Code Duplication - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 29-31 lines in 2 locations

src/gradient_free_optimizers/optimizers/exp_opt/random_annealing.py 1 location


    _name_ = "random_annealing"
    __name__ = "RandomAnnealingOptimizer"

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        epsilon=0.03,
        distribution="normal",
        n_neighbours=3,
        annealing_rate=0.98,
        start_temp=10,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
            epsilon=epsilon,
            distribution=distribution,
            n_neighbours=n_neighbours,
        )
        self.epsilon = epsilon
        self.distribution = distribution
        self.n_neighbours = n_neighbours
        self.annealing_rate = annealing_rate
        self.start_temp = start_temp
        self.temp = start_temp

    @HillClimbingOptimizer.track_new_pos
    @HillClimbingOptimizer.random_iteration

src/gradient_free_optimizers/optimizers/local_opt/simulated_annealing.py 1 location


    optimizer_type = "local"
    computationally_expensive = False

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        epsilon=0.03,
        distribution="normal",
        n_neighbours=3,
        annealing_rate=0.97,
        start_temp=1,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
            epsilon=epsilon,
            distribution=distribution,
            n_neighbours=n_neighbours,
        )

        self.annealing_rate = annealing_rate
        self.start_temp = start_temp
        self.temp = start_temp

    def _p_accept_default(self):
        # the 'minus' is omitted, because we maximize a score

		@@ 19-47 (lines=29) @@
16		optimizer_type = "local"
17		computationally_expensive = False
18
19		def __init__(
20		self,
21		search_space,
22		initialize={"grid": 4, "random": 2, "vertices": 4},
23		constraints=[],
24		random_state=None,
25		rand_rest_p=0,
26		nth_process=None,
27		epsilon=0.03,
28		distribution="normal",
29		n_neighbours=3,
30		annealing_rate=0.97,
31		start_temp=1,
32		):
33		super().__init__(
34		search_space=search_space,
35		initialize=initialize,
36		constraints=constraints,
37		random_state=random_state,
38		rand_rest_p=rand_rest_p,
39		nth_process=nth_process,
40		epsilon=epsilon,
41		distribution=distribution,
42		n_neighbours=n_neighbours,
43		)
44
45		self.annealing_rate = annealing_rate
46		self.start_temp = start_temp
47		self.temp = start_temp
48
49		def _p_accept_default(self):
50		# the 'minus' is omitted, because we maximize a score

		@@ 14-44 (lines=31) @@
11		_name_ = "random_annealing"
12		__name__ = "RandomAnnealingOptimizer"
13
14		def __init__(
15		self,
16		search_space,
17		initialize={"grid": 4, "random": 2, "vertices": 4},
18		constraints=[],
19		random_state=None,
20		rand_rest_p=0,
21		nth_process=None,
22		epsilon=0.03,
23		distribution="normal",
24		n_neighbours=3,
25		annealing_rate=0.98,
26		start_temp=10,
27		):
28		super().__init__(
29		search_space=search_space,
30		initialize=initialize,
31		constraints=constraints,
32		random_state=random_state,
33		rand_rest_p=rand_rest_p,
34		nth_process=nth_process,
35		epsilon=epsilon,
36		distribution=distribution,
37		n_neighbours=n_neighbours,
38		)
39		self.epsilon = epsilon
40		self.distribution = distribution
41		self.n_neighbours = n_neighbours
42		self.annealing_rate = annealing_rate
43		self.start_temp = start_temp
44		self.temp = start_temp
45
46		@HillClimbingOptimizer.track_new_pos
47		@HillClimbingOptimizer.random_iteration

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 29-31 lines in 2 locations

src/gradient_free_optimizers/optimizers/exp_opt/random_annealing.py 1 location

src/gradient_free_optimizers/optimizers/local_opt/simulated_annealing.py 1 location