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

Code Duplication Length = 47-47 lines in 2 locations

NiaPy/algorithms/other/mts.py 2 locations


		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
	return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR

def MTS_LS1v1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, BONUS1=10, BONUS2=1, sr_fix=0.4, rnd=rand, **ukwargs):
	r"""Multiple trajectory local search one version two.

	Args:
		Xk (numpy.ndarray): Current solution.
		Xk_fit (float): Current solutions fitness/function value.
		Xb (numpy.ndarray): Global best solution.
		Xb_fit (float): Global best solutions fitness/function value.
		improve (bool): Has the solution been improved.
		SR (numpy.ndarray): Search range.
		task (Task): Optimization task.
		BONUS1 (int): Bonus reward for improving global best solution.
		BONUS2 (int): Bonus reward for improving solution.
		sr_fix (numpy.ndarray): Fix when search range is to small.
		rnd (mtrand.RandomState): Random number generator.
		**ukwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray]:
			1. New solution.
			2. New solutions fitness/function value.
			3. Global best if found else old global best.
			4. Global bests function/fitness value.
			5. If solution has improved.
			6. Search range.
	"""
	if not improve:
		SR /= 2
		ifix = where(SR < 1e-15)
		SR[ifix] = task.bRange[ifix] * sr_fix
	improve, D, grade = False, rnd.uniform(-1, 1, task.D), 0.0
	for i in range(len(Xk)):
		Xk_i_old = Xk[i]
		Xk[i] = Xk_i_old - SR[i] * D[i]
		Xk = task.repair(Xk, rnd)
		Xk_fit_new = task.eval(Xk)
		if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, Xk, Xk_fit_new
		elif Xk_fit_new == Xk_fit: Xk[i] = Xk_i_old
		elif Xk_fit_new > Xk_fit:
			Xk[i] = Xk_i_old + 0.5 * SR[i]
			Xk = task.repair(Xk, rnd)
			Xk_fit_new = task.eval(Xk)
			if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, Xk, Xk_fit_new
			if Xk_fit_new >= Xk_fit: Xk[i] = Xk_i_old
			else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
	return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR

def genNewX(x, r, d, SR, op):
	r"""Move solution to other position based on operator.


__all__ = ['MultipleTrajectorySearch', 'MultipleTrajectorySearchV1', 'MTS_LS1', 'MTS_LS1v1', 'MTS_LS2', 'MTS_LS3', 'MTS_LS3v1']

def MTS_LS1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, BONUS1=10, BONUS2=1, sr_fix=0.4, rnd=rand, **ukwargs):
	r"""Multiple trajectory local search one.

	Args:
		Xk (numpy.ndarray): Current solution.
		Xk_fit (float): Current solutions fitness/function value.
		Xb (numpy.ndarray): Global best solution.
		Xb_fit (float): Global best solutions fitness/function value.
		improve (bool): Has the solution been improved.
		SR (numpy.ndarray): Search range.
		task (Task): Optimization task.
		BONUS1 (int): Bonus reward for improving global best solution.
		BONUS2 (int): Bonus reward for improving solution.
		sr_fix (numpy.ndarray): Fix when search range is to small.
		rnd (mtrand.RandomState): Random number generator.
		**ukwargs (Dict[str, Any]): Additional arguments.

	Returns:
		Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray]:
			1. New solution.
			2. New solutions fitness/function value.
			3. Global best if found else old global best.
			4. Global bests function/fitness value.
			5. If solution has improved.
			6. Search range.
	"""
	if not improve:
		SR /= 2
		ifix = where(SR < 1e-15)
		SR[ifix] = task.bRange[ifix] * sr_fix
	improve, grade = False, 0.0
	for i in range(len(Xk)):
		Xk_i_old = Xk[i]
		Xk[i] = Xk_i_old - SR[i]
		Xk = task.repair(Xk, rnd)
		Xk_fit_new = task.eval(Xk)
		if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, Xk, Xk_fit_new
		if Xk_fit_new == Xk_fit: Xk[i] = Xk_i_old
		elif Xk_fit_new > Xk_fit:
			Xk[i] = Xk_i_old + 0.5 * SR[i]
			Xk = task.repair(Xk, rnd)
			Xk_fit_new = task.eval(task.repair(Xk, rnd))
			if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, copy(Xk), Xk_fit_new
			if Xk_fit_new >= Xk_fit: Xk[i] = Xk_i_old
			else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
	return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR

def MTS_LS1v1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, BONUS1=10, BONUS2=1, sr_fix=0.4, rnd=rand, **ukwargs):
	r"""Multiple trajectory local search one version two.

		@@ 64-110 (lines=47) @@
61		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
62		return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR
63
64		def MTS_LS1v1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, BONUS1=10, BONUS2=1, sr_fix=0.4, rnd=rand, **ukwargs):
65		r"""Multiple trajectory local search one version two.
66
67		Args:
68		Xk (numpy.ndarray): Current solution.
69		Xk_fit (float): Current solutions fitness/function value.
70		Xb (numpy.ndarray): Global best solution.
71		Xb_fit (float): Global best solutions fitness/function value.
72		improve (bool): Has the solution been improved.
73		SR (numpy.ndarray): Search range.
74		task (Task): Optimization task.
75		BONUS1 (int): Bonus reward for improving global best solution.
76		BONUS2 (int): Bonus reward for improving solution.
77		sr_fix (numpy.ndarray): Fix when search range is to small.
78		rnd (mtrand.RandomState): Random number generator.
79		**ukwargs (Dict[str, Any]): Additional arguments.
80
81		Returns:
82		Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray]:
83		1. New solution.
84		2. New solutions fitness/function value.
85		3. Global best if found else old global best.
86		4. Global bests function/fitness value.
87		5. If solution has improved.
88		6. Search range.
89		"""
90		if not improve:
91		SR /= 2
92		ifix = where(SR < 1e-15)
93		SR[ifix] = task.bRange[ifix] * sr_fix
94		improve, D, grade = False, rnd.uniform(-1, 1, task.D), 0.0
95		for i in range(len(Xk)):
96		Xk_i_old = Xk[i]
97		Xk[i] = Xk_i_old - SR[i] * D[i]
98		Xk = task.repair(Xk, rnd)
99		Xk_fit_new = task.eval(Xk)
100		if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, Xk, Xk_fit_new
101		elif Xk_fit_new == Xk_fit: Xk[i] = Xk_i_old
102		elif Xk_fit_new > Xk_fit:
103		Xk[i] = Xk_i_old + 0.5 * SR[i]
104		Xk = task.repair(Xk, rnd)
105		Xk_fit_new = task.eval(Xk)
106		if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, Xk, Xk_fit_new
107		if Xk_fit_new >= Xk_fit: Xk[i] = Xk_i_old
108		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
109		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
110		return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR
111
112		def genNewX(x, r, d, SR, op):
113		r"""Move solution to other position based on operator.
		@@ 16-62 (lines=47) @@
13
14		__all__ = ['MultipleTrajectorySearch', 'MultipleTrajectorySearchV1', 'MTS_LS1', 'MTS_LS1v1', 'MTS_LS2', 'MTS_LS3', 'MTS_LS3v1']
15
16		def MTS_LS1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, BONUS1=10, BONUS2=1, sr_fix=0.4, rnd=rand, **ukwargs):
17		r"""Multiple trajectory local search one.
18
19		Args:
20		Xk (numpy.ndarray): Current solution.
21		Xk_fit (float): Current solutions fitness/function value.
22		Xb (numpy.ndarray): Global best solution.
23		Xb_fit (float): Global best solutions fitness/function value.
24		improve (bool): Has the solution been improved.
25		SR (numpy.ndarray): Search range.
26		task (Task): Optimization task.
27		BONUS1 (int): Bonus reward for improving global best solution.
28		BONUS2 (int): Bonus reward for improving solution.
29		sr_fix (numpy.ndarray): Fix when search range is to small.
30		rnd (mtrand.RandomState): Random number generator.
31		**ukwargs (Dict[str, Any]): Additional arguments.
32
33		Returns:
34		Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray]:
35		1. New solution.
36		2. New solutions fitness/function value.
37		3. Global best if found else old global best.
38		4. Global bests function/fitness value.
39		5. If solution has improved.
40		6. Search range.
41		"""
42		if not improve:
43		SR /= 2
44		ifix = where(SR < 1e-15)
45		SR[ifix] = task.bRange[ifix] * sr_fix
46		improve, grade = False, 0.0
47		for i in range(len(Xk)):
48		Xk_i_old = Xk[i]
49		Xk[i] = Xk_i_old - SR[i]
50		Xk = task.repair(Xk, rnd)
51		Xk_fit_new = task.eval(Xk)
52		if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, Xk, Xk_fit_new
53		if Xk_fit_new == Xk_fit: Xk[i] = Xk_i_old
54		elif Xk_fit_new > Xk_fit:
55		Xk[i] = Xk_i_old + 0.5 * SR[i]
56		Xk = task.repair(Xk, rnd)
57		Xk_fit_new = task.eval(task.repair(Xk, rnd))
58		if Xk_fit_new < Xb_fit: grade, Xb, Xb_fit = grade + BONUS1, copy(Xk), Xk_fit_new
59		if Xk_fit_new >= Xk_fit: Xk[i] = Xk_i_old
60		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
61		else: grade, improve, Xk_fit = grade + BONUS2, True, Xk_fit_new
62		return Xk, Xk_fit, Xb, Xb_fit, improve, grade, SR
63
64		def MTS_LS1v1(Xk, Xk_fit, Xb, Xb_fit, improve, SR, task, BONUS1=10, BONUS2=1, sr_fix=0.4, rnd=rand, **ukwargs):
65		r"""Multiple trajectory local search one version two.

NiaOrg / NiaPy

Code Duplication Length = 47-47 lines in 2 locations

NiaPy/algorithms/other/mts.py 2 locations