ga_math.calc_probability_vector() - Code Metrics - Inspection of "[ccore.ut] Update unit-test for K-Medoids." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 941fee...e45971 )

by Andrei

created 2017-09-16 14:38 UTC

ga_math.calc_probability_vector() B

↳ Parent: ga_math

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	5
dl	0
loc	35
rs	8.0894
c	0
b	0
f	0

"""!

@brief Genetic algorithm math API.

@authors Aleksey Kukushkin ([email protected])
@date 2014-2017
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""


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


class ga_math:
    """
    @brief Genetic algorithm math API.
    
    """

    @staticmethod
    def calc_count_centers(chromosome):
        return chromosome[chromosome.argmax()] + 1

    @staticmethod
    def get_clusters_representation(chromosome, count_clusters=None):
        """ Convert chromosome to cluster representation:
                chromosome : [0, 1, 1, 0, 2, 3, 3]
                clusters: [[0, 3], [1, 2], [4], [5, 6]]
        """

        if count_clusters is None:
            count_clusters = ga_math.calc_count_centers(chromosome)

        # Initialize empty clusters
        clusters = [[] for _ in range(count_clusters)]

        # Fill clusters with index of data
        for _idx_data in range(len(chromosome)):
            clusters[chromosome[_idx_data]].append(_idx_data)

        return clusters

    @staticmethod
    def get_centres(chromosomes, data, count_clusters):
        """!
        """

        centres = ga_math.calc_centers(chromosomes, data, count_clusters)

        return centres

    @staticmethod
    def calc_centers(chromosomes, data, count_clusters=None):
        """!
        """

        if count_clusters is None:
            count_clusters = ga_math.calc_count_centers(chromosomes[0])

        # Initialize center
        centers = np.zeros(shape=(len(chromosomes), count_clusters, len(data[0])))

        for _idx_chromosome in range(len(chromosomes)):

            # Get count data in clusters
            count_data_in_cluster = np.zeros(count_clusters)

            # Next data point
            for _idx in range(len(chromosomes[_idx_chromosome])):

                cluster_num = chromosomes[_idx_chromosome][_idx]

                centers[_idx_chromosome][cluster_num] += data[_idx]
                count_data_in_cluster[cluster_num] += 1

            for _idx_cluster in range(count_clusters):
                if count_data_in_cluster[_idx_cluster] != 0:
                    centers[_idx_chromosome][_idx_cluster] /= count_data_in_cluster[_idx_cluster]

        return centers

    @staticmethod
    def calc_probability_vector(fitness):
        """!
        """

        if len(fitness) == 0:
            raise AttributeError("Has no any fitness functions.")

        # Get 1/fitness function
        inv_fitness = np.zeros(len(fitness))

        #
        for _idx in range(len(inv_fitness)):

            if fitness[_idx] != 0.0:
                inv_fitness[_idx] = 1.0 / fitness[_idx]
            else:
                inv_fitness[_idx] = 0.0

        # Initialize vector
        prob = np.zeros(len(fitness))

        # Initialize first element
        prob[0] = inv_fitness[0]

        # Accumulate values in probability vector
        for _idx in range(1, len(inv_fitness)):
            prob[_idx] = prob[_idx - 1] + inv_fitness[_idx]

        # Normalize
        prob /= prob[-1]

        ga_math.set_last_value_to_one(prob)

        return prob

    @staticmethod
    def set_last_value_to_one(probabilities):
        """!
        @brief Update the last same probabilities to one.
        @details All values of probability list equals to the last element are set to 1.
        
        """

        # Start from the last elem
        back_idx = - 1

        # All values equal to the last elem should be set to 1
        last_val = probabilities[back_idx]

        # for all elements or if a elem not equal to the last elem
        for _ in range(-1, -len(probabilities) - 1):
            if probabilities[back_idx] == last_val:
                probabilities[back_idx] = 1
            else:
                break

    @staticmethod
    def get_uniform(probabilities):
        """!
        @brief Returns index in probabilities.

        @param[in] probabilities (list): List with segments in increasing sequence with val in [0, 1],
                   for example, [0 0.1 0.2 0.3 1.0].
        """

        # Initialize return value
        res_idx = None

        # Get random num in range [0, 1)
        random_num = np.random.rand()

        # Find segment with  val1 < random_num < val2
        for _idx in range(len(probabilities)):
            if random_num < probabilities[_idx]:
                res_idx = _idx
                break

        if res_idx is None:
            print('Probabilities : ', probabilities)
            raise AttributeError("'probabilities' should contain 1 as the end of last segment(s)")

        return res_idx



1			"""!
2
3			@brief Genetic algorithm math API.
4
5			@authors Aleksey Kukushkin ([email protected])
6			@date 2014-2017
7			@copyright GNU Public License
8
9			@cond GNU_PUBLIC_LICENSE
10			PyClustering is free software: you can redistribute it and/or modify
11			it under the terms of the GNU General Public License as published by
12			the Free Software Foundation, either version 3 of the License, or
13			(at your option) any later version.
14
15			PyClustering is distributed in the hope that it will be useful,
16			but WITHOUT ANY WARRANTY; without even the implied warranty of
17			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18			GNU General Public License for more details.
19
20			You should have received a copy of the GNU General Public License
21			along with this program. If not, see <http://www.gnu.org/licenses/>.
22			@endcond
23
24			"""
25
26
27			import numpy as np;
			0 ignored issues – show Configuration introduced 2017-09-16 14:11 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...
28
29
30			class ga_math:
31			"""
32			@brief Genetic algorithm math API.
33
34			"""
35
36			@staticmethod
37			def calc_count_centers(chromosome):
38			return chromosome[chromosome.argmax()] + 1
39
40			@staticmethod
41			def get_clusters_representation(chromosome, count_clusters=None):
42			""" Convert chromosome to cluster representation:
43			chromosome : [0, 1, 1, 0, 2, 3, 3]
44			clusters: [[0, 3], [1, 2], [4], [5, 6]]
45			"""
46
47			if count_clusters is None:
48			count_clusters = ga_math.calc_count_centers(chromosome)
49
50			# Initialize empty clusters
51			clusters = [[] for _ in range(count_clusters)]
52
53			# Fill clusters with index of data
54			for _idx_data in range(len(chromosome)):
55			clusters[chromosome[_idx_data]].append(_idx_data)
56
57			return clusters
58
59			@staticmethod
60			def get_centres(chromosomes, data, count_clusters):
61			"""!
62			"""
63
64			centres = ga_math.calc_centers(chromosomes, data, count_clusters)
65
66			return centres
67
68			@staticmethod
69			def calc_centers(chromosomes, data, count_clusters=None):
70			"""!
71			"""
72
73			if count_clusters is None:
74			count_clusters = ga_math.calc_count_centers(chromosomes[0])
75
76			# Initialize center
77			centers = np.zeros(shape=(len(chromosomes), count_clusters, len(data[0])))
78
79			for _idx_chromosome in range(len(chromosomes)):
80
81			# Get count data in clusters
82			count_data_in_cluster = np.zeros(count_clusters)
83
84			# Next data point
85			for _idx in range(len(chromosomes[_idx_chromosome])):
86
87			cluster_num = chromosomes[_idx_chromosome][_idx]
88
89			centers[_idx_chromosome][cluster_num] += data[_idx]
90			count_data_in_cluster[cluster_num] += 1
91
92			for _idx_cluster in range(count_clusters):
93			if count_data_in_cluster[_idx_cluster] != 0:
94			centers[_idx_chromosome][_idx_cluster] /= count_data_in_cluster[_idx_cluster]
95
96			return centers
97
98			@staticmethod
99			def calc_probability_vector(fitness):
100			"""!
101			"""
102
103			if len(fitness) == 0:
104			raise AttributeError("Has no any fitness functions.")
105
106			# Get 1/fitness function
107			inv_fitness = np.zeros(len(fitness))
108
109			#
110			for _idx in range(len(inv_fitness)):
111
112			if fitness[_idx] != 0.0:
113			inv_fitness[_idx] = 1.0 / fitness[_idx]
114			else:
115			inv_fitness[_idx] = 0.0
116
117			# Initialize vector
118			prob = np.zeros(len(fitness))
119
120			# Initialize first element
121			prob[0] = inv_fitness[0]
122
123			# Accumulate values in probability vector
124			for _idx in range(1, len(inv_fitness)):
125			prob[_idx] = prob[_idx - 1] + inv_fitness[_idx]
126
127			# Normalize
128			prob /= prob[-1]
129
130			ga_math.set_last_value_to_one(prob)
131
132			return prob
133
134			@staticmethod
135			def set_last_value_to_one(probabilities):
136			"""!
137			@brief Update the last same probabilities to one.
138			@details All values of probability list equals to the last element are set to 1.
139
140			"""
141
142			# Start from the last elem
143			back_idx = - 1
144
145			# All values equal to the last elem should be set to 1
146			last_val = probabilities[back_idx]
147
148			# for all elements or if a elem not equal to the last elem
149			for _ in range(-1, -len(probabilities) - 1):
150			if probabilities[back_idx] == last_val:
151			probabilities[back_idx] = 1
152			else:
153			break
154
155			@staticmethod
156			def get_uniform(probabilities):
157			"""!
158			@brief Returns index in probabilities.
159
160			@param[in] probabilities (list): List with segments in increasing sequence with val in [0, 1],
161			for example, [0 0.1 0.2 0.3 1.0].
162			"""
163
164			# Initialize return value
165			res_idx = None
166
167			# Get random num in range [0, 1)
168			random_num = np.random.rand()
169
170			# Find segment with val1 < random_num < val2
171			for _idx in range(len(probabilities)):
172			if random_num < probabilities[_idx]:
173			res_idx = _idx
174			break
175
176			if res_idx is None:
177			print('Probabilities : ', probabilities)
178			raise AttributeError("'probabilities' should contain 1 as the end of last segment(s)")
179
180			return res_idx
181
182

annoviko / pyclustering

Push — master ( 941fee...e45971 )

ga_math.calc_probability_vector() B

Complexity

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Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

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2. Missing init.py files