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

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Push — 0.7.dev ( bfeadc...20409b )

by Andrei

created 2017-09-13 16:22 UTC

genetic_algorithm._calc_fitness_function() B

↳ Parent: genetic_algorithm

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

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Metric	Value
cc	3
dl	0
loc	27
rs	8.8571
c	0
b	0
f	0

"""!

@brief Cluster analysis algorithm: Genetic clustering algorithm (GA).

@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
import math;

from pyclustering.cluster.ga_maths import ga_math;


class genetic_algorithm:
    """!
    @brief Class represents Genetic clustering algorithm.
    @details The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres.

    """

    def __init__(self, data, count_clusters, chromosome_count, population_count, count_mutation_gens=2,
                 coeff_mutation_count=0.25, select_coeff=1.0):
        """!
        @brief Initialize genetic clustering algorithm for cluster analysis.
        
        @param[in] data (numpy.array|list): Input data for clustering that is represented by two dimensional array where each row is a point,
                    for example, [[0.0, 2.1], [0.1, 2.0], [-0.2, 2.4]].
        @param[in] count_clusters (uint): Amount of clusters that should be allocated in the data.
        @param[in] chromosome_count (uint): Amount of chromosomes in each population.
        @param[in] population_count (uint): Amount of populations.
        @param[in] count_mutation_gens (uint): Amount of genes in chromosome that is mutated on each step.
        @param[in] coeff_mutation_count (float): Percent of chromosomes for mutation, destributed in range (0, 1] and
                    thus amount of chromosomes is defined as follows: 'chromosome_count' * 'coeff_mutation_count'.
        @param[in] select_coeff (float): Exponential coefficient for selection procedure that is used as follows: math.exp(1 + fitness(chromosome) * select_coeff).
        
        """
        
        # Initialize random
        np.random.seed()

        # Clustering data
        if type(data) is list:
            self.data = np.array(data)
        else:
            self.data = data

        # Count clusters
        self.count_clusters = count_clusters

        # Home many chromosome in population
        self.chromosome_count = chromosome_count

        # How many populations
        self.population_count = population_count

        # Count mutation genes
        self.count_mutation_gens = count_mutation_gens

        # Crossover rate
        self.crossover_rate = 1.0

        # Count of chromosome for mutation (range [0, 1])
        self.coeff_mutation_count = coeff_mutation_count

        # Exponential coeff for selection
        self.select_coeff = select_coeff


    def process(self):
        """!
        @brief Perform clustering procedure in line with rule of genetic clustering algorithm.
        
        @see get_clusters()
        
        """

        # Initialize population
        chromosomes = self._init_population(self.count_clusters, len(self.data), self.chromosome_count)

        # Initialize the Best solution
        best_chromosome, best_ff = self._get_best_chromosome(chromosomes, self.data, self.count_clusters)

        # Next population
        for _ in range(self.population_count):

            # Select
            chromosomes = self._select(chromosomes, self.data, self.count_clusters, self.select_coeff)

            # Crossover
            self._crossover(chromosomes)

            # Mutation
            self._mutation(chromosomes, self.count_clusters, self.count_mutation_gens, self.coeff_mutation_count)

            # Update the Best Solution
            new_best_chromosome, new_best_ff = self._get_best_chromosome(chromosomes, self.data, self.count_clusters)

            # Get best chromosome
            if new_best_ff < best_ff:
                best_ff = new_best_ff
                best_chromosome = new_best_chromosome

        return best_chromosome, best_ff



    def get_clusters(self):
        """!
        @brief Returns list of allocated clusters, each cluster contains indexes of objects from the data.
        
        @return (list) List of allocated clusters.
        
        @see process()
        
        """
        
        raise NameError("Implementation is require.");


    @staticmethod
    def _select(chromosomes, data, count_clusters, select_coeff):
        """!
        @brief Performs selection procedure where new chromosomes are calculated.
        
        @param[in] chromosomes (numpy.array): Chromosomes 
        
        """

        # Calc centers
        centres = ga_math.get_centres(chromosomes, data, count_clusters)


        # Calc fitness functions
        fitness = genetic_algorithm._calc_fitness_function(centres, data, chromosomes)

        for _idx in range(len(fitness)):
            fitness[_idx] = math.exp(1 + fitness[_idx] * select_coeff)

        # Calc probability vector
        probabilities = ga_math.calc_probability_vector(fitness)

        # Select P chromosomes with probabilities
        new_chromosomes = np.zeros(chromosomes.shape, dtype=np.int)

        # Selecting
        for _idx in range(len(chromosomes)):
            new_chromosomes[_idx] = chromosomes[ga_math.get_uniform(probabilities)]

        return new_chromosomes


    @staticmethod
    def _crossover(chromosomes):
        """!
        @brief Crossover procedure.
        
        """

        # Get pairs to Crossover
        pairs_to_crossover = np.array(range(len(chromosomes)))

        # Set random pairs
        np.random.shuffle(pairs_to_crossover)

        # Index offset ( pairs_to_crossover split into 2 parts : [V1, V2, .. | P1, P2, ...] crossover between V<->P)
        offset_in_pair = int(len(pairs_to_crossover) / 2)

        # For each pair
        for _idx in range(offset_in_pair):

            # Generate random mask for crossover
            crossover_mask = genetic_algorithm._get_crossover_mask(len(chromosomes[_idx]))

            # Crossover a pair
            genetic_algorithm._crossover_a_pair(chromosomes[pairs_to_crossover[_idx]],
                                               chromosomes[pairs_to_crossover[_idx + offset_in_pair]],
                                               crossover_mask)

    @staticmethod
    def _mutation(chromosomes, count_clusters, count_gen_for_mutation, coeff_mutation_count):
        """!
        @brief Mutation procedure.
        
        """

        # Count gens in Chromosome
        count_gens = len(chromosomes[0])

        # Get random chromosomes for mutation
        random_idx_chromosomes = np.array(range(len(chromosomes)))
        np.random.shuffle(random_idx_chromosomes)

        #
        for _idx_chromosome in range(int(len(random_idx_chromosomes) * coeff_mutation_count)):

            #
            for _ in range(count_gen_for_mutation):

                # Get random gen
                gen_num = np.random.randint(count_gens)

                # Set random cluster
                chromosomes[random_idx_chromosomes[_idx_chromosome]][gen_num] = np.random.randint(count_clusters)


    @staticmethod
    def _crossover_a_pair(chromosome_1, chromosome_2, mask):
        """!
        @brief Crossovers a pair of chromosomes.
        
        @param[in] chromosome_1 (numpy.array): The first chromosome for crossover.
        @param[in] chromosome_2 (numpy.array): The second chromosome for crossover.
        @param[in] mask (numpy.array): Crossover mask that defines which genes should be swapped.
        
        """

        for _idx in range(len(chromosome_1)):

            if mask[_idx] == 1:
                # Swap values
                chromosome_1[_idx], chromosome_2[_idx] = chromosome_2[_idx], chromosome_1[_idx]


    @staticmethod
    def _get_crossover_mask(mask_length):
        """!
        @brief Crossover mask to crossover a pair of chromosomes.
        
        @param[in] mask_length (uint): Length of the mask.
        
        """

        # Initialize mask
        mask = np.zeros(mask_length)

        # Set a half of array to 1
        mask[:int(int(mask_length) / 6)] = 1

        # Random shuffle
        np.random.shuffle(mask)

        return mask


    @staticmethod
    def _init_population(count_clusters, count_data, chromosome_count):
        """!
        @brief Returns first population as a uniform random choice.
        
        @param[in] count_clusters (uint):
        @param[in] count_data (uint):
        @param[in] chromosome_count (uint): 
        
        """

        population = np.random.randint(count_clusters, size=(chromosome_count, count_data))

        return population


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

        # Calc centers
        centres = ga_math.get_centres(chromosomes, data, count_clusters)

        # Calc Fitness functions
        fitness_function = genetic_algorithm._calc_fitness_function(centres, data, chromosomes)

        # Index of the best chromosome
        best_chromosome_idx = fitness_function.argmin()

        # Get chromosome with the best fitness function
        return chromosomes[best_chromosome_idx], fitness_function[best_chromosome_idx]


    @staticmethod
    def _calc_fitness_function(centres, data, chromosomes):
        """!
        @brief 
        
        """

        # Get count of chromosomes and clusters
        count_chromosome = len(chromosomes)

        # Initialize fitness function values
        fitness_function = np.zeros(count_chromosome)

        # Calc fitness function for each chromosome
        for _idx_chromosome in range(count_chromosome):

            # Get centers for a selected chromosome
            centres_data = np.zeros(data.shape)

            # Fill data centres
            for _idx in range(len(data)):
                centres_data[_idx] = centres[_idx_chromosome][chromosomes[_idx_chromosome][_idx]]

            # Get City Block distance for a chromosome
            fitness_function[_idx_chromosome] += np.sum(abs(data - centres_data))

        return fitness_function


Push — 0.7.dev ( bfeadc...20409b )

genetic_algorithm._calc_fitness_function() B

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

1		"""!
2
3		@brief Cluster analysis algorithm: Genetic clustering algorithm (GA).
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		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...
27		import math;
28
29		from pyclustering.cluster.ga_maths import ga_math;
30
31
32		class genetic_algorithm:
33		"""!
34		@brief Class represents Genetic clustering algorithm.
35		@details The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres.
36
37		"""
38
39		def __init__(self, data, count_clusters, chromosome_count, population_count, count_mutation_gens=2,
40		coeff_mutation_count=0.25, select_coeff=1.0):
41		"""!
42		@brief Initialize genetic clustering algorithm for cluster analysis.
43
44		@param[in] data (numpy.array\|list): Input data for clustering that is represented by two dimensional array where each row is a point,
45		for example, [[0.0, 2.1], [0.1, 2.0], [-0.2, 2.4]].
46		@param[in] count_clusters (uint): Amount of clusters that should be allocated in the data.
47		@param[in] chromosome_count (uint): Amount of chromosomes in each population.
48		@param[in] population_count (uint): Amount of populations.
49		@param[in] count_mutation_gens (uint): Amount of genes in chromosome that is mutated on each step.
50		@param[in] coeff_mutation_count (float): Percent of chromosomes for mutation, destributed in range (0, 1] and
51		thus amount of chromosomes is defined as follows: 'chromosome_count' * 'coeff_mutation_count'.
52		@param[in] select_coeff (float): Exponential coefficient for selection procedure that is used as follows: math.exp(1 + fitness(chromosome) * select_coeff).
53
54		"""
55
56		# Initialize random
57		np.random.seed()
58
59		# Clustering data
60		if type(data) is list:
61		self.data = np.array(data)
62		else:
63		self.data = data
64
65		# Count clusters
66		self.count_clusters = count_clusters
67
68		# Home many chromosome in population
69		self.chromosome_count = chromosome_count
70
71		# How many populations
72		self.population_count = population_count
73
74		# Count mutation genes
75		self.count_mutation_gens = count_mutation_gens
76
77		# Crossover rate
78		self.crossover_rate = 1.0
79
80		# Count of chromosome for mutation (range [0, 1])
81		self.coeff_mutation_count = coeff_mutation_count
82
83		# Exponential coeff for selection
84		self.select_coeff = select_coeff
85
86
87		def process(self):
88		"""!
89		@brief Perform clustering procedure in line with rule of genetic clustering algorithm.
90
91		@see get_clusters()
92
93		"""
94
95		# Initialize population
96		chromosomes = self._init_population(self.count_clusters, len(self.data), self.chromosome_count)
97
98		# Initialize the Best solution
99		best_chromosome, best_ff = self._get_best_chromosome(chromosomes, self.data, self.count_clusters)
100
101		# Next population
102		for _ in range(self.population_count):
103
104		# Select
105		chromosomes = self._select(chromosomes, self.data, self.count_clusters, self.select_coeff)
106
107		# Crossover
108		self._crossover(chromosomes)
109
110		# Mutation
111		self._mutation(chromosomes, self.count_clusters, self.count_mutation_gens, self.coeff_mutation_count)
112
113		# Update the Best Solution
114		new_best_chromosome, new_best_ff = self._get_best_chromosome(chromosomes, self.data, self.count_clusters)
115
116		# Get best chromosome
117		if new_best_ff < best_ff:
118		best_ff = new_best_ff
119		best_chromosome = new_best_chromosome
120
121		return best_chromosome, best_ff
122
123	View Code Duplication
		0 ignored issues – show Duplication introduced 2017-09-13 16:23 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
124		def get_clusters(self):
125		"""!
126		@brief Returns list of allocated clusters, each cluster contains indexes of objects from the data.
127
128		@return (list) List of allocated clusters.
129
130		@see process()
131
132		"""
133
134		raise NameError("Implementation is require.");
135
136
137		@staticmethod
138		def _select(chromosomes, data, count_clusters, select_coeff):
139		"""!
140		@brief Performs selection procedure where new chromosomes are calculated.
141
142		@param[in] chromosomes (numpy.array): Chromosomes
143
144		"""
145
146		# Calc centers
147	View Code Duplication	centres = ga_math.get_centres(chromosomes, data, count_clusters)
		0 ignored issues – show Duplication introduced 2017-09-13 16:23 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
148
149		# Calc fitness functions
150		fitness = genetic_algorithm._calc_fitness_function(centres, data, chromosomes)
151
152		for _idx in range(len(fitness)):
153		fitness[_idx] = math.exp(1 + fitness[_idx] * select_coeff)
154
155		# Calc probability vector
156		probabilities = ga_math.calc_probability_vector(fitness)
157
158		# Select P chromosomes with probabilities
159		new_chromosomes = np.zeros(chromosomes.shape, dtype=np.int)
160
161		# Selecting
162		for _idx in range(len(chromosomes)):
163		new_chromosomes[_idx] = chromosomes[ga_math.get_uniform(probabilities)]
164
165		return new_chromosomes
166
167
168		@staticmethod
169		def _crossover(chromosomes):
170		"""!
171		@brief Crossover procedure.
172
173		"""
174
175		# Get pairs to Crossover
176		pairs_to_crossover = np.array(range(len(chromosomes)))
177
178		# Set random pairs
179		np.random.shuffle(pairs_to_crossover)
180
181		# Index offset ( pairs_to_crossover split into 2 parts : [V1, V2, .. \| P1, P2, ...] crossover between V<->P)
182		offset_in_pair = int(len(pairs_to_crossover) / 2)
183
184		# For each pair
185		for _idx in range(offset_in_pair):
186
187		# Generate random mask for crossover
188		crossover_mask = genetic_algorithm._get_crossover_mask(len(chromosomes[_idx]))
189
190		# Crossover a pair
191		genetic_algorithm._crossover_a_pair(chromosomes[pairs_to_crossover[_idx]],
192		chromosomes[pairs_to_crossover[_idx + offset_in_pair]],
193		crossover_mask)
194
195		@staticmethod
196		def _mutation(chromosomes, count_clusters, count_gen_for_mutation, coeff_mutation_count):
197		"""!
198		@brief Mutation procedure.
199
200		"""
201
202		# Count gens in Chromosome
203		count_gens = len(chromosomes[0])
204
205		# Get random chromosomes for mutation
206		random_idx_chromosomes = np.array(range(len(chromosomes)))
207		np.random.shuffle(random_idx_chromosomes)
208
209		#
210		for _idx_chromosome in range(int(len(random_idx_chromosomes) * coeff_mutation_count)):
211
212		#
213		for _ in range(count_gen_for_mutation):
214
215		# Get random gen
216		gen_num = np.random.randint(count_gens)
217
218		# Set random cluster
219		chromosomes[random_idx_chromosomes[_idx_chromosome]][gen_num] = np.random.randint(count_clusters)
220
221
222		@staticmethod
223		def _crossover_a_pair(chromosome_1, chromosome_2, mask):
224		"""!
225		@brief Crossovers a pair of chromosomes.
226
227		@param[in] chromosome_1 (numpy.array): The first chromosome for crossover.
228		@param[in] chromosome_2 (numpy.array): The second chromosome for crossover.
229		@param[in] mask (numpy.array): Crossover mask that defines which genes should be swapped.
230
231		"""
232
233		for _idx in range(len(chromosome_1)):
234
235		if mask[_idx] == 1:
236		# Swap values
237		chromosome_1[_idx], chromosome_2[_idx] = chromosome_2[_idx], chromosome_1[_idx]
238
239
240		@staticmethod
241		def _get_crossover_mask(mask_length):
242		"""!
243		@brief Crossover mask to crossover a pair of chromosomes.
244
245		@param[in] mask_length (uint): Length of the mask.
246
247		"""
248
249		# Initialize mask
250		mask = np.zeros(mask_length)
251
252		# Set a half of array to 1
253		mask[:int(int(mask_length) / 6)] = 1
254
255		# Random shuffle
256		np.random.shuffle(mask)
257
258		return mask
259
260
261		@staticmethod
262		def _init_population(count_clusters, count_data, chromosome_count):
263		"""!
264		@brief Returns first population as a uniform random choice.
265
266		@param[in] count_clusters (uint):
267		@param[in] count_data (uint):
268		@param[in] chromosome_count (uint):
269
270		"""
271
272		population = np.random.randint(count_clusters, size=(chromosome_count, count_data))
273
274		return population
275
276
277		@staticmethod
278		def _get_best_chromosome(chromosomes, data, count_clusters):
279		"""!
280		@brief
281
282		"""
283
284		# Calc centers
285		centres = ga_math.get_centres(chromosomes, data, count_clusters)
286
287		# Calc Fitness functions
288		fitness_function = genetic_algorithm._calc_fitness_function(centres, data, chromosomes)
289
290		# Index of the best chromosome
291		best_chromosome_idx = fitness_function.argmin()
292
293		# Get chromosome with the best fitness function
294		return chromosomes[best_chromosome_idx], fitness_function[best_chromosome_idx]
295
296
297		@staticmethod
298		def _calc_fitness_function(centres, data, chromosomes):
299		"""!
300		@brief
301
302		"""
303
304		# Get count of chromosomes and clusters
305		count_chromosome = len(chromosomes)
306
307		# Initialize fitness function values
308		fitness_function = np.zeros(count_chromosome)
309
310		# Calc fitness function for each chromosome
311		for _idx_chromosome in range(count_chromosome):
312
313		# Get centers for a selected chromosome
314		centres_data = np.zeros(data.shape)
315
316		# Fill data centres
317		for _idx in range(len(data)):
318		centres_data[_idx] = centres[_idx_chromosome][chromosomes[_idx_chromosome][_idx]]
319
320		# Get City Block distance for a chromosome
321		fitness_function[_idx_chromosome] += np.sum(abs(data - centres_data))
322
323		return fitness_function
324

annoviko / pyclustering

Push — 0.7.dev ( bfeadc...20409b )

genetic_algorithm._calc_fitness_function() B

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