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):

		@@ 93-114 (lines=22) @@
90		self.p_current = self.individuals[nth_pop]
91		return self.p_current.init_pos()
92
93		@EvolutionaryAlgorithmOptimizer.track_new_pos
94		def iterate(self):
95		n_ind = len(self.individuals)
96
97		if n_ind == 1:
98		self.p_current = self.individuals[0]
99		return self.p_current.iterate()
100
101		self.sort_pop_best_score()
102		rnd_int = random.randint(0, len(self.pop_sorted) - 1)
103		self.p_current = self.pop_sorted[rnd_int]
104
105		total_rate = self.mutation_rate + self.crossover_rate
106		rand = np.random.uniform(low=0, high=total_rate)
107
108		if rand <= self.mutation_rate:
109		return self.p_current.iterate()
110		else:
111		if not self.offspring_l:
112		self._crossover()
113		self.p_current.pos_new = self.offspring_l.pop(0)
114		return self.p_current.pos_new
115
116		@EvolutionaryAlgorithmOptimizer.track_new_score
117		def evaluate(self, score_new):

		@@ 88-106 (lines=19) @@
85		self.p_current = self.individuals[nth_pop]
86		return self.p_current.init_pos()
87
88		@EvolutionaryAlgorithmOptimizer.track_new_pos
89		def iterate(self):
90		self.n_ind = len(self.individuals)
91
92		if self.n_ind == 1:
93		self.p_current = self.individuals[0]
94		return self.p_current.iterate()
95
96		self.sort_pop_best_score()
97		self.rnd_int = random.randint(0, len(self.pop_sorted) - 1)
98		self.p_current = self.pop_sorted[self.rnd_int]
99
100		total_rate = self.mutation_rate + self.crossover_rate
101		rand = np.random.uniform(low=0, high=total_rate)
102
103		if rand <= self.mutation_rate:
104		return self.p_current.iterate()
105		else:
106		return self._cross()
107
108		@EvolutionaryAlgorithmOptimizer.track_new_score
109		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