def run_tsp():
response_object = {'status': 'success'}
if request.method == 'GET':
response_object['problems'] = TSP_PROBLEMS
return response_object
else:
post_data = request.get_json()
variables, problem = post_data['variables'], post_data['problem']
towns, optimum = problem['points'], problem['optimum']
tsp_problem = tsp.Travelling_Salesman(towns, optimum)
if util.get_att("Algorithm", variables) == "genetic":
df, _, gen = genetic_algo(
tsp_problem,
generation_ceiling=util.get_att("Generation ceiling",
variables),
population_size=util.get_att("Population size", variables),
mutation_rate=util.get_att("Mutation rate", variables),
select=util.get_att("Selection type", variables),
elitism=util.get_att("Elitism", variables),
out_path=False)
else:
df, _, gen = firefly_algo(
tsp_problem,
iteration_ceiling=util.get_att("Iteration ceiling", variables),
population_size=util.get_att("Population size", variables),
alpha=util.get_att("Alpha", variables),
beta=util.get_att("Beta", variables),
gamma=util.get_att("Gamma", variables),
out_path=False)
best_solution = util.get_min_overall(df)
response_object['labels'] = [i + 1 for i in range(gen)]
response_object['datasets'] = [{
'data': util.get_max(df)
}, {
'data': util.get_mean(df)
}, {
'data': util.get_min(df)
}]
route = best_solution[1:-1]
normalized_towns = util.normalize_coordinates(towns, (0, 10))
response_object['route'] = [{
'x': normalized_towns[int(i) - 1][0],
'y': normalized_towns[int(i) - 1][1]
} for i in route] + [{
'x': normalized_towns[0][0],
'y': normalized_towns[0][1]
}]
response_object['best_solution'] = best_solution[-1]
return jsonify(response_object)
def find_similar(sent, lst):
dist = {}
lst = lst + split_and_add(lst)
for item in lst:
item = util.normalize(item)
sent = util.normalize(sent)
d = jarow(item, sent)
#print item , d
if d > 0.75:
dist[item] = d
#print dist
max_arr = util.get_max(dist)
return max_arr
def correct_right(lc, rc, index, llc=None):
lc = util.normalize(rc)
dist = {}
try:
right_ctx = index[rc]["right"]
for item in right_ctx:
d = jarow(item, rc)
#print item , (d * 1.0)/len(item)
if d > 0.75:
dist[item] = d
max_arr = util.get_max(dist)
#print dist
return max_arr
except:
return None
def run_sat():
response_object = {'status': 'success'}
if request.method == 'GET':
response_object['problems'] = SAT_PROBLEMS
return response_object
else:
post_data = request.get_json()
minimize = False
variables, problem = post_data['variables'], post_data['problem']
formula = util.get_sat_problem(problem)
sat_problem = sat.Satisfiability(formula)
df, data, gen = genetic_algo(
sat_problem,
generation_ceiling=util.get_att("Generation ceiling", variables),
population_size=util.get_att("Population size", variables),
mutation_rate=util.get_att("Mutation rate", variables),
select=util.get_att("Selection type", variables),
elitism=util.get_att("Elitism", variables),
out_path=False)
min_value = util.get_min_overall(df)[-1]
max_value = util.get_max_overall(df)[-1]
best_solution = min_value if minimize else max_value
response_object['labels'] = [i + 1 for i in range(gen)]
response_object['datasets'] = [{
'data': util.get_max(df)
}, {
'data': util.get_mean(df)
}, {
'data': util.get_min(df)
}]
size = util.get_att("Population size", variables)
# fitness distribution for each generation
response_object['distribution'] = []
response_object['dist_labels'] = ["" for _ in range(50)]
for i in range(gen):
chunk = data[i * size:i * size + size]
distribution = util.get_distribution(chunk, min_value, max_value)
response_object['distribution'].append(distribution)
response_object['best_solution'] = int(best_solution)
response_object['clauses'] = formula.clauses
response_object['satisfiable'] = True if int(
best_solution) >= sat_problem.clause_count else False
return jsonify(response_object)
def correct_left(rc, lc, index, rrc=None):
rc = util.normalize(rc)
dist = {}
try:
left_ctx = index[rc]["left"]
for item in left_ctx:
d = jarow(item, lc)
#print item , (d * 1.0)/len(item)
#print d
if d > 0.75:
dist[item] = d
max_arr = util.get_max(dist)
#print dist
return max_arr
except:
return None
def run_fe():
response_object = {'status': 'success'}
if request.method == 'GET':
response_object['problems'] = FE_PROBLEMS
return response_object
else:
post_data = request.get_json()
variables, problem = post_data['variables'], post_data['problem']
minimize = util_fe.get_problem_att(problem, "minimize")
fe_problem = fe.Function_Extreme(problem.lower(), 2, minimize=minimize)
if util.get_att("Algorithm", variables) == "genetic":
df, data, gen = genetic_algo(
fe_problem,
generation_ceiling=util.get_att("Generation ceiling",
variables),
population_size=util.get_att("Population size", variables),
mutation_rate=util.get_att("Mutation rate", variables),
select=util.get_att("Selection type", variables),
elitism=util.get_att("Elitism", variables),
out_path=False)
else:
df, data, gen = firefly_algo(
fe_problem,
iteration_ceiling=util.get_att("Iteration ceiling", variables),
population_size=util.get_att("Population size", variables),
alpha=util.get_att("Alpha", variables),
beta=util.get_att("Beta", variables),
gamma=util.get_att("Gamma", variables),
delta=util.get_att("Delta", variables),
out_path=False)
min_value = util.get_min_overall(df)[-1]
max_value = util.get_max_overall(df)[-1]
best_solution = min_value if minimize else max_value
response_object['labels'] = [i + 1 for i in range(gen)]
response_object['datasets'] = [{
'data': util.get_max(df)
}, {
'data': util.get_mean(df)
}, {
'data': util.get_min(df)
}]
size = util.get_att("Population size", variables)
# point data for each individual
response_object['points'] = []
for i in range(gen):
response_object['points'].append([])
for j in range(size):
response_object['points'][i].append({
'x': data[i * size + j][1],
'y': data[i * size + j][2]
})
# fitness distribution for each generation
response_object['distribution'] = []
response_object['dist_labels'] = ["" for _ in range(50)]
for i in range(gen):
chunk = data[i * size:i * size + size]
distribution = util.get_distribution(chunk)
response_object['distribution'].append(distribution)
response_object['best_solution'] = best_solution
return jsonify(response_object)
import cases
import sys
from util import get_max
# Latex font
from matplotlib import rc
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
rc('text', usetex=True)
if len(sys.argv) > 1:
name = sys.argv[1]
if name == "load":
filename = sys.argv[2]
tuple = get_max(filename, sys.argv[3:])
print(sys.argv[3:], tuple)
else:
case = getattr(cases, "case_" + name)
case()
else:
for name in dir(cases):
if name.startswith("case_"):
print(name[5:])
