Run example instance

- Save results to csv
- Generate csv attributes

src/file.py

@@ -2,5 +2,15 @@ import csv
 
 
 class File(object):
-    def __init__(self, filename):
+    def __init__(self, filename, header):
-        pass
+        self.file = open(filename, 'w')
+        self.writer = csv.DictWriter(self.file, fieldnames=header)
+
+    def write_header(self):
+        self.writer.writeheader()
+
+    def write_row(self, row):
+        self.writer.writerow(row)
+
+    def close(self):
+        self.file.close()

src/lifecycle.py

@@ -12,15 +12,14 @@ class Lifecycle(object):
 
     def start(self):
         for epoch in range(0, self.params["iter"]):
-            elite = round(self.params["elite"] * self.params["population_size"])
+            elite = self.params["elite"]
-            crossover = round((self.params["crossover"]
+            crossover = self.params["crossover"]
-                               * self.params["population_size"]) / 2)
             self.population.advance_generation(elite, crossover)
             self.best_fit.append(self.population.best_fitness())
             self.average_fit.append(self.population.avg_fitness())
 
-    def best_member(self):
+    def best_fitness(self):
-        return self.population.best_member()
+        return self.population.best_fitness()
 
     def generate_graph(self, show=False, location=None):
         plt.plot(self.best_fit)
@@ -30,3 +29,8 @@ class Lifecycle(object):
             plt.show()
         if location is not None:
             plt.savefig(location)
+
+    def get_csv(self):
+        id = {'id': self.id}
+        best_fitness = {'best_fit': self.best_fitness()}
+        return {**id, **best_fitness, **self.params}

src/main.py

@@ -1,16 +1,27 @@
 import os
 from lifecycle import Lifecycle
+from file import File
+
+param_example = {'population_size': 10,
+                 'elite': round(0.1 * 10),
+                 'crossover': round(0.6 * 10),
+                 'iter': 1000}
 
 
 def cls():
     os.system('cls' if os.name == 'nt' else 'clear')
 
 
-ga = Lifecycle(1, {'population_size': 10,
+output_file = File("output.csv", ['id', 'best_fit'] + list(param_example))
-                   'elite': 0.1,
+
-                   'crossover': 0.6,
+ga = Lifecycle(1, param_example)
-                   'iter': 1000})
 
 ga.start()
 
+output_file.write_header()
+
+output_file.write_row(ga.get_csv())
+
+output_file.close()
+
 ga.generate_graph(show=True)

src/population.py

@@ -68,7 +68,7 @@ class Population(object):
 
     def elite(self, amount):
         sorted_members = sorted(self.members, key=lambda
-            x: x.fitness(), reverse=True)[:amount]
+                                x: x.fitness(), reverse=True)[:amount]
 
         return sorted_members
 
@@ -92,7 +92,7 @@ class Population(object):
 
         self.members = new_generation
 
-        self.mutate(20)
+        self.mutate(10)
 
     def remove_member(self, member):
         self.members.remove(member)