genetic_algorithm_observer.get_global_best() - Code Metrics - Inspection of "Merge" - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — 0.7.dev ( 20409b...5de18e )

unknown

created 2017-09-14 10:13 UTC

genetic_algorithm_observer.get_global_best() A

↳ Parent: genetic_algorithm_observer

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
dl	0
loc	2
rs	10
c	0
b	0
f	0



import numpy as np
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3


class genetic_algorithm_observer:
    """!

    """

    def __init__(self, need_global_best=False, need_population_best=False, need_mean_ff=False):
        """ """

        # Global best chromosome and fitness function for each population
        self.global_best_result = {'chromosome': [], 'fitness_function': []}

        # Best chromosome and fitness function on a population
        self.best_population_result = {'chromosome': [], 'fitness_function': []}

        # Mean fitness function on each population
        self.mean_ff_result = []

        # Flags to collect
        self.need_global_best = need_global_best
        self.need_population_best = need_population_best
        self.need_mean_ff = need_mean_ff

    def collect_global_best(self, best_chromosome, best_fitness_function):
        """ """

        if not self.need_global_best:
            return

        self.global_best_result['chromosome'].append(best_chromosome)
        self.global_best_result['fitness_function'].append(best_fitness_function)

    def collect_population_best(self, best_chromosome, best_fitness_function):
        """ """

        if not self.need_population_best:
            return

        self.best_population_result['chromosome'].append(best_chromosome)
        self.best_population_result['fitness_function'].append(best_fitness_function)

    def collect_mean(self, fitness_functions):
        """ """

        if not self.need_mean_ff:
            return

        self.mean_ff_result.append(np.mean(fitness_functions))

    def get_global_best(self):
        return self.global_best_result

    def get_population_best(self):
        return self.best_population_result

    def get_mean_fitness_function(self):
        return self.mean_ff_result




1
2
3			import numpy as np
			0 ignored issues – show Configuration introduced 2017-09-14 10:15 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
4
5
6			class genetic_algorithm_observer:
7			"""!
8
9			"""
10
11			def __init__(self, need_global_best=False, need_population_best=False, need_mean_ff=False):
12			""" """
13
14			# Global best chromosome and fitness function for each population
15			self.global_best_result = {'chromosome': [], 'fitness_function': []}
16
17			# Best chromosome and fitness function on a population
18			self.best_population_result = {'chromosome': [], 'fitness_function': []}
19
20			# Mean fitness function on each population
21			self.mean_ff_result = []
22
23			# Flags to collect
24			self.need_global_best = need_global_best
25			self.need_population_best = need_population_best
26			self.need_mean_ff = need_mean_ff
27
28			def collect_global_best(self, best_chromosome, best_fitness_function):
29			""" """
30
31			if not self.need_global_best:
32			return
33
34			self.global_best_result['chromosome'].append(best_chromosome)
35			self.global_best_result['fitness_function'].append(best_fitness_function)
36
37			def collect_population_best(self, best_chromosome, best_fitness_function):
38			""" """
39
40			if not self.need_population_best:
41			return
42
43			self.best_population_result['chromosome'].append(best_chromosome)
44			self.best_population_result['fitness_function'].append(best_fitness_function)
45
46			def collect_mean(self, fitness_functions):
47			""" """
48
49			if not self.need_mean_ff:
50			return
51
52			self.mean_ff_result.append(np.mean(fitness_functions))
53
54			def get_global_best(self):
55			return self.global_best_result
56
57			def get_population_best(self):
58			return self.best_population_result
59
60			def get_mean_fitness_function(self):
61			return self.mean_ff_result
62
63
64

annoviko / pyclustering

Push — 0.7.dev ( 20409b...5de18e )

genetic_algorithm_observer.get_global_best() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

Duplication Side-by-Side

Filter issues like

2. Missing init.py files