signals = ['zA', 'zB', 'zC', 'zD', 'zE', 'zF', 'zG']
cost1 = {'zA': 12, 'zB': 26, 'zC': 27, 'zD': 30, 'zE': 37, 'zF': 39, 'zG': 41}
cost2 = {'zA': 19, 'zB': 20, 'zC': 21, 'zD': 23, 'zE': 36, 'zF': 37, 'zG': 38}
cost3 = {'zA': 16, 'zB': 17, 'zC': 31, 'zD': 33, 'zE': 36, 'zF': 56, 'zG': 57}
cost4 = {'zA': 3, 'zB': 34, 'zC': 36, 'zD': 39, 'zE': 41, 'zF': 43, 'zG': 58}
costlist = [cost1, cost2, cost3, cost4]
begintemp = {12: 8, 19: 4, 16: 8, 3: 14}
citer = 1
endtemp = [0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2]
with open("endtempstats.txt", 'a') as text:
for c in costlist:
for e in endtemp:
m = 0
print('end: ' + str(e))
text.write(str(citer) + ',')
text.write(str(begintemp[c['zA']]) + ',')
text.write(str(e) + ',')
for y in range(20):
map = readMap.read_complete_map('Russiadfs.txt')
map = sim_an_search.sim_an(map, c, signals, 200000,
begintemp[c['zA']], e)
freq = analyse.analyse_signal_frequentie(map)
costs = analyse.get_cost(freq, c)
text.write(str(costs) + ', ')
m += costs
print('mean: ' + str(m / 20))
text.write('\n')
citer += 1
import analyse
import readMap
import sim_an_search
signals = ['zA', 'zB', 'zC', 'zD', 'zE', 'zF', 'zG']
cost1 = {'zA':12, 'zB':26, 'zC':27, 'zD':30, 'zE':37, 'zF':39, 'zG':41}
cost2 = {'zA':19, 'zB':20, 'zC':21, 'zD':23, 'zE':36, 'zF':37, 'zG':38}
cost3 = {'zA':16, 'zB':17, 'zC':31, 'zD':33, 'zE':36, 'zF':56, 'zG':57}
cost4 = {'zA':3, 'zB':34, 'zC':36, 'zD':39, 'zE':41, 'zF':43, 'zG':58}
costlist = [cost1, cost2, cost3, cost4]
begintemp = [9, 5, 9, 15]
maplist = [readMap.read_complete_map('Ukrainedfs.txt'), readMap.read_complete_map('UnitedStatesdfs.txt'), readMap.read_complete_map('Russiadfs.txt')]
with open('temptation.txt', 'a') as text:
for m in range(3):
for c in range(4):
lowest_cost = 2000
for y in range(20):
map = maplist[m]
sim_an_search.sim_an(map, costlist[c], signals, begin_temp=begintemp[c], end_temp=0.5)
freq = analyse.analyse_signal_frequentie(map)
costs = analyse.get_cost(freq, costlist[c])
if costs < lowest_cost:
lowest_cost = costs
print(m, c + 1, y, lowest_cost)
text.write(str(m) + str(c + 1) + ': ' + str(lowest_cost))
text.write('\n')
import readMap
import dfs
import hill_solve
import analyse
map = readMap.read_complete_map('Russiadfs5.csv')
signal_costs = analyse.get_cost_scheme(1)
analyse.print_costs(map, signal_costs)
worst_costs = 0
#for l in range(loops):
#map = readMap.readStates("UnitedStates.txt")
#print(analyse.analyse_adjacent_states(map))
signals = ['zA', 'zB', 'zC', 'zD', 'zE', 'zF', 'zG']
costs = {'zA': 19, 'zB': 20, 'zC': 21, 'zD': 23, 'zE': 36, 'zF': 37, 'zG': 38}
algos = {
sim_an_search.sigmoidal_temperature: [],
sim_an_search.sigmoidal_temperature_2: [],
sim_an_search.sigmoidal_temperature_3: [],
sim_an_search.double_sigmoidal_temperature: [],
sim_an_search.sinus_double_sigmoidal_temperature: [],
sim_an_search.sinus_temperature: [],
sim_an_search.linear_temperature: [],
sim_an_search.sinus_linear_temperature: []
}
with open("sinsigmoid.txt", 'w') as text:
text.write('sinus_sigmoidal_temperature')
text.write(',')
for x in range(loops):
map = readMap.read_complete_map("UnitedStatesdfs.txt")
sim_an_search.stats_climber(map, costs, signals, 200000, 5, 0.01,
sim_an_search.sinus_sigmoidal_temperature)
freq = analyse.signal_frequentie(map)
hill_costs = analyse.get_cost(freq, costs)
text.write(str(hill_costs))
text.write(',')
print(str(x))
text.write('\n')