#This algorithm will try to find the shortest path that visits each point
import Graph_ShortestPath as Graph
import Utilities as Utils
import Algorithm as Optimize
#define where the cities are
Cities = [[1, 5], [2, 4], [3, 2], [4, 3], [5, 1], [8, 9], [6, 6], [4, 8]]
#run the simple algorithm
Optimal_Path = Optimize.Alg(Cities, 1000, 1000, 0)
print(Optimal_Path)
#fitness of the solution is 1/total_distance_traveled, closer to zero the better
Fitness = Utils.Fitness(Optimal_Path)
#graph the result
Graph.plotter("X-Axis", "Y-Axis", "Optimal Path", 0, 10, 0, 10, Cities,
Optimal_Path, "red")
print(Utils.Fitness(Optimal_Path))
tokens.append(
TokenClass.Token(authorIds[i], publicationIds[j],
udzial[i], u[i][j], w[i][j]))
disciplinesTokens[shelf] = tokens
file.close()
#############################
# MAIN CODE #
#############################
os.chdir(r'.\Resources')
shelvesNames = []
disciplinesTokens = {}
'''for fileName in os.listdir(os.getcwd()):
if fileName.endswith(".dat"):
shelvesNames.append(fileName[0:-4])
for shelf in shelvesNames:
TokenClass.Token.tokenIndex = 1
fillDiscipline(shelf)'''
dataShelve = shelve.open('sorted')
disciplinesTokens = dataShelve['disciplinesTokens']
dataShelve.close()
for discipline in disciplinesTokens:
print("Result in " + discipline)
for i in range(0, 26):
print(Algorithm.Alg(disciplinesTokens[discipline]))
