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INSERT INTO `pastes` VALUES (4,1,'NyU84w9G','C# WinApi keypress handling','csharp','private void textBoxCmd_KeyPress(object sender, KeyPressEventArgs e)\r\n{\r\n string Set = "";\r\n if (e.KeyChar == \'\\n\' || e.KeyChar == \'\\r\')\r\n {\r\n bool Uspat = false;\r\n string Vstup = textBoxCmd.Text.Trim().ToUpper();\r\n string reg = "(S10[0-5]|S11[0-5]|S12[0-7]|S13[0-7]|S14[0-4]|S15[0-4]|S16[0-1]|S17[0-4]|R1)";\r\n Regex test = new Regex(reg);\r\n if (test.IsMatch(Vstup))\r\n {\r\n if (Vstup.StartsWith("S"))\r\n {\r\n Uspat = true;\r\n Set = Vstup;\r\n }\r\n toolStripStatusLabel1.ForeColor = Color.Black;\r\n toolStripStatusLabel1.Text = "PROVÄ‚ÂDĂŤM";\r\n this.Update(); // prekresli se\r\n }\r\n else\r\n {\r\n toolStripStatusLabel1.ForeColor = Color.Red;\r\n toolStripStatusLabel1.Text = "CHYBA";\r\n return;\r\n }\r\n ttyClient.Instance.SendMessage(Vstup);\r\n string Data = ""; \r\n Data = ttyClient.Instance.ReadMessage();\r\n if (Vstup == "R1" && Data == "=>")\r\n {\r\n Data = ttyClient.Instance.ReadMessage();\r\n }\r\n if (!Uspat)\r\n { \r\n switch (Set)\r\n {\r\n case "S100":\r\n {\r\n Data += "V";\r\n break;\r\n }\r\n default:\r\n {\r\n Data += "xxx";\r\n break;\r\n }\r\n }\r\n lblDisplay.Text = Data;\r\n }\r\n else\r\n {\r\n Thread.Sleep(3000);\r\n }\r\n\r\n toolStripStatusLabel1.Text = "OK";\r\n textBoxCmd.Text = "";\r\n }\r\n}',1418668268,'never','public','1'),(2,1,'MvkCWRAI','Waritko NetSuite test','csharp','using System;\r\nusing System.Collections.Generic;\r\nusing System.ComponentModel;\r\nusing System.Linq;\r\nusing System.Text;\r\n\r\nnamespace question1\r\n{\r\n class Program\r\n {\r\n /// <summary>\r\n /// Removes duplicate entries from sorted array of integers\r\n /// </summary>\r\n /// <param name="values">Input sorted array of integers</param>\r\n /// <returns>Sorted array of integers without duplicate entries</returns>\r\n public int[] removeDuplicates(int[] values)\r\n {\r\n // First, check, if there is valid array and contains at least one member\r\n if (values == null || values.Length < 1) return null;\r\n\r\n int nullVal = values[0] - 1; // set this to be smaller than first member of array\r\n int lastVal = nullVal; // Initialize lastVal with invalid value\r\n int uniqueCount = 0;\r\n\r\n // Simply just go over the array and since it is sorted, just ommit the members, that are equal to previous one\r\n for(int i = 0; i < values.Length; i++)\r\n {\r\n if (values[i] != lastVal)\r\n {\r\n uniqueCount++;\r\n }\r\n else\r\n {\r\n values[i] = nullVal;\r\n }\r\n lastVal = values[i];\r\n }\r\n\r\n // Create output array in needed length\r\n int[] retValue = new int[uniqueCount];\r\n\r\n // Copy the unique ( != nullValue) elements to output array\r\n int outIdx = 0;\r\n for (int i = 0; i < values.Length; i++)\r\n {\r\n if (values[i] != nullVal)\r\n {\r\n retValue[outIdx] = values[i];\r\n outIdx++; // I could write retValue[idx++] ... but this is easier read\r\n }\r\n }\r\n return retValue;\r\n }\r\n // Answers to questions:\r\n // 1) Yes, the code is written as production, if needed to respect the limitations (no dynamic structures such as List<int>)\r\n // 2) No assumptions beyond C# typechecking, if given parameter is either null or empty array, I return back null value\r\n // 3) Number of unique items in array\r\n // 4) I think yes, since the array is already sorted, i can just go over it and compare current element with previous one\r\n // 5) It has linear complexity (O(n)) since I traverse the array fixed number of times (2x)\r\n\r\n\r\n\r\n\r\n /// <summary>\r\n /// Approximates square root of a number with given precision\r\n /// </summary>\r\n /// <param name="a">Number to approximate square root from</param>\r\n /// <param name="epsilon">How precisely to approximate (absolute value of retVal^2-a must be less than epsilon)</param>\r\n /// <returns>Approximated square root of a</returns>\r\n public double square_root(double a, double epsilon)\r\n {\r\n double x = a/3; // Divide number with quite random value to get the approximation started\r\n double lastX;\r\n\r\n // Approximation loop\r\n while (Math.Abs(x*x - a) > epsilon)\r\n {\r\n lastX = x;\r\n x = ((a/x) + lastX) / 2;\r\n }\r\n return x;\r\n }\r\n // Answers to questions:\r\n // 1) \r\n // 2) It will return zero, as abs(0-0) == 0\r\n // 3) 1.4142137800472\r\n }\r\n}\r\n',1418652802,'never','private','4'),(3,1,'EGm2OWCX','Waritko NetSuite test SQL','sql','-- I\'m using MS SQL dialect\r\n--\r\n--\r\n-- A\r\nSELECT\r\n [E].[name],\r\n [S].[name] [supervisor_name],\r\n [B].[nbonus]\r\nFROM\r\n [employees] [E]\r\nLEFT JOIN [employees] [S] ON [E].[supervisor] = [S].[empid]\r\nJOIN [bonus] [B] ON [B].[empid] = [E].[empid]\r\nWHERE\r\n [B].[nbonus] > 10000 --\r\n --\r\n -- B \r\n SELECT\r\n [E].[name],\r\n [E].[salary],\r\n [B].[nbonus],\r\n [TS].[total],\r\n [E].[location]\r\n FROM\r\n (\r\n SELECT\r\n [E].[empid],\r\n MAX (\r\n [E].[salary] + COALESCE ([B].[nbonus], 0)\r\n ) [total]\r\n FROM\r\n [employees] [E]\r\n LEFT JOIN [bonus] [B] ON [E].[empid] = [B].[empid]\r\n GROUP BY\r\n [E].[location],\r\n [E].[empid]\r\n ) [TS]\r\n JOIN [employees] [E] ON [E].[empid] = [TS].[empid]\r\n JOIN [bonus] [B] ON [TS].[empid] = [B].[empid] --\r\n --\r\n -- C\r\n UPDATE [employees]\r\n SET [supervisor] = (\r\n SELECT\r\n [supervisor]\r\n FROM\r\n [new_supervisor] [NS]\r\n WHERE\r\n [NS].[empid] = [employees].[empid]\r\n )\r\n WHERE\r\n [empid] IN (\r\n SELECT\r\n [empid]\r\n FROM\r\n [new_supervisor]\r\n )\r\n',1418652871,'never','private','1'),(5,1,'FQnwZlfU','EVO 1','cpp','/* OneMax.cc: Evolutionary solving of Queens palcement problem.\r\n *\r\n * Author(s): Jiri Jaros (jarosjir@fit.vutbr.cz)\r\n *\r\n */\r\n\r\n#include <stdio.h>\r\n#include <iostream>\r\n#include <string>\r\n#include <vector>\r\n#include <ga/ga.h>\r\n#include <algorithm>\r\n\r\n\r\n\r\nusing namespace std;\r\n\r\n//---------------------- Parameters of evolution ----------------------------//\r\nconst int dimension = 8;\r\n\r\n\r\n\r\n//------------------Fitness function for Queen problem-----------------------//\r\nfloat QueensFitness(GAGenome& g){\r\n //-- Fitness function is based on testing and counting conflicts between --//\r\n //-- paricular queens. In order to permutation encoding is used queens, --//\r\n //-- it is necessaty to test only diagonal directions --//\r\n\r\n GA1DArrayGenome<int> &genome = (GA1DArrayGenome<int> &)g;\r\n int fitness;\r\n \r\n for(int i = 0; i < dimension; i++)\r\n {\r\n for(int j = i+1; j < dimension; j++)\r\n {\r\n int left = genome.gene(i)-(j-i);\r\n int right = genome.gene(i)+(j-i);\r\n if(left >= 0 && right < dimension && (genome.gene(j) == left || genome.gene(j) == right))\r\n fitness++;\r\n }\r\n }\r\n \r\n \r\n \r\n return fitness;\r\n} //- OneMaxFitnessFunction -//\r\n\r\n\r\n//--------------------------- Init function ----------------------------------//\r\nvoid QueensInitializer(GAGenome& g) {\r\n //-- creation of permutation chromozome --//\r\n\r\n vector<int> tmp;\r\n GA1DArrayGenome<int> &genome = (GA1DArrayGenome<int> &) g;\r\n \r\n \r\n for(int i = 0; i < dimension; i++)\r\n {\r\n int num = GARandomInt(0, dimension - 1);\r\n \r\n if(!tmp.empty() && (std::find(tmp.begin(), tmp.end(), num)!=tmp.end()))\r\n {\r\n i--; \r\n continue;\r\n }\r\n tmp.push_back(num);\r\n genome.gene(i, num);\r\n }\r\n \r\n\r\n \r\n} //- Initializer -//\r\n\r\n//--------------------------- Print phenotype function -----------------------//\r\nvoid PrintPhenotype(const GAGenome &g) {\r\n\r\n GA1DArrayGenome<int> &genome = (GA1DArrayGenome<int> &)g;\r\n int i, j, x;\r\n\r\n cout << endl;\r\n for (x = 0; x < (dimension * 4) + 1; x++)\r\n cout << "-";\r\n cout << endl;\r\n\r\n for (j = 0; j < dimension; j++) {\r\n for (i = 0; i < dimension; i++) {\r\n cout << "|";\r\n if (genome.gene(i) == j)\r\n cout << " X ";\r\n else\r\n cout << " ";\r\n }\r\n cout << "|";\r\n cout << endl;\r\n for (x = 0; x < (dimension * 4) + 1; x++)\r\n cout << "-";\r\n cout << endl;\r\n }\r\n\r\n}\r\n\r\n\r\n//--------------------------- main function ----------------------------------//\r\nint main(int argc, char **argv) {\r\n \r\n //-- Declaration of population with genome size and Fitness function --//\r\n GA1DArrayGenome<int> genome(dimension, QueensFitness);\r\n \r\n //-- Genetic operations --//\r\n genome.initializer(::QueensInitializer);\r\n genome.mutator(GA1DArrayGenome<int>::SwapMutator); //- Fill mutator\r\n genome.crossover(GA1DArrayGenome<int>::PartialMatchCrossover); //- Fill Crossover\r\n\r\n \r\n //-- Declaration of genetic algorithm --//\r\n GASimpleGA ga(genome);\r\n \r\n// GATournamentSelector selector;\r\n// ga.selector(selector);\r\n //-- search for global maximum --//\r\n ga.minimize();\r\n \r\n //-- Parameters setting --//\r\n ga.parameters(argc,argv);\r\n \r\n //-- Start of evolution process --//\r\n ga.initialize();\r\n while(!ga.done()){\r\n ++ga; \r\n \r\n if ((ga.statistics().generation() % ga.scoreFrequency()) == 0)\r\n cout <<"Generation " << ga.statistics().generation() << " - Best fitness: " \r\n << ga.population().min() <<endl;\r\n \r\n if (ga.population().min() == 0) break;\r\n }\r\n ga.flushScores();\r\n \r\n \r\n //-- Print statistics --//\r\n cout <<endl << "Final statistics:" << endl;\r\n cout << ga.statistics() << endl;\r\n \r\n cout << "The best solution: " << ga.statistics().bestIndividual() <<" , Fitness = " << ga.population().min() << endl;\r\n PrintPhenotype(ga.statistics().bestIndividual());\r\n \r\n return 0;\r\n} //- main -//',1418668430,'never','public','1'),(6,1,'wyQIMnWZ','EVO 2','cpp','2 /* numericOptimistation.cc: Search for optimum of a numeric function.\r\n *\r\n * Author(s): Jiri Petrlik (ipetrlik@fit.vutbr.cz)\r\n *\r\n */\r\n\r\n#include <stdio.h>\r\n#include <iostream>\r\n#include <string>\r\n#include <vector>\r\n#include <ga/ga.h>\r\n#include <math.h>\r\n\r\n\r\n\r\nusing namespace std;\r\n\r\n//---------------------- Parameters of evolution ----------------------------//\r\nconst int dimension = 5; // dimension of optimised function\r\nconst int resolution=24; // number of bits per value\r\nconst double minValue=-500; // min value\r\nconst double maxValue=500; // max value\r\n\r\n\r\n//------------------Fitness function -----------------------//\r\nfloat fitness(GAGenome& g){\r\n //---------------Type your code here ---------------------//\r\n\r\n GA1DBinaryStringGenome &genome = (GA1DBinaryStringGenome &)g;\r\n\r\n double result=0;\r\n for(int i = 0; i < dimension; i++)\r\n {\r\n int tmp = 0;\r\n for(int j = 0; j < resolution; j++)\r\n {\r\n tmp = tmp << 1;\r\n tmp += genome.gene(i*resolution + j);\r\n }\r\n double dtmp = (double)tmp / (double)((1 << 24) - 1);\r\n dtmp = dtmp * (maxValue - minValue) + minValue;\r\n result += dtmp * sin(sqrt(fabs(dtmp)));\r\n }\r\n result = 418.9829 * dimension - result;\r\n \r\n\r\n return result;\r\n\r\n}\r\n\r\n//--------------------------- Print phenotype function -----------------------//\r\nvoid PrintPhenotype(const GAGenome &g) {\r\n\r\n GA1DBinaryStringGenome &genome = (GA1DBinaryStringGenome &)g; \r\n \r\n for(int i = 0; i < dimension; i++)\r\n {\r\n int tmp = 0;\r\n for(int j = 0; j < resolution; j++)\r\n {\r\n tmp = tmp << 1;\r\n tmp += genome.gene(i*resolution + j);\r\n }\r\n double dtmp = (double)tmp / (double)((1 << 24) - 1);\r\n dtmp = dtmp * (maxValue - minValue) + minValue;\r\n cout << "Gene " << i << ": " << dtmp << endl;\r\n }\r\n\r\n}\r\n\r\n\r\n//--------------------------- main function ----------------------------------//\r\nint main(int argc, char **argv) {\r\n \r\n //-- Declaration of population with genome size and Fitness function --//\r\n GA1DBinaryStringGenome genome(dimension*resolution, fitness);\r\n \r\n //-- Declaration of genetic algorithm --//\r\n GASimpleGA ga(genome);\r\n \r\n //-- search for global minimum --//\r\n ga.minimize();\r\n \r\n ga.nGenerations(1000);\r\n //ga.populationSize(1000);\r\n\r\n //-- Parameters setting --//\r\n ga.parameters(argc,argv);\r\n \r\n //-- Start of evolution process --//\r\n ga.initialize();\r\n while(!ga.done()){\r\n ++ga; \r\n \r\n if ((ga.statistics().generation() % ga.scoreFrequency()) == 0)\r\n cout <<"Generation " << ga.statistics().generation() << " - Best fitness: " \r\n << ga.population().min() <<endl;\r\n \r\n if (ga.population().min() == 0) break;\r\n }\r\n ga.flushScores();\r\n \r\n \r\n //-- Print statistics --//\r\n cout <<endl << "Final statistics:" << endl;\r\n cout << ga.statistics() << endl;\r\n \r\n cout << "The best solution: " << ga.statistics().bestIndividual() <<" , Fitness = " << ga.population().min() << endl;\r\n PrintPhenotype(ga.statistics().bestIndividual());\r\n \r\n return 0;\r\n} //- main -//',1418668469,'never','public','1'),(7,1,'8iKcyvVl','EVO 3','cpp','/* GP.cc: Program tries to approximate simple numerical function - params from command line.\r\n*\r\n* Author(s): Petr Pospichal (ipospichal@fit.vutbr.cz)\r\n*\r\n*/\r\n\r\n#include <stdio.h>\r\n#include <iostream>\r\n#include <string>\r\n#include <ga/ga.h>\r\n#include <math.h>\r\n#include <stack>\r\n#include <vector>\r\n//#include <limits.h>\r\n\r\n\r\n\r\nusing namespace std;\r\n\r\n\r\n#define VARIABLE 0\r\n#define CONSTANT 1\r\n#define UNARY_OPERATOR 2\r\n#define BINARY_OPERATOR 3\r\n\r\n\r\nstruct GPTreeItem_T {\r\n short type;\r\n string name;\r\n float const_value;\r\n float (*unaryfunc)(float);\r\n float (*binaryfunc)(float,float);\r\n};\r\n\r\n#define TREE_ITEM_VARIABLE(name) { VARIABLE, name, 0.0, NULL, NULL }\r\n#define TREE_ITEM_CONSTANT(name,value) { CONSTANT, name, value, NULL, NULL }\r\n#define TREE_ITEM_UNARY_OPERATOR(name,ptr) { UNARY_OPERATOR, name, 0.0, ptr, NULL }\r\n#define TREE_ITEM_BINARY_OPERATOR(name,ptr) { BINARY_OPERATOR, name, 0.0, NULL, ptr }\r\n\r\nfloat Plus(float a, float b) { return a+b; }\r\nfloat Minus(float a, float b) { return a-b; }\r\nfloat Multiplication(float a, float b) { return a*b; }\r\nfloat Division(float a, float b) { return a/b; }\r\n\r\n\r\nconst GPTreeItem_T TreeItems[] = {\r\n TREE_ITEM_VARIABLE("x"),\r\n TREE_ITEM_CONSTANT("-1.0",-1.0),\r\n TREE_ITEM_CONSTANT("1.0",1.0),\r\n TREE_ITEM_BINARY_OPERATOR("+",Plus),\r\n TREE_ITEM_BINARY_OPERATOR("-",Minus),\r\n TREE_ITEM_BINARY_OPERATOR("*",Multiplication),\r\n TREE_ITEM_BINARY_OPERATOR("/",Division)\r\n};\r\n\r\nconst unsigned int TreeItemsCount = (sizeof TreeItems)/(sizeof TreeItems[0]);\r\n\r\n\r\n#define CROSSOVER_MOD 0.8\r\n#define MUTATION_MOD 1.2\r\n#define GENERATIONS_MOD 100\r\n#define CHROM_LEN_PENALIZATION 2\r\n//#define CHROM_LEN_PENALIZATION 0.1\r\n//#define CHROM_LEN_PENALIZATION 10\r\n\r\n#define NO_SCALING\r\n//#define SCALING_LINEAR\r\n//#define SCALING_SIGMA\r\n\r\n//#define GOAL_FUNC1\r\n//#define GOAL_FUNC2\r\n//#define GOAL_FUNC3\r\n#define GOAL_FUNC4\r\n\r\n\r\n#define DEBUG_POPULATION\r\n\r\nconst int MAX_DEPTH = 3; // max random tree depth param\r\n\r\n\r\n#ifdef GOAL_FUNC1\r\n// x\r\n #define GOAL_STRING "x"\r\nconst float GoalFunc[][2] = {\r\n {0.0 , 0.0},\r\n {1.0 , 1.0},\r\n {2.0 , 2.0},\r\n {3.0 , 3.0},\r\n {4.0 , 4.0}\r\n};\r\n#endif\r\n\r\n#ifdef GOAL_FUNC2\r\n// x^2+1\r\n #define GOAL_STRING "x^2 + 1"\r\nconst float GoalFunc[][2] = {\r\n {0.0 , 1.0},\r\n {1.0 , 2.0},\r\n {2.0 , 5.0},\r\n {3.0 , 10.0},\r\n {4.0 , 17.0}\r\n};\r\n#endif\r\n\r\n#ifdef GOAL_FUNC3\r\n// x^2+x+1\r\n #define GOAL_STRING "x^2 + x + 1"\r\nconst float GoalFunc[][2] = {\r\n {0.0 , 1.0},\r\n {1.0 , 3.0},\r\n {2.0 , 7.0},\r\n {3.0 , 13.0},\r\n {4.0 , 21.0}\r\n};\r\n#endif\r\n\r\n#ifdef GOAL_FUNC4\r\n// TODO find function for these points\r\n #define GOAL_STRING "to be found"\r\nconst float GoalFunc[][2] = {\r\n {0.0 , 1.0},\r\n {1.0 , 5.0},\r\n {2.0 , 31.0},\r\n {3.0 , 121.0},\r\n {4.0 , 341.0}\r\n};\r\n#endif\r\n\r\n\r\n\r\nclass GaMyTreeIter : public GATreeIterBASE {\r\npublic:\r\n GaMyTreeIter(const GATreeBASE & t) : GATreeIterBASE(t) {} // just call parent constructor\r\n\r\n GANodeBASE* getRootNode()\r\n { return this->node; } // this allows us to get to root node pointer so we can traverse tree easily\r\n \r\n};\r\n\r\n\r\n\r\n// The objective function is declared here and defined below.\r\nfloat objective(GAGenome &);\r\n\r\n// This is the declaration for the initialization operator for our trees.\r\nvoid TreeInitializer(GAGenome &);\r\n\r\n// This is a recursive function that will be used in the \'write\' method for \r\n// our tree genomes.\r\nvoid WriteNode(ostream & os, GANode<int> * n);\r\n\r\nvoid PreorderWalk( GANode<int> * n, void function(GANodeBASE * node), void* params );\r\nvoid PostoderWalk( GANode<int> * n, void function(GANodeBASE * node, void* params), void* params );\r\n\r\nstring GetNodeName(int n) { return TreeItems[n].name; }\r\n\r\nint GetTreeItemType(int n) { return TreeItems[n].type; }\r\n\r\nvoid PrintNodeType(GANodeBASE * node, void* params) { cout << GetNodeName(((GANode<int> *)node)->contents) << " "; }\r\n\r\n\r\nint\r\nmain(int argc, char *argv[])\r\n{\r\n \r\n //cout << GoalFunc[1][1] << endl;\r\n //cout << TreeItems[0].name << endl;\r\n cout.flush();\r\n \r\n // See if we\'ve been given a seed to use (for testing purposes). When you\r\n // specify a random seed, the evolution will be exactly the same each time\r\n // you use that seed number.\r\n \r\n unsigned int seed = 0;\r\n for(int i=1; i<argc; i++) {\r\n if(strcmp(argv[i++],"seed") == 0) {\r\n //seed = atoi(argv[i]);\r\n GARandomSeed(atoi(argv[i]));\r\n }\r\n }\r\n \r\n // Set the default values of the parameters.\r\n\r\n const int popsize=10; // this param is used twice\r\n\r\n GAParameterList params;\r\n GASimpleGA::registerDefaultParameters(params);\r\n params.set(gaNpopulationSize, popsize);\r\n params.set(gaNpCrossover, CROSSOVER_MOD);\r\n params.set(gaNpMutation, MUTATION_MOD);\r\n params.set(gaNnGenerations, GENERATIONS_MOD);\r\n params.set(gaNelitism,true);\r\n //params.set(gaNnBestGenomes,10);\r\n //params.set(gaNminimaxi,true);\r\n //params.set(gaNminimaxi,true);\r\n //params.set(gaNscoreFilename, "bog.dat");\r\n //params.set(gaNscoreFrequency, 1); // record score every 10th generation\r\n //params.set(gaNflushFrequency, 10); // dump scores every 10th recorded score\r\n //params.parse(argc, argv, gaFalse); // Parse the command line for GAlib args.\r\n\r\n GATreeGenome<int> genome(objective),genome2(objective);\r\n genome.initializer(TreeInitializer);\r\n genome.mutator(GATreeGenome<int>::SwapSubtreeMutator);\r\n genome.crossover(GATreeGenome<int>::OnePointCrossover); \r\n \r\n GASimpleGA ga(genome); \r\n ga.parameters(params);\r\n #ifdef NO_SCALING\r\n GANoScaling scale;\r\n #endif\r\n #ifdef SCALING_LINEAR\r\n GALinearScaling scale;\r\n #endif\r\n #ifdef SCALING_SIGMA\r\n GASigmaTruncationScaling scale;\r\n #endif\r\n\r\n ga.scaling(scale);\r\n ga.minimize();\r\n ga.initialize();\r\n int gen=0;\r\n while(!ga.done())\r\n {\r\n gen++;\r\n #ifdef DEBUG_POPULATION\r\n //cout << "============== generation " << gen << endl << "--- new objective evaluations" << endl;\r\n #endif\r\n ga.step(); // do one generation of the evolution\r\n #ifdef DEBUG_POPULATION\r\n //cout << "--- current population" << endl;\r\n for (int i=0; i<popsize;i++)\r\n {\r\n //cout << i << " (" << ga.population().individual(i).fitness() << "): " << ga.population().individual(i);\r\n }\r\n\r\n genome2=ga.statistics().bestIndividual();\r\n //cout << "--- best" << endl;\r\n float objvalue = ::objective(genome2);\r\n //cout << " (" << objvalue << ", " << genome2.fitness() << "):" << ga.statistics().bestIndividual() << endl;\r\n #endif\r\n }\r\n\r\n genome2 = ga.statistics().bestIndividual();\r\n cout << "=================" << endl << "The GA generated this tree (sould be " << GOAL_STRING << ")\\n" << genome2;\r\n cout << "objective: " << genome2.score() << " fitness:" << genome2.fitness() << ", " << genome2.size() << " nodes, " << genome2.depth() << " levels deep.\\n\\n";\r\n cout << "==============" << endl;\r\n cout << genome2.score() - CHROM_LEN_PENALIZATION * genome2.size() << endl;\r\n cout << "==============" << endl;\r\n\r\n //cout << ga.statistics() << endl;\r\n \r\n \r\n return 0;\r\n}\r\n\r\n/* ----------------------------------------------------------------------------\r\n\r\n This function is calculating intermetiate results of the tree interpretation. It uses stack to store operands, inputs and outputs. \r\n\r\n*/\r\n\r\nvoid calculateResult(GANodeBASE * node, void* params)\r\n{\r\n //cout << "calculateResult " << endl;\r\n vector<float>* stack = (std::vector<float>*)params; // stack is passed using void*\r\n float x=stack->at(0); // value of x is stored on the first position\r\n\r\n int treeitemindex=((GANode<int> *)node)->contents; // get value of tree item (node), use it to index constant array of tree elements\r\n int type=GetTreeItemType(treeitemindex); // get note type\r\n float op1,op2,result=0.0;\r\n switch(type)\r\n {\r\n // do operation depending on note type\r\n case BINARY_OPERATOR: //cout << "binary" << endl; \r\n op1=stack->back();stack->pop_back(); // binary operator -> use two operands from stack\r\n op2=stack->back();stack->pop_back();\r\n result=TreeItems[treeitemindex].binaryfunc(op1,op2); // ... calculate result\r\n stack->push_back(result); // and put it back on the stack\r\n break;\r\n case UNARY_OPERATOR: //cout << "unary" << endl; \r\n op1=stack->back();stack->pop_back();\r\n result=TreeItems[treeitemindex].unaryfunc(op1);\r\n stack->push_back(result);\r\n break;\r\n case CONSTANT: //cout << "constant" << endl; \r\n stack->push_back(TreeItems[treeitemindex].const_value); // push constant to stack\r\n break;\r\n case VARIABLE: //cout << "variable" << endl; \r\n stack->push_back(x); // push x value to stack\r\n break;\r\n default:\r\n cout<< "error no operator defined" << endl; exit(0);\r\n break; \r\n }\r\n\r\n}\r\n\r\n\r\n\r\n/*----------------------------------------------------------------------------\r\n\r\n Objective function of getting "fitness" of tree chromosome. Compares values of GoalFunc on defined x points with interpreted tree chromosome. Fitness is defined as sum of all differences between goal function and interpreted tree plus possible penalization for longer chromosomes. \r\n\r\n*/\r\n\r\nfloat objective(GAGenome & t){\r\n GATreeGenome<int> & tree= (GATreeGenome<int> &)t;\r\n\r\n vector<float> stack; // stack for storing elements and intermediate results as we walk the tree\r\n\r\n GaMyTreeIter *iterator = new GaMyTreeIter(tree); \r\n GANodeBASE * nod = iterator->getRootNode(); // root node pointer used as traversal start point\r\n \r\n\r\n int GoalFuncPoints = sizeof(GoalFunc)/sizeof(GoalFunc[0]);\r\n float objective=0.0;\r\n \r\n // for each point of the goal function, interpret the tree\r\n // could be optimized - how?\r\n for(int point=0;point<GoalFuncPoints;point++)\r\n {\r\n stack.clear(); \r\n float x=GoalFunc[point][0];\r\n float reference=GoalFunc[point][1];\r\n float result=0.0;\r\n\r\n stack.push_back(x); // use stack for sending x-value to calculation function\r\n //cout << "point " << point << " x " << reference << " " << endl;\r\n\r\n // call tree traversal with calculateResult func, which interpretes the tree for defined x value\r\n PostoderWalk((GANode<int> *)nod, calculateResult, static_cast<void*>(& stack) );\r\n\r\n // get result value from stack after interpretation\r\n result=stack.back();\r\n if(stack.size()!=2) // stack should contain only x-value and result, otherwise traversal was incorrect (possibly damaged or not initialized tree)\r\n {\r\n cout << " error interpreting expression, incorrect derivation tree " << endl; exit(1);\r\n }\r\n\r\n //cout << " size: " << stack.size() << endl;\r\n //cout << " reference: " << reference << " result:" << result << endl;\r\n objective+=fabs(reference-result); // get sum of difference beween goal func and interpreted result\r\n }\r\n\r\n if(isnan(objective) || isinf(objective)) objective=1e5; // get rid of NaN and Inf results (division by zero)\r\n objective+=tree.size()*CHROM_LEN_PENALIZATION; // possibly penalize longer chromosomes\r\n\r\n #ifdef DEBUG_POPULATION \r\n cout << " (" << objective << "): " << tree;\r\n #endif \r\n\r\n return objective; \r\n}\r\n\r\n\r\n\r\n\r\n// Initializer for tree chromosomes to maximum depth of MAX_DEPTH constant. Ensures that trees are traversable, eg. binary operator has 2 children, unary only one etc.\r\nvoid \r\nMakeRandomTreeNode(GATreeGenome<int> &tree, int depth, bool after)\r\n{\r\n int randomitem=GARandomInt(0,TreeItemsCount-1);\r\n //GPTreeItem_T randomitem=TreeItems[randomitemnum];\r\n short randomitemtype=TreeItems[randomitem].type;\r\n \r\n // if we reached maximum depth, create only terminals\r\n if(depth>=MAX_DEPTH)\r\n {\r\n // not entirely optimal :)\r\n while(randomitemtype!=CONSTANT && randomitemtype!=VARIABLE)\r\n {\r\n randomitem=GARandomInt(0,TreeItemsCount-1);\r\n //randomitem=TreeItems[randomitemnum];\r\n randomitemtype=TreeItems[randomitem].type;\r\n }\r\n // cout << " depth, below " << GetNodeName(randomitem) << endl;\r\n tree.insert(randomitem,GATreeBASE::BELOW);\r\n return;\r\n }\r\n \r\n // insert side-by-side or below current node?\r\n if(after)\r\n {\r\n tree.insert(randomitem,GATreeBASE::AFTER);\r\n } else {\r\n tree.insert(randomitem,GATreeBASE::BELOW);\r\n }\r\n \r\n // constants and variables do not have children\r\n if(randomitemtype==CONSTANT || randomitemtype==VARIABLE)\r\n { \r\n return;\r\n } else if(randomitemtype==UNARY_OPERATOR)\r\n { // unary operator has one children\r\n MakeRandomTreeNode(tree, depth+1, false);\r\n } else if(randomitemtype==BINARY_OPERATOR)\r\n { // binary has two children\r\n //cout << " binary operator " << endl;\r\n MakeRandomTreeNode(tree, depth+1, false);\r\n tree.parent(); // move the iterator up one level\r\n MakeRandomTreeNode(tree, depth+1, false);\r\n } else {\r\n cout << "MakeRandomTreeNode error, unknown operator number :" << randomitemtype;\r\n }\r\n \r\n tree.parent(); // up one level\r\n}\r\n\r\n\r\n\r\n\r\n/* ----------------------------------------------------------------------------\r\nHere is the initializer for our genomes. It builds a tree of n items of type\r\nint. Notice that we first destroy any tree that is already in the genome \r\nbefore we do our initialization. This is so that the genomes can be re-used.\r\nWhen you re-run a GA, it does not destroy the individuals in the population - \r\nit reuses them. Thus, the initializer must make sure that the genome is \r\ncleaned up before it tries to initialize it.\r\n---------------------------------------------------------------------------- */\r\nvoid\r\nTreeInitializer(GAGenome & c)\r\n{\r\n GATreeGenome<int> &child=(GATreeGenome<int> &)c;\r\n \r\n // destroy any pre-existing tree\r\n child.root();\r\n child.destroy();\r\n\r\n // make random tree\r\n MakeRandomTreeNode(child,0,false);\r\n}\r\n\r\n\r\n// just for inspiration\r\nvoid \r\nPreorderWalk( GANode<int> * n, void function(GANodeBASE * node, void* params), void* params )\r\n{\r\n if(!n) return;\r\n GANodeBASE * node = (GANodeBASE *)n;\r\n \r\n //cout << GetNodeName(((GANode<GPTreeItem_T> *)node)->contents) << " ";\r\n //cout << GetNodeName(((GANode<int> *)node)->contents) << " aaa" << endl;\r\n function(node,params);\r\n \r\n PreorderWalk((GANode<int> *)node->child, function, params);\r\n \r\n for(GANodeBASE * tmp=node->next; tmp && tmp != node; tmp=tmp->next){\r\n //cout << GetNodeName(((GANode<GPTreeItem_T> *)tmp)->contents) << " ";\r\n function(tmp,params);\r\n PreorderWalk((GANode<int> *)tmp->child, function, params);\r\n }\r\n}\r\n\r\n\r\n// Function for walking the tree genome. Results in calling "function" in reverse-polish-notation order on nodes\r\nvoid \r\nPostoderWalk( GANode<int> * n, void function(GANodeBASE * node, void* params), void* params )\r\n{\r\n\r\n if(!n) return;\r\n GANodeBASE * node = (GANodeBASE *)n;\r\n //cout << "PostorderWalk: " << n << endl; \r\n //cout << GetNodeName(((GANode<int> *)node)->contents) << " aaa" << endl;\r\n \r\n PostoderWalk((GANode<int> *)node->child, function, params);\r\n\r\n for(GANodeBASE * tmp=node->next; tmp && tmp != node; tmp=tmp->next){\r\n //cout << GetNodeName(((GANode<GPTreeItem_T> *)tmp)->contents) << " ";\r\n PostoderWalk((GANode<int> *)tmp->child, function, params);\r\n function(tmp,params);\r\n }\r\n\r\n function(node,params); \r\n \r\n}\r\n\r\n\r\n\r\n/* ----------------------------------------------------------------------------\r\nTree genome method overrides\r\n-------------------------------------------------------------------------------\r\nHere we override the built-in methods for the tree class. We can do this\r\nbecause the tree class is template-ized - when the compiler looks for an\r\ninstance of the tree class methods, it finds these so it won\'t generate an\r\ninstance from the templates. You can do this with ANY method of a template\r\nclass. Here we do it only for the write method.\r\nThe default write operator prints out po //TreeItemsCount\r\ninters to the contents of each node.\r\nHere we print out the actual contents of each node. This assumes that the \r\nobject in our node has the operator<< defined for it.\r\n---------------------------------------------------------------------------- */\r\nvoid \r\nWriteNode(ostream & os, GANode<int> * n)\r\n{\r\n if(!n) return;\r\n GANodeBASE * node = (GANodeBASE *)n;\r\n os.width(10);\r\n os << (((GANode<int> *)node)) << " ";\r\n os.width(10);\r\n os << GetNodeName(((GANode<int> *)node)->contents) << " ";\r\n //os << GetNodeName((*(GANode<int> *)node)) << " ";\r\n os.width(10); \r\n if(node->parent) os << ((GANode<int> *)node->parent) << " ";\r\n else os << "_" << " ";\r\n os.width(10); \r\n if(node->child) os << ((GANode<int> *)node->child) << " ";\r\n else os << "_" << " ";\r\n os.width(10);\r\n if(node->next) os << (((GANode<int> *)node->next)) << " ";\r\n else os << "_" << " ";\r\n os.width(10);\r\n if(node->prev) os << (((GANode<int> *)node->prev)) << "\\n";\r\n else os << ".\\n";\r\n WriteNode(os, (GANode<int> *)node->child);\r\n \r\n for(GANodeBASE * tmp=node->next; tmp && tmp != node; tmp=tmp->next){\r\n os.width(10);\r\n os << (((GANode<int> *)tmp)) << " ";\r\n os.width(10);\r\n os << GetNodeName(((GANode<int> *)tmp)->contents) << " ";\r\n //os << GetNodeName(((GANode<int> *)tmp)) << " ";\r\n os.width(10);\r\n if(tmp->parent) os << (((GANode<int> *)tmp->parent)) << " ";\r\n else os << "_" << " ";\r\n os.width(10); \r\n if(tmp->child) os << (((GANode<int> *)tmp->child)) << " ";\r\n else os << "_" << " ";\r\n os.width(10); \r\n if(tmp->next) os << (((GANode<int> *)tmp->next)) << " ";\r\n else os << "_" << " ";\r\n os.width(10); \r\n if(tmp->prev) os << (((GANode<int> *)tmp->prev)) << "\\n";\r\n else os << ".\\n";\r\n WriteNode(os, (GANode<int> *)tmp->child);\r\n }\r\n}\r\n\r\n\r\n// function for printing out the phenotype to screen\r\ntemplate<> int\r\nGATreeGenome<int>::write(ostream & os) const\r\n{\r\n //os << " ptr type parent child next prev\\n";\r\n //WriteNode(os, (GANode<int> *)rt);\r\n //PreorderWalk((GANode<int> *)rt, PrintNodeType, 0);\r\n PostoderWalk((GANode<int> *)rt, PrintNodeType, 0);\r\n cout << endl;\r\n return os.fail() ? 1 : 0;\r\n}\r\n\r\n\r\n\r\n// force instantiations for compilers that do not do auto instantiation\r\n// for some compilers (e.g. metrowerks) this must come after any\r\n// specializations or you will get \'multiply-defined errors when you compile.\r\n#if !defined(GALIB_USE_AUTO_INST)\r\n#include <ga/GATree.C>\r\n#include <ga/GATreeGenome.C>\r\nGALIB_INSTANTIATION_PREFIX GATreeGenome<int>;\r\nGALIB_INSTANTIATION_PREFIX GATree<int>;\r\n#endif',1418668544,'never','public','1'),(8,1,'XAPC2WfS','Untitled','javascript','{\r\n {\r\n "@timestamp": "2014-12-16T02:49:04-0800",\r\n "@version": "1",\r\n "bytes": 121,\r\n "clientip": "192.168.253.127",\r\n "cookie": "-",\r\n "set-cookie": "-",\r\n "duration": 18794935,\r\n "firstlinereq": "POST /app/rdc/rdcservice.nl?NS_VER=2015.1.0 HTTP/1.1",\r\n "message": "192.168.253.127 - - [16/Dec/2014:02:49:04 -0800] \\"POST /app/rdc/rdcservice.nl?NS_VER=2015.1.0 HTTP/1.1\\" 200 121",\r\n "method": "POST",\r\n "referer": "-",\r\n "remotehost": "192.168.253.127",\r\n "request": "/DEV/201510/app/rdc/rdcservice.nl?NS_VER=2015.1.0",\r\n "requestreceived": "[16/Dec/2014:02:49:04 -0800]",\r\n "ssl_protocol": "-",\r\n "ssl_cipher": "-",\r\n "status": 200,\r\n "tags": ["apache"],\r\n "urlpath": "/DEV/201510/app/rdc/rdcservice.nl",\r\n "urlquery": "?NS_VER=2015.1.0",\r\n "useragent": "Jakarta Commons-HttpClient/3.0.1"\r\n }, {\r\n "@timestamp": "2014-12-16T02:51:07-0800",\r\n "@version": "1",\r\n "bytes": 5289,\r\n "clientip": "127.0.0.1",\r\n "cookie": "-",\r\n "set-cookie": "JSESSIONID=BVWxJQTQx6hJBRmJgvpvYM25hM8JcLbNQcyXsfWRR4zydwv2RfGZ12T1nLv9Jn0yHXG19xQHN0LRJpt2P3LvwTywL7SmJ8wtdQ8VvjWnpxv1QN1JZlJ8zLPLn9T9H9F0!829942195; 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