def test_can_be_initialised_from_random(self): """ Test that the population can be initialised from random individuals. """ myPop = Population.from_random( size = 128 ) self.assertIsInstance(myPop, Population)
def from_random_pop(cls, size, *args, **kwargs): """ Create the model from a random initial condition. Inputs ====== size : int size of the population *args, **kwargs : passed to default constructor of model """ pop = Population.from_random(size) return Model( pop, *args, **kwargs )
def test_records_assortment(self): """ Check that the model records assortment. """ gens = 26 pop = Population.from_random(32) assort0 = pop.average_assortment myModel = Model( pop, h = 0.4, s = 0.6, delta = 0.3, generations = gens) myModel.go() self.assertEqual( len( myModel.desired_assortment ), gens ) self.assertEqual( assort0, myModel.desired_assortment[0] )
def test_initialiser_raises_value_error_with_odd_individuals(self): """ Population must be created with an even number of individuals. Test that valueerror is raised if this is not the case. """ list_of_individuals = [ Individual.from_random() for _ in range(127) ] with self.assertRaises(ValueError): myPop = Population( list_of_individuals ) with self.assertRaises(ValueError): myPop = Population.from_random(127)
def setUp(self): """ Make some dummy lists of individuals and popualtions, that can be tested repeastedly. """ self.random_individuals = [ Individual.from_random() for _ in range(128) ] self.random_pop = Population.from_random(128) I1 = Individual( 1,1 , 0,0 ) I2 = Individual( 1,1 , 0,0 ) I3 = Individual( 1,0 , 0,1 ) I4 = Individual( 0,0 , 1,0 ) self.small_pop = Population( [I1,I2,I3,I4] )
def test_records_fairness(self): """ Test that the model record fairness, simply by asserting that the model has a list of the length of the number of generations. """ gens = 26 pop = Population.from_random(32) fairness0 = pop.fairness myModel = Model( pop, h = 0.4, s = 0.6, delta = 0.3, generations = gens) myModel.go() self.assertEqual( len( myModel.fairness ), gens ) self.assertEqual( fairness0, myModel.fairness[0] )
def test_pairing_is_sensible(self): """ Test that the pairing of individuals is sensible. The corelation of phentypes should be roughly equal to the average value of desired assortment. """ ##Start fro scratch, incase this messes with something else pop = Population.from_random(10000) pop.pair_population() x = [ p[0].phenotypic_value for p in pop.pairs ] y = [ p[1].phenotypic_value for p in pop.pairs ] corr = np.corrcoef( x, y )[0,1] average_assortment = np.mean( [ I.desired_assortment for I in pop.individuals ] ) ##This seems to be a very approximate law, so let's be quite generous in what we allow self.assertAlmostEqual( corr, average_assortment, places = 1 )
def test_next_gen_returns_sensible_children(self): """ Call the next gen method for a population with three pairs. Assert that the returned frequencies are as expected. """ ##All the individuals want maximum assortment I1 = Individual( 1,1, 0, 0 ) I2 = Individual( 1,1, 0, 1 ) I3 = Individual( 1,1, 1, 1 ) ##A popwith two of each kind should pair up exactly... pop = Population( [I1,I1,I2,I2,I3,I3] ) ##...so long as we set this bit by hand pop.males = [I1,I2,I3] pop.females = [I1,I2,I3] ##Make the fitnesses exactly addative, for ease M = np.array( [ [ 0, 0.5 ],[ 0.5, 1. ] ] ) delta = 0 new_pops = [ pop.new_generation(M,delta = delta, mu_strat = 0, mu_assort = 0) for _ in range(5000) ] ##We shhould have ratios of roughly 2:1:0 for phenotypic values of 1:0.5:0 ##Making the mean 2.5/3 = mean_fairness = np.mean( [ p.fairness for p in new_pops ] ) self.assertAlmostEqual( mean_fairness, 2.5/3, places = 2 )
def setUp(self): self.random_pop = Population.from_random(128)
