Code Duplication - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 76-77 lines in 2 locations

NiaPy/benchmarks/schaffer.py 1 location


			return 0.5 + (cos(sin(x[0] ** 2 - x[1] ** 2)) ** 2 - 0.5) / (1 + 0.001 * (x[0] ** 2 + x[1] ** 2)) ** 2
		return f

class ExpandedSchaffer(Benchmark):
	r"""Implementations of Expanded Schaffer functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**Expanded Schaffer Function**
	:math:`f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2`

	**Input domain:**
	The function can be defined on any input domain but it is usually
	evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.

	**Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (420.968746,...,420.968746)`

	LaTeX formats:
	Inline:
	$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$

	Equation:
	\begin{equation} f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2 \end{equation}

	Domain:
	$-100 \leq x_i \leq 100$

	Reference:
		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
	"""
	Name = ['ExpandedSchaffer']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of Expanded Scaffer benchmark.

		Args:
			Lower (Optional[float]): Lower bound of problem.
			Upper (Optional[float]): Upper bound of problem.

		See Also:
			:func:`NiaPy.benchmarks.Benchmark.__init__`
		"""
		Benchmark.__init__(self, Lower, Upper)

	@staticmethod
	def latex_code():
		r"""Return the latex code of the problem.

		Returns:
			str: Latex code
		"""
		return r'''$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$'''

	def function(self):
		r"""Return benchmark evaluation function.

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def g(x, y): return 0.5 + (sin(sqrt(x ** 2 + y ** 2)) ** 2 - 0.5) / (1 + 0.001 * (x ** 2 + y ** 2)) ** 2
		def f(D, x):
			r"""Fitness function.

			Args:
				D (int): Dimensionality of the problem
				sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.

			Returns:
				float: Fitness value for the solution.
			"""
			val = 0.0
			for i in range(1, D): val += g(x[i - 1], x[i])
			return g(x[D - 1], x[0]) + val
		return f

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


NiaPy/benchmarks/schwefel.py 1 location


			return 418.9829 * D - val
		return f

class ExpandedScaffer(Benchmark):
	r"""Implementations of High Conditioned Elliptic functions.

	Date: 2018

	Author: Klemen Berkovič

	License: MIT

	Function:
	**High Conditioned Elliptic Function**

		:math:`f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2`

		**Input domain:**
		The function can be defined on any input domain but it is usually
		evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.

		**Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (420.968746,...,420.968746)`

	LaTeX formats:
		Inline:
				$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$

		Equation:
				\begin{equation} f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2 \end{equation}

		Domain:
				$-100 \leq x_i \leq 100$

	Reference:
		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
	"""
	Name = ['ExpandedScaffer']

	def __init__(self, Lower=-100.0, Upper=100.0):
		r"""Initialize of Expanded Scaffer benchmark.

		Args:
			Lower (Optional[float]): Lower bound of problem.
			Upper (Optional[float]): Upper bound of problem.

		See Also:
			:func:`NiaPy.benchmarks.Benchmark.__init__`
		"""
		Benchmark.__init__(self, Lower=Lower, Upper=Lower)

	@staticmethod
	def latex_code():
		r"""Return the latex code of the problem.

		Returns:
			str: Latex code
		"""
		return r'''$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$'''

	def function(self):
		r"""Return benchmark evaluation function.

		Returns:
			Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
		"""
		def g(x, y): return 0.5 + (sin(sqrt(x ** 2 + y ** 2)) ** 2 - 0.5) / (1 + 0.001 * (x ** 2 + y ** 2)) ** 2
		def f(D, x):
			r"""Fitness function.

			Args:
				D (int): Dimensionality of the problem
				sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.

			Returns:
				float: Fitness value for the solution.
			"""
			val = 0.0
			for i in range(1, D): val += g(x[i - 1], x[i])
			return g(x[D - 1], x[0]) + val
		return f

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


		@@ 158-233 (lines=76) @@
155		return 0.5 + (cos(sin(x[0] 2 - x[1] 2)) ** 2 - 0.5) / (1 + 0.001 * (x[0] 2 + x[1] 2)) ** 2
156		return f
157
158		class ExpandedSchaffer(Benchmark):
159		r"""Implementations of Expanded Schaffer functions.
160
161		Date: 2018
162
163		Author: Klemen Berkovič
164
165		License: MIT
166
167		Function:
168		Expanded Schaffer Function
169		:math:`f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2`
170
171		Input domain:
172		The function can be defined on any input domain but it is usually
173		evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.
174
175		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
176
177		LaTeX formats:
178		Inline:
179		$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$
180
181		Equation:
182		\begin{equation} f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2 \end{equation}
183
184		Domain:
185		$-100 \leq x_i \leq 100$
186
187		Reference:
188		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
189		"""
190		Name = ['ExpandedSchaffer']
191
192		def __init__(self, Lower=-100.0, Upper=100.0):
193		r"""Initialize of Expanded Scaffer benchmark.
194
195		Args:
196		Lower (Optional[float]): Lower bound of problem.
197		Upper (Optional[float]): Upper bound of problem.
198
199		See Also:
200		:func:`NiaPy.benchmarks.Benchmark.__init__`
201		"""
202		Benchmark.__init__(self, Lower, Upper)
203
204		@staticmethod
205		def latex_code():
206		r"""Return the latex code of the problem.
207
208		Returns:
209		str: Latex code
210		"""
211		return r'''$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$'''
212
213		def function(self):
214		r"""Return benchmark evaluation function.
215
216		Returns:
217		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
218		"""
219		def g(x, y): return 0.5 + (sin(sqrt(x 2 + y 2)) ** 2 - 0.5) / (1 + 0.001 * (x 2 + y 2)) ** 2
220		def f(D, x):
221		r"""Fitness function.
222
223		Args:
224		D (int): Dimensionality of the problem
225		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
226
227		Returns:
228		float: Fitness value for the solution.
229		"""
230		val = 0.0
231		for i in range(1, D): val += g(x[i - 1], x[i])
232		return g(x[D - 1], x[0]) + val
233		return f
234
235		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
236

		@@ 333-409 (lines=77) @@
330		return 418.9829 * D - val
331		return f
332
333		class ExpandedScaffer(Benchmark):
334		r"""Implementations of High Conditioned Elliptic functions.
335
336		Date: 2018
337
338		Author: Klemen Berkovič
339
340		License: MIT
341
342		Function:
343		High Conditioned Elliptic Function
344
345		:math:`f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2`
346
347		Input domain:
348		The function can be defined on any input domain but it is usually
349		evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`.
350
351		Global minimum: :math:`f(x^) = 0`, at :math:`x^ = (420.968746,...,420.968746)`
352
353		LaTeX formats:
354		Inline:
355		$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$
356
357		Equation:
358		\begin{equation} f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2 \end{equation}
359
360		Domain:
361		$-100 \leq x_i \leq 100$
362
363		Reference:
364		http://www5.zzu.edu.cn/__local/A/69/BC/D3B5DFE94CD2574B38AD7CD1D12_C802DAFE_BC0C0.pdf
365		"""
366		Name = ['ExpandedScaffer']
367
368		def __init__(self, Lower=-100.0, Upper=100.0):
369		r"""Initialize of Expanded Scaffer benchmark.
370
371		Args:
372		Lower (Optional[float]): Lower bound of problem.
373		Upper (Optional[float]): Upper bound of problem.
374
375		See Also:
376		:func:`NiaPy.benchmarks.Benchmark.__init__`
377		"""
378		Benchmark.__init__(self, Lower=Lower, Upper=Lower)
379
380		@staticmethod
381		def latex_code():
382		r"""Return the latex code of the problem.
383
384		Returns:
385		str: Latex code
386		"""
387		return r'''$f(\textbf{x}) = g(x_D, x_1) + \sum_{i=2}^D g(x_{i - 1}, x_i) \\ g(x, y) = 0.5 + \frac{\sin \left(\sqrt{x^2 + y^2} \right)^2 - 0.5}{\left( 1 + 0.001 (x^2 + y^2) \right)}^2$'''
388
389		def function(self):
390		r"""Return benchmark evaluation function.
391
392		Returns:
393		Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function
394		"""
395		def g(x, y): return 0.5 + (sin(sqrt(x 2 + y 2)) ** 2 - 0.5) / (1 + 0.001 * (x 2 + y 2)) ** 2
396		def f(D, x):
397		r"""Fitness function.
398
399		Args:
400		D (int): Dimensionality of the problem
401		sol (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
402
403		Returns:
404		float: Fitness value for the solution.
405		"""
406		val = 0.0
407		for i in range(1, D): val += g(x[i - 1], x[i])
408		return g(x[D - 1], x[0]) + val
409		return f
410
411		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
412

NiaOrg / NiaPy

Code Duplication Length = 76-77 lines in 2 locations

NiaPy/benchmarks/schaffer.py 1 location

NiaPy/benchmarks/schwefel.py 1 location