NiaPy.util.utility.randRepair() - Code Metrics - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

NiaPy.util.utility.randRepair() A
last analyzed 2020-11-16 11:17 UTC

↳ Parent: NiaPy.util.utility

Complexity

Conditions

Size

Total Lines	20
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	6
nop	5
dl	0
loc	20
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.

    """

    # TODO: Add one-liner np.clip approach

    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			# TODO: Add one-liner np.clip approach
49
50			ir = where(x < Lower)
51			x[ir] = Lower[ir]
52			ir = where(x > Upper)
53			x[ir] = Upper[ir]
54			return x
55
56
57			def limitInversRepair(x, Lower, Upper, **kwargs):
58			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
59
60			Arguments:
61			x (numpy.ndarray): Solution to check and repair if needed.
62			Lower (numpy.ndarray): Lower bounds of search space.
63			Upper (numpy.ndarray): Upper bounds of search space.
64			kwargs (Dict[str, Any]): Additional arguments.
65
66			Returns:
67			numpy.ndarray: Solution in search space.
68
69			"""
70
71			ir = where(x < Lower)
72			x[ir] = Upper[ir]
73			ir = where(x > Upper)
74			x[ir] = Lower[ir]
75			return x
76
77
78			def wangRepair(x, Lower, Upper, **kwargs):
79			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
80
81			Arguments:
82			x (numpy.ndarray): Solution to check and repair if needed.
83			Lower (numpy.ndarray): Lower bounds of search space.
84			Upper (numpy.ndarray): Upper bounds of search space.
85			kwargs (Dict[str, Any]): Additional arguments.
86
87			Returns:
88			numpy.ndarray: Solution in search space.
89
90			"""
91
92			ir = where(x < Lower)
93			x[ir] = amin([Upper[ir], 2 * Lower[ir] - x[ir]], axis=0)
94			ir = where(x > Upper)
95			x[ir] = amax([Lower[ir], 2 * Upper[ir] - x[ir]], axis=0)
96			return x
97
98
99			def randRepair(x, Lower, Upper, rnd=rand, **kwargs):
100			r"""Repair solution and put the solution in the random position inside of the bounds of problem.
101
102			Arguments:
103			x (numpy.ndarray): Solution to check and repair if needed.
104			Lower (numpy.ndarray): Lower bounds of search space.
105			Upper (numpy.ndarray): Upper bounds of search space.
106			rnd (mtrand.RandomState): Random generator.
107			kwargs (Dict[str, Any]): Additional arguments.
108
109			Returns:
110			numpy.ndarray: Fixed solution.
111
112			"""
113
114			ir = where(x < Lower)
115			x[ir] = rnd.uniform(Lower[ir], Upper[ir])
116			ir = where(x > Upper)
117			x[ir] = rnd.uniform(Lower[ir], Upper[ir])
118			return x
119
120
121			def reflectRepair(x, Lower, Upper, **kwargs):
122			r"""Repair solution and put the solution in search space with reflection of how much the solution violates a bound.
123
124			Args:
125			x (numpy.ndarray): Solution to be fixed.
126			Lower (numpy.ndarray): Lower bounds of search space.
127			Upper (numpy.ndarray): Upper bounds of search space.
128			kwargs (Dict[str, Any]): Additional arguments.
129
130			Returns:
131			numpy.ndarray: Fix solution.
132
133			"""
134
135			ir = where(x > Upper)
136			x[ir] = Lower[ir] + x[ir] % (Upper[ir] - Lower[ir])
137			ir = where(x < Lower)
138			x[ir] = Lower[ir] + x[ir] % (Upper[ir] - Lower[ir])
139			return x
140
141
142			def fullArray(a, D):
143			r"""Fill or create array of length D, from value or value form a.
144
145			Arguments:
146			a (Union[int, float, numpy.ndarray], Iterable[Any]): Input values for fill.
147			D (int): Length of new array.
148
149			Returns:
150			numpy.ndarray: Array filled with passed values or value.
151
152			"""
153
154			A = []
155
156			if isinstance(a, (int, float)):
157			A = full(D, a)
158			elif isinstance(a, (ndarray, list, tuple)):
159			if len(a) == D:
160			A = a if isinstance(a, ndarray) else asarray(a)
161			elif len(a) > D:
162			A = a[:D] if isinstance(a, ndarray) else asarray(a[:D])
163			else:
164			for i in range(int(ceil(float(D) / len(a)))):
165			A.extend(a[:D if (D - i * len(a)) >= len(a) else D - i * len(a)])
166			A = asarray(A)
167			return A
168
169
170			def objects2array(objs):
171			r"""Convert `Iterable` array or list to `NumPy` array.
172
173			Args:
174			objs (Iterable[Any]): Array or list to convert.
175
176			Returns:
177			numpy.ndarray: Array of objects.
178
179			"""
180
181			a = empty(len(objs), dtype=object)
182			for i, e in enumerate(objs):
183			a[i] = e
184			return a
185

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NiaPy.util.utility.randRepair() A last analyzed 2020-11-16 11:17 UTC

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NiaPy.util.utility.randRepair() A
last analyzed 2020-11-16 11:17 UTC