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

Code Duplication Length = 18-20 lines in 2 locations

src/gradient_free_optimizers/optimizers/smb_opt/surrogate_models.py 1 location


        super().__init__(min_variance=min_variance, **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


search_data6 = opt6.search_data


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=1
        )

    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__(min_variance=min_variance, **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:

		@@ 79-96 (lines=18) @@
76		search_data6 = opt6.search_data
77
78
79		class GPR:
80		def __init__(self):
81		nu_param = 0.5
82		matern = Matern(
83		# length_scale=length_scale_param,
84		# length_scale_bounds=length_scale_bounds_param,
85		nu=nu_param,
86		)
87
88		self.gpr = GaussianProcessRegressor(
89		kernel=matern + RBF() + WhiteKernel(), n_restarts_optimizer=1
90		)
91
92		def fit(self, X, y):
93		self.gpr.fit(X, y)
94
95		def predict(self, X, return_std=False):
96		return self.gpr.predict(X, return_std=return_std)
97
98
99		bayesian_optimizer_para = [

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 18-20 lines in 2 locations

src/gradient_free_optimizers/optimizers/smb_opt/surrogate_models.py 1 location

tests/test_optimizers/test_parameter/test_bayesian_optimizer_para_init.py 1 location