NiaPy.algorithms.basic.cso.CatSwarmOptimization.setParameters() - Code Metrics - Inspection of "Added Cat Swarm Optimization algorithm (#216)" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( bf6d5c...a49e28 )

by Grega

created 2019-06-07 17:28 UTC

CatSwarmOptimization.setParameters() A

↳ Parent: NiaPy.algorithms.basic.cso

Complexity

Conditions

Size

Total Lines	19
Code Lines	4

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	4
nop	10
dl	0
loc	19
rs	10
c	0
b	0
f	0

How to fix Many Parameters

# encoding=utf8
# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, no-self-use, line-too-long, arguments-differ, bad-continuation
import logging

import numpy as np
import math
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')

__all__ = ['CatSwarmOptimization']

class CatSwarmOptimization(Algorithm):
    r"""Implementation of Cat swarm optimiization algorithm.

    **Algorithm:** Cat swarm optimization

    **Date:** 2019

    **Author:** Mihael Baketarić

    **License:** MIT

    **Reference paper:** Chu, Shu-Chuan & Tsai, Pei-Wei & Pan, Jeng-Shyang. (2006). Cat Swarm Optimization. 854-858. 10.1007/11801603_94.
    """
    Name = ['CatSwarmOptimization', 'CSO']

    @staticmethod

    def typeParameters(): return {
        'NP': lambda x: isinstance(x, int) and x > 0,
        'MR': lambda x: isinstance(x, (int, float)) and 0 <= x <= 1,
        'C1': lambda x: isinstance(x, (int, float)) and x >= 0,
        'SMP': lambda x: isinstance(x, int) and x > 0,
        'SPC': lambda x: isinstance(x, bool),
        'CDC': lambda x: isinstance(x, (int, float)) and 0 <= x <= 1,
        'SRD': lambda x: isinstance(x, (int, float)) and 0 <= x <= 1,
        'vMax': lambda x: isinstance(x, (int, float)) and x > 0
    }

    def setParameters(self, NP=30, MR=0.1, C1=2.05, SMP=3, SPC=True, CDC=0.85, SRD=0.2, vMax=1.9, **ukwargs):
        r"""Set the algorithm parameters.

        Arguments:
            NP (int): Number of individuals in population
            MR (float): Mixture ratio
            C1 (float): Constant in tracing mode
            SMP (int): Seeking memory pool
            SPC (bool): Self-position considering
            CDC (float): Decides how many dimensions will be varied
            SRD (float): Seeking range of the selected dimension
            vMax (float): Maximal velocity

            See Also:
                * :func:`NiaPy.algorithms.Algorithm.setParameters`
        """
        Algorithm.setParameters(self, NP=NP, **ukwargs)
        self.MR, self.C1, self.SMP, self.SPC, self.CDC, self.SRD, self.vMax = MR, C1, SMP, SPC, CDC, SRD, vMax
        if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))

    def initPopulation(self, task):
        r"""Initialize population.

        Args:
            task (Task): Optimization task.

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
                1. Initialized population.
                2. Initialized populations fitness/function values.
                3. Additional arguments:
                    * Dictionary of modes (seek or trace) and velocities for each cat
        See Also:
            * :func:`NiaPy.algorithms.Algorithm.initPopulation`
        """
        pop, fpop, d = Algorithm.initPopulation(self, task)
        d['modes'] = self.randomSeekTrace()
        d['velocities'] = self.uniform(-self.vMax, self.vMax, [len(pop), task.D])
        return pop, fpop, d

    def repair(self, x, l, u):

        r"""Repair array to range.

        Args:
            x (numpy.ndarray): Array to repair.
            l (numpy.ndarray): Lower limit of allowed range.
            u (numpy.ndarray): Upper limit of allowed range.

        Returns:
            numpy.ndarray: Repaired array.
        """
        ir = np.where(x < l)
        x[ir] = l[ir]
        ir = np.where(x > u)
        x[ir] = u[ir]
        return x

    def randomSeekTrace(self):
        r"""Set cats into seeking/tracing mode.

        Returns:
            numpy.ndarray: One or zero. One means tracing mode. Zero means seeking mode. Length of list is equal to NP.
        """
        lista = np.zeros((self.NP,), dtype=int)

        indexes = np.arange(self.NP)
        self.Rand.shuffle(indexes)
        lista[indexes[:int(self.NP * self.MR)]] = 1
        return lista

    def weightedSelection(self, weights):
        r"""Random selection considering the weights.

        Args:
            weights (numpy.ndarray): weight for each potential position.

        Returns:
            int: index of selected next position.
        """
        cumulative_sum = np.cumsum(weights)
        return np.argmax(cumulative_sum >= (self.rand() * cumulative_sum[-1]))

    def seekingMode(self, task, cat, fcat, pop, fpop, fxb):
        r"""Seeking mode.

        Args:
            task (Task): Optimization task.
            cat (numpy.ndarray): Individual from population.
            fcat (float): Current individual's fitness/function value.
            pop (numpy.ndarray): Current population.
            fpop (numpy.ndarray): Current population fitness/function values.
            fxb (float): Current best cat fitness/function value.

        Returns:
            Tuple[numpy.ndarray, float, numpy.ndarray, float]:
                1. Updated individual's position
                2. Updated individual's fitness/function value
                3. Updated global best position
                4. Updated global best fitness/function value
        """
        cat_copies = []
        cat_copies_fs = []
        for j in range(self.SMP - 1 if self.SPC else self.SMP):
            cat_copies.append(cat.copy())
            indexes = np.arange(task.D)
            self.Rand.shuffle(indexes)
            to_vary_indexes = indexes[:int(task.D * self.CDC)]
            if self.randint(2) == 1:
                cat_copies[j][to_vary_indexes] += cat_copies[j][to_vary_indexes] * self.SRD
            else:
                cat_copies[j][to_vary_indexes] -= cat_copies[j][to_vary_indexes] * self.SRD
            cat_copies[j] = task.repair(cat_copies[j])
            cat_copies_fs.append(task.eval(cat_copies[j]))
        if self.SPC:
            cat_copies.append(cat.copy())
            cat_copies_fs.append(fcat)

        cat_copies_select_probs = np.ones(len(cat_copies))
        fmax = np.max(cat_copies_fs)
        fmin = np.min(cat_copies_fs)
        if any(x != cat_copies_fs[0] for x in cat_copies_fs):
            fb = fmax
            if math.isinf(fb):
                cat_copies_select_probs = np.full(len(cat_copies), fb)
            else:
                cat_copies_select_probs = np.abs(cat_copies_fs - fb) / (fmax - fmin)
        if fmin < fxb:
            fxb = fmin
            ind = self.randint(self.NP, 1, 0)
            pop[ind] = cat_copies[np.where(cat_copies_fs == fmin)[0][0]]
            fpop[ind] = fmin
        sel_index = self.weightedSelection(cat_copies_select_probs)
        return cat_copies[sel_index], cat_copies_fs[sel_index], pop, fpop

    def tracingMode(self, task, cat, velocity, xb):
        r"""Tracing mode.

        Args:
            task (Task): Optimization task.
            cat (numpy.ndarray): Individual from population.
            velocity (numpy.ndarray): Velocity of individual.
            xb (numpy.ndarray): Current best individual.
        Returns:
            Tuple[numpy.ndarray, float, numpy.ndarray]:
                1. Updated individual's position
                2. Updated individual's fitness/function value
                3. Updated individual's velocity vector
        """
        Vnew = self.repair(velocity + (self.uniform(0, 1, len(velocity)) * self.C1 * (xb - cat)), np.full(task.D, -self.vMax), np.full(task.D, self.vMax))
        cat_new = task.repair(cat + Vnew)
        return cat_new, task.eval(cat_new), Vnew

    def runIteration(self, task, pop, fpop, xb, fxb, velocities, modes, **dparams):
        r"""Core function of Cat Swarm Optimization algorithm.

        Args:
            task (Task): Optimization task.
            pop (numpy.ndarray): Current population.
            fpop (numpy.ndarray): Current population fitness/function values.
            xb (numpy.ndarray): Current best individual.
            fxb (float): Current best cat fitness/function value.
            velocities (numpy.ndarray): Velocities of individuals.
            modes (numpy.ndarray): Flag of each individual.
            **dparams (Dict[str, Any]): Additional function arguments.

        Returns:
            Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
                1. New population
                2. New population fitness/function values
                3. Additional arguments:
                    * Dictionary of modes (seek or trace) and velocities for each cat
        """
        pop_copies = pop.copy()
        for k in range(len(pop_copies)):
            if modes[k] == 0:
                pop_copies[k], fpop[k], pop_copies[:], fpop[:] = self.seekingMode(task, pop_copies[k], fpop[k], pop_copies, fpop, fxb)
            else:  # if cat in tracing mode
                pop_copies[k], fpop[k], velocities[k] = self.tracingMode(task, pop_copies[k], velocities[k], xb)
        return pop_copies, fpop, {'velocities': velocities, 'modes': self.randomSeekTrace()}

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


1		# encoding=utf8
2		# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, no-self-use, line-too-long, arguments-differ, bad-continuation
3		import logging
4
5		import numpy as np
6		import math
7		from NiaPy.algorithms.algorithm import Algorithm
8		logging.basicConfig()
9		logger = logging.getLogger('NiaPy.algorithms.basic')
10		logger.setLevel('INFO')
11
12		__all__ = ['CatSwarmOptimization']
13
14		class CatSwarmOptimization(Algorithm):
15		r"""Implementation of Cat swarm optimiization algorithm.
16
17		Algorithm: Cat swarm optimization
18
19		Date: 2019
20
21		Author: Mihael Baketarić
22
23		License: MIT
24
25		Reference paper: Chu, Shu-Chuan & Tsai, Pei-Wei & Pan, Jeng-Shyang. (2006). Cat Swarm Optimization. 854-858. 10.1007/11801603_94.
26		"""
27		Name = ['CatSwarmOptimization', 'CSO']
28
29	View Code Duplication	@staticmethod
		0 ignored issues – show Duplication introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
30		def typeParameters(): return {
31		'NP': lambda x: isinstance(x, int) and x > 0,
32		'MR': lambda x: isinstance(x, (int, float)) and 0 <= x <= 1,
33		'C1': lambda x: isinstance(x, (int, float)) and x >= 0,
34		'SMP': lambda x: isinstance(x, int) and x > 0,
35		'SPC': lambda x: isinstance(x, bool),
36		'CDC': lambda x: isinstance(x, (int, float)) and 0 <= x <= 1,
37		'SRD': lambda x: isinstance(x, (int, float)) and 0 <= x <= 1,
38		'vMax': lambda x: isinstance(x, (int, float)) and x > 0
39		}
40
41		def setParameters(self, NP=30, MR=0.1, C1=2.05, SMP=3, SPC=True, CDC=0.85, SRD=0.2, vMax=1.9, **ukwargs):
42		r"""Set the algorithm parameters.
43
44		Arguments:
45		NP (int): Number of individuals in population
46		MR (float): Mixture ratio
47		C1 (float): Constant in tracing mode
48		SMP (int): Seeking memory pool
49		SPC (bool): Self-position considering
50		CDC (float): Decides how many dimensions will be varied
51		SRD (float): Seeking range of the selected dimension
52		vMax (float): Maximal velocity
53
54		See Also:
55		* :func:`NiaPy.algorithms.Algorithm.setParameters`
56		"""
57		Algorithm.setParameters(self, NP=NP, **ukwargs)
58		self.MR, self.C1, self.SMP, self.SPC, self.CDC, self.SRD, self.vMax = MR, C1, SMP, SPC, CDC, SRD, vMax
59		if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
60
61		def initPopulation(self, task):
62		r"""Initialize population.
63
64		Args:
65		task (Task): Optimization task.
66
67		Returns:
68		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
69		1. Initialized population.
70		2. Initialized populations fitness/function values.
71		3. Additional arguments:
72		* Dictionary of modes (seek or trace) and velocities for each cat
73		See Also:
74		* :func:`NiaPy.algorithms.Algorithm.initPopulation`
75		"""
76		pop, fpop, d = Algorithm.initPopulation(self, task)
77		d['modes'] = self.randomSeekTrace()
78		d['velocities'] = self.uniform(-self.vMax, self.vMax, [len(pop), task.D])
79		return pop, fpop, d
80
81	View Code Duplication	def repair(self, x, l, u):
		0 ignored issues – show Duplication introduced 2019-06-07 14:55 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
82		r"""Repair array to range.
83
84		Args:
85		x (numpy.ndarray): Array to repair.
86		l (numpy.ndarray): Lower limit of allowed range.
87		u (numpy.ndarray): Upper limit of allowed range.
88
89		Returns:
90		numpy.ndarray: Repaired array.
91		"""
92		ir = np.where(x < l)
93		x[ir] = l[ir]
94		ir = np.where(x > u)
95		x[ir] = u[ir]
96		return x
97
98		def randomSeekTrace(self):
99		r"""Set cats into seeking/tracing mode.
100
101		Returns:
102		numpy.ndarray: One or zero. One means tracing mode. Zero means seeking mode. Length of list is equal to NP.
103		"""
104		lista = np.zeros((self.NP,), dtype=int)
		0 ignored issues – show Comprehensibility Best Practice introduced 2019-06-07 14:55 UTC by Report Bug Copy Issue Report The variable `int` does not seem to be defined. Loading history...
105		indexes = np.arange(self.NP)
106		self.Rand.shuffle(indexes)
107		lista[indexes[:int(self.NP * self.MR)]] = 1
108		return lista
109
110		def weightedSelection(self, weights):
111		r"""Random selection considering the weights.
112
113		Args:
114		weights (numpy.ndarray): weight for each potential position.
115
116		Returns:
117		int: index of selected next position.
118		"""
119		cumulative_sum = np.cumsum(weights)
120		return np.argmax(cumulative_sum >= (self.rand() * cumulative_sum[-1]))
121
122		def seekingMode(self, task, cat, fcat, pop, fpop, fxb):
123		r"""Seeking mode.
124
125		Args:
126		task (Task): Optimization task.
127		cat (numpy.ndarray): Individual from population.
128		fcat (float): Current individual's fitness/function value.
129		pop (numpy.ndarray): Current population.
130		fpop (numpy.ndarray): Current population fitness/function values.
131		fxb (float): Current best cat fitness/function value.
132
133		Returns:
134		Tuple[numpy.ndarray, float, numpy.ndarray, float]:
135		1. Updated individual's position
136		2. Updated individual's fitness/function value
137		3. Updated global best position
138		4. Updated global best fitness/function value
139		"""
140		cat_copies = []
141		cat_copies_fs = []
142		for j in range(self.SMP - 1 if self.SPC else self.SMP):
143		cat_copies.append(cat.copy())
144		indexes = np.arange(task.D)
145		self.Rand.shuffle(indexes)
146		to_vary_indexes = indexes[:int(task.D * self.CDC)]
147		if self.randint(2) == 1:
148		cat_copies[j][to_vary_indexes] += cat_copies[j][to_vary_indexes] * self.SRD
149		else:
150		cat_copies[j][to_vary_indexes] -= cat_copies[j][to_vary_indexes] * self.SRD
151		cat_copies[j] = task.repair(cat_copies[j])
152		cat_copies_fs.append(task.eval(cat_copies[j]))
153		if self.SPC:
154		cat_copies.append(cat.copy())
155		cat_copies_fs.append(fcat)
156
157		cat_copies_select_probs = np.ones(len(cat_copies))
158		fmax = np.max(cat_copies_fs)
159		fmin = np.min(cat_copies_fs)
160		if any(x != cat_copies_fs[0] for x in cat_copies_fs):
161		fb = fmax
162		if math.isinf(fb):
163		cat_copies_select_probs = np.full(len(cat_copies), fb)
164		else:
165		cat_copies_select_probs = np.abs(cat_copies_fs - fb) / (fmax - fmin)
166		if fmin < fxb:
167		fxb = fmin
168		ind = self.randint(self.NP, 1, 0)
169		pop[ind] = cat_copies[np.where(cat_copies_fs == fmin)[0][0]]
170		fpop[ind] = fmin
171		sel_index = self.weightedSelection(cat_copies_select_probs)
172		return cat_copies[sel_index], cat_copies_fs[sel_index], pop, fpop
173
174		def tracingMode(self, task, cat, velocity, xb):
175		r"""Tracing mode.
176
177		Args:
178		task (Task): Optimization task.
179		cat (numpy.ndarray): Individual from population.
180		velocity (numpy.ndarray): Velocity of individual.
181		xb (numpy.ndarray): Current best individual.
182		Returns:
183		Tuple[numpy.ndarray, float, numpy.ndarray]:
184		1. Updated individual's position
185		2. Updated individual's fitness/function value
186		3. Updated individual's velocity vector
187		"""
188		Vnew = self.repair(velocity + (self.uniform(0, 1, len(velocity)) * self.C1 * (xb - cat)), np.full(task.D, -self.vMax), np.full(task.D, self.vMax))
189		cat_new = task.repair(cat + Vnew)
190		return cat_new, task.eval(cat_new), Vnew
191
192		def runIteration(self, task, pop, fpop, xb, fxb, velocities, modes, **dparams):
193		r"""Core function of Cat Swarm Optimization algorithm.
194
195		Args:
196		task (Task): Optimization task.
197		pop (numpy.ndarray): Current population.
198		fpop (numpy.ndarray): Current population fitness/function values.
199		xb (numpy.ndarray): Current best individual.
200		fxb (float): Current best cat fitness/function value.
201		velocities (numpy.ndarray): Velocities of individuals.
202		modes (numpy.ndarray): Flag of each individual.
203		**dparams (Dict[str, Any]): Additional function arguments.
204
205		Returns:
206		Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
207		1. New population
208		2. New population fitness/function values
209		3. Additional arguments:
210		* Dictionary of modes (seek or trace) and velocities for each cat
211		"""
212		pop_copies = pop.copy()
213		for k in range(len(pop_copies)):
214		if modes[k] == 0:
215		pop_copies[k], fpop[k], pop_copies[:], fpop[:] = self.seekingMode(task, pop_copies[k], fpop[k], pop_copies, fpop, fxb)
216		else: # if cat in tracing mode
217		pop_copies[k], fpop[k], velocities[k] = self.tracingMode(task, pop_copies[k], velocities[k], xb)
218		return pop_copies, fpop, {'velocities': velocities, 'modes': self.randomSeekTrace()}
219
220		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
221

NiaOrg / NiaPy

Push — master ( bf6d5c...a49e28 )

CatSwarmOptimization.setParameters() A

Complexity

Size

Duplication

Importance

How to fix Many Parameters

Many Parameters

Duplication Side-by-Side

Filter issues like