Added monitors and statistics, also made a module with some

specific python stuff in __init__.py

eo/src/pyeo/test/test_mo.py

@@ -1,7 +1,115 @@
 from maxone import *
+from math import exp
 import unittest
 
-class TestSGA(unittest.TestCase):
+
+class MyInit(eoInit):
+    def __call__(self, eo):
+	eo.genome = [rng().normal(), rng().normal(), rng().normal()];
+
+class MyMutate(eoMonOp):
+    def __call__(self, eo):
+	
+	std = 0.05
+	eo.genome = copy(eo.genome)
+	
+	eo.genome[0] += rng().normal() * std
+	eo.genome[1] += rng().normal() * std
+	eo.genome[2] += rng().normal() * std	
+	return 1
+
+class AnEval(eoEvalFunc):
+    def __init__(self):
+	eoEvalFunc.__init__(self)
+	
+	setObjectivesSize(2);
+	setObjectivesValue(0,1);
+	setObjectivesValue(1,1);
+    
+    def __call__(self, eo):
+	x = abs(eo.genome[0])
+	y = abs(eo.genome[1])
+	z = abs(eo.genome[2])
+	
+	eo.fitness = [ x / (x+y+z), y /(x+y+z) ]
+
+import Gnuplot
+
+g = Gnuplot.Gnuplot()
+g.reset()
+
+def do_plot(pop):
+    l1 = []
+    l2 = []
+
+    for indy in pop:
+	l1.append(indy.fitness[0])
+	l2.append(indy.fitness[1])
+
+    d = Gnuplot.Data(l1,l2, with = 'points')
+    
+    d2 = Gnuplot.Data([0,1],[1,0], with='lines')
+    
+    g.plot(d,d2)
+
+
+
+class NSGA_II(eoAlgo):
+    def __init__(self, ngens):
+	
+	self.cont = eoGenContinue(ngens);
+	
+	self.selectOne = eoDetTournamentSelect(2);
+	self.evaluate = AnEval()
+	self.mutate = MyMutate()
+	self.init = MyInit()
+	
+	self.seq = eoProportionalOp()
+	self.seq.add(self.mutate, 1.0)
+    	
+	self.perf2worth = eoNDSorting_II()
+	
+    def __call__(self, pop):
+	
+	sz = len(pop)
+	i = 0
+	while self.cont(pop):
+	    newpop = eoPop()	
+	    populator = eoSelectivePopulator(pop, newpop, self.selectOne);
+
+	    while len(newpop) < sz:
+		self.seq(populator)
+	
+	    for indy in newpop: 
+		self.evaluate(indy)
+		pop.push_back(indy)
+
+	    self.perf2worth(pop)
+	    self.perf2worth.sort_pop(pop)
+	    
+	    #print pop[0].fitness, pop[0].genome
+	    pop.resize(sz)
+	    
+	    #worth = self.perf2worth.getValue()
+	    #print worth[0], worth[sz-1]
+	    
+	    i += 1
+	    if i%100 == 0:
+		pass #do_plot(pop)
+	    
+	worths = self.perf2worth.getValue()
+	
+	w0 = int(worths[0]-0.001)
+	
+	for i in range(len(pop)):
+	    if worths[i] <= w0:
+		break;
+
+	    print pop[i].genome
+	    print pop[i].fitness
+	
+	
+class TestNSGA_II(unittest.TestCase):
     
     def testIndividuals(self):
 	setObjectivesSize(2);
@@ -26,12 +134,11 @@ class TestSGA(unittest.TestCase):
 	self.failUnlessEqual(dominates(eo2, eo2), 0)
    
     def testNDSorting(self):
-	
 	setObjectivesSize(2);
 	setObjectivesValue(0,-1)
 	setObjectivesValue(1,-1);
 	
-	pop = Pop()
+	pop = eoPop()
 	pop.resize(6)
 
 	pop[5].fitness = [0.15,0.87]
@@ -50,10 +157,20 @@ class TestSGA(unittest.TestCase):
 	    print indy.fitness
 
 	
-	worths = srt.value()
+	worths = srt.getValue()
 	print worths
 	print type(worths)
 
+    def testNSGA_II(self):
+	
+	init = MyInit();
+	evaluate = AnEval();
+	pop = eoPop(25, init)
+	for indy in pop: evaluate(indy)
+	
+	nsga = NSGA_II(50)
 
+	nsga(pop)
+	
 if __name__=='__main__':
     unittest.main()