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

Code Duplication Length = 18-20 lines in 2 locations

gradient_free_optimizers/optimizers/sequence_model/surrogate_models.py 1 location


        super().__init__(**kwargs)


class GPR:
    def __init__(self):
        length_scale_param = 1
        length_scale_bounds_param = (1e-05, 100000.0)
        nu_param = 0.5
        matern = Matern(
            # length_scale=length_scale_param,
            # length_scale_bounds=length_scale_bounds_param,
            nu=nu_param,
        )

        self.gpr = GaussianProcessRegressor(
            kernel=matern + WhiteKernel(), n_restarts_optimizer=0
        )

    def fit(self, X, y):
        self.gpr.fit(X, y)

    def predict(self, X, return_std=False):
        return self.gpr.predict(X, return_std=return_std)


class GPR_linear:

tests/test_optimizers/test_parameter/test_bayesian_optimizer_para_init.py 1 location


)


class GPR:
    def __init__(self):
        nu_param = 0.5
        matern = Matern(
            # length_scale=length_scale_param,
            # length_scale_bounds=length_scale_bounds_param,
            nu=nu_param,
        )

        self.gpr = GaussianProcessRegressor(
            kernel=matern + RBF() + WhiteKernel(), n_restarts_optimizer=0
        )

    def fit(self, X, y):
        self.gpr.fit(X, y)

    def predict(self, X, return_std=False):
        return self.gpr.predict(X, return_std=return_std)


bayesian_optimizer_para = [

		@@ 100-119 (lines=20) @@
97		super().__init__(**kwargs)
98
99
100		class GPR:
101		def __init__(self):
102		length_scale_param = 1
103		length_scale_bounds_param = (1e-05, 100000.0)
104		nu_param = 0.5
105		matern = Matern(
106		# length_scale=length_scale_param,
107		# length_scale_bounds=length_scale_bounds_param,
108		nu=nu_param,
109		)
110
111		self.gpr = GaussianProcessRegressor(
112		kernel=matern + WhiteKernel(), n_restarts_optimizer=0
113		)
114
115		def fit(self, X, y):
116		self.gpr.fit(X, y)
117
118		def predict(self, X, return_std=False):
119		return self.gpr.predict(X, return_std=return_std)
120
121
122		class GPR_linear:

		@@ 28-45 (lines=18) @@
25		)
26
27
28		class GPR:
29		def __init__(self):
30		nu_param = 0.5
31		matern = Matern(
32		# length_scale=length_scale_param,
33		# length_scale_bounds=length_scale_bounds_param,
34		nu=nu_param,
35		)
36
37		self.gpr = GaussianProcessRegressor(
38		kernel=matern + RBF() + WhiteKernel(), n_restarts_optimizer=0
39		)
40
41		def fit(self, X, y):
42		self.gpr.fit(X, y)
43
44		def predict(self, X, return_std=False):
45		return self.gpr.predict(X, return_std=return_std)
46
47
48		bayesian_optimizer_para = [

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 18-20 lines in 2 locations

gradient_free_optimizers/optimizers/sequence_model/surrogate_models.py 1 location

tests/test_optimizers/test_parameter/test_bayesian_optimizer_para_init.py 1 location