Code Duplication - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Hyperactive

Code Duplication Length = 11-11 lines in 2 locations

examples/optimizers/bayesian_optimization.py 1 location


    def predict(self, X):
        return self.m.predict(X)

class GPR1:
    def __init__(self):
        self.gpr = GaussianProcessRegressor(
                kernel=Matern(nu=2.5), normalize_y=True, n_restarts_optimizer=10
            )
        
    def fit(self, X, y):
        self.gpr.fit(X, y)

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


opt = Hyperactive(X, y)

hyperactive/opt_args.py 1 location


from numpy.random import normal


class GPR:
    def __init__(self):
        self.gpr = GaussianProcessRegressor(
                kernel=Matern(nu=2.5), normalize_y=True, n_restarts_optimizer=10
            )
        
    def fit(self, X, y):
        self.gpr.fit(X, y)

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


class Arguments:

		@@ 44-54 (lines=11) @@
41		def predict(self, X):
42		return self.m.predict(X)
43
44		class GPR1:
45		def __init__(self):
46		self.gpr = GaussianProcessRegressor(
47		kernel=Matern(nu=2.5), normalize_y=True, n_restarts_optimizer=10
48		)
49
50		def fit(self, X, y):
51		self.gpr.fit(X, y)
52
53		def predict(self, X):
54		return self.gpr.predict(X, return_std=True)
55
56
57		opt = Hyperactive(X, y)

		@@ 12-22 (lines=11) @@
9		from numpy.random import normal
10
11
12		class GPR:
13		def __init__(self):
14		self.gpr = GaussianProcessRegressor(
15		kernel=Matern(nu=2.5), normalize_y=True, n_restarts_optimizer=10
16		)
17
18		def fit(self, X, y):
19		self.gpr.fit(X, y)
20
21		def predict(self, X):
22		return self.gpr.predict(X, return_std=True)
23
24
25		class Arguments:

SimonBlanke / Hyperactive

Code Duplication Length = 11-11 lines in 2 locations

examples/optimizers/bayesian_optimization.py 1 location

hyperactive/opt_args.py 1 location