def test_with_all_hypothesis(self): brave = BraveHypothesis() wimpy = WimpyHypothesis() knn3 = KNearestNeighbors(3) knn5 = KNearestNeighbors(5) knn7 = KNearestNeighbors(7) knn11 = KNearestNeighbors(11) rando = RandoHypothesis() prob = SimpleProbabilityHypothesis() # drP3 = OptimusPerceptron(11) # drP5 = DrPerceptron(5) # drP7 = DrPerceptron(7) # drP11 = DrPerceptron(11) # self.setUpDecisioner(brave, wimpy, knn3, knn5, knn7, knn11, rando, prob, # drP3, # drP5, # drP7, # drP11, ) def create_monster(): color = randint(1, 100) if color < 70: if random() >= 0.3: return Monster(0, [color], 'passive') return Monster(1, [color], 'aggressive') else: if random() >= 0.7: return Monster(0, [color], 'passive') return Monster(1, [color], 'aggressive') for i in range(100): monster = create_monster() self.decisioner.update(monster.color, 1, monster.action(True)) maximum_value = 1000.0 actual_value = 0.0 for i in range(1000): monster = create_monster() guess = self.decisioner.get_guess(monster.color) outcome = monster.action(guess) self.decisioner.update(monster.color, guess, outcome) maximum_value -= monster._aggressive actual_value += outcome
def test_GroupedAggroByColor_WithWimpyBraveAndKNN(self): brave = BraveHypothesis() wimpy = WimpyHypothesis() knn = KNearestNeighbors(3) self.setUpDecisioner(brave, wimpy, knn) def create_monster(): color = randint(1, 100) # Every monster below 50 is aggressive if color < 50: return Monster(1, [color], 'aggressive') # Otherwise if they are above 50 they are # passive else: return Monster(0, [color], 'passive') # Next we load up the training data for i in range(100): # Create the monster and generate all the data, by default # we expect everything in the training period to be true # meaning attack for data monster = create_monster() self.assertTrue(self.decisioner.get_guess(monster.color)) self.decisioner.update(monster.color, 1, monster.action(True)) # Finally we need to know that normal KNN was matched as the best # fitness for the data set self.assertGreater(knn.fitness(), brave.fitness()) self.assertGreater(knn.fitness(), wimpy.fitness()) # Then we are going to run over all of the data sets # We want to also track the actual value and the maximum # value for comparison maximum_value = 5000.0 actual_value = 0.0 for i in range(5000): # Create the monster, guess on it and grab the outcome # which is all required information for the loop monster = create_monster() guess = self.decisioner.get_guess(monster.color) outcome = monster.action(guess) # update values for comparison after loop maximum_value -= monster._aggressive actual_value += outcome # self.decisioner.update(monster.color, guess, outcome) # We need to know that KNN is still our best fit for this # data set self.assertGreater(knn.fitness(), brave.fitness()) self.assertGreater(knn.fitness(), wimpy.fitness()) # Finally we expect that the standard KNN process will obtain # within 10 percent margin of the best possible solution self.assertGreater(actual_value / maximum_value, 0.9)
def test_update_ratio_matches_expected(self): """Test the KNN algorithm with our built in rolling window""" knn = KNearestNeighbors(1) # First we define the method for generating our two # categories in a single dimension def generate_monster(): color = randint(1, 100) # All colors less than 50 are passive if color < 50: return Monster(0, [color], 'passive') # Otherwise the monster is aggresive return Monster(1, [color], 'aggressive') # Run the inital training phase for i in range(100): monster = generate_monster() knn.update(monster.color, 1, monster.action(True)) # Then we need to run over a set of values, we should see # a relatively high ratio of actual value vs maximum value actual_value = 0.0 maximum_value = 5000.0 for i in range(5000): monster = generate_monster() guess = knn.get_guess(monster.color) outcome = monster.action(guess) maximum_value -= monster._aggressive actual_value += outcome knn.update(monster.color, guess, outcome) self.assertGreaterEqual(actual_value / maximum_value, 0.9)
def create_monster(): color = randint(1, 100) if color < 70: if random() < 0.3: return Monster(1, [color], 'aggressive') return Monster(0, [color], 'passive') else: if random() < 0.7: return Monster(1, [color], 'aggressive') return Monster(0, [color], 'passive') hyps = [ BraveHypothesis(), WimpyHypothesis(), KNearestNeighbors(3), KNearestNeighbors(5), KNearestNeighbors(7), KNearestNeighbors(11), KNearestNeighbors(17), KNearestNeighbors(23), KNearestNeighbors(29), KNearestNeighbors(31), KNearestNeighbors(37), KNearestNeighbors(41), KNearestNeighbors(43), KNearestNeighbors(47), SimpleProbabilityHypothesis(), RandoHypothesis(), OptimusPerceptron(47), # mod 47 universe OptimusPerceptron(43), # mod 43 universe
def setUp(self): # We set the window to 99 to allow the first set to key # on 100 values updated self._hypothesis = KNearestNeighbors(3, window=99)