Rosenbrock.evaluate() - Code Metrics - Inspection of "Merge pull request #32 from lucijabrezocnik/featur..." - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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by Grega

created 2018-02-15 08:26 UTC

Rosenbrock.evaluate() A

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dl	0
loc	9
rs	9.6666
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cc	2

# encoding=utf8
# pylint: disable=anomalous-backslash-in-string
"""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			# encoding=utf8
2			# pylint: disable=anomalous-backslash-in-string
3			"""Implementation of Rosenbrock benchmark function.
4
5			Date: 2018
6
7			Authors: Iztok Fister Jr. and Lucija Brezočnik
8
9			License: MIT
10
11			Function: Rosenbrock function
12
13			Input domain:
14			The function can be defined on any input domain but it is usually
15			evaluated on the hypercube x_i ∈ [-30, 30], for all i = 1, 2,..., D.
16
17			Global minimum:
18			f(x) = 0, at x = (1,...,1)
19
20			LaTeX formats:
21			Inline: $f(x) = \sum_{i=1}^{D-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)$
22			Equation: \begin{equation}
23			f(x) = \sum_{i=1}^{D-1} (100 (x_{i+1} - x_i^2)^2 + (x_i - 1)^2)
24			\end{equation}
25			Domain: $-30 \leq x_i \leq 30$
26
27			Reference paper:
28			Jamil, M., and Yang, X. S. (2013).
29			A literature survey of benchmark functions for global optimisation problems.
30			International Journal of Mathematical Modelling and Numerical Optimisation,
31			4(2), 150-194.
32			"""
33
34			import math
35
36			__all__ = ['Rosenbrock']
37
38
39			class Rosenbrock(object):
40
41			def __init__(self, Lower=-30, Upper=30):
42			self.Lower = Lower
43			self.Upper = Upper
44
45			@classmethod
46			def function(cls):
47			def evaluate(D, sol):
48
49			val = 0.0
50
51			for i in range(D - 1):
52			val += 100 * math.pow(sol[i + 1] - math.pow((sol[i]), 2), 2) + math.pow((sol[i] - 1), 2)
53
54			return val
55
56			return evaluate
57

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