def solve(randsol,
fitness,
solver=None,
budget=None,
effort=1.5,
fine_ops=True,
str_trace=True):
##### PARAMETERS
### create a tracer object
tr = Tracer(rs=randsol, tt=str_trace)
### associate wrapper and tracer
tr.acquire_wrapper(wr)
### create an instance of the search operators
ops = OPS(fitness, fine_ops, tr)
##### BUDGET AND EFFORT
if not budget:
# If budget is given, use that to determine the iteration
# budget. This allows us to specify the number of iterations
# directly, which is useful for fair benchmarking. Otherwise,
# estimate genotype size and calculate budget.
budget = int(avg_size_trace(tr)**effort)
# print(" budget = ", budget)
##### SOLVER
if solver in solver_name:
#### import solver module
solver_module = __import__(solver_name[solver])
#### get solver class
Solver_Class = getattr(
solver_module, solver) # assumes class name same as solver acronym
#### create an instance of the solver
search_algorithm = Solver_Class(ops, budget)
#### run the solver
(sol, fit) = search_algorithm.run()
#### collect data
solve.data = search_algorithm.data
#### release wrapper
tr.release_wrapper(wr)
#### return solution
return (sol.pheno, fit)
else:
print("solver %s not available!" % solver)
print("solver = RS | HC | LA | EA | PS")
import sys
sys.path.append('../solver')
sys.path.append('../tracer')
sys.path.append('../analysis')
from tracer import Tracer
from wrapper import wr
from traceable_random import random, random_function
#### TEST
'''
tr.str_addr = True
'''
'''
n=5
def generator(problem_size=n):
return [random.choice([0,1]) for x in range(problem_size)]
(output1, trace1) = tr.get_trace(generator)
print output1
tr.display_trace(trace1)
#print trace1
