/* Now wrap everything up in a loop, and run it.
*/
int main(int argc, const char * argv[]) {
// for grins, let's bump up the repr_size:
repr_size = 100;
population_size = 1000;
population_type p(population_size, repr_type(repr_size, 0));
// each time through this loop is an "update":
for(std::size_t i=0; i<10; ++i) {
// let's first print out the mean & max fitness each time through:
double sum=0.0;
double max_fitness=0.0;
for(population_type::iterator j=p.begin(); j!=p.end(); ++j) {
double f = all_ones(*j);
sum += f;
max_fitness = std::max(max_fitness, f);
}
std::cout << "mean: " << sum/p.size() << " max: " << max_fitness << std::endl;
// and now the EA:
compete(p);
random_selection_with_generic_mutation(p);
}
return 0;
}
int maximalRectangle(vector<vector<char> > &matrix) {
int H = matrix.size(), W = H ? matrix[0].size() : 0;
int max_rect = 0;
for (int i = 0; i < H; ++i) {
vector<char> all_ones(W, '1');
for (int j = i; j < H; ++j) {
all_ones = And(all_ones, matrix[j]);
// Cannot exceed max_rect for this j
if ((j - i + 1) * W <= max_rect) continue;
int max_run = maxRun(all_ones);
// Cannot exceed max_rect for the rest of j for this i
if ((H - i) * max_run < max_rect) break;
max_rect = max(max_rect, (j - i + 1) * max_run);
}
}
return max_rect;
}
