def test_frequencyResponse_forHarmonic_WithOptimusPerceptron(self): brave = BraveHypothesis() wimpy = WimpyHypothesis() oPP = OptimusPerceptron() self.setUpDecisioner(brave, wimpy, oPP) # First we will start with the basic training case # which is the first 100 in the range for i in range(101): nonAggro = 0 # this means not aggro color = [randint(1, 100)] monster = Monster(nonAggro, color, 'passive') # Get the guess from the decisioner for the first 100, # we expect every guess to be 1 self.assertTrue(self.decisioner.get_guess(monster.color)) # Then we will update from this guess self.decisioner.update(monster.color, 1, monster.action(True)) # Dr. Perceptron should do better than brave and wimpy # when encountering monsters that repeat with a frequency # that is trainable given Dr.Perceptron's window size. # In otherwords, Dr. Perceptron trains on a frequency of # Monsters but training on a pattern is limited to the # input size of Dr. Perceptron (which as of this check-in # is 5) # # We test a staggered input with a repeating pattern for i in range(1000): aggroPattern = [0, 1, 1, 0, 1] aggroIdx = i%len(aggroPattern) color = [randint(1, 100)] monster = Monster(aggroPattern[aggroIdx], color, 'aggressiveish') # Then we will update from this guess self.decisioner.update(monster.color, aggroPattern[aggroIdx], aggroPattern[aggroIdx]) # drP should always be better than wimpy self.assertGreater(oPP.fitness(), wimpy.fitness()) # after a lot of training for a pattern that is harmonic within the input size Dr.P should beat out brave self.assertGreater(oPP.fitness(), brave.fitness()) # Since the pattern is highly regular within the input size, the fitness should be really really close to 1. # With great harmony results great trainability and therefore great fitness self.assertGreater(oPP.fitness(), 0.99)
def test_frequencyResponse_WithOptimusPerceptron(self): brave = BraveHypothesis() wimpy = WimpyHypothesis() oPP = OptimusPerceptron(4) self.setUpDecisioner(brave, wimpy, oPP) # First we will start with the basic training case # which is the first 100 in the range for i in range(101): nonAggro = 0 # this means not aggro color = [randint(1, 100)] monster = Monster(nonAggro, color, 'passive') # Get the guess from the decisioner for the first 100, # we expect every guess to be 1 self.assertTrue(self.decisioner.get_guess(monster.color)) # Then we will update from this guess self.decisioner.update(monster.color, 1, monster.action(True)) # Dr. Perceptron should do better than brave and wimpy # when encountering monsters that repeat with a frequency # that is trainable given Dr.Perceptron's window size. # In otherwords, Dr. Perceptron trains on a frequency of # Monsters but training on a pattern is limited to the # input size of Dr. Perceptron (which as of this check-in # is 5) # # We test a staggered input for i in range(100): aggro = 1 passive = 0 evenMonstersPassive = i%2 color = [randint(1, 100)] monster = Monster(evenMonstersPassive, color, 'aggressiveish') # Then we will update from this guess self.decisioner.update(monster.color, evenMonstersPassive, evenMonstersPassive) #print ('i:'+str(i)+' '+str(evenMonstersPassive)+' '+str(oPP.fitness())) self.assertGreater(oPP.fitness(), wimpy.fitness()) self.assertGreater(oPP.fitness(), brave.fitness())