def solve_functions(dp):
global function_object, data_function
data_function = dp
_, function_object = Main.find_function_by_id(int(data_function['problem']))
function_object.set_n_dimension(int(data_function['dimension']))
isHybrid = data_function['isHybrid']
j = json.loads(open(os.path.dirname(__file__) + "/../JSON/functions-methods.json", 'r').read())
callable_method = j[data_function['firstMethod']['name-method']]['callable-method']
initial_time = time.time()
result_first = globals()[callable_method](data_function['firstMethod'], None)
result_first_done = {'decimal-best': {}, 'value-best': str(result_first[3]), 'time': str(time.time() - initial_time)}
PLOTS = PLots()
pl_3d, pl_contour = PLOTS.plot_function3d(function_object)
j = 1
for i in result_first[2]:
result_first_done['decimal-best'][j] = str(i)
j += 1
if not isHybrid:
err1 = PLOTS.plot_err(result_first[1])
return {
'problem':function_object.name,
'problem-description':str(function_object),
'isHybrid':isHybrid,
'result-first':result_first_done,
'3d':pl_3d,
'contour':pl_contour,
'err1':err1
}
initial_time = time.time()
result_second = globals()[callable_method](data_function['firstMethod'], result_first[0])
result_second_done = {'decimal-best': {}, 'value-best': str(result_second[3]), 'time': str(time.time() - initial_time)}
j = 1
for i in result_second[2]:
result_second_done['decimal-best'][j] = str(i)
j += 1
err1, err2, err3 = PLOTS.plot_err(result_first[1], result_second[1])
return {
'problem':function_object.name,
'problem-description':str(function_object),
'isHybrid':isHybrid,
'result-first':result_first_done,
'result-second':result_second_done,
'3d': pl_3d,
'contour': pl_contour,
'err1':err1,
'err2':err2,
'err3':err3,
'hibridization-analysis':str('The FIRST hit the value SOMEVALUE in TIME seconds.\nStarting on the best found value, the SECOND *CONDITION got a improve DIFFERENCE in the solution, in TIME seconds. Hybridization reached a value of FINAL-VALUE in a total of FINAL-TIME seconds, considered a effective result, because one method support the other.')
}
def simule_functions(dp, repetitions):
global function_object, data_function
data_function = dp
_, function_object = Main.find_function_by_id(int(data_function['problem']))
function_object.set_n_dimension(int(data_function['dimension']))
isHybrid = data_function['isHybrid']
j = json.loads(open(os.path.dirname(__file__) + "/../JSON/functions-methods.json", 'r').read())
callable_method = j[data_function['firstMethod']['name-method']]['callable-method']
vec_times = []
vec_costs = []
for i in range(repetitions):
initial_time = time.time()
result_first = globals()[callable_method](data_function['firstMethod'], None)
if isHybrid:
result_second = globals()[callable_method](data_function['secondMethod'], result_first[0])
vec_costs.append(result_second[3])
else:
vec_costs.append(result_first[3])
vec_times.append(time.time() - initial_time)
PLOTS = PLots()
pl_3d, pl_contour = PLOTS.plot_function3d(function_object)
box_times, box_costs, scatter = PLOTS.plot_simulation(vec_times, vec_costs)
return {
'problem':function_object.name,
'problem-description':str(function_object),
'isHybrid':isHybrid,
'times':vec_times,
'costs':vec_costs,
'3d':pl_3d,
'contour':pl_contour,
'box_times':box_times,
'box_costs':box_costs,
'scatter':scatter
}