NiaPy.util.utility.limit_repair() - Code Metrics - Inspection of "Upgrade runner" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#206)

by Grega

created 2019-04-25 10:11 UTC

NiaPy.util.utility.limit_repair() A

↳ Parent: NiaPy.util.utility

Complexity

Conditions

Size

Total Lines	19
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	6
nop	4
dl	0
loc	19
rs	10
c	0
b	0
f	0

# encoding=utf8

"""Implementation of various utility functions."""

import logging

from numpy import (
    ndarray,
    asarray,
    full,
    empty,
    where,
    random as rand,
    ceil,
    amin,
    amax
)

logging.basicConfig()
logger = logging.getLogger("NiaPy.util.utility")
logger.setLevel("INFO")

__all__ = [
    "limit_repair",
    "limitInversRepair",
    "objects2array",
    "wangRepair",
    "randRepair",
    "fullArray",
    "reflectRepair"
]


def limit_repair(x, Lower, Upper, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            Lower (numpy.ndarray): Lower bounds of search space.
            Upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Solution in search space.

    """

    ir = where(x < Lower)
    x[ir] = Lower[ir]
    ir = where(x > Upper)
    x[ir] = Upper[ir]
    return x


def limitInversRepair(x, Lower, Upper, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            Lower (numpy.ndarray): Lower bounds of search space.
            Upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Solution in search space.

    """

    ir = where(x < Lower)
    x[ir] = Upper[ir]
    ir = where(x > Upper)
    x[ir] = Lower[ir]
    return x


def wangRepair(x, Lower, Upper, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            Lower (numpy.ndarray): Lower bounds of search space.
            Upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Solution in search space.

    """

    ir = where(x < Lower)
    x[ir] = amin([Upper[ir], 2 * Lower[ir] - x[ir]], axis=0)
    ir = where(x > Upper)
    x[ir] = amax([Lower[ir], 2 * Upper[ir] - x[ir]], axis=0)
    return x


def randRepair(x, Lower, Upper, rnd=rand, **kwargs):
    r"""Repair solution and put the solution in the random position inside of the bounds of problem.

    Arguments:
            x (numpy.ndarray): Solution to check and repair if needed.
            Lower (numpy.ndarray): Lower bounds of search space.
            Upper (numpy.ndarray): Upper bounds of search space.
            rnd (mtrand.RandomState): Random generator.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Fixed solution.

    """

    ir = where(x < Lower)
    x[ir] = rnd.uniform(Lower[ir], Upper[ir])
    ir = where(x > Upper)
    x[ir] = rnd.uniform(Lower[ir], Upper[ir])
    return x


def reflectRepair(x, Lower, Upper, **kwargs):
    r"""Repair solution and put the solution in search space with reflection of how much the solution violates a bound.

    Args:
            x (numpy.ndarray): Solution to be fixed.
            Lower (numpy.ndarray): Lower bounds of search space.
            Upper (numpy.ndarray): Upper bounds of search space.
            kwargs (Dict[str, Any]): Additional arguments.

    Returns:
            numpy.ndarray: Fix solution.

    """

    ir = where(x > Upper)
    x[ir] = Lower[ir] + x[ir] % (Upper[ir] - Lower[ir])
    ir = where(x < Lower)
    x[ir] = Lower[ir] + x[ir] % (Upper[ir] - Lower[ir])
    return x


def fullArray(a, D):
    r"""Fill or create array of length D, from value or value form a.

    Arguments:
        a (Union[int, float, numpy.ndarray], Iterable[Any]): Input values for fill.
        D (int): Length of new array.

    Returns:
        numpy.ndarray: Array filled with passed values or value.

    """

    A = []

    if isinstance(a, (int, float)):
        A = full(D, a)
    elif isinstance(a, (ndarray, list, tuple)):
        if len(a) == D:
            A = a if isinstance(a, ndarray) else asarray(a)
        elif len(a) > D:
            A = a[:D] if isinstance(a, ndarray) else asarray(a[:D])
        else:
            for i in range(int(ceil(float(D) / len(a)))):
                A.extend(a[:D if (D - i * len(a)) >= len(a) else D - i * len(a)])
            A = asarray(A)
    return A


def objects2array(objs):
    r"""Convert `Iterable` array or list to `NumPy` array.

    Args:
        objs (Iterable[Any]): Array or list to convert.

    Returns:
        numpy.ndarray: Array of objects.

    """

    a = empty(len(objs), dtype=object)
    for i, e in enumerate(objs):
        a[i] = e
    return a


1			# encoding=utf8
2
3			"""Implementation of various utility functions."""
4
5			import logging
6
7			from numpy import (
8			ndarray,
9			asarray,
10			full,
11			empty,
12			where,
13			random as rand,
14			ceil,
15			amin,
16			amax
17			)
18
19			logging.basicConfig()
20			logger = logging.getLogger("NiaPy.util.utility")
21			logger.setLevel("INFO")
22
23			__all__ = [
24			"limit_repair",
25			"limitInversRepair",
26			"objects2array",
27			"wangRepair",
28			"randRepair",
29			"fullArray",
30			"reflectRepair"
31			]
32
33
34			def limit_repair(x, Lower, Upper, **kwargs):
35			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
36
37			Arguments:
38			x (numpy.ndarray): Solution to check and repair if needed.
39			Lower (numpy.ndarray): Lower bounds of search space.
40			Upper (numpy.ndarray): Upper bounds of search space.
41			kwargs (Dict[str, Any]): Additional arguments.
42
43			Returns:
44			numpy.ndarray: Solution in search space.
45
46			"""
47
48			ir = where(x < Lower)
49			x[ir] = Lower[ir]
50			ir = where(x > Upper)
51			x[ir] = Upper[ir]
52			return x
53
54
55			def limitInversRepair(x, Lower, Upper, **kwargs):
56			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
57
58			Arguments:
59			x (numpy.ndarray): Solution to check and repair if needed.
60			Lower (numpy.ndarray): Lower bounds of search space.
61			Upper (numpy.ndarray): Upper bounds of search space.
62			kwargs (Dict[str, Any]): Additional arguments.
63
64			Returns:
65			numpy.ndarray: Solution in search space.
66
67			"""
68
69			ir = where(x < Lower)
70			x[ir] = Upper[ir]
71			ir = where(x > Upper)
72			x[ir] = Lower[ir]
73			return x
74
75
76			def wangRepair(x, Lower, Upper, **kwargs):
77			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
78
79			Arguments:
80			x (numpy.ndarray): Solution to check and repair if needed.
81			Lower (numpy.ndarray): Lower bounds of search space.
82			Upper (numpy.ndarray): Upper bounds of search space.
83			kwargs (Dict[str, Any]): Additional arguments.
84
85			Returns:
86			numpy.ndarray: Solution in search space.
87
88			"""
89
90			ir = where(x < Lower)
91			x[ir] = amin([Upper[ir], 2 * Lower[ir] - x[ir]], axis=0)
92			ir = where(x > Upper)
93			x[ir] = amax([Lower[ir], 2 * Upper[ir] - x[ir]], axis=0)
94			return x
95
96
97			def randRepair(x, Lower, Upper, rnd=rand, **kwargs):
98			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
99
100			Arguments:
101			x (numpy.ndarray): Solution to check and repair if needed.
102			Lower (numpy.ndarray): Lower bounds of search space.
103			Upper (numpy.ndarray): Upper bounds of search space.
104			rnd (mtrand.RandomState): Random generator.
105			kwargs (Dict[str, Any]): Additional arguments.
106
107			Returns:
108			numpy.ndarray: Fixed solution.
109
110			"""
111
112			ir = where(x < Lower)
113			x[ir] = rnd.uniform(Lower[ir], Upper[ir])
114			ir = where(x > Upper)
115			x[ir] = rnd.uniform(Lower[ir], Upper[ir])
116			return x
117
118
119			def reflectRepair(x, Lower, Upper, **kwargs):
120			r"""Repair solution and put the solution in search space with reflection of how much the solution violates a bound.
121
122			Args:
123			x (numpy.ndarray): Solution to be fixed.
124			Lower (numpy.ndarray): Lower bounds of search space.
125			Upper (numpy.ndarray): Upper bounds of search space.
126			kwargs (Dict[str, Any]): Additional arguments.
127
128			Returns:
129			numpy.ndarray: Fix solution.
130
131			"""
132
133			ir = where(x > Upper)
134			x[ir] = Lower[ir] + x[ir] % (Upper[ir] - Lower[ir])
135			ir = where(x < Lower)
136			x[ir] = Lower[ir] + x[ir] % (Upper[ir] - Lower[ir])
137			return x
138
139
140			def fullArray(a, D):
141			r"""Fill or create array of length D, from value or value form a.
142
143			Arguments:
144			a (Union[int, float, numpy.ndarray], Iterable[Any]): Input values for fill.
145			D (int): Length of new array.
146
147			Returns:
148			numpy.ndarray: Array filled with passed values or value.
149
150			"""
151
152			A = []
153
154			if isinstance(a, (int, float)):
155			A = full(D, a)
156			elif isinstance(a, (ndarray, list, tuple)):
157			if len(a) == D:
158			A = a if isinstance(a, ndarray) else asarray(a)
159			elif len(a) > D:
160			A = a[:D] if isinstance(a, ndarray) else asarray(a[:D])
161			else:
162			for i in range(int(ceil(float(D) / len(a)))):
163			A.extend(a[:D if (D - i * len(a)) >= len(a) else D - i * len(a)])
164			A = asarray(A)
165			return A
166
167
168			def objects2array(objs):
169			r"""Convert `Iterable` array or list to `NumPy` array.
170
171			Args:
172			objs (Iterable[Any]): Array or list to convert.
173
174			Returns:
175			numpy.ndarray: Array of objects.
176
177			"""
178
179			a = empty(len(objs), dtype=object)
180			for i, e in enumerate(objs):
181			a[i] = e
182			return a
183

NiaOrg / NiaPy

Pull Request — master (#206)

NiaPy.util.utility.limit_repair() A

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