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

Code Duplication Length = 31-33 lines in 3 locations

NiaPy/algorithms/basic/de.py 3 locations


	x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossRand2(pop, ic, x_b, f, cr, rnd=rand, *args):
	r"""Mutation strategy with crossover.

	Mutation strategy uses five different random individuals from population.

	Mutation:
		Name: de/best/1

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{r_1, G} + F \cdot (\mathbf{x}_{r_2, G} - \mathbf{x}_{r_3, G}) + F \cdot (\mathbf{x}_{r_4, G} - \mathbf{x}_{r_5, G})`
		where :math:`r_1, r_2, r_3, r_4, r_5` are random indexes representing current population individuals.

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		*args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 5 else None
	r = rnd.choice(len(pop), 5, replace=not len(pop) >= 5, p=p)
	x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) + f * (pop[r[3]][i] - pop[r[4]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossBest2(pop, ic, x_b, f, cr, rnd=rand, *args):
	r"""Mutation strategy with crossover.

	x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossCurr2Rand1(pop, ic, x_b, f, cr, rnd=rand, *args):
	r"""Mutation strategy with crossover.

	Mutation:
		Name: de/curr2rand/1

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{i, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		*args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 4 else None
	r = rnd.choice(len(pop), 4, replace=not len(pop) >= 4, p=p)
	x = [pop[ic][i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossCurr2Best1(pop, ic, x_b, f, cr, rnd=rand, **kwargs):
	r"""Mutation strategy with crossover.

	x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) + f * (pop[r[3]][i] - pop[r[4]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossBest2(pop, ic, x_b, f, cr, rnd=rand, *args):
	r"""Mutation strategy with crossover.

	Mutation:
		Name: de/best/2

		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{best, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals.

	Crossover:
		Name: Binomial crossover

		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`

	Args:
		pop (numpy.ndarray[Individual]): Current population.
		ic (int): Index of individual being mutated.
		x_b (Individual): Current global best individual.
		f (float): Scale factor.
		cr (float): Crossover probability.
		rnd (mtrand.RandomState): Random generator.
		*args (list): Additional arguments.

	Returns:
		numpy.ndarray: mutated and mixed individual.
	"""
	j = rnd.randint(len(pop[ic]))
	p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 4 else None
	r = rnd.choice(len(pop), 4, replace=not len(pop) >= 4, p=p)
	x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
	return asarray(x)

def CrossCurr2Rand1(pop, ic, x_b, f, cr, rnd=rand, *args):
	r"""Mutation strategy with crossover.

		@@ 85-117 (lines=33) @@
82		x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
83		return asarray(x)
84
85		def CrossRand2(pop, ic, x_b, f, cr, rnd=rand, *args):
86		r"""Mutation strategy with crossover.
87
88		Mutation strategy uses five different random individuals from population.
89
90		Mutation:
91		Name: de/best/1
92
93		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{r_1, G} + F \cdot (\mathbf{x}_{r_2, G} - \mathbf{x}_{r_3, G}) + F \cdot (\mathbf{x}_{r_4, G} - \mathbf{x}_{r_5, G})`
94		where :math:`r_1, r_2, r_3, r_4, r_5` are random indexes representing current population individuals.
95
96		Crossover:
97		Name: Binomial crossover
98
99		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`
100
101		Args:
102		pop (numpy.ndarray[Individual]): Current population.
103		ic (int): Index of individual being mutated.
104		x_b (Individual): Current global best individual.
105		f (float): Scale factor.
106		cr (float): Crossover probability.
107		rnd (mtrand.RandomState): Random generator.
108		*args (list): Additional arguments.
109
110		Returns:
111		numpy.ndarray: mutated and mixed individual.
112		"""
113		j = rnd.randint(len(pop[ic]))
114		p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 5 else None
115		r = rnd.choice(len(pop), 5, replace=not len(pop) >= 5, p=p)
116		x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) + f * (pop[r[3]][i] - pop[r[4]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
117		return asarray(x)
118
119		def CrossBest2(pop, ic, x_b, f, cr, rnd=rand, *args):
120		r"""Mutation strategy with crossover.
		@@ 151-181 (lines=31) @@
148		x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
149		return asarray(x)
150
151		def CrossCurr2Rand1(pop, ic, x_b, f, cr, rnd=rand, *args):
152		r"""Mutation strategy with crossover.
153
154		Mutation:
155		Name: de/curr2rand/1
156
157		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{i, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
158		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals
159
160		Crossover:
161		Name: Binomial crossover
162
163		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`
164
165		Args:
166		pop (numpy.ndarray[Individual]): Current population.
167		ic (int): Index of individual being mutated.
168		x_b (Individual): Current global best individual.
169		f (float): Scale factor.
170		cr (float): Crossover probability.
171		rnd (mtrand.RandomState): Random generator.
172		*args (list): Additional arguments.
173
174		Returns:
175		numpy.ndarray: mutated and mixed individual.
176		"""
177		j = rnd.randint(len(pop[ic]))
178		p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 4 else None
179		r = rnd.choice(len(pop), 4, replace=not len(pop) >= 4, p=p)
180		x = [pop[ic][i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
181		return asarray(x)
182
183		def CrossCurr2Best1(pop, ic, x_b, f, cr, rnd=rand, **kwargs):
184		r"""Mutation strategy with crossover.
		@@ 119-149 (lines=31) @@
116		x = [pop[r[0]][i] + f * (pop[r[1]][i] - pop[r[2]][i]) + f * (pop[r[3]][i] - pop[r[4]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
117		return asarray(x)
118
119		def CrossBest2(pop, ic, x_b, f, cr, rnd=rand, *args):
120		r"""Mutation strategy with crossover.
121
122		Mutation:
123		Name: de/best/2
124
125		:math:`\mathbf{v}_{i, G} = \mathbf{x}_{best, G} + F \cdot (\mathbf{x}_{r_1, G} - \mathbf{x}_{r_2, G}) + F \cdot (\mathbf{x}_{r_3, G} - \mathbf{x}_{r_4, G})`
126		where :math:`r_1, r_2, r_3, r_4` are random indexes representing current population individuals.
127
128		Crossover:
129		Name: Binomial crossover
130
131		:math:`\mathbf{x}_{i, G+1} = \begin{cases} \mathbf{u}_{i, G+1}, & \text{if $f(\mathbf{u}_{i, G+1}) \leq f(\mathbf{x}_{i, G})$}, \\ \mathbf{x}_{i, G}, & \text{otherwise}. \end{cases}`
132
133		Args:
134		pop (numpy.ndarray[Individual]): Current population.
135		ic (int): Index of individual being mutated.
136		x_b (Individual): Current global best individual.
137		f (float): Scale factor.
138		cr (float): Crossover probability.
139		rnd (mtrand.RandomState): Random generator.
140		*args (list): Additional arguments.
141
142		Returns:
143		numpy.ndarray: mutated and mixed individual.
144		"""
145		j = rnd.randint(len(pop[ic]))
146		p = [1 / (len(pop) - 1.0) if i != ic else 0 for i in range(len(pop))] if len(pop) > 4 else None
147		r = rnd.choice(len(pop), 4, replace=not len(pop) >= 4, p=p)
148		x = [x_b[i] + f * (pop[r[0]][i] - pop[r[1]][i]) + f * (pop[r[2]][i] - pop[r[3]][i]) if rnd.rand() < cr or i == j else pop[ic][i] for i in range(len(pop[ic]))]
149		return asarray(x)
150
151		def CrossCurr2Rand1(pop, ic, x_b, f, cr, rnd=rand, *args):
152		r"""Mutation strategy with crossover.

NiaOrg / NiaPy

Code Duplication Length = 31-33 lines in 3 locations

NiaPy/algorithms/basic/de.py 3 locations