Remove verbosity parameters.

examples/1D_optimization_example.ipynb

@@ -21,7 +21,7 @@
    "outputs": [],
    "source": [
     "from gaft.components import BinaryIndividual\n",
-    "indv = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)"
+    "indv = BinaryIndividual(ranges=[(0, 10)], eps=0.001)"
    ]
   },
   {
@@ -182,12 +182,12 @@
      "output_type": "stream",
      "text": [
       "gaft.ConsoleOutput   INFO     Generation number: 50 Population number: 50\n",
-      "gaft.ConsoleOutput   INFO     Generation: 1, best fitness: 15.136, scaled fitness: 15.136\n",
-      "gaft.ConsoleOutput   INFO     Generation: 2, best fitness: 15.136, scaled fitness: 15.136\n",
-      "gaft.ConsoleOutput   INFO     Generation: 3, best fitness: 15.136, scaled fitness: 15.136\n",
-      "gaft.ConsoleOutput   INFO     Generation: 4, best fitness: 15.136, scaled fitness: 15.136\n",
-      "gaft.ConsoleOutput   INFO     Generation: 5, best fitness: 15.154, scaled fitness: 15.154\n",
-      "gaft.ConsoleOutput   INFO     Generation: 6, best fitness: 15.154, scaled fitness: 15.154\n",
+      "gaft.ConsoleOutput   INFO     Generation: 1, best fitness: 15.077, scaled fitness: 15.077\n",
+      "gaft.ConsoleOutput   INFO     Generation: 2, best fitness: 15.164, scaled fitness: 15.164\n",
+      "gaft.ConsoleOutput   INFO     Generation: 3, best fitness: 15.164, scaled fitness: 15.164\n",
+      "gaft.ConsoleOutput   INFO     Generation: 4, best fitness: 15.164, scaled fitness: 15.164\n",
+      "gaft.ConsoleOutput   INFO     Generation: 5, best fitness: 15.164, scaled fitness: 15.164\n",
+      "gaft.ConsoleOutput   INFO     Generation: 6, best fitness: 15.164, scaled fitness: 15.164\n",
       "gaft.ConsoleOutput   INFO     Generation: 7, best fitness: 15.164, scaled fitness: 15.164\n",
       "gaft.ConsoleOutput   INFO     Generation: 8, best fitness: 15.164, scaled fitness: 15.164\n",
       "gaft.ConsoleOutput   INFO     Generation: 9, best fitness: 15.164, scaled fitness: 15.164\n",
@@ -232,7 +232,7 @@
       "gaft.ConsoleOutput   INFO     Generation: 48, best fitness: 15.164, scaled fitness: 15.164\n",
       "gaft.ConsoleOutput   INFO     Generation: 49, best fitness: 15.164, scaled fitness: 15.164\n",
       "gaft.ConsoleOutput   INFO     Generation: 50, best fitness: 15.164, scaled fitness: 15.164\n",
-      "gaft.ConsoleOutput   INFO     Optimal solution: ([0.8935546875], 15.163825486392565)\n"
+      "gaft.ConsoleOutput   INFO     Optimal solution: ([0.892333984375], 15.164338102247847)\n"
      ]
     }
    ],
@@ -278,7 +278,7 @@
     {
      "data": {
       "text/plain": [
-       "[0.8935546875]"
+       "[0.892333984375]"
       ]
      },
      "execution_count": 11,
@@ -305,7 +305,7 @@
     {
      "data": {
       "text/plain": [
-       "15.163825486392565"
+       "15.164338102247847"
       ]
      },
      "execution_count": 12,

tests/dynamic_linear_scaling_test.py

@@ -24,7 +24,7 @@ def test_run(self):
         '''
         Make sure GA engine can run correctly.
         '''
-        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
+        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)
         population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.

tests/engine_test.py

@@ -24,7 +24,7 @@ def test_run(self):
         '''
         Make sure GA engine can run correctly.
         '''
-        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
+        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)
         population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.

tests/exponential_ranking_selection_test.py

@@ -19,7 +19,7 @@ def fitness(indv):
         self.fitness = fitness
 
     def test_selection(self):
-        indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 30)])
         p = Population(indv)
         p.init()
 

tests/flip_bit_big_mutation_test.py

@@ -22,7 +22,7 @@ def setUp(self):
     def test_mutate(self):
         ''' Make sure the individual can be mutated correctly.
         '''
-        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
+        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)
         population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.

tests/flip_bit_mutation_test.py

@@ -17,7 +17,7 @@ def setUp(self):
     def test_mutate_binary_indv(self):
         ''' Make sure the individual can be mutated correctly.
         '''
-        indv = BinaryIndividual(ranges=[(0, 1)], verbosity=0).init(solution=[0.398])
+        indv = BinaryIndividual(ranges=[(0, 1)]).init(solution=[0.398])
         mutation = FlipBitMutation(pm=1.0)
         chromsome_before = [0, 1, 1, 0, 0, 1, 0, 1, 1]
         chromsome_after = [1, 0, 0, 1, 1, 0, 1, 0, 0]

tests/individual_test.py

@@ -16,7 +16,7 @@ def setUp(self):
     def test_binary_encoding(self):
         ''' Make sure individual can decode and encode binary gene correctly.
         '''
-        indv = BinaryIndividual(ranges=[(0, 1)], eps=0.001, verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 1)], eps=0.001)
         indv.init(solution=[0.398])
 
         # Test binary chromsome.
@@ -26,7 +26,7 @@ def test_binary_encoding(self):
         # Test decode.
         self.assertListEqual(indv.decode(), [0.396484375])
 
-        indv = BinaryIndividual(ranges=[(0, 1), (-1, 1)],  eps=0.001, verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 1), (-1, 1)],  eps=0.001)
         indv.init(solution=[0.398, 0.66])
 
         # Test binary chromsome.
@@ -39,15 +39,15 @@ def test_binary_encoding(self):
     def test_descriptors(self):
         ''' Make sure descriptors can check the parameters correctly.
         '''
-        self.assertRaises(TypeError, BinaryIndividual, ranges=0.1, eps=0.001, verbosity=0)
-        self.assertRaises(TypeError, BinaryIndividual, ranges=[(0, 1)], eps='asdf', verbosity=0)
-        self.assertRaises(ValueError, BinaryIndividual, ranges=[(0, 1)], eps=10.0, verbosity=0)
-        self.assertRaises(ValueError, BinaryIndividual, ranges=[(0, 1)], eps=[1, 2], verbosity=0)
+        self.assertRaises(TypeError, BinaryIndividual, ranges=0.1, eps=0.001)
+        self.assertRaises(TypeError, BinaryIndividual, ranges=[(0, 1)], eps='asdf')
+        self.assertRaises(ValueError, BinaryIndividual, ranges=[(0, 1)], eps=10.0)
+        self.assertRaises(ValueError, BinaryIndividual, ranges=[(0, 1)], eps=[1, 2])
 
     def test_init(self):
         ''' Make sure the individual can be initialized correctly.
         '''
-        indv = BinaryIndividual(ranges=[(0, 1)], eps=0.001, verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 1)], eps=0.001)
 
         # Check chromsome initialization.
         indv.init(chromsome=[0, 1, 1, 0, 0, 0, 1, 1, 1, 0])
@@ -65,8 +65,7 @@ def test_clone(self):
         ''' Make sure individual can be cloned correctly.
         '''
         indv = BinaryIndividual(ranges=[(0, 1)],
-                                eps=0.001,
-                                verbosity=0).init(solution=[0.398])
+                                eps=0.001).init(solution=[0.398])
         indv_clone = indv.clone()
 
         self.assertListEqual(indv.chromsome, indv_clone.chromsome)
@@ -77,9 +76,7 @@ def test_clone(self):
     def test_multi_precisions(self):
         ''' Make sure we can construct individual using different decrete precisions.
         '''
-        indv = BinaryIndividual(ranges=[(0, 1), (0, 10)],
-                                eps=[0.01, 1.0],
-                                verbosity=0).init(solution=[0.3, 0.5])
+        indv = BinaryIndividual(ranges=[(0, 1), (0, 10)], eps=[0.01, 1.0]).init(solution=[0.3, 0.5])
         self.assertNotEqual(indv.precisions[0], indv.precisions[1])
 
 if '__main__' == __name__:

tests/linear_ranking_selection_test.py

@@ -19,7 +19,7 @@ def fitness(indv):
         self.fitness = fitness
 
     def test_selection(self):
-        indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 30)])
         p = Population(indv)
         p.init()
 

tests/linear_scaling_test.py

@@ -24,7 +24,7 @@ def test_run(self):
         '''
         Make sure GA engine can run correctly.
         '''
-        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
+        indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)
         population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.

tests/population_test.py

@@ -12,7 +12,7 @@ class PopulationTest(unittest.TestCase):
 
     def setUp(self):
         self.maxDiff = True
-        self.indv_template = BinaryIndividual(ranges=[(0, 1)], verbosity=0)
+        self.indv_template = BinaryIndividual(ranges=[(0, 1)])
         def fitness(indv):
             x, = indv.solution
             return x**3 - 60*x**2 + 900*x + 100

tests/roulette_wheel_selection_test.py

@@ -19,7 +19,7 @@ def fitness(indv):
         self.fitness = fitness
 
     def test_selection(self):
-        indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 30)])
         p = Population(indv)
         p.init()
 

tests/tournament_selection_test.py

@@ -19,7 +19,7 @@ def fitness(indv):
         self.fitness = fitness
 
     def test_selection(self):
-        indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
+        indv = BinaryIndividual(ranges=[(0, 30)])
         p = Population(indv)
         p.init()
 

tests/uniform_crossover_test.py

@@ -17,8 +17,8 @@ def setUp(self):
     def test_cross(self):
         ''' Make sure individuals can be crossed correctly.
         '''
-        father = BinaryIndividual(ranges=[(0, 1)], verbosity=0).init(solution=[0.398])
-        mother = BinaryIndividual(ranges=[(0, 1)], verbosity=0).init(solution=[0.298])
+        father = BinaryIndividual(ranges=[(0, 1)]).init(solution=[0.398])
+        mother = BinaryIndividual(ranges=[(0, 1)]).init(solution=[0.298])
         crossover = UniformCrossover(pc=1.0, pe=0.5)
         child1, child2 = crossover.cross(father, mother)