sklearn_cv = SVCCVSkGridLinear(\
C_range = [2 ** i for i in range(-5, 5, 2)],
cv_method = KFold(20, 5))
meta_model = DSESSVCLinearMetaModel(\
window_size = 10,
scaling = ScalingStandardscore(),
crossvalidation = sklearn_cv,
repair_mode = 'mirror')
method = ORIDSESAlignedSVC(\
mu = 15,
lambd = 100,
theta = 0.3,
pi = 70,
initial_sigma = matrix([[4.5, 4.5]]),
delta = 4.5,
tau0 = 0.5,
tau1 = 0.6,
initial_pos = matrix([[10.0, 10.0]]),
beta = 0.9,
meta_model = meta_model)
return method
if __name__ == "__main__":
problem = TRProblem()
optfit = problem.optimum_fitness()
sim = Simulator(get_method(), problem, Accuracy(optfit, 10**(-6)))
results = sim.simulate()
'''
from sys import path
path.append("../../../..")
from numpy import matrix
from evopy.strategies.ori_dses import ORIDSES
from evopy.problems.tr_problem import TRProblem
from evopy.simulators.simulator import Simulator
from evopy.operators.termination.accuracy import Accuracy
def get_method():
method = ORIDSES(\
mu = 15,
lambd = 100,
theta = 0.3,
pi = 70,
initial_sigma = matrix([[4.5, 4.5]]),
delta = 4.5,
tau0 = 0.5,
tau1 = 0.6,
initial_pos = matrix([[10.0, 10.0]]))
return method
if __name__ == "__main__":
problem = TRProblem()
termination = Accuracy(problem.optimum_fitness(), pow(10, -6))
sim = Simulator(get_method(), problem, termination)
results = sim.simulate()
