X-Git-Url: https://ruin.nu/git/?a=blobdiff_plain;ds=sidebyside;f=src%2Fmain.cpp;h=2e074957b459fbd72c3b5b2ada38fd7ca96753bb;hb=5202e2201f0d303478cefb4c365306d146189b45;hp=153aff40e608bce08a91bfe77a4980f017379ca8;hpb=f8575ea6c0982bba5ccf42771b2994d19500c0a3;p=germs.git diff --git a/src/main.cpp b/src/main.cpp index 153aff4..2e07495 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -12,20 +12,28 @@ using namespace std; #include "modelidentifier.h" #include "genesorter.h" #include "sortaction.h" +#include "genealgorithms.h" #include "model.h" int main(int argc, char** argv){ string ann = "default.ann"; + Model model(0); + bool detectModel = true; int opt; - while ((opt = getopt(argc, argv, "n:h")) != -1) { + while ((opt = getopt(argc, argv, "m:n:h")) != -1) { switch (opt) { + case 'm': + model = Model::modelFactory(optarg); + detectModel = false; + break; case 'n': ann = optarg; break; case 'h': cout << "Usage: " << argv[0] << " [OPTION] [FILE]" << endl + << endl << " -m Specifies which model to use for sorting: Whirl, X, Zipper, FatX or Cloud " << endl << " -n Specifies which artificial neural network to use for identification. '" << ann << "' is used by default" << endl << " -h Prints this help message" << endl << endl @@ -60,15 +68,30 @@ int main(int argc, char** argv){ //TODO: Identify ModelIdentifier mi(ann); priority_queue > pq = mi.identify(go); + if (detectModel){ + model = pq.top().second; + } while (pq.size() > 0){ cout << "Model: " << pq.top().second.name() << " score: " << pq.top().first << endl; pq.pop(); } + cout << "Using model: " << model.name() << endl; + + cout << "Distance: " << inversionDistance(go) << endl; + //copy(go.begin(), go.end(), ostream_iterator(cout, " ")); + //cout << endl; //TODO: Chose a sorter GeneSorter so; //TODO: Sort - GeneSorter::ActionList al = so.sort(go); + GeneSorter::ActionList al = so.sort(go,model); + + double score = 0; + for (GeneSorter::ActionList::iterator sa = al.begin(); sa != al.end(); ++sa){ + cout << "Action: " << sa->toString() << " model score: " << model.score(*sa,go) << endl; + score += model.score(*sa,go); + } + cout << "Avg score: " << score / al.size() << endl; //TODO: Print result return EXIT_SUCCESS; }