Rosenbrock.function() - Code Metrics - Inspection of "Feature functions2" - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#32)

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created 2018-02-15 07:44 UTC

Rosenbrock.function() A

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Complexity

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Lines	0
Ratio	0 %

Importance

Changes	1
Bugs	0	Features	1

Metric	Value
dl	0
loc	13
rs	9.4285
c	1
b	0
f	1
cc	3

"""Implementation of Rosenbrock benchmark function.


Date: 2018

Authors: Iztok Fister Jr. and Lucija Brezočnik

License: MIT

Function: Rosenbrock function

Input domain:
    The function can be defined on any input domain but it is usually
    evaluated on the hypercube x_i ∈ [-30, 30], for all i = 1, 2,..., D.

Global minimum:
    f(x*) = 0, at x* = (1,...,1)

LaTeX formats:
    Inline: $f(x) = \sum_{i=1}^{D-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)$
    Equation: \begin{equation}
              f(x) = \sum_{i=1}^{D-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)
              \end{equation}
    Domain: $-30 \leq x_i \leq 30$

Reference paper:
    Jamil, M., and Yang, X. S. (2013).
    A literature survey of benchmark functions for global optimisation problems.
    International Journal of Mathematical Modelling and Numerical Optimisation,
    4(2), 150-194.
"""

import math

__all__ = ['Rosenbrock']


class Rosenbrock(object):

    def __init__(self, Lower=-30, Upper=30):
        self.Lower = Lower
        self.Upper = Upper

    @classmethod
    def function(cls):
        def evaluate(D, sol):

            val = 0.0

            for i in range(D - 1):
                val += 100 * math.pow(sol[i + 1] - math.pow((sol[i]), 2), 2) + math.pow((sol[i] - 1), 2)

            return val

        return evaluate


1			"""Implementation of Rosenbrock benchmark function.
			0 ignored issues – show Bug introduced 2018-02-15 07:45 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\s` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2018-02-15 07:45 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\e` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history... Bug introduced 2018-02-15 07:45 UTC by Report Bug Copy Issue Report A suspicious escape sequence `\l` was found. Did you maybe forget to add an `r` prefix? Escape sequences in Python are generally interpreted according to rules similar to standard C. Only if strings are prefixed with `r` or `R` are they interpreted as regular expressions. The escape sequence that was used indicates that you might have intended to write a regular expression. Learn more about the available escape sequences. in the Python documentation. Loading history...
2
3			Date: 2018
4
5			Authors: Iztok Fister Jr. and Lucija Brezočnik
6
7			License: MIT
8
9			Function: Rosenbrock function
10
11			Input domain:
12			The function can be defined on any input domain but it is usually
13			evaluated on the hypercube x_i ∈ [-30, 30], for all i = 1, 2,..., D.
14
15			Global minimum:
16			f(x) = 0, at x = (1,...,1)
17
18			LaTeX formats:
19			Inline: $f(x) = \sum_{i=1}^{D-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)$
20			Equation: \begin{equation}
21			f(x) = \sum_{i=1}^{D-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)
22			\end{equation}
23			Domain: $-30 \leq x_i \leq 30$
24
25			Reference paper:
26			Jamil, M., and Yang, X. S. (2013).
27			A literature survey of benchmark functions for global optimisation problems.
28			International Journal of Mathematical Modelling and Numerical Optimisation,
29			4(2), 150-194.
30			"""
31
32			import math
33
34			__all__ = ['Rosenbrock']
35
36
37			class Rosenbrock(object):
38
39			def __init__(self, Lower=-30, Upper=30):
40			self.Lower = Lower
41			self.Upper = Upper
42
43			@classmethod
44			def function(cls):
45			def evaluate(D, sol):
46
47			val = 0.0
48
49			for i in range(D - 1):
50			val += 100 * math.pow(sol[i + 1] - math.pow((sol[i]), 2), 2) + math.pow((sol[i] - 1), 2)
51
52			return val
53
54			return evaluate
55

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Pull Request — master (#32)

Rosenbrock.function() A

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