Change name: variants -> solution.

examples/ex01/ex01.py

@@ -8,7 +8,7 @@
 from math import sin, cos
 
 from gaft import GAEngine
-from gaft.components import GAIndividual
+from gaft.components import BinaryIndividual
 from gaft.components import GAPopulation
 from gaft.operators import TournamentSelection
 from gaft.operators import UniformCrossover
@@ -21,7 +21,7 @@
 from gaft.analysis.fitness_store import FitnessStore
 
 # Define population.
-indv_template = GAIndividual(ranges=[(0, 10)], encoding='binary', eps=0.001)
+indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)
 population = GAPopulation(indv_template=indv_template, size=50).init()
 
 # Create genetic operators.
@@ -37,7 +37,7 @@
 # Define fitness function.
 @engine.fitness_register
 def fitness(indv):
-    x, = indv.variants
+    x, = indv.solution
     return x + 10*sin(5*x) + 7*cos(4*x)
 
 # Define on-the-fly analysis.
@@ -53,7 +53,7 @@ def register_step(self, g, population, engine):
 
     def finalize(self, population, engine):
         best_indv = population.best_indv(engine.fitness)
-        x = best_indv.variants
+        x = best_indv.solution
         y = engine.fitness(best_indv)
         msg = 'Optimal solution: ({}, {})'.format(x, y)
         self.logger.info(msg)

gaft/analysis/console_output.py

@@ -27,7 +27,7 @@ def register_step(self, g, population, engine):
 
     def finalize(self, population, engine):
         best_indv = population.best_indv(engine.fitness)
-        x = best_indv.variants
+        x = best_indv.solution
         y = engine.fitness(best_indv)
         msg = 'Optimal solution: ({}, {})'.format(x, y)
         self.logger.info(msg)

gaft/analysis/fitness_store.py

@@ -18,22 +18,22 @@ def setup(self, ng, engine):
         # Best fitness in each generation.
         self.fitness_values = []
 
-        # Best variants.
-        self.variants = []
+        # Best solution.
+        self.solution = []
 
     def register_step(self, g, population, engine):
         # Collect data.
         best_indv = population.best_indv(engine.fitness)
         best_fit = engine.ori_fitness(best_indv)
 
         self.ngs.append(g)
-        self.variants.append(best_indv.variants)
+        self.solution.append(best_indv.solution)
         self.fitness_values.append(best_fit)
 
     def finalize(self, population, engine):
         with open('best_fit.py', 'w', encoding='utf-8') as f:
             f.write('best_fit = [\n')
-            for ng, x, y in zip(self.ngs, self.variants, self.fitness_values):
+            for ng, x, y in zip(self.ngs, self.solution, self.fitness_values):
                 f.write('    ({}, {}, {}),\n'.format(ng, x, y))
             f.write(']\n\n')