Fix syntax errors

algorithms/genetic_algorithm.py

@@ -44,16 +44,16 @@ class GeneticAlgorithm(BaseAlgorithm):
         self.bit_resolution = options.bit_resolution
         self.mutation_factor = options.mutation_factor
         self.overwrite_mutations = options.overwrite_mutations
-        
+
         self.stop_criterion = options.stop_criterion # time, target, iter
         self.gen_to_repeat = options.gen_to_repeat
         self.time_limit = options.time_limit
         self.stop_after_score = options.stop_after_score
-        
+
         self.generations = 0
 
         self.chromosomes = []
-        
+
         self.buckets = None
         if options.use_buckets:
             self.bucket_count = options.bucket_count
@@ -67,15 +67,15 @@ class GeneticAlgorithm(BaseAlgorithm):
         if self.buckets is not None:
             assert len(self.null_degrees) == 1
             assert self.bucket_count & 1 == 0
-            assert self.stop_criterion is in ["time", "target", "iter"]
+            assert self.stop_criterion in ["time", "target", "iter"]
 
     def solve(self):
         self.initialize_sample()
         self.organize_sample()
         solve_function = getattr(self, "solve_" + self.stop_criterion)
         solve_function()
-        return (self.make_weights(self.chromosomes[0]), self.chromosomes[0].get_score(), generations)
-    
+        return (self.make_weights(self.chromosomes[0]), self.chromosomes[0].get_score(), self.generations)
+
     def solve_time(self):
         start_time = time_ns()
         while (time_ns() - start_time) // 10**6 <= self.time_limit:
@@ -83,13 +83,13 @@ class GeneticAlgorithm(BaseAlgorithm):
 
     def solve_target(self):
         start_time = time_ns()
-        while (time_ns() - start_time) // 10**6 <= 10 * 1000
+        while (time_ns() - start_time) // 10**6 <= 10 * 1000:
             self.step()
 
     def solve_iter(self):
         for generation in range(self.gen_to_repeat):
             self.step()
-        
+
     def step(self):
         self.create_children()
         self.mutate_sample()
@@ -98,7 +98,7 @@ class GeneticAlgorithm(BaseAlgorithm):
     def create_children(self):
         """Using the better half of the population, creates children overwriting the bottom half by doing crossovers.
         If use_buckets is True, uses AM-GM–based crossover. Otherwise, it uses the basic merger crossover."""
-        
+
         for child in range(self.sample_size // 2, self.sample_size - 1, 2):
             if self.buckets is None:
                 p1, p2 = sample(range(self.sample_size // 2), 2)
@@ -115,13 +115,13 @@ class GeneticAlgorithm(BaseAlgorithm):
                     key=lambda x: abs(x.pattern + p1.pattern)
                 ) if len(self.buckets[bucket_opp]) > 0 else choice(self.chromosomes)
                 self.crossover_bucket(p1, p2, child, child + 1)
-        
+
         self.generations += 1
 
     def organize_sample(self):
         """Reorganizes the sample by updating pattern for all chromosomes and sorting them by their scores.
         Optionally, if use_buckets is True, allocates each chromosome to its respective bucket."""
-        
+
         # Update pattern
         for chromosome in self.chromosomes:
             if chromosome.needs_update:
@@ -149,7 +149,7 @@ class GeneticAlgorithm(BaseAlgorithm):
     def mutate_sample(self):
         """Mutates the sample excluding the best chromosome.
         Overwrites the previous chromosomes if overwrite_mutations is True."""
-        
+
         if self.overwrite_mutations:
             # For all except the best chromosome
             for chromosome in self.chromosomes[1:]:
@@ -171,16 +171,16 @@ class GeneticAlgorithm(BaseAlgorithm):
 
     def make_weights(self, chromosome):
         """Returns e^{iθ} value for a chromosome's θs"""
-        
+
         weights = []
         for bits in chromosome.gene:
             angle = (bits - (2 ** self.bit_count - 1) / 2) * 2 * pi / (2 ** self.bit_resolution)
-            weights.append(exp(1j * angle))
+            weights.append(complex(cos(angle), sin(angle)))
         return weights
 
     def crossover(self, p1, p2, c1, c2):
         """Merges two parents' genes to create two children"""
-        
+
         self.chromosomes[c1] = deepcopy(self.chromosomes[p1])
         self.chromosomes[c2] = deepcopy(self.chromosomes[p2])
         for i in range(self.N):
@@ -192,7 +192,7 @@ class GeneticAlgorithm(BaseAlgorithm):
 
     def crossover_bucket(self, p1, p2, c1, c2):
         """Creates two children from parents' genes using the AM-GM approximation"""
-        
+
         for ii in range(self.N):
             g1 = p1.gene[ii]
             g2 = p2.gene[ii]
@@ -201,7 +201,8 @@ class GeneticAlgorithm(BaseAlgorithm):
 
     def initialize_sample(self):
         """Destroys all chromosomes and creates a new random population"""
-        
+
+        self.generations = 0
         self.chromosomes.clear()
         if self.overwrite_mutations:
             self.chromosomes = [