Change class name: GAPopulation -> Population.

examples/ex01/ex01.py

@@ -9,7 +9,7 @@
 
 from gaft import GAEngine
 from gaft.components import BinaryIndividual
-from gaft.components import GAPopulation
+from gaft.components import Population
 from gaft.operators import TournamentSelection
 from gaft.operators import UniformCrossover
 from gaft.operators import FlipBitMutation
@@ -22,7 +22,7 @@
 
 # Define population.
 indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)
-population = GAPopulation(indv_template=indv_template, size=50).init()
+population = Population(indv_template=indv_template, size=50).init()
 
 # Create genetic operators.
 selection = TournamentSelection()

examples/ex02/ex02.py

@@ -9,7 +9,7 @@
 
 from gaft import GAEngine
 from gaft.components import BinaryIndividual
-from gaft.components import GAPopulation
+from gaft.components import Population
 from gaft.operators import TournamentSelection
 from gaft.operators import UniformCrossover
 from gaft.operators import FlipBitBigMutation
@@ -20,7 +20,7 @@
 
 # Define population.
 indv_template = BinaryIndividual(ranges=[(-2, 2), (-2, 2)], eps=0.001)
-population = GAPopulation(indv_template=indv_template, size=50).init()
+population = Population(indv_template=indv_template, size=50).init()
 
 # Create genetic operators.
 #selection = RouletteWheelSelection()

gaft/components/__init__.py

@@ -1,4 +1,4 @@
 from .individual import IndividualBase
 from .binary_individual import BinaryIndividual
-from .population import GAPopulation
+from .population import Population
 

gaft/components/population.py

@@ -33,7 +33,7 @@ def __call__(self, fitness):
             return self.result
 
 
-class GAIndividuals(object):
+class Individuals(object):
     '''
     Descriptor for all individuals in population.
     '''
@@ -49,10 +49,10 @@ def __set__(self, instance, value):
         instance.update_flag()
 
 
-class GAPopulation(object):
+class Population(object):
 
     # All individuals.
-    individuals = GAIndividuals('individuals')
+    individuals = Individuals('individuals')
 
     def __init__(self, indv_template, size=100):
         '''

gaft/engine.py

@@ -12,7 +12,7 @@
 import pstats
 import os
 
-from .components import IndividualBase, GAPopulation
+from .components import IndividualBase, Population
 from .plugin_interfaces.operators import GASelection, GACrossover, GAMutation
 from .plugin_interfaces.analysis import OnTheFlyAnalysis
 from .mpiutil import mpi
@@ -106,7 +106,7 @@ def __init__(self, population, selection, crossover, mutation,
         the engine object unites these informations and provide means for running
         a genetic algorthm optimization.
 
-        :param population: The GAPopulation to be reproduced in evolution iteration.
+        :param population: The Population to be reproduced in evolution iteration.
         :param selection: The GASelection to be used for individual seleciton.
         :param crossover: The GACrossover to be used for individual crossover.
         :param mutation: The GAMutation to be used for individual mutation.
@@ -231,8 +231,8 @@ def _check_parameters(self):
         '''
         Helper function to check parameters of engine.
         '''
-        if not isinstance(self.population, GAPopulation):
-            raise TypeError('population must be a GAPopulation object')
+        if not isinstance(self.population, Population):
+            raise TypeError('population must be a Population object')
         if not isinstance(self.selection, GASelection):
             raise TypeError('selection operator must be a GASelection instance')
         if not isinstance(self.crossover, GACrossover):

gaft/plugin_interfaces/analysis.py

@@ -36,7 +36,7 @@ def register_step(self, g, population, engine):
         :type g: int
 
         :param population: The up to date population of the iteration.
-        :type population: GAPopulation
+        :type population: Population
 
         :param engine: The current GAEngine where the analysis is running.
         :type engine: GAEngine
@@ -49,7 +49,7 @@ def finalize(self, population, engine):
         post-processing of the collected data.
 
         :param population: The up to date population of the iteration.
-        :type population: GAPopulation
+        :type population: Population
 
         :param engine: The current GAEngine where the analysis is running.
         :type engine: GAEngine

gaft/plugin_interfaces/metaclasses.py

@@ -6,7 +6,7 @@
 from functools import wraps
 
 from ..components.individual import IndividualBase
-from ..components.population import GAPopulation
+from ..components.population import Population
 from ..mpiutil import master_only
 
 
@@ -153,8 +153,8 @@ def _wrapped_select(self, population, fitness):
             ''' Wrapper to add parameters type checking.
             '''
             # Check parameter types.
-            if not isinstance(population, GAPopulation):
-                raise TypeError('population must be GAPopulation object')
+            if not isinstance(population, Population):
+                raise TypeError('population must be Population object')
             if not callable(fitness):
                 raise TypeError('fitness must be a callable object')
 

tests/dynamic_linear_scaling_test.py

@@ -9,7 +9,7 @@
 
 from gaft import GAEngine
 from gaft.components import BinaryIndividual
-from gaft.components import GAPopulation
+from gaft.components import Population
 from gaft.operators import RouletteWheelSelection
 from gaft.operators import UniformCrossover
 from gaft.operators import FlipBitMutation
@@ -25,7 +25,7 @@ def test_run(self):
         Make sure GA engine can run correctly.
         '''
         indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
-        population = GAPopulation(indv_template=indv_template, size=50).init()
+        population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.
         selection = RouletteWheelSelection()

tests/engine_test.py

@@ -9,7 +9,7 @@
 
 from gaft import GAEngine
 from gaft.components import BinaryIndividual
-from gaft.components import GAPopulation
+from gaft.components import Population
 from gaft.operators import RouletteWheelSelection
 from gaft.operators import UniformCrossover
 from gaft.operators import FlipBitMutation
@@ -25,7 +25,7 @@ def test_run(self):
         Make sure GA engine can run correctly.
         '''
         indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
-        population = GAPopulation(indv_template=indv_template, size=50).init()
+        population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.
         selection = RouletteWheelSelection()

tests/exponential_ranking_selection_test.py

@@ -6,7 +6,7 @@
 
 import unittest
 
-from gaft.components import GAPopulation, BinaryIndividual
+from gaft.components import Population, BinaryIndividual
 from gaft.operators import ExponentialRankingSelection
 
 class ExponentialRankingSelectionTest(unittest.TestCase):
@@ -20,7 +20,7 @@ def fitness(indv):
 
     def test_selection(self):
         indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
-        p = GAPopulation(indv)
+        p = Population(indv)
         p.init()
 
         selection = ExponentialRankingSelection()

tests/flip_bit_big_mutation_test.py

@@ -9,7 +9,7 @@
 
 from gaft import GAEngine
 from gaft.components import BinaryIndividual
-from gaft.components import GAPopulation
+from gaft.components import Population
 from gaft.operators import RouletteWheelSelection
 from gaft.operators import UniformCrossover
 from gaft.operators.mutation.flip_bit_mutation import FlipBitBigMutation
@@ -23,7 +23,7 @@ def test_mutate(self):
         ''' Make sure the individual can be mutated correctly.
         '''
         indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
-        population = GAPopulation(indv_template=indv_template, size=50).init()
+        population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.
         selection = RouletteWheelSelection()

tests/linear_ranking_selection_test.py

@@ -6,7 +6,7 @@
 
 import unittest
 
-from gaft.components import GAPopulation, BinaryIndividual
+from gaft.components import Population, BinaryIndividual
 from gaft.operators import LinearRankingSelection
 
 class LinearRankingSelectionTest(unittest.TestCase):
@@ -20,7 +20,7 @@ def fitness(indv):
 
     def test_selection(self):
         indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
-        p = GAPopulation(indv)
+        p = Population(indv)
         p.init()
 
         selection = LinearRankingSelection()

tests/linear_scaling_test.py

@@ -9,7 +9,7 @@
 
 from gaft import GAEngine
 from gaft.components import BinaryIndividual
-from gaft.components import GAPopulation
+from gaft.components import Population
 from gaft.operators import RouletteWheelSelection
 from gaft.operators import UniformCrossover
 from gaft.operators import FlipBitMutation
@@ -25,7 +25,7 @@ def test_run(self):
         Make sure GA engine can run correctly.
         '''
         indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001, verbosity=0)
-        population = GAPopulation(indv_template=indv_template, size=50).init()
+        population = Population(indv_template=indv_template, size=50).init()
 
         # Create genetic operators.
         selection = RouletteWheelSelection()

tests/population_test.py

@@ -1,12 +1,12 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-''' Test case for GAPopulation
+''' Test case for Population
 '''
 
 import unittest
 
-from gaft.components import GAPopulation, BinaryIndividual
+from gaft.components import Population, BinaryIndividual
 
 class PopulationTest(unittest.TestCase):
 
@@ -20,7 +20,7 @@ def fitness(indv):
 
     def test_initialization(self):
         ''' Make sure a population can be initialized correctly. '''
-        population = GAPopulation(indv_template=self.indv_template, size=10)
+        population = Population(indv_template=self.indv_template, size=10)
 
         self.assertListEqual(population.individuals, [])
 
@@ -32,14 +32,14 @@ def test_initialization(self):
 
     def test_new_population(self):
         ''' Make sure population can clone a new population. '''
-        population = GAPopulation(indv_template=self.indv_template, size=10)
+        population = Population(indv_template=self.indv_template, size=10)
         population.init()
         new_population = population.new()
         self.assertEqual(new_population.size, 10)
         self.assertListEqual(new_population.individuals, [])
 
     def test_all_fits(self):
-        population = GAPopulation(indv_template=self.indv_template, size=10)
+        population = Population(indv_template=self.indv_template, size=10)
         population.init()
         all_fits = population.all_fits(fitness=self.fitness)
 

tests/roulette_wheel_selection_test.py

@@ -6,7 +6,7 @@
 
 import unittest
 
-from gaft.components import GAPopulation, BinaryIndividual
+from gaft.components import Population, BinaryIndividual
 from gaft.operators.selection.roulette_wheel_selection import RouletteWheelSelection
 
 class RouletteWheelSelectionTest(unittest.TestCase):
@@ -20,7 +20,7 @@ def fitness(indv):
 
     def test_selection(self):
         indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
-        p = GAPopulation(indv)
+        p = Population(indv)
         p.init()
 
         selection = RouletteWheelSelection()

tests/tournament_selection_test.py

@@ -6,7 +6,7 @@
 
 import unittest
 
-from gaft.components import GAPopulation, BinaryIndividual
+from gaft.components import Population, BinaryIndividual
 from gaft.operators import TournamentSelection
 
 class TournamentSelectionTest(unittest.TestCase):
@@ -20,7 +20,7 @@ def fitness(indv):
 
     def test_selection(self):
         indv = BinaryIndividual(ranges=[(0, 30)], verbosity=0)
-        p = GAPopulation(indv)
+        p = Population(indv)
         p.init()
 
         selection = TournamentSelection()