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

Completed

Push — 0.7.dev ( 20409b...5de18e )

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created 2017-09-14 10:13 UTC

ga_math.get_clusters_representation() A

↳ Parent: ga_math

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	4
dl	0
loc	18
rs	9.2
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 ga_math:
    """
    """

    @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			import numpy as np
			0 ignored issues – show Configuration introduced 2017-09-08 16:24 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...
3
4
5			class ga_math:
6			"""
7			"""
8
9			@staticmethod
10			def calc_count_centers(chromosome):
11			return chromosome[chromosome.argmax()] + 1
12
13			@staticmethod
14			def get_clusters_representation(chromosome, count_clusters=None):
15			""" Convert chromosome to cluster representation:
16			chromosome : [0, 1, 1, 0, 2, 3, 3]
17			clusters: [[0, 3], [1, 2], [4], [5, 6]]
18			"""
19
20			if count_clusters is None:
21			count_clusters = ga_math.calc_count_centers(chromosome)
22
23			# Initialize empty clusters
24			clusters = [[] for _ in range(count_clusters)]
25
26			# Fill clusters with index of data
27			for _idx_data in range(len(chromosome)):
28			clusters[chromosome[_idx_data]].append(_idx_data)
29
30			return clusters
31
32			@staticmethod
33			def get_centres(chromosomes, data, count_clusters):
34			""" """
35
36			centres = ga_math.calc_centers(chromosomes, data, count_clusters)
37
38			return centres
39
40			@staticmethod
41			def calc_centers(chromosomes, data, count_clusters=None):
42			""" """
43
44			if count_clusters is None:
45			count_clusters = ga_math.calc_count_centers(chromosomes[0])
46
47			# Initialize center
48			centers = np.zeros(shape=(len(chromosomes), count_clusters, len(data[0])))
49
50			for _idx_chromosome in range(len(chromosomes)):
51
52			# Get count data in clusters
53			count_data_in_cluster = np.zeros(count_clusters)
54
55			# Next data point
56			for _idx in range(len(chromosomes[_idx_chromosome])):
57
58			cluster_num = chromosomes[_idx_chromosome][_idx]
59
60			centers[_idx_chromosome][cluster_num] += data[_idx]
61			count_data_in_cluster[cluster_num] += 1
62
63			for _idx_cluster in range(count_clusters):
64			if count_data_in_cluster[_idx_cluster] != 0:
65			centers[_idx_chromosome][_idx_cluster] /= count_data_in_cluster[_idx_cluster]
66
67			return centers
68
69			@staticmethod
70			def calc_probability_vector(fitness):
71			""" """
72
73			if len(fitness) == 0:
74			raise AttributeError("Has no any fitness functions.")
75
76			# Get 1/fitness function
77			inv_fitness = np.zeros(len(fitness))
78
79			#
80			for _idx in range(len(inv_fitness)):
81
82			if fitness[_idx] != 0.0:
83			inv_fitness[_idx] = 1.0 / fitness[_idx]
84			else:
85			inv_fitness[_idx] = 0.0
86
87			# Initialize vector
88			prob = np.zeros(len(fitness))
89
90			# Initialize first element
91			prob[0] = inv_fitness[0]
92
93			# Accumulate values in probability vector
94			for _idx in range(1, len(inv_fitness)):
95			prob[_idx] = prob[_idx - 1] + inv_fitness[_idx]
96
97			# Normalize
98			prob /= prob[-1]
99
100			ga_math.set_last_value_to_one(prob)
101
102			return prob
103
104			@staticmethod
105			def set_last_value_to_one(probabilities):
106			"""!
107			@brief Update the last same probabilities to one.
108			@details All values of probability list equals to the last element are set to 1.
109			"""
110
111			# Start from the last elem
112			back_idx = - 1
113
114			# All values equal to the last elem should be set to 1
115			last_val = probabilities[back_idx]
116
117			# for all elements or if a elem not equal to the last elem
118			for _ in range(-1, -len(probabilities) - 1):
119			if probabilities[back_idx] == last_val:
120			probabilities[back_idx] = 1
121			else:
122			break
123
124			@staticmethod
125			def get_uniform(probabilities):
126			"""!
127			@brief Returns index in probabilities.
128
129			@param[in] probabilities (list): List with segments in increasing sequence with val in [0, 1],
130			for example, [0 0.1 0.2 0.3 1.0].
131			"""
132
133			# Initialize return value
134			res_idx = None
135
136			# Get random num in range [0, 1)
137			random_num = np.random.rand()
138
139			# Find segment with val1 < random_num < val2
140			for _idx in range(len(probabilities)):
141			if random_num < probabilities[_idx]:
142			res_idx = _idx
143			break
144
145			if res_idx is None:
146			print('Probabilities : ', probabilities)
147			raise AttributeError("'probabilities' should contain 1 as the end of last segment(s)")
148
149			return res_idx
150
151

annoviko / pyclustering

Push — 0.7.dev ( 20409b...5de18e )

ga_math.get_clusters_representation() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

Duplication Side-by-Side

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2. Missing init.py files