Lin Chi Yu

Dae galera, estou com um problema aqui e queria ver se tem alguem para me ajudar. O programa abaixo está em Ruby e queria saber se alguem consegue traduzir ele para C. Não entendo nada de ruby e por isso precisava dessa ajudinha. def euc_2d(c1, c2) Math.sqrt((c1[0] - c2[0])**2.0 + (c1[1] - c2[1])**2.0).round end def random_permutation(cities) perm = Array.new(cities.size){|i| i} perm.each_index do |i| r = rand(perm.size-i) + i perm[r], perm[i] = perm[i], perm[r] end return perm end def stochastic_two_opt(permutation) perm = Array.new(permutation) c1, c2 = rand(perm.size), rand(perm.size) exclude = [c1] exclude << ((c1==0) ? perm.size-1 : c1-1) exclude << ((c1==perm.size-1) ? 0 : c1+1) c2 = rand(perm.size) while exclude.include?(c2) c1, c2 = c2, c1 if c2 < c1 perm[c1...c2] = perm[c1...c2].reverse return perm end def augmented_cost(permutation, penalties, cities, lambda) distance, augmented = 0, 0 permutation.each_with_index do |c1, i| c2 = (i==permutation.size-1) ? permutation[0] : permutation[i+1] c1, c2 = c2, c1 if c2 < c1 d = euc_2d(cities[c1], cities[c2]) distance += d augmented += d + (lambda * (penalties[c1][c2])) end return [distance, augmented] end def cost(cand, penalties, cities, lambda) cost, acost = augmented_cost(cand[:vector], penalties, cities, lambda) cand[:cost], cand[:aug_cost] = cost, acost end def local_search(current, cities, penalties, max_no_improv, lambda) cost(current, penalties, cities, lambda) count = 0 begin candidate = {:vector=> stochastic_two_opt(current[:vector])} cost(candidate, penalties, cities, lambda) count = (candidate[:aug_cost] < current[:aug_cost]) ? 0 : count+1 current = candidate if candidate[:aug_cost] < current[:aug_cost] end until count >= max_no_improv return current end def calculate_feature_utilities(penal, cities, permutation) utilities = Array.new(permutation.size,0) permutation.each_with_index do |c1, i| c2 = (i==permutation.size-1) ? permutation[0] : permutation[i+1] c1, c2 = c2, c1 if c2 < c1 utilities[i] = euc_2d(cities[c1], cities[c2]) / (1.0 + penal[c1][c2]) end return utilities end def update_penalties!(penalties, cities, permutation, utilities) max = utilities.max() permutation.each_with_index do |c1, i| c2 = (i==permutation.size-1) ? permutation[0] : permutation[i+1] c1, c2 = c2, c1 if c2 < c1 penalties[c1][c2] += 1 if utilities[i] == max end return penalties end def search(max_iterations, cities, max_no_improv, lambda) current = {:vector=>random_permutation(cities)} best = nil penalties = Array.new(cities.size){ Array.new(cities.size, 0) } max_iterations.times do |iter| current=local_search(current, cities, penalties, max_no_improv, lambda) utilities=calculate_feature_utilities(penalties,cities,current[:vector]) update_penalties!(penalties, cities, current[:vector], utilities) best = current if best.nil? or current[:cost] < best[:cost] puts " > iter=#{(iter+1)}, best=#{best[:cost]}, aug=#{best[:aug_cost]}" end return best end if __FILE__ == $0 # problem configuration berlin52 = [[565,575],[25,185],[345,750],[945,685],[845,655], [880,660],[25,230],[525,1000],[580,1175],[650,1130],[1605,620], [1220,580],[1465,200],[1530,5],[845,680],[725,370],[145,665], [415,635],[510,875],[560,365],[300,465],[520,585],[480,415], [835,625],[975,580],[1215,245],[1320,315],[1250,400],[660,180], [410,250],[420,555],[575,665],[1150,1160],[700,580],[685,595], [685,610],[770,610],[795,645],[720,635],[760,650],[475,960], [95,260],[875,920],[700,500],[555,815],[830,485],[1170,65], [830,610],[605,625],[595,360],[1340,725],[1740,245]] # algorithm configuration max_iterations = 150 max_no_improv = 20 alpha = 0.3 local_search_optima = 12000.0 lambda = alpha * (local_search_optima/berlin52.size.to_f) # execute the algorithm best = search(max_iterations, berlin52, max_no_improv, lambda) puts "Done. Best Solution: c=#{best[:cost]}, v=#{best[:vector].inspect}" end Vai ser de grande ajuda. Obrigado.