter, die im SLM Geschrieben werden
# zum berechnen der Normierungskoeffizienten
holo = dataIO('CGHWindow') # initialisierung des Holograms
time.sleep(1)
holo.setParam('yholos',10)
holo.setParam('xholos',20)
brutergliste = []
nettergliste = []
brutbestresults=[]
netbestresults=[]
z=0.0 #Ausrechnung der Kennlinie mit dem Chromosom
probkl=[] # (0,0.5,0,0,0,0)
while z<1:
if z==0.0:
Wert=0
else:
Wert=(0+0.5*z+0*z**2 +0*z**3)*z**0+0
if Wert>1:
Wert=1
if Wert<0:
Wert=0
Wert=int(Wert*255)
probkl.append(Wert)
z=z+0.003921569
for t in range(200):
holo.setParam('activeholo',t)
holo.setParam('period',periode)
holo.setParam('orientation',orientations)
holo.setParam('maxvalue',255)
holo.setParam('curve',probkl)
holo.setParam('redraw',0)
cam = dataIO('Vistek') # Bild aufnahme
cam.setParam("exposure",0.08)
cam.startDevice()
cam. acquire()
time.sleep(0.3)
cam. acquire()
time.sleep(0.3)
cam. acquire()
time.sleep(0.3)
time.sleep(0.3)
img = dataObject()
cam.getVal(img)
if(prnt0):
plot(img)
cam.stopDevice()
normliste = []
for o in range(200): #Ausrechnung der Intensitaetswerte
sume=0
for w in range (matrix_sp_messbreite[o][2]*2+1):
for q in range (matrix_sp_messbreite[o][2]*2+1):
pixel=img[int(matrix_sp_messbreite[o][0]-matrix_sp_messbreite[o][2]+q),int(matrix_sp_messbreite[o][1]-matrix_sp_messbreite[o][2]+w)]
sume+=pixel
total=(sume/((2*matrix_sp_messbreite[o][2]+1)**2))
if (total==0.0):
total=10
normliste.append(total)
totalnormliste=[i+j for i,j in zip(normliste,hintergrundliste)] # Substraktion der
normkoeff=[] #Hintergrundsintensitaeten
for h in range (200):
wertkoeff=totalnormliste[h]/max(totalnormliste) #Normierungs koefizient jedes Gitter
normkoeff.append(wertkoeff) #=(Intensitaet je Gitter)/max gitter intensitaet
#-------Ausgaben zur Normierung--------
if(prnt2):
print("Brutto inten der Normierung",normliste )
if(prnt2):
print ("Netto inten der Normierung",totalnormliste)
if(prnt3):
print (" normierungs koeff",normkoeff)
del cam
"""
Still very simple demo for optimizing something but this time
it is demonstrated how to add your own MerritFunction by deriving a
class from GeneticOptimization
"""
# Schritt 1.2:
#Optimisation mit der Evaluationsfunktion bei ableiten einer Klasse aus GeneticOptimization
#-----------------------------------------------------------------------
# Here comes the derived class
class MyOptimization(GeneticOptimization):
"""
Simple example
"""
# ----------------------------------------
# Evaluate the fitness of Individual with number index.
#def evaluateIndividual(self, index): # Example methode
def evaluatefitness(self,bulean):
for v in range(200):
poli1=self.mPopulation.mIndividual[v].mChromosome[0]
poli2=self.mPopulation.mIndividual[v].mChromosome[1]
poli3=self.mPopulation.mIndividual[v].mChromosome[2]
poli4=self.mPopulation.mIndividual[v].mChromosome[3]
poli5=self.mPopulation.mIndividual[v].mChromosome[4]
poli6=self.mPopulation.mIndividual[v].mChromosome[5]
if(prnt4):
print ("Chromo aller indiv",poli1,poli2,poli3,poli4,poli5,poli6)
z=0.0 #Ausrechnung der Kennlinie für jedem Individuum
kl=[] #aus seine Chromosomen
while z<1:
if z==0.0:
Wert=0
else:
Wert=(poli1+poli2*z+poli3*z**2 +poli4*z**3)*z**poli5+poli6
if Wert>1:
Wert=1
if Wert<0:
Wert=0
Wert=int(Wert*255)
kl.append(Wert)
z=z+0.003921569
holo.setParam('activeholo',v)
holo.setParam('period',periode)
holo.setParam('orientation',orientations)
holo.setParam('maxvalue',255)
holo.setParam('curve',kl)
if(prnt5):
print("kennlinie aller indiv", kl)
holo.setParam('redraw',0)
cam = dataIO('Vistek') # Bild Aufnahme
cam.setParam("exposure",0.08)
cam.startDevice()
cam. acquire()
time.sleep(0.3)
img = dataObject()
cam.getVal(img)
if(prnt6):
plot(img)
elif(bulean):
plot(img)
cam.stopDevice()
intensitaetliste = []
for o in range(200): #Ausrechnung der Intensitaetswerte
sume=0
for w in range (2*matrix_sp_messbreite[o][2]+1):
for q in range (2*matrix_sp_messbreite[o][2]+1):
pixel=img[int(matrix_sp_messbreite[o][0]-matrix_sp_messbreite[o][2]+q),int(matrix_sp_messbreite[o][1]-matrix_sp_messbreite[o][2]+w)]
sume+=pixel
total=(-sume/((2*matrix_sp_messbreite[o][2]+1)**2))
intensitaetliste.append(total)
#Ausrechnung der Gitter intensitaeten mit Substraktion der
# Hintergrundsintensitaetenund division der normierungskoefizient
totalintenliste=[i-j for i,j in zip(intensitaetliste,hintergrundliste)]
total_int_liste_mit_koeff=[]
for c,d in zip(totalintenliste,normkoeff):
total_int_liste_mit_koeff.append(c/d)
enumObj = enumerate(total_int_liste_mit_koeff)
self.mEvaluations = [ [value,idx] for [idx,value] in enumObj ]
brutergliste.append(sum(intensitaetliste)/200)
nettergliste.append(sum(total_int_liste_mit_koeff)/200)
netbestresults.append(min(total_int_liste_mit_koeff))
brutbestresults.append(min(intensitaetliste))
#-------Ausgaben zur Evaluation -----
if(prnt7):
print("individuelle Brutto intensitäten ",intensitaetliste)
if(prnt8):
print("gesamt bruto inten",sum(intensitaetliste)/200)
if(prnt8):
print("gesamt netto inten",total_int_liste_mit_koeff)
if(prnt9):
print("nummerierte indiv Netto intensitäten",self.mEvaluations)
if(prnt11):
print(" Brutto Best Result in generation",min(intensitaetliste) )
print(" Netto Best Result in generation", min(total_int_liste_mit_koeff))
del cam
return self.mEvaluations
#-----------------------------------------------------------------------
def main():
try:
# --------------------------------------------------------------
# Referenz zu Schritt 0: Alle wichtige Parameter
ChromosomeLength=6 # Length of the Chromosome
PopulationSize = 200 # Number of Individuals per Generation
CntGenerations =generationen_anzahl # Number of Generations
Survivors = uberlebende # Survivors per Generation
Crossover = False # No Crossover
CoolingParameter =cooling_param # Exponential Decrease per Generation of Mutation Rate
# --------------------------------------------------------------
# Schritt 2 : Now we generate a population and initialize them with some individuals
popu = Population()
for t in range(PopulationSize):
indi = Individual(ChromosomeLength) # The allowed range for all genes
indi.setMinMax(0,a0min,a0max)
indi.setMinMax(1,a1min,a1max)
indi.setMinMax(2,a2min,a2max)
indi.setMinMax(3,a3min,a3max)
indi.setMinMax(4,a4min,a4max)
indi.setMinMax(5,a5min,a5max)
# erstellung zufaellige Chromosome fur den Zufällig erstellte gitter u=0 bis 200
va=[]
vb=[]
vc=[]
vd=[]
ve=[]
vg=[]
for u in range(200):
va.append(random.uniform(a0min,a0max))
vb.append(random.uniform(a1min,a1max))
vc.append(random.uniform(a2min,a2max))
vd.append(random.uniform(a3min,a3max))
ve.append(random.uniform(a4min,a4max))
vg.append(random.uniform(a5min,a5max))
if(gen_one): # Erstellung der erste Generation
indi.setValue(0,va[t]) # entweder Zufällig oder predeterminiert
indi.setValue(1,vb[t]) #
indi.setValue(2,vc[t])
indi.setValue(3,vd[t])
indi.setValue(4,ve[t])
indi.setValue(5,vg[t])
else:
indi.setValue(0,chrom0)
indi.setValue(1,chrom1)
indi.setValue(2,chrom2)
indi.setValue(3,chrom3)
indi.setValue(4,chrom4)
indi.setValue(5,chrom5)
popu.append(indi)
# Step 3: Optimize ------------------------------------------------------------
optimizer = MyOptimization(popu, CoolingParameter) # 0 -> Exp.Cooling
for generations in range(CntGenerations):
bulean=generations in plt_gen
optimizer.evaluatefitness(bulean) # Evaluate the Fitness of all Individuals
optimizer.selection(Survivors) # Decide who is surviving
optimizer.mating(Survivors,0.5) # Mating of the Survivors
optimizer.mutateAll(Survivors) # Mutate the Children
optimizer.cooling(0) # 0 -> Exponential Cooling
print("Generation:", generations,"MutationRate:" , optimizer.mMutationRate)
if(prnt11):
print("Liste Beste Results Netto je gen",netbestresults)
print("Liste Beste Results Brutto je gen" ,brutbestresults)
#-------Ausgaben zur Optimisation -----
if(prnt10):
print ("Gesamt Brutto Intensitaeten",brutergliste)
print("Gesamt Netto Intensitaeten",nettergliste)
#popu.show()
print("evaluating")
for q in range (5):
print("...")
if(prnt13):
print("Netto Best Result:",optimizer.mBestMerrit)
print("Verbesserung von:", ((netbestresults[29]-netbestresults[0])/netbestresults[0])*100, "%")
if(prnt12):
print("Chromosome Best individual")
optimizer.mBestIndividual.show()
if(prnt10):
print ("Gesamt Brutto Intensitaeten",brutergliste)
print("Gesamt Netto Intensitaeten",nettergliste)
if(prnt11):
print("Liste Beste Results Netto je gen",netbestresults)
#print("Liste Beste Results Brutto je gen" ,brutbestresults)
# --------------------------------------------------------------
except KeyboardInterrupt:
pass
if __name__ == '__main__':
main()
del holo
|