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metaheuristic_algorithms_python | Various metaheuristic algorithms | Learning library

 by   tadatoshi Python Version: Current License: No License

 by   tadatoshi Python Version: Current License: No License

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kandi X-RAY | metaheuristic_algorithms_python Summary

metaheuristic_algorithms_python is a Python library typically used in Tutorial, Learning, Example Codes applications. metaheuristic_algorithms_python has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install metaheuristic_algorithms_python' or download it from GitHub, PyPI.
Various metaheuristic algorithms implemented in Python.
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kandi-support Support

  • metaheuristic_algorithms_python has a low active ecosystem.
  • It has 33 star(s) with 10 fork(s). There are 7 watchers for this library.
  • It had no major release in the last 12 months.
  • metaheuristic_algorithms_python has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of metaheuristic_algorithms_python is current.
This Library - Support
Best in #Learning
Average in #Learning
This Library - Support
Best in #Learning
Average in #Learning

quality kandi Quality

  • metaheuristic_algorithms_python has no bugs reported.
This Library - Quality
Best in #Learning
Average in #Learning
This Library - Quality
Best in #Learning
Average in #Learning

securitySecurity

  • metaheuristic_algorithms_python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
This Library - Security
Best in #Learning
Average in #Learning
This Library - Security
Best in #Learning
Average in #Learning

license License

  • metaheuristic_algorithms_python does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
This Library - License
Best in #Learning
Average in #Learning
This Library - License
Best in #Learning
Average in #Learning

buildReuse

  • metaheuristic_algorithms_python releases are not available. You will need to build from source code and install.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
This Library - Reuse
Best in #Learning
Average in #Learning
This Library - Reuse
Best in #Learning
Average in #Learning
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metaheuristic_algorithms_python Key Features

Various metaheuristic algorithms implemented in Python.

Installation

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pip3 install metaheuristic_algorithms_python

Programming

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    from metaheuristic_algorithms.function_wrappers.abstract_wrapper import AbstractWrapper

    class RosenbrookFunctionWrapper(AbstractWrapper):

        def maximum_decision_variable_values(self):
            return [5, 5]

        def minimum_decision_variable_values(self):
            return [-5, -5]

        def objective_function_value(self, decision_variable_values):
            return (1 - decision_variable_values[0])**2 + 100 * (decision_variable_values[1] - decision_variable_values[0]**2)**2

        def initial_decision_variable_value_estimates(self):
            return [2, 2]

Command line execution

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    $ metaheuristic_algorithms_python --objective_function 'nonsmooth_multipeak_function' --number_of_variables 2 --objective 'maximization' firefly_algorithm --number_of_fireflies 10 --maximun_generation 10 --randomization_parameter_alpha 0.2 --absorption_coefficient_gamma 1.0

Community Discussions

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Trending Discussions on Learning

QUESTION

How do purely functional languages handle index-based algorithms?

Asked 2022-Apr-05 at 12:51

I have been trying to learn about functional programming, but I still struggle with thinking like a functional programmer. One such hangup is how one would implement index-heavy operations which rely strongly on loops/order-of-execution.

For example, consider the following Java code:

public class Main {
    public static void main(String[] args) {
        List<Integer> nums = Arrays.asList(1,2,3,4,5,6,7,8,9);
        System.out.println("Nums:\t"+ nums);
        System.out.println("Prefix:\t"+prefixList(nums));
    }
  
    private static List<Integer> prefixList(List<Integer> nums){
      List<Integer> prefix = new ArrayList<>(nums);
      for(int i = 1; i < prefix.size(); ++i)
        prefix.set(i, prefix.get(i) + prefix.get(i-1));
      return prefix;
    }
}
/*
System.out: 
Nums:   [1, 2, 3, 4, 5, 6, 7, 8, 9]
Prefix: [1, 3, 6, 10, 15, 21, 28, 36, 45]
*/

Here, in the prefixList function, the nums list is first cloned, but then there is the iterative operation performed on it, where the value on index i relies on index i-1 (i.e. order of execution is required). Then this value is returned.

What would this look like in a functional language (Haskell, Lisp, etc.)? I have been learning about monads and think they may be relevant here, but my understanding is still not great.

ANSWER

Answered 2022-Mar-07 at 21:17

This is not an index-heavy operation, in fact you can do this with a one-liner with scanl1 :: (a -> a -> a) -> [a] -> [a]:

prefixList = scanl1 (+)

indeed, for the list of Nums, we get:

Prelude> prefixList [1 .. 9]
[1,3,6,10,15,21,28,36,45]

scanl1 takes the first item of the original list as initial value for the accumulator, and yields that. Then each time it takes the accumulator and the next item of the given list, and sums these up as new accumulator, and yields the new accumulator value.

Often one does not need indexing, but enumerating over the list is sufficient. Imperative programming languages often work with for loops with indexes, but in many cases these can be replaced by foreach loops that thus do not take the index into account. In Haskell this also often helps to make algorithms more lazy.

If you really need random access lookups, you can work with data structures such as defined in the array and vector packages.

Source https://stackoverflow.com/questions/71387267

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install metaheuristic_algorithms_python

You can install using 'pip install metaheuristic_algorithms_python' or download it from GitHub, PyPI.
You can use metaheuristic_algorithms_python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

It's not tested on Python 2.6 or 2.7 yet.

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