Code Duplication - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 19-22 lines in 2 locations

src/gradient_free_optimizers/optimizers/pop_opt/genetic_algorithm.py 1 location


        self.p_current = self.individuals[nth_pop]
        return self.p_current.init_pos()

    @EvolutionaryAlgorithmOptimizer.track_new_pos
    def iterate(self):
        n_ind = len(self.individuals)

        if n_ind == 1:
            self.p_current = self.individuals[0]
            return self.p_current.iterate()

        self.sort_pop_best_score()
        rnd_int = random.randint(0, len(self.pop_sorted) - 1)
        self.p_current = self.pop_sorted[rnd_int]

        total_rate = self.mutation_rate + self.crossover_rate
        rand = np.random.uniform(low=0, high=total_rate)

        if rand <= self.mutation_rate:
            return self.p_current.iterate()
        else:
            if not self.offspring_l:
                self._crossover()
            self.p_current.pos_new = self.offspring_l.pop(0)
            return self.p_current.pos_new

    @EvolutionaryAlgorithmOptimizer.track_new_score
    def evaluate(self, score_new):

src/gradient_free_optimizers/optimizers/pop_opt/evolution_strategy.py 1 location


        self.p_current = self.individuals[nth_pop]
        return self.p_current.init_pos()

    @EvolutionaryAlgorithmOptimizer.track_new_pos
    def iterate(self):
        self.n_ind = len(self.individuals)

        if self.n_ind == 1:
            self.p_current = self.individuals[0]
            return self.p_current.iterate()

        self.sort_pop_best_score()
        self.rnd_int = random.randint(0, len(self.pop_sorted) - 1)
        self.p_current = self.pop_sorted[self.rnd_int]

        total_rate = self.mutation_rate + self.crossover_rate
        rand = np.random.uniform(low=0, high=total_rate)

        if rand <= self.mutation_rate:
            return self.p_current.iterate()
        else:
            return self._cross()

    @EvolutionaryAlgorithmOptimizer.track_new_score
    def evaluate(self, score_new):

		@@ 83-104 (lines=22) @@
80		self.p_current = self.individuals[nth_pop]
81		return self.p_current.init_pos()
82
83		@EvolutionaryAlgorithmOptimizer.track_new_pos
84		def iterate(self):
85		n_ind = len(self.individuals)
86
87		if n_ind == 1:
88		self.p_current = self.individuals[0]
89		return self.p_current.iterate()
90
91		self.sort_pop_best_score()
92		rnd_int = random.randint(0, len(self.pop_sorted) - 1)
93		self.p_current = self.pop_sorted[rnd_int]
94
95		total_rate = self.mutation_rate + self.crossover_rate
96		rand = np.random.uniform(low=0, high=total_rate)
97
98		if rand <= self.mutation_rate:
99		return self.p_current.iterate()
100		else:
101		if not self.offspring_l:
102		self._crossover()
103		self.p_current.pos_new = self.offspring_l.pop(0)
104		return self.p_current.pos_new
105
106		@EvolutionaryAlgorithmOptimizer.track_new_score
107		def evaluate(self, score_new):

		@@ 77-95 (lines=19) @@
74		self.p_current = self.individuals[nth_pop]
75		return self.p_current.init_pos()
76
77		@EvolutionaryAlgorithmOptimizer.track_new_pos
78		def iterate(self):
79		self.n_ind = len(self.individuals)
80
81		if self.n_ind == 1:
82		self.p_current = self.individuals[0]
83		return self.p_current.iterate()
84
85		self.sort_pop_best_score()
86		self.rnd_int = random.randint(0, len(self.pop_sorted) - 1)
87		self.p_current = self.pop_sorted[self.rnd_int]
88
89		total_rate = self.mutation_rate + self.crossover_rate
90		rand = np.random.uniform(low=0, high=total_rate)
91
92		if rand <= self.mutation_rate:
93		return self.p_current.iterate()
94		else:
95		return self._cross()
96
97		@EvolutionaryAlgorithmOptimizer.track_new_score
98		def evaluate(self, score_new):

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 19-22 lines in 2 locations

src/gradient_free_optimizers/optimizers/pop_opt/genetic_algorithm.py 1 location

src/gradient_free_optimizers/optimizers/pop_opt/evolution_strategy.py 1 location