- Timestamp:
- 09/09/23 15:10:49 (16 months ago)
- Location:
- cpp/frams/genetics
- Files:
-
- 8 edited
-
fB/fB_oper.cpp (modified) (13 diffs)
-
fB/fB_oper.h (modified) (3 diffs)
-
fH/fH_oper.cpp (modified) (3 diffs)
-
fL/fL_general.cpp (modified) (7 diffs)
-
fL/fL_general.h (modified) (4 diffs)
-
fL/fL_oper.cpp (modified) (16 diffs)
-
genman.cpp (modified) (2 diffs)
-
genooperators.h (modified) (1 diff)
Legend:
- Unmodified
- Added
- Removed
-
cpp/frams/genetics/fB/fB_oper.cpp
r1130 r1273 1 1 // This file is a part of Framsticks SDK. http://www.framsticks.com/ 2 // Copyright (C) 1999-202 1Maciej Komosinski and Szymon Ulatowski.2 // Copyright (C) 1999-2023 Maciej Komosinski and Szymon Ulatowski. 3 3 // See LICENSE.txt for details. 4 5 //TODO parsing quotes/neurons seems too relaxed, for example the genotype aag"S""acalaaaafbc is considered valid 6 //TODO Same with numbers: "----1.23" is valid 7 //TODO reconsider: Horizontal gene transfer - copying a single random gene from each parent to the beginning of the other parent: should the gene be copied (seems to cause bloat!) or rather moved? 8 //Neurons ("N") can grow even without using quotes and providing neuron classname in the genotype, for example aaaaabbccvaaapdgfddaalaandwddbaajt (this works likely as designed, but investigate and reconsider); also valid neuron definitions inside the genotype are sometimes not expressed 4 9 5 10 #include <frams/util/sstring.h> … … 19 24 { "Genetics: fB: Mutation", }, 20 25 { "Genetics: fB: Crossover", }, 21 { "fB_mut_substitut ion", 1, 0, "Substitution", "f 0 1 0.6", FIELD(mutationprobs[FB_SUBSTITUTION]), "Probability of mutation by changing single random letter ingenotype", },22 { "fB_mut_insert ion", 1, 0, "Insertion", "f 0 1 0.095", FIELD(mutationprobs[FB_INSERTION]), "Probability of mutation by inserting characters in random place ofgenotype", },23 { "fB_mut_ nclassins", 1, 0, "Insertion of neuron class definition", "f 0 1 0.005", FIELD(mutationprobs[FB_NCLASSINS]), "Probability of mutation by inserting neuron class definition inrandom place of genotype", },24 { "fB_mut_delet ion", 1, 0, "Deletion", "f 0 1 0.1", FIELD(mutationprobs[FB_DELETION]), "Probability of mutation by deleting random characters ingenotype", },25 { "fB_mut_duplicat ion", 1, 0, "Duplication", "f 0 1 0.0", FIELD(mutationprobs[FB_DUPLICATION]), "Probability of mutation by copying single *gene* ofgenotype and appending it to the beginning of this genotype", },26 { "fB_mut_translocat ion", 1, 0, "Translocation", "f 0 1 0.15", FIELD(mutationprobs[FB_TRANSLOCATION]), "Probability of mutation by replacing two substrings ingenotype", },27 { "fB_cross_gene_transfer", 2, 0, "Horizontal gene transfer", "f 0 1 0.0", FIELD(crossoverprobs[FB_GENE_TRANSFER]), "Probability of crossing over by transferring single genes from both parents to beginning of each other", },28 { "fB_cross_crossover", 2, 0, "Crossing over", "f 0 1 1.0", FIELD(crossoverprobs[FB_CROSSING_OVER]), "Probability of crossing over byrandom distribution of genes from both parents to both children", },26 { "fB_mut_substitute", 1, 0, "Substitution", "f 0 100 1", FIELD(mutationprobs[FB_SUBSTITUTION]), "Relative probability of changing a single random character (or a neuron) in the genotype", }, 27 { "fB_mut_insert", 1, 0, "Insertion", "f 0 100 3", FIELD(mutationprobs[FB_INSERTION]), "Relative probability of inserting a random character in a random place of the genotype", }, 28 { "fB_mut_insert_neuron", 1, 0, "Insertion of a neuron", "f 0 100 3", FIELD(mutationprobs[FB_INSERTION_NEURON]), "Relative probability of inserting a neuron in a random place of genotype", }, 29 { "fB_mut_delete", 1, 0, "Deletion", "f 0 100 4", FIELD(mutationprobs[FB_DELETION]), "Relative probability of deleting a random character (or a neuron) in the genotype", }, 30 { "fB_mut_duplicate", 1, 0, "Duplication", "f 0 100 0", FIELD(mutationprobs[FB_DUPLICATION]), "Relative probability of copying a single *gene* of the genotype and appending it to the beginning of this genotype", }, 31 { "fB_mut_translocate", 1, 0, "Translocation", "f 0 100 4", FIELD(mutationprobs[FB_TRANSLOCATION]), "Relative probability of swapping two substrings in the genotype", }, 32 { "fB_cross_gene_transfer", 2, 0, "Horizontal gene transfer", "f 0 100 0", FIELD(crossoverprobs[FB_GENE_TRANSFER]), "Relative probability of crossing over by copying a single random gene from each parent to the beginning of the other parent", }, 33 { "fB_cross_crossover", 2, 0, "Crossing over", "f 0 100 100", FIELD(crossoverprobs[FB_CROSSING_OVER]), "Relative probability of crossing over by a random distribution of genes from both parents to both children", }, 29 34 { 0, }, 30 35 }; … … 128 133 if (!genotype.getNextToken(pos, strdims, '\n')) 129 134 { 130 return GENOPER_O PFAIL;135 return GENOPER_OK; 131 136 } 132 137 // parse dimension … … 134 139 if (!ExtValue::parseInt(strdims.c_str(), dims, true, false)) 135 140 { 136 return GENOPER_O PFAIL;141 return GENOPER_OK; 137 142 } 138 143 SString line; … … 171 176 else 172 177 { 173 return GENOPER_O PFAIL;178 return GENOPER_OK; 174 179 } 175 180 } … … 247 252 { 248 253 std::list<SString> tokenized = tokenizeSequence(line); 249 int rndid = rndUint( tokenized.size()); // select random letter from genotype254 int rndid = rndUint((int)tokenized.size()); // select random letter from genotype 250 255 // increment/decrement character - when overflow happens, this method 251 256 // uses the "reflect" approach … … 282 287 break; 283 288 } 284 case FB_ NCLASSINS:289 case FB_INSERTION_NEURON: 285 290 { 286 291 std::list<SString> tokenized = tokenizeSequence(line); 287 292 std::list<SString>::iterator it = tokenized.begin(); 288 int rndid = rndUint( tokenized.size()); // select random insertion point293 int rndid = rndUint((int)tokenized.size()); // select random insertion point 289 294 std::advance(it, rndid); 290 295 NeuroClass *cls = getRandomNeuroClass(Model::SHAPETYPE_BALL_AND_STICK); … … 307 312 chg = 1.0 / line.length(); 308 313 std::list<SString> tokenized = tokenizeSequence(line); 309 int rndid = rndUint( tokenized.size()); // select random insertion point314 int rndid = rndUint((int)tokenized.size()); // select random insertion point 310 315 std::list<SString>::iterator it = tokenized.begin(); 311 316 std::advance(it, rndid); … … 321 326 std::list<SString> tokenized = tokenizeSequence(line); 322 327 std::list<SString>::iterator it = tokenized.begin(); 323 int rndid = rndUint( tokenized.size()); // select random deletion point328 int rndid = rndUint((int)tokenized.size()); // select random deletion point 324 329 std::advance(it, rndid); 325 330 tokenized.erase(it); … … 343 348 for (int i = 0; i < 4; i++) 344 349 { 345 cuts[i] = rndUint( tokenized.size());350 cuts[i] = rndUint((int)tokenized.size()); 346 351 } 347 352 std::sort(cuts.begin(), cuts.end()); … … 407 412 case FB_GENE_TRANSFER: 408 413 { 409 // get random gene fromfirst parent414 // get a random gene from the first parent 410 415 int choice = rndUint(fB_GenoHelpers::geneCount(parent1)); 411 416 int start, end; … … 414 419 child2 = gene + parent2; 415 420 chg2 = (float)parent2.length() / (float)child2.length(); 416 // do the same for second parent421 // do the same for the second parent 417 422 choice = rndUint(fB_GenoHelpers::geneCount(parent2)); 418 423 gene = fB_GenoHelpers::getGene(choice, parent2, start, end); … … 554 559 { 555 560 pos--; 556 if (isdigit(geno[pos]) == 0) 557 { 558 return GENSTYLE_CS(0, GENSTYLE_INVALID); 559 } 560 } 561 return GENSTYLE_RGBS(0, 0, 200, GENSTYLE_BOLD); 562 } 563 if (islower(ch) == 0) 564 { 565 return GENSTYLE_CS(0, GENSTYLE_INVALID); 566 } 567 uint32_t style = GENSTYLE_CS(GENCOLOR_TEXT, GENSTYLE_NONE); 568 if (ch == 'a' && pos > 0 && (geno[pos - 1] == 'a' || geno[pos - 1] == '\n')) 561 if (isdigit(geno[pos]) == 0) //going left we encountered some non-digit character 562 { 563 return GENSTYLE_CS(GENCOLOR_NUMBER, GENSTYLE_NONE); //so 'ch' is any digit in the genotype (neural property value etc.); for simplicity, digits as parts of neuroclass name or property name also get included here 564 } 565 } 566 return GENSTYLE_RGBS(0, 0, 200, GENSTYLE_BOLD); //only digits up to the beginning, so this is the dimensionality value 567 } 568 if (ch == '-' || ch == '.') 569 return GENSTYLE_CS(GENCOLOR_NUMBER, GENSTYLE_NONE); 570 if (ch == '"') 571 return GENSTYLE_RGBS(150, 0, 150, GENSTYLE_BOLD); //quotes encompass neuron definitions. To further distinguish the text inside quotes from the text outside quotes, we would need to determine the number of '"' from the beginning, i.e. linear search through the entire genotype. We don't want to do it - it would mean the complexity of len(geno)^2 if performed for each symbol in the genotype independently, like this function does. Below we perform an approximate partial scan. 572 if (isupper(ch) || strchr("@|*", ch)) 573 return GENSTYLE_RGBS(150, 0, 150, GENSTYLE_BOLD); //neuroclass 574 if (strchr(":,=", ch)) 575 return GENSTYLE_RGBS(150, 0, 150, GENSTYLE_NONE); //these symbols occur exclusively inside "...neuron...", so let's make the entire neuron section "...neuron..." more visually uniform by using the same violet color as the neuroclass name and quotes have 576 if (islower(ch)) //how to color the current lower-case letter? 577 { 578 static const int SCAN_RANGE = 8; //how many characters before the current one to scan to discover some context and find out if we are likely in the neuroclass name or the property name. Reduces computational complexity. Example genotype fragments: abcabc"T:r=0.9, ry=4.088, rz=1.213"abcabc or abc"N:in=0.0, fo=0.17, si=999.0"abc 579 int i = pos; 580 while (i > 0 && pos - i < SCAN_RANGE) 581 { 582 i--; //go back one char 583 if (isupper(geno[i])) 584 return GENSTYLE_RGBS(150, 0, 150, GENSTYLE_BOLD); //neuroclass 585 if (geno[i] == ',' || geno[i] == ':') //this is what must occur before property name starts 586 return GENSTYLE_RGBS(255, 140, 0, GENSTYLE_BOLD); //property 587 if (!(isalpha(geno[i]) || isspace(geno[i]))) //going left we encountered any char that is not a letter or space 588 break; 589 } 590 } 591 592 uint32_t style = GENSTYLE_CS(GENCOLOR_TEXT, GENSTYLE_NONE); //if the current character did not fall into any of the above cases, assume default black style 593 if (ch == 'a' && (geno[pos + 1] == 'a' || (pos > 0 && geno[pos - 1] == 'a'))) //start codon, "aa" 569 594 { 570 595 style = GENSTYLE_RGBS(0, 200, 0, GENSTYLE_BOLD); 571 596 } 572 else if (ch == 'z' && pos > 0 && geno[pos - 1] == 'z')597 else if (ch == 'z' && (geno[pos + 1] == 'z' || (pos > 0 && geno[pos - 1] == 'z'))) //stop codon, "zz" 573 598 { 574 599 style = GENSTYLE_RGBS(200, 0, 0, GENSTYLE_BOLD); -
cpp/frams/genetics/fB/fB_oper.h
r821 r1273 1 1 // This file is a part of Framsticks SDK. http://www.framsticks.com/ 2 // Copyright (C) 1999-20 18Maciej Komosinski and Szymon Ulatowski.2 // Copyright (C) 1999-2023 Maciej Komosinski and Szymon Ulatowski. 3 3 // See LICENSE.txt for details. 4 4 … … 11 11 /** @name Codes for general fB mutation types */ 12 12 //@{ 13 #define FB_SUBSTITUTION 0 ///<Relative probability of mutation by changing single random letter in genotype (substitution)14 #define FB_INSERTION 1 ///<Relative probability of mutation by inserting characters in random place ofgenotype15 #define FB_ NCLASSINS 2 ///<Relative probability of mutation by inserting neuron class definition inrandom place of genotype16 #define FB_DELETION 3 ///<Relative probability of mutation by deleting random characters ingenotype17 #define FB_DUPLICATION 4 ///<Relative probability of mutation by copying single *gene* ofgenotype and appending it to the beginning of this genotype18 #define FB_TRANSLOCATION 5 ///<Relative probability of mutation by replacing two substrings ingenotype19 #define FB_MUT_COUNT 6 ///<Count of mutation types13 #define FB_SUBSTITUTION 0 ///<Relative probability of changing a single random character (or a neuron) in the genotype 14 #define FB_INSERTION 1 ///<Relative probability of inserting a random character in a random place of the genotype 15 #define FB_INSERTION_NEURON 2 ///<Relative probability of inserting a neuron in a random place of genotype 16 #define FB_DELETION 3 ///<Relative probability of deleting a random character (or a neuron) in the genotype 17 #define FB_DUPLICATION 4 ///<Relative probability of copying a single *gene* of the genotype and appending it to the beginning of this genotype 18 #define FB_TRANSLOCATION 5 ///<Relative probability of swapping two substrings in the genotype 19 #define FB_MUT_COUNT 6 ///<Count of mutation types 20 20 //@} 21 21 22 22 /** @name Codes for fB cross over types */ 23 23 //@{ 24 #define FB_GENE_TRANSFER 0 ///<Relative probability of crossing over by transferring single genes from both parents to beginning of each other25 #define FB_CROSSING_OVER 1 ///<Relative probability of crossing over by random distribution of genes from both parents to both children24 #define FB_GENE_TRANSFER 0 ///<Relative probability of crossing over by copying a single random gene from each parent to the beginning of the other parent 25 #define FB_CROSSING_OVER 1 ///<Relative probability of crossing over by a random distribution of genes from both parents to both children 26 26 #define FB_XOVER_COUNT 2 ///<Count of crossing over types 27 27 //@} … … 48 48 int crossOver(char *&g1, char *&g2, float& chg1, float& chg2); 49 49 50 virtual const char* getSimplest() { return " 5\naaazz"; }50 virtual const char* getSimplest() { return "3\naaazz"; } 51 51 52 52 uint32_t style(const char *geno, int pos); -
cpp/frams/genetics/fH/fH_oper.cpp
r1257 r1273 14 14 { 15 15 { "Genetics: fH", 1, FH_OPCOUNT + FH_ADD_OPCOUNT, }, 16 { "fH_mut_addition", 0, 0, "Add element", "f 0 100 30", FIELD(operations[FH_ADD]), "Probability of adding new element to genotype", },17 { "fH_mut_add_joint", 0, 0, " - add joint", "f 0 100 33", FIELD(addoperations[FH_ADD_STICK]), "Probability of addingnew stick handle", },18 { "fH_mut_add_neuron", 0, 0, " - add neuron", "f 0 100 3 3", FIELD(addoperations[FH_ADD_NEURO]), "Probability of addingnew neuron handle", },19 { "fH_mut_add_connection", 0, 0, " - add neural connection", "f 0 100 33", FIELD(addoperations[FH_ADD_CONN]), "Probability of addingnew neuron connection handle", },20 { "fH_mut_deletion", 0, 0, "Delete element", "f 0 100 10", FIELD(operations[FH_DEL]), "Probability of removing element from genotype", },21 { "fH_mut_handle", 0, 0, "Modify vectors of handles", "f 0 100 30", FIELD(operations[FH_HANDLE]), "Probability of changing values in vectors ofhandle", },22 { "fH_mut_property", 0, 0, "Modify properties of handles", "f 0 100 30", FIELD(operations[FH_PROP]), "Probability of changing properties of handles", },16 { "fH_mut_addition", 0, 0, "Add element", "f 0 100 4", FIELD(operations[FH_ADD]), "Probability of adding a new element", }, 17 { "fH_mut_add_joint", 0, 0, " - add joint", "f 0 100 4", FIELD(addoperations[FH_ADD_STICK]), "Probability of adding a new stick handle", }, 18 { "fH_mut_add_neuron", 0, 0, " - add neuron", "f 0 100 3", FIELD(addoperations[FH_ADD_NEURO]), "Probability of adding a new neuron handle", }, 19 { "fH_mut_add_connection", 0, 0, " - add neural connection", "f 0 100 1", FIELD(addoperations[FH_ADD_CONN]), "Probability of adding a new neuron connection handle", }, 20 { "fH_mut_deletion", 0, 0, "Delete element", "f 0 100 4", FIELD(operations[FH_DEL]), "Probability of removing an element", }, 21 { "fH_mut_handle", 0, 0, "Modify vectors of handles", "f 0 100 1", FIELD(operations[FH_HANDLE]), "Probability of changing values in vectors of a handle", }, 22 { "fH_mut_property", 0, 0, "Modify properties of handles", "f 0 100 4", FIELD(operations[FH_PROP]), "Probability of changing properties of handles", }, 23 23 { 0, }, 24 24 }; … … 53 53 return 1; 54 54 } 55 return 0;55 return GENOPER_OK; 56 56 } 57 57 … … 63 63 fH_Builder builder; 64 64 int err = builder.parseGenotype(geno); 65 // if parsing failed, then it is impossible to repair genotype65 // if parsing failed, then it is impossible to repair the genotype 66 66 if (err != 0) 67 67 { 68 return GENOPER_O PFAIL;69 } 70 // method removes definitions of neurons that have invalid genotype68 return GENOPER_OK; 69 } 70 // method removes definitions of neurons that have an invalid genotype 71 71 int amount = builder.removeNeuronsWithInvalidClasses(); 72 // if there were any warnings, then rewrite genotype72 // if there were any warnings, then rewrite the genotype 73 73 if (eh.getErrorWarningCount() > 0 || amount > 0) 74 74 { -
cpp/frams/genetics/fL/fL_general.cpp
r973 r1273 1 1 // This file is a part of Framsticks SDK. http://www.framsticks.com/ 2 // Copyright (C) 1999-202 0Maciej Komosinski and Szymon Ulatowski.2 // Copyright (C) 1999-2023 Maciej Komosinski and Szymon Ulatowski. 3 3 // See LICENSE.txt for details. 4 4 … … 9 9 #include <iterator> 10 10 11 const char *fL_part_names[FL_PART_PROPS_COUNT] = { " dn", "fr", "ing", "as" };12 const char *fL_part_fullnames[FL_PART_PROPS_COUNT] = { " details", "friction", "ingestion", "assimilation" };13 14 const char *fL_joint_names[FL_JOINT_PROPS_COUNT] = { " stif", "rotstif", "stam" };15 const char *fL_joint_fullnames[FL_JOINT_PROPS_COUNT] = { " stiffness", "rotationstiffness", "stamina" };11 const char *fL_part_names[FL_PART_PROPS_COUNT] = { "fr" }; // "dn", "ing", "as" }; 12 const char *fL_part_fullnames[FL_PART_PROPS_COUNT] = { "friction" }; // "density", "ingestion", "assimilation" }; 13 14 const char *fL_joint_names[FL_JOINT_PROPS_COUNT] = { "rotstif" }; //"stif", , "stam" }; //see the comment to fH_joint_names in fH_general.cpp 15 const char *fL_joint_fullnames[FL_JOINT_PROPS_COUNT] = { "rotation stiffness" }; //"stiffness", "stamina" }; 16 16 17 17 #define FIELDSTRUCT fL_Word … … 502 502 fL_Word *stick = new fL_Word(true); 503 503 stick->name = "S"; 504 stick->npar = 8;504 stick->npar = FL_PART_PROPS_COUNT + FL_JOINT_PROPS_COUNT + 1; //MacKo 2023-07: was hardcoded "8" but crashed when the number of props in Part and Joint was decreased; I think it should be like it is now (4+3+1 - or another value depending on prop counts) 505 505 for (int i = 0; i < FL_PART_PROPS_COUNT; i++) 506 506 { 507 507 stick->mut.addProperty(NULL, fL_part_names[i], "s", fL_part_fullnames[i], fL_part_fullnames[i], PARAM_CANOMITNAME, 0, -1); 508 508 } 509 510 509 for (int i = 0; i < FL_JOINT_PROPS_COUNT; i++) 511 510 { … … 1096 1095 { 1097 1096 delete newpart; 1098 logMessage("fL_Builder", "developModel", LOG_ERROR, 1099 "Error parsing word parameter"); 1097 logMessage("fL_Builder", "developModel", LOG_ERROR, "Error parsing word parameter"); 1100 1098 return 1; 1101 1099 } … … 1123 1121 if (word->parevals[FL_PART_PROPS_COUNT + i]->evaluateRPN(jointprop) != 0) 1124 1122 { 1125 logMessage("fL_Builder", "developModel", LOG_ERROR, 1126 "Error parsing word parameter"); 1123 logMessage("fL_Builder", "developModel", LOG_ERROR, "Error parsing word parameter"); 1127 1124 delete newjoint; 1128 1125 return 1; … … 1153 1150 } 1154 1151 model->addNeuro(neu); 1152 1153 1154 // MacKo 2023-07: embody (i.e., attach to body) sensors and effectors. 1155 // Ad hoc modification; should be reconsidered as it was added without reminding and understanding of the entire logic of the phenotype development and assumptions, and may not be "the best performing" or "the most reasonable" approach. Without this embodiment, genotypes with sensors and effectors not attached to body had warnings (such neurons "could not be initialized") so such genotypes were not accepted when creatwarnfail==1; even with creatwarnfail==0, non-embodied sensor and effector neurons were useless. 1156 //printf("Neuron: -------------- %s\n", details.c_str()); 1157 switch (neu->getClass()->getPreferredLocation()) 1158 { 1159 case NeuroClass::PrefLocation::PREFER_PART: //attach to currstate.currpart 1160 if (currstate.currpart != NULL) 1161 neu->attachToPart(currstate.currpart); 1162 break; 1163 case NeuroClass::PrefLocation::PREFER_JOINT: //attach to the last joint incident with currstate.currpart 1164 if (currstate.currpart != NULL) 1165 { 1166 Joint *j = NULL; 1167 for (int i = 0; i < model->getJointCount(); i++) 1168 { 1169 Joint *jj = model->getJoint(i); 1170 if (jj->part1 == currstate.currpart || jj->part2 == currstate.currpart) 1171 j = jj; 1172 } 1173 if (j != NULL) 1174 neu->attachToJoint(j); 1175 } 1176 break; 1177 default: 1178 break; 1179 } 1180 1181 1155 1182 if (using_mapping) neu->addMapping(IRange(word->begin, word->end)); 1156 1183 if (neu->getClass()->getPreferredInputs() != 0) … … 1251 1278 { 1252 1279 connsbuffer[i].second->addInput(connsbuffer[i].second, weight); 1253 if (using_mapping) neu->addMapping(1280 if (using_mapping) connsbuffer[i].second->addMapping( //MacKo 2023-07: changed 'neu' (which is NULL here so crashed) to connsbuffer[i].second (no guarantee this is a proper fix, only inspired by the analogy to the difference in both branches of "if" in the line above) 1254 1281 IRange((*connsbuffer[i].first)->begin, 1255 1282 (*connsbuffer[i].first)->end)); -
cpp/frams/genetics/fL/fL_general.h
r821 r1273 1 1 // This file is a part of Framsticks SDK. http://www.framsticks.com/ 2 // Copyright (C) 1999-20 18Maciej Komosinski and Szymon Ulatowski.2 // Copyright (C) 1999-2023 Maciej Komosinski and Szymon Ulatowski. 3 3 // See LICENSE.txt for details. 4 4 … … 25 25 /** @name Constants used in fL methods */ 26 26 //@{ 27 #define FL_PART_PROPS_COUNT 4///<Count of part properties28 #define FL_JOINT_PROPS_COUNT 3///<Count of joint properties27 #define FL_PART_PROPS_COUNT 1 ///<Count of part properties 28 #define FL_JOINT_PROPS_COUNT 1 ///<Count of joint properties 29 29 #define FL_PE_NEURO_DET "d" ///<Id of details type definition in f0_neuro_paramtab 30 30 #define FL_PE_CONN_WEIGHT "w" ///<Id of weight type definition in f0_neuroconn_paramtab 31 31 #define FL_PE_CONN_ATTR "attr" ///<Id of attractor of neural connection 32 32 #define FL_DEFAULT_LENGTH 1.0 ///<Default length of a stick in fL encoding 33 #define FL_MINIMAL_LENGTH 0. 0///<Minimal length of a stick in fL encoding33 #define FL_MINIMAL_LENGTH 0.1 ///<Minimal length of a stick in fL encoding 34 34 #define FL_MAXIMAL_LENGTH 2.0 ///<Maximal length of a stick in fL encoding 35 35 #define FL_MAXITER "100.0" ///<Maximal iteration available in fL … … 404 404 405 405 /** 406 * Develop es L-System from given genotype and buildsFramsticks Model from it.407 * When using_checkpoints is enabled, method generatescheckpoint for each408 * step defined in t imestamp.409 * @param neededtime reference to a time value after stopping development (usually it will be equal to t ime specified in the time field, unless the number of allowed words will be exceeded earlier)406 * Develops an L-System from a given genotype and builds a Framsticks Model from it. 407 * When using_checkpoints is enabled, this method generates a checkpoint for each 408 * step defined in the timestamp. 409 * @param neededtime reference to a time value after stopping development (usually it will be equal to the time specified in the time field, unless the number of allowed words will be exceeded earlier) 410 410 * @return final model from a fL genotype 411 411 */ … … 413 413 414 414 /** 415 * Creates new checkpoint for a given model based on current state ofgenotype.416 * @param model reference to model417 * @return 0 if developing went successfully, 1 otherwise415 * Creates a new checkpoint for a given model based on the current state of the genotype. 416 * @param model reference to the model 417 * @return 0 if the development was successfull, 1 otherwise 418 418 */ 419 419 int buildModelFromSequence(Model *model); -
cpp/frams/genetics/fL/fL_oper.cpp
r1075 r1273 1 1 // This file is a part of Framsticks SDK. http://www.framsticks.com/ 2 // Copyright (C) 1999-202 0Maciej Komosinski and Szymon Ulatowski.2 // Copyright (C) 1999-2023 Maciej Komosinski and Szymon Ulatowski. 3 3 // See LICENSE.txt for details. 4 4 … … 8 8 #include <algorithm> 9 9 10 11 12 //TODO fix: occasionally happens (note that fL extensively uses Param parsing, see fL_word_paramtab): Param.loadSingleLine: Unknown property 'Word.w0(n1' (ignored) 13 //TODO reconsider and maybe improve sensor and effector embodiment; see "MacKo 2023-07: embody" in fL_general.cpp. 14 15 16 10 17 #define FIELDSTRUCT Geno_fL 11 18 static ParamEntry geno_fL_paramtab[] = … … 14 21 {"Genetics: fL: Probabilities of mutating axiom and rules", }, 15 22 {"Genetics: fL: Probabilities of mutation types", }, 16 {"fL_maxdefinedwords", 0, 0, "Maximum number of defined words", "d 0 100 10", FIELD(maxdefinedwords), "Maximum number of words that can be defined in L-System", },17 18 {"fL_axm_mut_prob", 1, 0, "Axiom mutation", "f 0 1 0.2", FIELD(groupprobabilities[FL_AXM_WORD_MUT_PROB]), "Probability of performing mutation operations on axiom", },19 {"fL_rul_mut_prob", 1, 0, "Rule's successor mutation", "f 0 1 0.8", FIELD(groupprobabilities[FL_RUL_WORD_MUT_PROB]), "Probability of performing mutation operations on the successor ofrandom rule", },20 21 {"fL_mut_addition", 2, 0, "Addition of word to sequence", "f 0 1 0.2", FIELD(operations[FL_ADD_WORD]), "Probability of adding random existing word to the axiom orone of successors", },22 23 {"fL_mut_add_stick", 2, 0, " - addition of stick", "f 0 1 0.2", FIELD(addtypes[FL_ADD_STICK]), "Probability of addingstick", },24 {"fL_mut_add_neuro", 2, 0, " - addition of neuron", "f 0 1 0.2", FIELD(addtypes[FL_ADD_NEURO]), "Probability of addingneuron", },25 {"fL_mut_add_conn", 2, 0, " - addition of neuron connection", "f 0 1 0.2", FIELD(addtypes[FL_ADD_CONN]), "Probability of addingconnection", },26 {"fL_mut_add_rot", 2, 0, " - addition of rotation words", "f 0 1 0.2", FIELD(addtypes[FL_ADD_ROT]), "Probability of adding one of rotation words", },27 {"fL_mut_add_branch", 2, 0, " - addition of branched stick", "f 0 1 0.2", FIELD(addtypes[FL_ADD_BRANCH]), "Probability of adding branch with rotation andstick", },28 {"fL_mut_add_other", 2, 0, " - addition of defined words", "f 0 1 0.4", FIELD(addtypes[FL_ADD_OTHER]), "Probability of adding other word, defined ingenotype", },29 30 {"fL_mut_worddefaddition", 2, 0, "Addition of new word definition", "f 0 1 0.05", FIELD(operations[FL_ADD_WDEF]), "Probability of addingnew word definition to the genotype", },31 {"fL_mut_ruleaddition", 2, 0, "Addition of new rule definition", "f 0 1 0.1", FIELD(operations[FL_ADD_RULE]), "Probability of adding new rule definition forexisting word", },32 {"fL_mut_rulecond", 2, 0, "Modification of rule condition", "f 0 1 0.1", FIELD(operations[FL_CHG_COND]), "Probability of modifyingrandom rule condition", },33 34 {"fL_mut_changeword", 2, 0, "Change of random word", "f 0 1 0.3", FIELD(operations[FL_CHG_WORD]), "Probability of changing word name or formula of a random word fromaxiom or one of successors", },35 {"fL_mut_changeword_formula", 2, 0, " - change of formula", "f 0 1 0.7", FIELD(chgoperations[FL_CHG_WORD_FORMULA]), "Probability of changing formula inword", },36 {"fL_mut_changeword_name", 2, 0, " - change of name", "f 0 1 0.3", FIELD(chgoperations[FL_CHG_WORD_NAME]), "Probability of changing name inword", },37 38 {"fL_mut_changeiter", 2, 0, "Change of L-System iteration", "f 0 1 0.3", FIELD(operations[FL_CHG_ITER]), "Probability of changing number of iterations of L-Systems", },39 {"fL_mut_changeiter_step", 2, 0, "Step of iteration changing", "f 0 1 1.0", FIELD(iterchangestep), "Minimal step that should be used for changing iterations in L-Systems", },40 {"fL_mut_deletion", 2, 0, "Deletion of random word", "f 0 1 0.2", FIELD(operations[FL_DEL_WORD]), "Probability of deleting random word from axiom or random successor (also deletes rule if there is only one word insuccessor)", },23 {"fL_maxdefinedwords", 0, 0, "Maximum number of defined words", "d 0 100 10", FIELD(maxdefinedwords), "Maximum number of words that can be defined in the L-System", }, 24 25 {"fL_axm_mut_prob", 1, 0, "Axiom mutation", "f 0 100 4", FIELD(groupprobabilities[FL_AXM_WORD_MUT_PROB]), "Probability of performing mutation operations on axiom", }, 26 {"fL_rul_mut_prob", 1, 0, "Rule's successor mutation", "f 0 100 1", FIELD(groupprobabilities[FL_RUL_WORD_MUT_PROB]), "Probability of performing mutation operations on the successor of a random rule", }, 27 28 {"fL_mut_addition", 2, 0, "Addition of a word to a sequence", "f 0 100 4", FIELD(operations[FL_ADD_WORD]), "Probability of adding a random existing word to the axiom or to one of successors", }, 29 30 {"fL_mut_add_stick", 2, 0, " - addition of a stick", "f 0 100 1", FIELD(addtypes[FL_ADD_STICK]), "Probability of adding a stick", }, 31 {"fL_mut_add_neuro", 2, 0, " - addition of a neuron", "f 0 100 4", FIELD(addtypes[FL_ADD_NEURO]), "Probability of adding a neuron", }, 32 {"fL_mut_add_conn", 2, 0, " - addition of a neuron connection", "f 0 100 4", FIELD(addtypes[FL_ADD_CONN]), "Probability of adding a neuron connection", }, 33 {"fL_mut_add_rot", 2, 0, " - addition of rotation words", "f 0 100 2", FIELD(addtypes[FL_ADD_ROT]), "Probability of adding one of rotation words", }, 34 {"fL_mut_add_branch", 2, 0, " - addition of a branched stick", "f 0 100 4", FIELD(addtypes[FL_ADD_BRANCH]), "Probability of adding a branch with a rotation and a stick", }, 35 {"fL_mut_add_other", 2, 0, " - addition of defined words", "f 0 100 1", FIELD(addtypes[FL_ADD_OTHER]), "Probability of adding another word defined in the genotype", }, 36 37 {"fL_mut_worddefaddition", 2, 0, "Addition of a new word definition", "f 0 100 1", FIELD(operations[FL_ADD_WDEF]), "Probability of adding a new word definition to the genotype", }, 38 {"fL_mut_ruleaddition", 2, 0, "Addition of a new rule definition", "f 0 100 1", FIELD(operations[FL_ADD_RULE]), "Probability of adding a new rule definition for an existing word", }, 39 {"fL_mut_rulecond", 2, 0, "Modification of a rule condition", "f 0 100 1", FIELD(operations[FL_CHG_COND]), "Probability of modifying a random rule condition", }, 40 41 {"fL_mut_changeword", 2, 0, "Change a random word", "f 0 100 4", FIELD(operations[FL_CHG_WORD]), "Probability of changing a word name or a formula of a random word from an axiom or one of successors", }, 42 {"fL_mut_changeword_formula", 2, 0, " - change of a formula", "f 0 100 4", FIELD(chgoperations[FL_CHG_WORD_FORMULA]), "Probability of changing a formula in a word", }, 43 {"fL_mut_changeword_name", 2, 0, " - change of a name", "f 0 100 2", FIELD(chgoperations[FL_CHG_WORD_NAME]), "Probability of changing a name in a word", }, 44 45 {"fL_mut_changeiter", 2, 0, "Change the number of iterations", "f 0 100 1", FIELD(operations[FL_CHG_ITER]), "Probability of changing the number of iterations of the L-System", }, 46 {"fL_mut_changeiter_step", 2, 0, "Step of the iteration change", "f 0 1 1.0", FIELD(iterchangestep), "The minimal step that should be used for changing iterations in the L-System", }, 47 {"fL_mut_deletion", 2, 0, "Deletion of a random word", "f 0 100 4", FIELD(operations[FL_DEL_WORD]), "Probability of deleting a random word from an axiom or a random successor (also deletes the rule if there is only one word in the successor)", }, 41 48 { 0, }, 42 49 }; … … 66 73 if (builder.countSticksInSequence(&builder.genotype) == 0) 67 74 { 68 return GENOPER_OPFAIL;75 return 1; 69 76 } 70 77 double neededtime = 0; 71 78 Model *m = builder.developModel(neededtime); 72 if ( !m)73 { 74 return GENOPER_OPFAIL;79 if (m == NULL) 80 { 81 return 1; 75 82 } 76 83 if (!m->isValid()) 77 84 { 78 85 delete m; 79 return GENOPER_OPFAIL;86 return 1; 80 87 } 81 88 delete m; 82 83 89 84 90 return GENOPER_OK; … … 93 99 if (err != 0) 94 100 { 95 return err;101 return GENOPER_OK; 96 102 } 97 103 double neededtime = 0; 98 104 Model *m = builder.developModel(neededtime); 105 if (m == NULL) 106 { 107 return GENOPER_OK; 108 } 99 109 if (!m->isValid()) 100 110 { 101 111 delete m; 102 return GENOPER_O PFAIL;112 return GENOPER_OK; 103 113 } 104 114 if (neededtime != builder.time) … … 171 181 else 172 182 { 173 int rid = rndUint( creature->rules.size());183 int rid = rndUint((unsigned int)creature->rules.size()); 174 184 list = &creature->rules[rid]->objsucc; 175 185 numparams = creature->rules[rid]->objpred->npar; … … 303 313 if (creature->rules.size() > 0) 304 314 { 305 int ruleid = rndUint( creature->rules.size());315 int ruleid = rndUint((unsigned int)creature->rules.size()); 306 316 if (!creature->rules[ruleid]->condeval) 307 317 { … … 340 350 if (wordswithnorules.size() > 0) 341 351 { 342 int predid = rndUint( wordswithnorules.size());352 int predid = rndUint((unsigned int)wordswithnorules.size()); 343 353 fL_Rule *newrule = new fL_Rule(0, 0); 344 354 fL_Word *pred = new fL_Word(); … … 352 362 else if (creature->rules.size() > 0) 353 363 { 354 int ruleid = rndUint( creature->rules.size());364 int ruleid = rndUint((unsigned int)creature->rules.size()); 355 365 fL_Rule *newrule = new fL_Rule(0, 0); 356 366 fL_Word *pred = new fL_Word(); … … 455 465 else 456 466 { 457 int rndid = rndUint( list->size());467 int rndid = rndUint((unsigned int)list->size()); 458 468 std::list<fL_Word *>::iterator it = list->begin(); 459 469 std::advance(it, rndid); … … 482 492 int tmp = 0; 483 493 std::list<fL_Word *> *list = selectRandomSequence(creature, numpars, tmp); 484 int rndid = rndUint( list->size());494 int rndid = rndUint((unsigned int)list->size()); 485 495 std::list<fL_Word *>::iterator it = list->begin(); 486 496 std::advance(it, rndid); … … 531 541 int tmp = 0; 532 542 std::list<fL_Word *> *list = selectRandomSequence(creature, numpars, tmp); 533 int rndid = rndUint( list->size());543 int rndid = rndUint((unsigned int)list->size()); 534 544 std::list<fL_Word *>::iterator selectedword = list->begin(); 535 545 std::advance(selectedword, rndid); … … 545 555 int numpars = 0; 546 556 std::list<fL_Word *> *list = selectRandomSequence(creature, numpars, tmp); 547 int rndid = rndUint( list->size());557 int rndid = rndUint((unsigned int)list->size()); 548 558 std::list<fL_Word *>::iterator it = list->begin(); 549 559 std::advance(it, rndid); … … 577 587 if (available.size() > 0) 578 588 { 579 int newnameid = rndUint( available.size());589 int newnameid = rndUint((unsigned int)available.size()); 580 590 (*selectedword)->name = available[newnameid]->name; 581 591 } … … 752 762 for (int i = 0; i < numselrules; i++) 753 763 { 754 int rulid = rndUint( from->rules.size());764 int rulid = rndUint((unsigned int)from->rules.size()); 755 765 fL_Rule *rul = from->rules[rulid]; 756 766 fL_Rule *newrule = new fL_Rule(0, 0); … … 894 904 else if (strchr("<>$[]&\\ @|*", ch) != NULL) // other allowed symbols and special neuron symbols 895 905 { 896 style = GENSTYLE_CS(GENCOLOR_TEXT, ch =='[' || ch==']' ? GENSTYLE_BOLD : GENSTYLE_NONE);906 style = GENSTYLE_CS(GENCOLOR_TEXT, ch == '[' || ch == ']' ? GENSTYLE_BOLD : GENSTYLE_NONE); 897 907 } 898 908 return style; -
cpp/frams/genetics/genman.cpp
r1238 r1273 12 12 13 13 14 #define GENMAN_REPEAT_FAILED 100 //how many times GenMan tries to repeat a mutation or crossover when the operator does not return a cceptable genotype14 #define GENMAN_REPEAT_FAILED 100 //how many times GenMan tries to repeat a mutation or crossover when the operator does not return an acceptable genotype 15 15 #define STRINGIFY_1(x) #x 16 16 #define STRINGIFY(x) STRINGIFY_1(x) //this second-level macro allows the parameter to be a macro itself and to stringify its value, not its name … … 575 575 if (g[i] == '<') latex += "$<$"; else if (g[i] == '>') latex += "$>$"; else 576 576 if (g[i] == '-') latex += "$-$"; else if (g[i] == '|') latex += "$|$"; else 577 if (g[i] == '$') latex += "\\$"; else if (g[i] == '%') latex += "\\%"; else latex += g[i]; 577 if (g[i] == '$') latex += "\\$"; else if (g[i] == '%') latex += "\\%"; else 578 if (g[i] == '#') latex += "\\#"; else latex += g[i]; 578 579 if ((i % 3) == 0 && g[i] == ' ') latex += "\n"; //for readability, insert some newlines into latex... 579 580 if (i % 10 == 0) latex += "{\\hskip 0pt}"; // https://tex.stackexchange.com/questions/33526/automatic-line-breaking-of-long-lines-of-text -
cpp/frams/genetics/genooperators.h
r1254 r1273 164 164 virtual const char* getSimplest() { return NULL; } 165 165 166 /**You may want to have your genotype colored. This method provides desired character styles for genes. 166 /**Provides color styles for individual characters of the genotype. For efficiency, 167 this function may be approximate and do not perform the full, proper analysis of the syntax. 167 168 \param geno genotype 168 169 \param pos 0-based char offset 169 \retval number-encoded visual style (and validity) of the genotype char at \a geno[pos]. 170 Assume white background. 170 \retval number-encoded visual style (and validity) of the genotype char at \a geno[pos]. Assume white background. 171 171 \sa GENSTYLE_* macros, like GENSTYLE_BOLD*/ 172 172 virtual uint32_t style(const char *geno, int pos) { return GENSTYLE_RGBS(0, 0, 0, GENSTYLE_NONE); }
Note: See TracChangeset
for help on using the changeset viewer.
