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50 Motivating Fitness Girls On Instagram To Follow

By Sunny Singh "Akash"
All the inspiration you have to get fit as a fiddle—and remain there. 50 Fitness Girls on Instagram To Follow From ballet performers to weightlifters and big name coaches, these sharp ladies flaunt the best exercise inspiration on Instagram. Track with the expectation of complimentary exercise thoughts, wonderful photography, and inspiration to get dynamic both inside and outside the rec center. Here, our 50 most loved fit young ladies on Instagram. | 1 | Sonia Silva View this post on Instagram Press PLAY ▶️ Treinar não é de todo a prioridade neste momento mas ficar em casa não significa ficar parado e a manutenção da saúde mental é imperativa para que todos nós saibamos cumprir a nossa missão #staythefuckhome. Quanto mais cedo nos isolarmos mais cedo nos abraçaremos. Permaneçam fortes para que consigamos quebrar as cadeias e contrariar todas as estatísticas pela positiva. #covid19 Powered by @helena.santos10, a minha companhia de isolamento. #fitme #fitmept #fitmeever...
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Debug fact failed!

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hello,once I run my program beneath, i recieve this error:Debug assertion failed!Expression: BLOCK_TYPE_IS_VALID(pHead->nBlockUse)this exception happens when getting back from fitness characteristic.

#consist of <map> #include <algorithm> #include <vector> #include <fstream> #consist of<time.h> #include <math.h> the usage of namespace std; #outline generations 10000 #outline popSize 120 // population size #define makeRandNumber ((double)rand()/RAND_MAX) int b=2; const int l= sixteen; struct mychromo mychromo() ~mychromo() //delete genes; delete [] genes; double makeRandNum(int n) srand(time(NULL)); return ((double)rand()/RAND_MAX)*(n-1); inline double makeRandNum() srand(time(NULL)); return ((double)rand()/RAND_MAX); int genes[l]; double healthy; ; bool comval (std::pair<int,int> p1, std::pair<int,int> p2) return p1.second<p2.2d; double health(mychromo gen) // error accurs when coming back from this characteristic!! int fitness=0; bool flag; for (int i=0; i< l; i=i+2) flag=genuine; for (int j=0; j< b; j++) if(gen.genes[i+j]==0) flag= false; if(flag) fitness+=b; return (double)health; void mutate(vector <mychromo> & pop) int mi1,temp; for (int i=0; i< popSize; i++) mi1 = (int)(ceil(makeRandNumber*(l-1))); if(pop[i].genes[mi1]==0) pop[i].genes[mi1]=1; else pop[i].genes[mi1]=0; pop[i].healthy=health(pop[i]); void twoPointCrossOver(mychromo& c1,mychromo& c2) int ind1, ind2,tmp; ind1= makeRandNumber*(l-1); ind2= makeRandNumber*(l-1); whereas (ind1== ind2) ind2= makeRandNumber*(l-1); if(ind1> ind2) tmp=ind2;ind2=ind1;ind1=tmp; mychromo temp; for (int i=ind1; i<ind2+1 ; i++) temp.genes[i]= c1.genes[i]; c1.genes[i]=c2.genes[i]; c2.genes[i]= temp.genes[i]; c1.fit= health(c1); c2.fit= fitness(c2); return; bool compfit(mychromo chromo1,mychromo chromo2)// kind nozooli return chromo1.healthy > chromo2.healthy; int chance(const double likelihood) if(makeRandNumber<probability) return 1; // crossover else return 0; // mutation void make_population_greedy(vector <mychromo> chromoPop) mychromo chromosome; for ( int i = 0; i < popSize; i++ ) for (int j=0;j<l; j++) if(makeRandNumber<0.5) chromosome.genes[j]=0; else chromosome.genes[j]=1; chromosome.fit= health(chromosome); chromoPop.push_back(chromosome); return; void SelectPopulation(vector <mychromo> chromoPop,vector <mychromo>& selectedPop) // calculate agrigate probability double fitSum=0; for (int i=0;i< popSize; i++) fitSum+= chromoPop[i].fit; chromoPop[0].healthy= chromoPop[0].fit/fitSum; for (int i=1;i< popSize; i++) chromoPop[i].fit= chromoPop[i].healthy/fitSum+ chromoPop[i-1].fit; // choice part for (int i=0;i< popSize; i++) double cmpVal= makeRandNumber; if(cmpVal<= chromoPop[0].fit) selectedPop.push_back(chromoPop[0]);proceed; for(int j=1; j< popSize; j++) if(cmpVal> chromoPop[j-1].healthy && cmpVal<= chromoPop[j].fit ) selectedPop.push_back(chromoPop[j]); form(selectedPop.begin(), selectedPop.end(), compfit); void replacePopulation_eliteReplacement(vector <mychromo> &chromoPop,vector <mychromo>& selectedPop) for (int i=0; i<popSize; i++ ) int index= makeRandNumber*(popSize-1); if(selectedPop[i].healthy> chromoPop[index].fit) chromoPop[index].fit= selectedPop[i].fit; for(int j=0; j<l; j++) chromoPop[index].genes[j]=selectedPop[i].genes[j]; return; void main() int n=16; srand(time(NULL)); int idx=0; int count=0; int minx = 0, finx = 0; //father or mother (father & mother) indexes double crp = 0.6; // crossover probability int parentDistance=0; vector <mychromo> chromoPop; vector <mychromo> selectedPop; int bestfit=999; make_population_greedy(chromoPop); for(int p=0;p<generations;p++) kind(chromoPop.begin(), chromoPop.conclusion(), compfit); //alternative SelectPopulation(chromoPop,selectedPop); //CrossOver minx = ceil((popSize-1)* makeRandNumber); finx = ceil((popSize-1)* makeRandNumber); while(finx==minx) finx = ceil((popSize-1)* makeRandNumber); if(makeRandNumber< crp) twoPointCrossOver(selectedPop[minx], selectedPop[minx]); //substitute replacePopulation_eliteReplacement(chromoPop, selectedPop); //Mutation mutate(chromoPop); return ;

I haven't any thought whats incorrect with it!thanks for any advice in boost.

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