ga_math.get_uniform() - Code Metrics - Inspection of "#16: Correction for one-dimensional data, implemen..." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — 0.7.dev ( bfeadc...20409b )

by Andrei

created 2017-09-13 16:22 UTC

ga_math.get_uniform() B

↳ Parent: ga_math

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	4
dl	0
loc	26
rs	8.5806
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 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):
        """ """

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

annoviko / pyclustering

Push — 0.7.dev ( bfeadc...20409b )

ga_math.get_uniform() B

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