From 9d272b73dacef0520709e9340498935206b02ee3 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:26:13 +0530 Subject: [PATCH 01/28] Update test_agents.py pep8 changes, showed flake8 errors --- tests/test_agents.py | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/tests/test_agents.py b/tests/test_agents.py index 89ee3fcf3..7d251f547 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -1,21 +1,23 @@ from agents import Direction + def test_move_forward(): d = Direction("up") - l1 = d.move_forward((0,0)) - assert l1 == (0,-1) + l1 = d.move_forward((0, 0)) + assert l1 == (0, -1) d = Direction(Direction.R) - l1 = d.move_forward((0,0)) - assert l1 == (1,0) + l1 = d.move_forward((0, 0)) + assert l1 == (1, 0) d = Direction(Direction.D) - l1 = d.move_forward((0,0)) - assert l1 == (0,1) + l1 = d.move_forward((0, 0)) + assert l1 == (0, 1) d = Direction("left") - l1 = d.move_forward((0,0)) - assert l1 == (-1,0) - l2 = d.move_forward((1,0)) - assert l2 == (0,0) + l1 = d.move_forward((0, 0)) + assert l1 == (-1, 0) + l2 = d.move_forward((1, 0)) + assert l2 == (0, 0) + def test_add(): d = Direction(Direction.U) l1 = d + "right" @@ -36,5 +38,4 @@ def test_add(): l1 = d + Direction.R l2 = d + Direction.L assert l1.direction == Direction.U - assert l2.direction == Direction.D #fixed - + assert l2.direction == Direction.D # fixed From 1581f7a72b3f91267e5a87fcfffa0f07d0140beb Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:32:01 +0530 Subject: [PATCH 02/28] Update test_agents.py --- tests/test_agents.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/test_agents.py b/tests/test_agents.py index 7d251f547..aece69d7f 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -17,7 +17,6 @@ def test_move_forward(): l2 = d.move_forward((1, 0)) assert l2 == (0, 0) - def test_add(): d = Direction(Direction.U) l1 = d + "right" @@ -38,4 +37,4 @@ def test_add(): l1 = d + Direction.R l2 = d + Direction.L assert l1.direction == Direction.U - assert l2.direction == Direction.D # fixed + assert l2.direction == Direction.D From 7f9d0534f52faa3f137c5a14f3ed45a75080bf52 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:34:40 +0530 Subject: [PATCH 03/28] Update test_agents.py --- tests/test_agents.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/test_agents.py b/tests/test_agents.py index aece69d7f..20aa33af6 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -16,7 +16,8 @@ def test_move_forward(): assert l1 == (-1, 0) l2 = d.move_forward((1, 0)) assert l2 == (0, 0) - + + def test_add(): d = Direction(Direction.U) l1 = d + "right" @@ -37,4 +38,4 @@ def test_add(): l1 = d + Direction.R l2 = d + Direction.L assert l1.direction == Direction.U - assert l2.direction == Direction.D + assert l2.direction == Direction.D From 594736d3cb27954e496f3f171fcb41ade76078ca Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:38:02 +0530 Subject: [PATCH 04/28] Update test_agents.py --- tests/test_agents.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_agents.py b/tests/test_agents.py index 20aa33af6..2512ccddf 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -16,8 +16,8 @@ def test_move_forward(): assert l1 == (-1, 0) l2 = d.move_forward((1, 0)) assert l2 == (0, 0) - - + + def test_add(): d = Direction(Direction.U) l1 = d + "right" From b6f9de464bda1d5bf2ef5c5a16f4da59f9881805 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:41:23 +0530 Subject: [PATCH 05/28] Update test_text.py added missing whitespace after comma --- tests/test_text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_text.py b/tests/test_text.py index d58cd497a..577ad661b 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -54,7 +54,7 @@ def test_viterbi_segmentation(): P = UnigramTextModel(wordseq) text = "itiseasytoreadwordswithoutspaces" - s, p = viterbi_segment(text,P) + s, p = viterbi_segment(text, P) assert s == [ 'it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] From 7d5dcba1de45cd511e85e44ce197eccf2f717c31 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:48:32 +0530 Subject: [PATCH 06/28] Update utils.py added space after comma --- utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils.py b/utils.py index cfdc88d37..73dd63d63 100644 --- a/utils.py +++ b/utils.py @@ -194,7 +194,7 @@ def probability(p): return p > random.uniform(0.0, 1.0) -def weighted_sample_with_replacement(n,seq, weights): +def weighted_sample_with_replacement(n, seq, weights): """Pick n samples from seq at random, with replacement, with the probability of each element in proportion to its corresponding weight.""" From 2783b6a981a4cef8c7f653fe16f4a82092393e82 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:49:12 +0530 Subject: [PATCH 07/28] Update search.py added space after comma --- search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.py b/search.py index c8885a9ed..94f4949d2 100644 --- a/search.py +++ b/search.py @@ -587,7 +587,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): new_population = [] for i in range(len(population)): fitnesses = map(fitness_fn, population) - p1, p2 = weighted_sample_with_replacement(2,population, fitnesses) + p1, p2 = weighted_sample_with_replacement(2, population, fitnesses) child = p1.mate(p2) if random.uniform(0, 1) < pmut: child.mutate() From 99cbbaf72c3be44c84ed5e0b4a5958934bbb55e6 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:49:35 +0530 Subject: [PATCH 08/28] Update probability.py added space after comma --- probability.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/probability.py b/probability.py index fa856c330..1d7992e6d 100644 --- a/probability.py +++ b/probability.py @@ -643,5 +643,5 @@ def particle_filtering(e, N, HMM): w[i] = float("{0:.4f}".format(w[i])) # STEP 2 - s = weighted_sample_with_replacement(N,s,w) + s = weighted_sample_with_replacement(N, s, w) return s From 620cb4182fed8b4fb4af005a69d0e8b6607deb41 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:50:29 +0530 Subject: [PATCH 09/28] Update learning.py added space after comma --- learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learning.py b/learning.py index 4917a2cf0..2b86ff8e4 100644 --- a/learning.py +++ b/learning.py @@ -754,7 +754,7 @@ def weighted_replicate(seq, weights, n): wholes = [int(w * n) for w in weights] fractions = [(w * n) % 1 for w in weights] return (flatten([x] * nx for x, nx in zip(seq, wholes)) + - weighted_sample_with_replacement(n - sum(wholes),seq, fractions, )) + weighted_sample_with_replacement(n - sum(wholes), seq, fractions)) def flatten(seqs): return sum(seqs, []) From ab26cd8971b4eaa9cd37dc11781be9507ab62972 Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 17:52:40 +0530 Subject: [PATCH 10/28] Update planning.py added double_tennis_problem --- planning.py | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/planning.py b/planning.py index a17677460..17028e4c6 100644 --- a/planning.py +++ b/planning.py @@ -526,3 +526,34 @@ def spare_tire_graphplan(): graphplan.graph.expand_graph() if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff(): return None + +def double_tennis_problem(): + init = [expr('At(A, LeftBaseLine)'), + expr('At(B, RightNet)'), + expr('Approaching(Ball, RightBaseLine)'), + expr('Partner(A,B)'), + expr('Partner(A,B)')] + + def goal_test(kb): + required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')] + for q in required: + if kb.ask(q) is False: + return False + return True + + ##actions + #hit + precond_pos=[expr("Approaching(Ball,loc)"), expr("At(actor,loc)")] + precond_neg=[] + effect_add=[expr("Returned(Ball)")] + effect_rem = [] + hit = Action(expr("Hit(actor,Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + #go + precond_pos = [ expr("At(actor,loc)")] + precond_neg = [] + effect_add = [expr("At(actor,to)")] + effect_rem = [expr("At(actor,loc)")] + go = Action(expr("Go(actor,to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + return PDLL(init, [hit, go], goal_test) From f69e7c1e7ab1689da33766ae856fb7f55b2ee8ad Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Sun, 19 Mar 2017 12:43:08 +0530 Subject: [PATCH 11/28] Update rl.py In the pseudocode figure 21.8, the first 'if' starts with argument 's', which is the previous state, not s1(i.e, the current state). --- rl.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rl.py b/rl.py index 5241710fe..77a04f98a 100644 --- a/rl.py +++ b/rl.py @@ -154,13 +154,13 @@ def __call__(self, percept): s1, r1 = self.update_state(percept) Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r alpha, gamma, terminals, actions_in_state = self.alpha, self.gamma, self.terminals, self.actions_in_state - if s1 in terminals: - Q[s1, None] = r1 + if s in terminals: + Q[s, None] = r1 if s is not None: Nsa[s, a] += 1 Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1] for a1 in actions_in_state(s1)) - Q[s, a]) - if s1 in terminals: + if s in terminals: self.s = self.a = self.r = None else: self.s, self.r = s1, r1 From fce259abe690e0fd7380b1490c3502dfefacf440 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Sun, 19 Mar 2017 15:05:45 +0530 Subject: [PATCH 12/28] Update search.py the 'uniform_cost_search' in notebook 'search-4e.ipynb' resembles more to the pseudocode in book. --- search.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/search.py b/search.py index 94f4949d2..416855008 100644 --- a/search.py +++ b/search.py @@ -268,9 +268,22 @@ def best_first_graph_search(problem, f): return None -def uniform_cost_search(problem): - "[Figure 3.14]" - return best_first_graph_search(problem, lambda node: node.path_cost) +def uniform_cost_search(problem, costfn=lambda node: node.path_cost): + """[Figure 3.14]""" + frontier = FrontierPQ(Node(problem.initial), costfn) + explored = set() + while frontier: + node = frontier.pop() + if problem.is_goal(node.state): + return node + explored.add(node.state) + for action in problem.actions(node.state): + child = node.child(problem, action) + if child.state not in explored and child not in frontier: + frontier.add(child) + elif child in frontier and frontier.cost[child] < child.path_cost: + frontier.replace(child) + return None def depth_limited_search(problem, limit=50): From 6b82783aebe359b43e11119f942b1ff3ecb38f79 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Sun, 19 Mar 2017 15:17:16 +0530 Subject: [PATCH 13/28] Update search.py --- search.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/search.py b/search.py index 416855008..342daf13a 100644 --- a/search.py +++ b/search.py @@ -268,9 +268,10 @@ def best_first_graph_search(problem, f): return None -def uniform_cost_search(problem, costfn=lambda node: node.path_cost): - """[Figure 3.14]""" - frontier = FrontierPQ(Node(problem.initial), costfn) +def uniform_cost_search(problem, costfn = lambda node: node.path_cost): + "[Figure 3.14]" + node = Node(problem.initial) + frontier = PriorityQueue(node, costfn) explored = set() while frontier: node = frontier.pop() From 5e628be627aaec90265c66b6c95120532033ecf9 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Sun, 19 Mar 2017 15:24:37 +0530 Subject: [PATCH 14/28] Update search.py --- search.py | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/search.py b/search.py index 342daf13a..44d6961be 100644 --- a/search.py +++ b/search.py @@ -268,23 +268,9 @@ def best_first_graph_search(problem, f): return None -def uniform_cost_search(problem, costfn = lambda node: node.path_cost): +def uniform_cost_search(problem): "[Figure 3.14]" - node = Node(problem.initial) - frontier = PriorityQueue(node, costfn) - explored = set() - while frontier: - node = frontier.pop() - if problem.is_goal(node.state): - return node - explored.add(node.state) - for action in problem.actions(node.state): - child = node.child(problem, action) - if child.state not in explored and child not in frontier: - frontier.add(child) - elif child in frontier and frontier.cost[child] < child.path_cost: - frontier.replace(child) - return None +return best_first_graph_search(problem, lambda node: node.path_cost) def depth_limited_search(problem, limit=50): From 86f10d5eefacc6d8261a16fe144927b5fb6d3194 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Sun, 19 Mar 2017 15:28:04 +0530 Subject: [PATCH 15/28] Update search.py --- search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.py b/search.py index 44d6961be..94f4949d2 100644 --- a/search.py +++ b/search.py @@ -270,7 +270,7 @@ def best_first_graph_search(problem, f): def uniform_cost_search(problem): "[Figure 3.14]" -return best_first_graph_search(problem, lambda node: node.path_cost) + return best_first_graph_search(problem, lambda node: node.path_cost) def depth_limited_search(problem, limit=50): From 706838b4b3868f8d7e2f9ccd44819cc76db07993 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Tue, 21 Mar 2017 12:34:39 +0530 Subject: [PATCH 16/28] Removed flake8 test for pytest directory (#386) --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index aa875cc38..e6563f0fe 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,7 +14,6 @@ install: - pip install -r requirements.txt script: - - flake8 tests/ - py.test - python -m doctest -v *.py From c8115cead6fe9f6a74f1c30347e937939fd7ba6e Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 22 Mar 2017 12:17:01 +0530 Subject: [PATCH 17/28] edits in search.py (#384) * added documentation, standardised docstring quotes * made search.py pep8 compatible using flake8 * fixed bug in OnlineDFSAgent --- search.py | 136 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 70 insertions(+), 66 deletions(-) diff --git a/search.py b/search.py index c8885a9ed..545a24e5c 100644 --- a/search.py +++ b/search.py @@ -86,7 +86,7 @@ class Node: subclass this class.""" def __init__(self, state, parent=None, action=None, path_cost=0): - "Create a search tree Node, derived from a parent by an action." + """Create a search tree Node, derived from a parent by an action.""" self.state = state self.parent = parent self.action = action @@ -102,23 +102,23 @@ def __lt__(self, node): return self.state < node.state def expand(self, problem): - "List the nodes reachable in one step from this node." + """List the nodes reachable in one step from this node.""" return [self.child_node(problem, action) for action in problem.actions(self.state)] def child_node(self, problem, action): - "[Figure 3.10]" + """[Figure 3.10]""" next = problem.result(self.state, action) return Node(next, self, action, problem.path_cost(self.path_cost, self.state, action, next)) def solution(self): - "Return the sequence of actions to go from the root to this node." + """Return the sequence of actions to go from the root to this node.""" return [node.action for node in self.path()[1:]] def path(self): - "Return a list of nodes forming the path from the root to this node." + """Return a list of nodes forming the path from the root to this node.""" node, path_back = self, [] while node: path_back.append(node) @@ -144,10 +144,15 @@ class SimpleProblemSolvingAgentProgram: """Abstract framework for a problem-solving agent. [Figure 3.1]""" def __init__(self, initial_state=None): + """State is an sbstract representation of the state + of the world, and seq is the list of actions required + to get to a particular state from the initial state(root).""" self.state = initial_state self.seq = [] def __call__(self, percept): + """[Figure 3.1] Formulate a goal and problem, then + search for a sequence of actions to solve it.""" self.state = self.update_state(self.state, percept) if not self.seq: goal = self.formulate_goal(self.state) @@ -204,22 +209,22 @@ def graph_search(problem, frontier): def breadth_first_tree_search(problem): - "Search the shallowest nodes in the search tree first." + """Search the shallowest nodes in the search tree first.""" return tree_search(problem, FIFOQueue()) def depth_first_tree_search(problem): - "Search the deepest nodes in the search tree first." + """Search the deepest nodes in the search tree first.""" return tree_search(problem, Stack()) def depth_first_graph_search(problem): - "Search the deepest nodes in the search tree first." + """Search the deepest nodes in the search tree first.""" return graph_search(problem, Stack()) def breadth_first_search(problem): - "[Figure 3.11]" + """[Figure 3.11]""" node = Node(problem.initial) if problem.goal_test(node.state): return node @@ -269,12 +274,12 @@ def best_first_graph_search(problem, f): def uniform_cost_search(problem): - "[Figure 3.14]" + """[Figure 3.14]""" return best_first_graph_search(problem, lambda node: node.path_cost) def depth_limited_search(problem, limit=50): - "[Figure 3.17]" + """[Figure 3.17]""" def recursive_dls(node, problem, limit): if problem.goal_test(node.state): return node @@ -295,7 +300,7 @@ def recursive_dls(node, problem, limit): def iterative_deepening_search(problem): - "[Figure 3.18]" + """[Figure 3.18]""" for depth in range(sys.maxsize): result = depth_limited_search(problem, depth) if result != 'cutoff': @@ -304,6 +309,7 @@ def iterative_deepening_search(problem): # ______________________________________________________________________________ # Informed (Heuristic) Search + greedy_best_first_graph_search = best_first_graph_search # Greedy best-first search is accomplished by specifying f(n) = h(n). @@ -320,7 +326,7 @@ def astar_search(problem, h=None): def recursive_best_first_search(problem, h=None): - "[Figure 3.26]" + """[Figure 3.26]""" h = memoize(h or problem.h, 'h') def RBFS(problem, node, flimit): @@ -368,12 +374,13 @@ def hill_climbing(problem): def exp_schedule(k=20, lam=0.005, limit=100): - "One possible schedule function for simulated annealing" + """One possible schedule function for simulated annealing""" return lambda t: (k * math.exp(-lam * t) if t < limit else 0) def simulated_annealing(problem, schedule=exp_schedule()): - "[Figure 4.5]" + """[Figure 4.5] CAUTION: This differs from the pseudocode as it + returns a state instead of a Node.""" current = Node(problem.initial) for t in range(sys.maxsize): T = schedule(t) @@ -389,7 +396,7 @@ def simulated_annealing(problem, schedule=exp_schedule()): def and_or_graph_search(problem): - """Used when the environment is nondeterministic and completely observable. + """[Figure 4.11]Used when the environment is nondeterministic and completely observable. Contains OR nodes where the agent is free to choose any action. After every action there is an AND node which contains all possible states the agent may reach due to stochastic nature of environment. @@ -397,10 +404,10 @@ def and_or_graph_search(problem): may end up in any of them). Returns a conditional plan to reach goal state, or failure if the former is not possible.""" - "[Figure 4.11]" # functions used by and_or_search def or_search(state, problem, path): + """returns a plan as a list of actions""" if problem.goal_test(state): return [] if state in path: @@ -412,7 +419,7 @@ def or_search(state, problem, path): return [action, plan] def and_search(states, problem, path): - "Returns plan in form of dictionary where we take action plan[s] if we reach state s." # noqa + """Returns plan in form of dictionary where we take action plan[s] if we reach state s.""" # noqa plan = {} for s in states: plan[s] = or_search(s, problem, path) @@ -426,10 +433,10 @@ def and_search(states, problem, path): class OnlineDFSAgent: - """The abstract class for an OnlineDFSAgent. Override update_state - method to convert percept to state. While initializing the subclass - a problem needs to be provided which is an instance of a subclass - of the Problem class. [Figure 4.21] """ + """[Figure 4.21] The abstract class for an OnlineDFSAgent. Override + update_state method to convert percept to state. While initializing + the subclass a problem needs to be provided which is an instance of + a subclass of the Problem class.""" def __init__(self, problem): self.problem = problem @@ -449,7 +456,7 @@ def __call__(self, percept): if self.s is not None: if s1 != self.result[(self.s, self.a)]: self.result[(self.s, self.a)] = s1 - unbacktracked[s1].insert(0, self.s) + self.unbacktracked[s1].insert(0, self.s) if len(self.untried[s1]) == 0: if len(self.unbacktracked[s1]) == 0: self.a = None @@ -466,8 +473,8 @@ def __call__(self, percept): return self.a def update_state(self, percept): - '''To be overridden in most cases. The default case - assumes the percept to be of type state.''' + """To be overridden in most cases. The default case + assumes the percept to be of type state.""" return percept # ______________________________________________________________________________ @@ -477,8 +484,8 @@ class OnlineSearchProblem(Problem): """ A problem which is solved by an agent executing actions, rather than by just computation. - Carried in a deterministic and a fully observable environment. - """ + Carried in a deterministic and a fully observable environment.""" + def __init__(self, initial, goal, graph): self.initial = initial self.goal = goal @@ -491,15 +498,11 @@ def output(self, state, action): return self.graph.dict[state][action] def h(self, state): - """ - Returns least possible cost to reach a goal for the given state. - """ + """Returns least possible cost to reach a goal for the given state.""" return self.graph.least_costs[state] def c(self, s, a, s1): - """ - Returns a cost estimate for an agent to move from state 's' to state 's1'. - """ + """Returns a cost estimate for an agent to move from state 's' to state 's1'.""" return 1 def update_state(self, percept): @@ -538,8 +541,8 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept # self.result[(self.s, self.a)] = s1 # no need as we are using problem.output # minimum cost for action b in problem.actions(s) - self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H) - for b in self.problem.actions(self.s)) + self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), + self.H) for b in self.problem.actions(self.s)) # costs for action b in problem.actions(s1) costs = [self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H) @@ -551,10 +554,8 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept return self.a def LRTA_cost(self, s, a, s1, H): - """ - Returns cost to move from state 's' to state 's1' plus - estimated cost to get to goal from s1. - """ + """Returns cost to move from state 's' to state 's1' plus + estimated cost to get to goal from s1.""" print(s, a, s1) if s1 is None: return self.problem.h(s) @@ -571,8 +572,7 @@ def LRTA_cost(self, s, a, s1, H): def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): - """ - Call genetic_algorithm on the appropriate parts of a problem. + """Call genetic_algorithm on the appropriate parts of a problem. This requires the problem to have states that can mate and mutate, plus a value method that scores states.""" s = problem.initial_state @@ -582,12 +582,12 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): - "[Figure 4.8]" + """[Figure 4.8]""" for i in range(ngen): new_population = [] for i in range(len(population)): fitnesses = map(fitness_fn, population) - p1, p2 = weighted_sample_with_replacement(2,population, fitnesses) + p1, p2 = weighted_sample_with_replacement(2, population, fitnesses) child = p1.mate(p2) if random.uniform(0, 1) < pmut: child.mutate() @@ -598,18 +598,18 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): class GAState: - "Abstract class for individuals in a genetic search." + """Abstract class for individuals in a genetic search.""" def __init__(self, genes): self.genes = genes def mate(self, other): - "Return a new individual crossing self and other." + """Return a new individual crossing self and other.""" c = random.randrange(len(self.genes)) return self.__class__(self.genes[:c] + other.genes[c:]) def mutate(self): - "Change a few of my genes." + """Change a few of my genes.""" raise NotImplementedError # _____________________________________________________________________________ @@ -641,10 +641,10 @@ def __init__(self, dict=None, directed=True): self.make_undirected() def make_undirected(self): - "Make a digraph into an undirected graph by adding symmetric edges." + """Make a digraph into an undirected graph by adding symmetric edges.""" for a in list(self.dict.keys()): - for (b, distance) in self.dict[a].items(): - self.connect1(b, a, distance) + for (b, dist) in self.dict[a].items(): + self.connect1(b, a, dist) def connect(self, A, B, distance=1): """Add a link from A and B of given distance, and also add the inverse @@ -654,7 +654,7 @@ def connect(self, A, B, distance=1): self.connect1(B, A, distance) def connect1(self, A, B, distance): - "Add a link from A to B of given distance, in one direction only." + """Add a link from A to B of given distance, in one direction only.""" self.dict.setdefault(A, {})[B] = distance def get(self, a, b=None): @@ -668,12 +668,12 @@ def get(self, a, b=None): return links.get(b) def nodes(self): - "Return a list of nodes in the graph." + """Return a list of nodes in the graph.""" return list(self.dict.keys()) def UndirectedGraph(dict=None): - "Build a Graph where every edge (including future ones) goes both ways." + """Build a Graph where every edge (including future ones) goes both ways.""" return Graph(dict=dict, directed=False) @@ -705,6 +705,7 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g + """ [Figure 3.2] Simplified road map of Romania """ @@ -734,7 +735,8 @@ def distance_to_node(n): """ [Figure 4.9] Eight possible states of the vacumm world Each state is represented as - * "State of the left room" "State of the right room" "Room in which the agent is present" + * "State of the left room" "State of the right room" "Room in which the agent + is present" 1 - DDL Dirty Dirty Left 2 - DDR Dirty Dirty Right 3 - DCL Dirty Clean Left @@ -745,14 +747,14 @@ def distance_to_node(n): 8 - CCR Clean Clean Right """ vacumm_world = Graph(dict( - State_1 = dict(Suck = ['State_7', 'State_5'], Right = ['State_2']), - State_2 = dict(Suck = ['State_8', 'State_4'], Left = ['State_2']), - State_3 = dict(Suck = ['State_7'], Right = ['State_4']), - State_4 = dict(Suck = ['State_4', 'State_2'], Left = ['State_3']), - State_5 = dict(Suck = ['State_5', 'State_1'], Right = ['State_6']), - State_6 = dict(Suck = ['State_8'], Left = ['State_5']), - State_7 = dict(Suck = ['State_7', 'State_3'], Right = ['State_8']), - State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7']) + State_1=dict(Suck=['State_7', 'State_5'], Right=['State_2']), + State_2=dict(Suck=['State_8', 'State_4'], Left=['State_2']), + State_3=dict(Suck=['State_7'], Right=['State_4']), + State_4=dict(Suck=['State_4', 'State_2'], Left=['State_3']), + State_5=dict(Suck=['State_5', 'State_1'], Right=['State_6']), + State_6=dict(Suck=['State_8'], Left=['State_5']), + State_7=dict(Suck=['State_7', 'State_3'], Right=['State_8']), + State_8=dict(Suck=['State_8', 'State_6'], Left=['State_7']) )) """ [Figure 4.23] @@ -888,6 +890,7 @@ def goal_test(self, state): # Inverse Boggle: Search for a high-scoring Boggle board. A good domain for # iterative-repair and related search techniques, as suggested by Justin Boyan. + ALPHABET = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' cubes16 = ['FORIXB', 'MOQABJ', 'GURILW', 'SETUPL', @@ -906,6 +909,7 @@ def random_boggle(n=4): # The best 5x5 board found by Boyan, with our word list this board scores # 2274 words, for a score of 9837 + boyan_best = list('RSTCSDEIAEGNLRPEATESMSSID') @@ -1019,7 +1023,7 @@ def __init__(self, board=None): self.set_board(board) def set_board(self, board=None): - "Set the board, and find all the words in it." + """Set the board, and find all the words in it.""" if board is None: board = random_boggle() self.board = board @@ -1050,17 +1054,17 @@ def find(self, lo, hi, i, visited, prefix): visited.pop() def words(self): - "The words found." + """The words found.""" return list(self.found.keys()) scores = [0, 0, 0, 0, 1, 2, 3, 5] + [11] * 100 def score(self): - "The total score for the words found, according to the rules." + """The total score for the words found, according to the rules.""" return sum([self.scores[len(w)] for w in self.words()]) def __len__(self): - "The number of words found." + """The number of words found.""" return len(self.found) # _____________________________________________________________________________ @@ -1134,7 +1138,7 @@ def __getattr__(self, attr): def __repr__(self): return '<{:4d}/{:4d}/{:4d}/{}>'.format(self.succs, self.goal_tests, - self.states, str(self.found)[:4]) + self.states, str(self.found)[:4]) def compare_searchers(problems, header, From 6a1b84be56061a1d8ebeae5d5a8d86a9359d7801 Mon Sep 17 00:00:00 2001 From: ESHAN PANDEY Date: Wed, 22 Mar 2017 12:18:38 +0530 Subject: [PATCH 18/28] Upadte search.py (#389) minor error fixes. --- search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.py b/search.py index 545a24e5c..c9b6280b4 100644 --- a/search.py +++ b/search.py @@ -829,7 +829,7 @@ class GraphProblemStochastic(GraphProblem): def result(self, state, action): return self.graph.get(state, action) - def path_cost(): + def path_cost(self): raise NotImplementedError From 9f1b1ee7da67c581df659ebd1b06974d5075b8c1 Mon Sep 17 00:00:00 2001 From: ESHAN PANDEY Date: Wed, 22 Mar 2017 12:19:06 +0530 Subject: [PATCH 19/28] Update learning.py (#388) created a parameter size in the function leave_one_out(learner, dataset, size=None): --- learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learning.py b/learning.py index 4917a2cf0..8308fe607 100644 --- a/learning.py +++ b/learning.py @@ -850,7 +850,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): size += 1 -def leave_one_out(learner, dataset): +def leave_one_out(learner, dataset, size=None): """Leave one out cross-validation over the dataset.""" return cross_validation(learner, size, dataset, k=len(dataset.examples)) From 4548aaef4398d6fee318d3240aa7858fa9b2e94b Mon Sep 17 00:00:00 2001 From: articuno12 Date: Wed, 22 Mar 2017 12:22:21 +0530 Subject: [PATCH 20/28] Updated docstring for ModelBasedReflexAgentProgram in agent.py (#391) --- agents.py | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/agents.py b/agents.py index afd5e6408..047eb3fd6 100644 --- a/agents.py +++ b/agents.py @@ -144,7 +144,7 @@ def program(percept): def ModelBasedReflexAgentProgram(rules, update_state, model): - """This agent takes action based on the percept and state. [Figure 2.8]""" + """This agent takes action based on the percept and state. [Figure 2.12]""" def program(percept): program.state = update_state(program.state, program.action, percept, model) rule = rule_match(program.state, rules) @@ -443,7 +443,7 @@ def move_to(self, thing, destination): # obs.thing_added(thing) def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): - """Adds things to the world. If (exclude_duplicate_class_items) then the item won't be + """Adds things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class.""" if (self.is_inbounds(location)): if (exclude_duplicate_class_items and @@ -523,7 +523,7 @@ class Wall(Obstacle): class GraphicEnvironment(XYEnvironment): def __init__(self, width=10, height=10, boundary=True, color={}, display=False): - """define all the usual XYEnvironment characteristics, + """define all the usual XYEnvironment characteristics, but initialise a BlockGrid for GUI too""" super().__init__(width, height) self.grid = BlockGrid(width, height, fill=(200,200,200)) @@ -534,14 +534,14 @@ def __init__(self, width=10, height=10, boundary=True, color={}, display=False): self.visible = False self.bounded = boundary self.colors = color - + #def list_things_at(self, location, tclass=Thing): # need to override because locations # """Return all things exactly at a given location.""" # return [thing for thing in self.things # if thing.location == location and isinstance(thing, tclass)] - + def get_world(self): - """Returns all the items in the world in a format + """Returns all the items in the world in a format understandable by the ipythonblocks BlockGrid""" result = [] x_start, y_start = (0, 0) @@ -552,9 +552,9 @@ def get_world(self): row.append(self.list_things_at([x, y])) result.append(row) return result - + """def run(self, steps=1000, delay=1): - "" "Run the Environment for given number of time steps, + "" "Run the Environment for given number of time steps, but update the GUI too." "" for step in range(steps): sleep(delay) @@ -569,7 +569,7 @@ def get_world(self): self.reveal() """ def run(self, steps=1000, delay=1): - """Run the Environment for given number of time steps, + """Run the Environment for given number of time steps, but update the GUI too.""" for step in range(steps): self.update(delay) @@ -577,7 +577,7 @@ def run(self, steps=1000, delay=1): break self.step() self.update(delay) - + def update(self, delay=1): sleep(delay) if self.visible: @@ -585,9 +585,9 @@ def update(self, delay=1): self.reveal() else: self.reveal() - + def reveal(self): - """display the BlockGrid for this world - the last thing to be added + """display the BlockGrid for this world - the last thing to be added at a location defines the location color""" #print("Grid={}".format(self.grid)) self.draw_world() @@ -595,7 +595,7 @@ def reveal(self): # self.grid.show() self.grid.show() self.visible == True - + def draw_world(self): self.grid[:] = (200, 200, 200) world = self.get_world() @@ -606,14 +606,14 @@ def draw_world(self): self.grid[y, x] = self.colors[world[x][y][-1].__class__.__name__] #print('location: ({}, {}) got color: {}' #.format(y, x, self.colors[world[x][y][-1].__class__.__name__])) - + def conceal(self): """hide the BlockGrid for this world""" self.visible = False display(HTML('')) - - - + + + From 4bac57176bc6f3bd9e3b1904d382b1a18fb241f3 Mon Sep 17 00:00:00 2001 From: articuno12 Date: Wed, 22 Mar 2017 12:22:52 +0530 Subject: [PATCH 21/28] Added testcase for ReflexVacuumAgent and ModelBasedVacuumAgent (#394) * Added testcase for agents.py * spacing around commas was wrong --- tests/test_agents.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/tests/test_agents.py b/tests/test_agents.py index 89ee3fcf3..77421c2c7 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -1,4 +1,5 @@ from agents import Direction +from agents import ReflexVacuumAgent, ModelBasedVacuumAgent, TrivialVacuumEnvironment def test_move_forward(): d = Direction("up") @@ -38,3 +39,26 @@ def test_add(): assert l1.direction == Direction.U assert l2.direction == Direction.D #fixed +def test_ReflexVacuumAgent() : + # create an object of the ReflexVacuumAgent + agent = ReflexVacuumAgent() + # create an object of TrivialVacuumEnvironment + environment = TrivialVacuumEnvironment() + # add agent to the environment + environment.add_thing(agent) + # run the environment + environment.run() + # check final status of the environment + assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'} + +def test_ModelBasedVacuumAgent() : + # create an object of the ModelBasedVacuumAgent + agent = ModelBasedVacuumAgent() + # create an object of TrivialVacuumEnvironment + environment = TrivialVacuumEnvironment() + # add agent to the environment + environment.add_thing(agent) + # run the environment + environment.run() + # check final status of the environment + assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'} From c38675a611be633d410cbf2bbdebb8e6bf0b8541 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 22 Mar 2017 08:54:32 +0200 Subject: [PATCH 22/28] Add Perceptron to Notebook (#387) * Add Perceptron Section * Add Perceptron Image --- images/perceptron.png | Bin 0 -> 21245 bytes learning.ipynb | 365 +++++++++++++++++++++++++++++++++--------- 2 files changed, 293 insertions(+), 72 deletions(-) create mode 100644 images/perceptron.png diff --git a/images/perceptron.png b/images/perceptron.png new file mode 100644 index 0000000000000000000000000000000000000000..a83cc048d3d1c81be7c2d91b0e02308aef0bfa28 GIT binary patch literal 21245 zcmeFZ`9IWc*grhBgo?6-LXj+43XyHdK8$T_*(v*$E!jm~iO^!g2w7&B5mWYElcZ!{ z8at6?2-(IuJm*~3{k@;>U-10&{NOdO&vGuG^E}RDc^~h?GZQ1-)6Bfg5D4V7zMhsD z1VXzBfzXVcJOS>oj8^i3A9{BUxCR9BF_q=enE_lg-PgMl1c97sq5jb*G9ZM(O`c$F z>tKYxTX3jzpey9Qv!`FMl%H#`pq!M9)OA@oI@@pv#4KE2OT+x3!^*_zx8_4(?dypm zE^&#HIvmemTrNv=Gc`BYD;{ApF^{vxy2O!qudCY$>6ph|);aePF<4A*U%oeHa8@shmReP1WARuySpvSb^mDE*`Y*sGtQ5Xgf)fEX9neN zzHQk6bA)J}BdCBMHdA`=kq~vQvz>I{_pG)IEA=7A|Nrp+<3-?Xa60NznA)-|vm^PA zRdW)N0FdbOR@NmXJG4zuA5<^sT@b1v$Bg@r~Wwh!CnU>lgSCUio>Uf<<#@a+f%?b^;H6zVMID{)q z+>6^o7^@EbH!L^FDSD*bbuv42l63llVlVxsY6N*}wQ0ve)~x5Z6=?RwzA%2zyR7aj z{W@t>tmv%^o_ZXv{GSUj|HIvNF#my~)c<$> z7FPDIaeK{C2c!Sf!P(A+@Xfk1NP{;~>Hta|q2A?er!h+IJN+i*V5dek6MKRZeX!yl z4W25Z|SiBQKNd-VMOd3N-b&)-KjBUi>bE_AuH26en#Z)F$o zWRKc2o5jbI^R8pnzLQ0{(O%N7MNUWMKcp(s!J}A@M7T^YE5qj9ug^dJcLU}l6Z8E_ z`mh0btS)!uW=6~IOw!5~a3zR3NnM`Ugv%L>;PSHgb;6c}Y5T@Xh|Co8cX%D39K7g6yAOtq zU>7lz+1myU)zwbUOK9zFZEtT+Iz+6v#_bzrD}`0Kb#15}?cR+z4PFscj@x6lt#C`W zva(tznj9P)%m{o#ZEAaRzU0eHTM}GwEiRm4mwAi2K8E0Cf|-w7_ghahKaO4r23@RT zfL-v?Hqmj%uX)dHEk$|tgQ}$J{$hdKK^y~%kFkOHI(Fs#H2C;e&z+i?J#YNkU7zjx zZOyQay2e+dUBvOYp@Iz#hgXNz$aX8X`tT{eXbGAJf+d3!|?ixGlU!;ZPV)IT zI`0ooMy#ohf%SP|2!==c^@&-0em0tqfFb*4iLB?s7P3P-T^Y6lf4|g4{pfg|-mum@ zX2-Ac@I&1x(B>X!Qv)W7(*j&{A66s&L}q)$56PN`RCPrj zzRL==Uwmz>wmw?r?Or_vwgMR-~J@UQN83EVFO;a|~L{Bs;0mRDMb;=_W2LzR+f$XC_og`9|P^38}j zq}8D>to6IwXr(7+d`_Ndcb6(OGf7Tr8eG?`O@@+;d?)IMB919LE9f_Jrj}!}rlgJ| z%0a}zY6CvZ&&oaoIRS)|&5?eSsi1KWizwPpGV*-&z5}Wg(kG@HMDN$MZMhkvikB#> z{d_0@7Onf_JUfijPbZasMOq0x?NCQDwG9@(sG9pjs-e>X^pzkTxA%A;A5muW9L1Gw zQ|a+?f)^gn|9{jMm>d5;T-p8N@1=@UJMs~v#dXKP?^mcQM?LavXX^r2_R@G=g}|V$ zb05|`e4N`c@?#VIe^d0h0wzVV?MgC2|Eu8iHDpqsS7r=+dnrUQ4No4nn$1UGsTxq- zjIFZ(E@Aop@Ba4k0^@d7Rh31};^6UJzqu)H)){i!@l~k$<-}d@f-=WoIQhP<)+{k}M zO1ZK}8^bE7<3K)Zf!iy?A6v5&16)e9iw1!w75f)_%ssiwx!_ERd+w0@s7zgaVX>tNus3Wm~e7BeR&C%d`4XIYYWexqFkT9ylC=nk2DhatWrf6=({Cnlpoa^=*PJ!dk$Z`UXWh(XBJy5g zJ<>BI)<0PBidOORrB5#Oe(VsV>_4f3c%~1hsqsT}q2oL!=!r$LhO71awpE$3?x?YW zl_hcrl(ae}uWF>~Kq>WOhd|=Ov|fp{-jtIATES~+5PMDO$@6NTkr_$r51$I#MOUuK zn^?l0cNC>HLmMcYkj7ZuW(2A3`ACHDyxu#%co! zzTbI3NY;_!9ER*W7jbZA*8$n{JWF3fVthxwn4Dr2QE&M-VEKJ4?O`wp8lDs0oWvzL z^HdTdOixg8xipcF0LS5j?MvZNZaJH5rB|i;SeR=c&)eb^KHj`gNj;%mnDW3TEUKBA zyshNnA_);}J~@9@qT|XL>M|JyCe0cPE!!js$}iXsy4PbYnS&<#0=xa zF6!b_yYJCJW}Z0le=K+hr-9$^!ve+lVbQ$Pqkr=`kF4Qby3;gHAP8lgXSytGTjQ6l z0Q@jZ=zU=9z0Hy`# zzN$#lc5nTu@ThTCJNc$)Tv+awfGUJ71Z=D-g9H{F-$l*JTEWStRIiYb5RoeJRr%rg zp1Skl*<0bH5SB@dgH>EOBtaxDO!y!1NU9*1)0->IHL&vB_!2WESN~pCO26ayL65!U zwQ!1@#!W>VJn;@*0V1e$a{i?#?3PJR2slN9i)Cue;_>e09Kqt;YR}dzu6y3}?OVnY z<aYjP(%A(7o(=mf8*?S#Pv zT%A+C^Oe)>&y=9Ek>ECHI+mb?3k`yg}e&{v$_cu-U5MW6%C}3ODq{ZS`8UI z)^|=6LEmQZK3;CRm&Okf2deH{-+%5K?fKID@A-(jzI!eT+!_USHlJAfigx<4UHcAx z6b3*

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zbQLtDV9WYu972htwbIJ}fh2o(%j|Jl!|#z~Wh&fSzi=8pJw5u(L}b|{*b~a^!36oY zpTk2!;?nVR_Sn+T9NLgOH#2)GeXXBgB5xS9L+gxs?X%X|1SW`IiXd?gvn#C#2OFk_ z$5X#Bqkbs|n4~1SL5ZA}f$V4a3FiWG<+clR#*wOa?Q((az1AY%jx_IH1qtj?fl zo%c73T6sUTUnFrr)r*oo#`b^%%m4Wc@U)yi#Jh>@ye2_zB;CL*C-5@cIScSw{Y57H fzu*}Q24<{>cRKP-%WV|sWE0O#KJUogB7{E!!?WX` literal 0 HcmV?d00001 diff --git a/learning.ipynb b/learning.ipynb index b81fa3ca8..9f2d91add 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -9,16 +9,30 @@ "source": [ "# Learning\n", "\n", - "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from learning module." + "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from the module:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from learning import *" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Contents\n", "\n", - "* Dataset\n", + "* Datasets\n", "* Machine Learning Overview\n", "* Plurality Learner Classifier\n", " * Overview\n", @@ -28,6 +42,10 @@ " * Overview\n", " * Implementation\n", " * Example\n", + "* Perceptron Classifier\n", + " * Overview\n", + " * Implementation\n", + " * Example\n", "* MNIST Handwritten Digits Classification\n", " * Loading and Visualising\n", " * Testing\n", @@ -41,9 +59,13 @@ "editable": true }, "source": [ - "## Dataset\n", + "## Datasets\n", + "\n", + "For the following tutorials we will use a range of datasets, to better showcase the strengths and weaknesses of the algorithms. The datasests are the following:\n", + "\n", + "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv). Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n", "\n", - "The dataset we will be using for the following tutorials is [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv). Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica." + "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." ] }, { @@ -154,7 +176,7 @@ "source": [ "### Example\n", "\n", - "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead (and only for this algorithm) we will use the zoo dataset, found [here](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." + "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead we will use the zoo dataset." ] }, { @@ -175,8 +197,6 @@ } ], "source": [ - "from learning import DataSet, PluralityLearner\n", - "\n", "zoo = DataSet(name=\"zoo\")\n", "\n", "pL = PluralityLearner(zoo)\n", @@ -240,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, @@ -300,8 +320,6 @@ } ], "source": [ - "from learning import DataSet, NearestNeighborLearner\n", - "\n", "iris = DataSet(name=\"iris\")\n", "\n", "kNN = NearestNeighborLearner(iris,k=3)\n", @@ -320,7 +338,147 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Perceptron Classifier\n", + "\n", + "### Overview\n", + "\n", + "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n", + "\n", + "You can think of it as a single neuron. It has *n* synapses, each with its own weight. Each synapse corresponds to one item feature. Perceptron multiplies each item feature with the corresponding synapse weight and then adds them together (aka, the dot product) and checks whether this value is greater than the threshold. If yes, it returns 1. It returns 0 otherwise.\n", + "\n", + "![perceptron](images/perceptron.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Implementation\n", + "\n", + "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights. If the result is greater than a predefined threshold (usually 0.5, 0 or 1), it returns 1. If it is less than the threshold, it returns 0.\n", + "\n", + "NOTE: The current implementation of the algorithm classifies an item into one of two classes. It is a binary classifier and will not work well for multi-class datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def PerceptronLearner(dataset, learning_rate=0.01, epochs=100):\n", + " \"\"\"Logistic Regression, NO hidden layer\"\"\"\n", + " i_units = len(dataset.inputs)\n", + " o_units = 1 # As of now, dataset.target gives only one index.\n", + " hidden_layer_sizes = []\n", + " raw_net = network(i_units, hidden_layer_sizes, o_units)\n", + " learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs)\n", + "\n", + " def predict(example):\n", + " # Input nodes\n", + " i_nodes = learned_net[0]\n", + "\n", + " # Activate input layer\n", + " for v, n in zip(example, i_nodes):\n", + " n.value = v\n", + "\n", + " # Forward pass\n", + " for layer in learned_net[1:]:\n", + " for node in layer:\n", + " inc = [n.value for n in node.inputs]\n", + " in_val = dotproduct(inc, node.weights)\n", + " node.value = node.activation(in_val)\n", + "\n", + " # Hypothesis\n", + " o_nodes = learned_net[-1]\n", + " pred = [o_nodes[i].value for i in range(o_units)]\n", + " return 1 if pred[0] >= 0.5 else 0\n", + "\n", + " return predict" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The weights are trained from the `BackPropagationLearner`. Note that the perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one node, with the weights calculated.\n", + "\n", + "`PerceptronLearner` returns `predict`, a function that can be used to classify a new item.\n", + "\n", + "That function passes the input/example through the network, calculating the dot product of the input and the weights. If that value is greater than or equal to 0.5, it returns 1. Otherwise it returns 0." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Example\n", + "\n", + "We will train the Perceptron on the iris dataset. Because, though, the algorithm is a binary classifier (which means it classifies an item in one of two classes) and the iris dataset has three classes, we need to transform the dataset into a proper form, with only two classes. Therefore, we will remove the third and final class of the dataset, *Virginica*.\n", + "\n", + "Then, we will try and classify the item/flower with measurements of 5,3,1,0.1." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "iris = DataSet(name=\"iris\")\n", + "iris.remove_examples(\"virginica\")\n", + "iris.classes_to_numbers()\n", + "\n", + "perceptron = PerceptronLearner(iris)\n", + "print(perceptron([5,3,1,0.1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The output is 0, which means the item is classified in the first class, *setosa*. This is indeed correct. Note that the Perceptron algorithm is not perfect and may produce false classifications." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## MNIST Handwritten Digits Classification\n", "\n", @@ -337,7 +495,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Loading MNIST digits data\n", "\n", @@ -346,9 +507,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -366,29 +529,37 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ "def load_MNIST(path=\"aima-data/MNIST\"):\n", " \"helper function to load MNIST data\"\n", - " with open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\") as train_img_file:\n", - " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", - " tr_img = array.array(\"B\", train_img_file.read())\n", + " train_img_file = open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\")\n", + " train_lbl_file = open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\")\n", + " test_img_file = open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\")\n", + " test_lbl_file = open(os.path.join(path, 't10k-labels-idx1-ubyte'), \"rb\")\n", " \n", - " with open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\") as train_lbl_file:\n", - " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", - " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", + " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", + " tr_img = array.array(\"B\", train_img_file.read())\n", + " train_img_file.close() \n", + " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", + " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", + " train_lbl_file.close()\n", " \n", - " with open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\") as test_img_file:\n", - " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", - " te_img = array.array(\"B\", test_img_file.read())\n", - " \n", - " with open(os.path.join(path, \"t10k-labels-idx1-ubyte\"), \"rb\") as test_lbl_file:\n", - " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", - " te_lbl = array.array(\"b\", test_lbl_file.read())\n", + " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", + " te_img = array.array(\"B\", test_img_file.read())\n", + " test_img_file.close()\n", + " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", + " te_lbl = array.array(\"b\", test_lbl_file.read())\n", + " test_lbl_file.close()\n", + "\n", + "# print(len(tr_img), len(tr_lbl), tr_size)\n", + "# print(len(te_img), len(te_lbl), te_size)\n", " \n", " train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.int16)\n", " train_lbl = np.zeros((tr_size,), dtype=np.int8)\n", @@ -407,16 +578,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are going to use to train and classify hand-written digits in various learning approaches." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -425,7 +601,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n", "\n", @@ -434,9 +613,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -459,7 +640,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Visualizing MNIST digits data\n", "\n", @@ -468,9 +652,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -504,16 +690,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "data": { - "image/png": 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kihQp4nWz4pKyZcuG/NkiRYpQpEgRPvzwQwoXLkzhwoXNa5s3b8564xQlhrGi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A4ePAjYOyu3IMmMsgN0Ijk2I0aMMDH+Dz74APBNRPd//Y033uiqhHpBEurz5cvH4sWLk33dZZdd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\n", 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1BODrr79m5syZYW3fT2We2bNnB+Dll18G4J577mH06NEA9O7dO+LtRrPk+oUX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0bjcO1F8uTJY0J5Er4MVC4fMGCACTuI35hXyZUrF2AdHwkVSLL9559/zgMPPABEnxIlSGgvMTGR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\nr9GsWTMA3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -529,14 +717,16 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "data": { - "image/png": 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iKEqY6ERKURRFURQlTHQipSiKoiiKEiY6kVIURVEURQkTnUgpiqIoiqKEiU6k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TQ5fvy4qWiWG+qKFSt6Nc0uXLiwSVBPyaleWLBgQUh8szS0pyiKoiiKEiCuUqRk1Swrypw5c5oS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\n83im9WI/fvzQESm7x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UrVoVcN1dcgObO3euyVr8448/vDEwRCQcokyZMmYiKwHJ/fr1S/f+Q4cOAaknl8Fu3H5C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\n3Vi/YpPKykrf8wTdb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -550,16 +740,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Let's have a look at the average of all the images of training and testing data." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -596,9 +791,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -622,7 +819,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -663,7 +860,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Testing\n", "\n", @@ -672,9 +872,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -695,35 +897,44 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "from learning import DataSet, manhattan_distance\n", - "\n", "# takes ~8 seconds to execute this\n", "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Moving forward we can use `MNIST_DataSet` to test our algorithms." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### k-Nearest Neighbors\n", "\n", @@ -734,9 +945,11 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -757,16 +970,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "To make sure that the output we got is correct, let's plot that image along with its label." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -779,10 +997,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -790,7 +1008,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -804,7 +1022,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", "\n", From 2922ab68d374d61d92ac9fdcfad545f6a72e8d7e Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 22 Mar 2017 12:25:49 +0530 Subject: [PATCH 23/28] Games notebook updates (#383) * updated games.ipynb with refactored games.py * fixed typos in games.ipynb --- games.ipynb | 230 ++++++++++++++++++++++++++++++++++------------------ 1 file changed, 152 insertions(+), 78 deletions(-) diff --git a/games.ipynb b/games.ipynb index 1dc5f5ca9..da7652cf8 100644 --- a/games.ipynb +++ b/games.ipynb @@ -18,7 +18,7 @@ "outputs": [], "source": [ "from games import (GameState, Game, Fig52Game, TicTacToe, query_player, random_player, \n", - " alphabeta_player, play_game, minimax_decision, alphabeta_full_search,\n", + " alphabeta_player, minimax_decision, alphabeta_full_search,\n", " alphabeta_search, Canvas_TicTacToe)" ] }, @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -237,7 +237,7 @@ "output_type": "stream", "text": [ "a1\n", - "a3\n" + "a1\n" ] } ], @@ -250,12 +250,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `alphabeta_player(game, state)` will always give us the best move possible:" + "The `alphabeta_player(game, state)` will always give us the best move possible, for the relevant player (MAX or MIN):" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -296,7 +296,7 @@ "'a1'" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -318,7 +318,7 @@ "'a1'" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -354,18 +354,47 @@ "3" ] }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "game52.play_game(alphabeta_player, alphabeta_player)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B3\n" + ] + }, + { + "data": { + "text/plain": [ + "8" + ] + }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "play_game(game52, alphabeta_player, alphabeta_player)" + "game52.play_game(alphabeta_player, random_player)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -374,41 +403,68 @@ "name": "stdout", "output_type": "stream", "text": [ - "B2\n" + "current state:\n", + "A\n", + "available moves: ['a2', 'a1', 'a3']\n", + "\n", + "Your move? a3\n", + "D3\n" ] }, { "data": { "text/plain": [ - "12" + "2" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "play_game(game52, alphabeta_player, random_player)" + "game52.play_game(query_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current state:\n", + "B\n", + "available moves: ['b1', 'b3', 'b2']\n", + "\n", + "Your move? b3\n", + "B3\n" + ] + }, + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#play_game(game52, query_player, alphabeta_player)\n", - "#play_game(game52, alphabeta_player, query_player)" + "game52.play_game(alphabeta_player, query_player)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that, here, if you are the first player, the alphabeta_player plays as MIN, and if you are the second player, the alphabeta_player plays as MAX. This happens because that's the way the game is defined in the class Fig52Game. Having a look at the code of this class should make it clear." + "Note that if you are the first player then alphabeta_player plays as MIN, and if you are the second player then alphabeta_player plays as MAX. This happens because that's the way the game is defined in the class Fig52Game. Having a look at the code of this class should make it clear." ] }, { @@ -421,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -439,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -469,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -490,12 +546,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So, how does this game state looks like?" + "So, how does this game state look like?" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -523,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -531,10 +587,10 @@ { "data": { "text/plain": [ - "(3, 3)" + "(3, 2)" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -545,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -556,7 +612,7 @@ "(3, 2)" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -574,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -585,7 +641,7 @@ "(2, 2)" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -603,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -612,29 +668,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "O X O \n", - "O . X \n", - "O X X \n", - "-1\n" + "O O O \n", + "X X . \n", + ". X . \n" ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(play_game(ttt, random_player, alphabeta_player))" + "ttt.play_game(random_player, alphabeta_player)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output is -1, hence `random_player` loses implies `alphabeta_player` wins. \n", + "The output is (usually) -1, because `random_player` loses to `alphabeta_player`. Sometimes, however, `random_player` manages to draw with `alphabeta_player`.\n", " \n", - " Since, an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" + " Since an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -688,7 +753,7 @@ ], "source": [ "for _ in range(10):\n", - " print(play_game(ttt, alphabeta_player, alphabeta_player))" + " print(ttt.play_game(alphabeta_player, alphabeta_player))" ] }, { @@ -700,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -709,52 +774,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "X . . \n", - "O O O \n", - ". X X \n", - "-1\n", - "O O O \n", - "X X O \n", - "X X . \n", - "-1\n", - "O X . \n", - ". O X \n", - "X . O \n", - "-1\n", - "O . . \n", - ". O X \n", - "X X O \n", - "-1\n", + "O . X \n", "X O X \n", - "X O O \n", - ". O X \n", + ". . O \n", "-1\n", - "O . X \n", + "X O X \n", + "O O X \n", "X O . \n", - ". X O \n", "-1\n", - "O O X \n", + "O X O \n", "X O X \n", + "X O X \n", + "0\n", + "O X O \n", "X O . \n", + "O X X \n", "-1\n", - "O O O \n", + ". . O \n", + ". O X \n", "O X X \n", - "X . X \n", "-1\n", + "O O O \n", "X X O \n", - "O O X \n", - "O X . \n", + ". X X \n", + "-1\n", + "O O O \n", + ". . X \n", + ". X X \n", "-1\n", - "X . X \n", "O O O \n", + ". X X \n", ". X . \n", + "-1\n", + "X O X \n", + ". O X \n", + ". O . \n", + "-1\n", + "O X O \n", + "X O X \n", + "O X . \n", "-1\n" ] } ], "source": [ "for _ in range(10):\n", - " print(play_game(ttt, random_player, alphabeta_player))" + " print(ttt.play_game(random_player, alphabeta_player))" ] }, { @@ -770,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -828,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -881,12 +946,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Yay! We win. But we cannot win against an `alphabeta_player`, however hard we try." + "Yay! We (usually) win. But we cannot win against an `alphabeta_player`, however hard we try." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -934,6 +999,15 @@ "source": [ "ab_play = Canvas_TicTacToe('ab_play', 'human', 'alphabeta')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -952,7 +1026,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From 2b07ba93156ec4a9a6013bb752227e254006e507 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 22 Mar 2017 12:28:49 +0530 Subject: [PATCH 24/28] Fixed bugs in games.py (#380) * move play_game into games class * display current state before prompting for action * fixed player swap bug * display available moves to human players * make tests pass --- games.py | 31 ++++++++++++++++++------------- tests/test_games.py | 6 +++--- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/games.py b/games.py index f5061f4c8..d98b7473c 100644 --- a/games.py +++ b/games.py @@ -136,6 +136,10 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): """Make a move by querying standard input.""" + print("current state:") + game.display(state) + print("available moves: {}".format(game.actions(state))) + print("") move_string = input('Your move? ') try: move = eval(move_string) @@ -153,18 +157,6 @@ def alphabeta_player(game, state): return alphabeta_full_search(state, game) -def play_game(game, *players): - """Play an n-person, move-alternating game.""" - - state = game.initial - while True: - for player in players: - move = player(game, state) - state = game.result(state, move) - if game.terminal_test(state): - game.display(state) - return game.utility(state, game.to_move(game.initial)) - # ______________________________________________________________________________ # Some Sample Games @@ -204,6 +196,17 @@ def display(self, state): def __repr__(self): return '<{}>'.format(self.__class__.__name__) + + def play_game(self, *players): + """Play an n-person, move-alternating game.""" + state = self.initial + while True: + for player in players: + move = player(self, state) + state = self.result(state, move) + if self.terminal_test(state): + self.display(state) + return self.utility(state, self.to_move(self.initial)) class Fig52Game(Game): @@ -255,7 +258,9 @@ def actions(self, state): def result(self, state, move): if move not in state.moves: - return state # Illegal move has no effect + return GameState(to_move=('O' if state.to_move == 'X' else 'X'), + utility=self.compute_utility(state.board, move, state.to_move), + board=state.board, moves=state.moves) # Illegal move has no effect board = state.board.copy() board[move] = state.to_move moves = list(state.moves) diff --git a/tests/test_games.py b/tests/test_games.py index 28644fbc5..35df9c827 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -60,13 +60,13 @@ def test_alphabeta_full_search(): def test_random_tests(): - assert play_game(Fig52Game(), alphabeta_player, alphabeta_player) == 3 + assert Fig52Game().play_game(alphabeta_player, alphabeta_player) == 3 # The player 'X' (one who plays first) in TicTacToe never loses: - assert play_game(ttt, alphabeta_player, alphabeta_player) >= 0 + assert ttt.play_game(alphabeta_player, alphabeta_player) >= 0 # The player 'X' (one who plays first) in TicTacToe never loses: - assert play_game(ttt, alphabeta_player, random_player) >= 0 + assert ttt.play_game(alphabeta_player, random_player) >= 0 if __name__ == '__main__': From efa5628126987d16c7f7a2e6f25b1f58f2146705 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 22 Mar 2017 09:29:51 +0200 Subject: [PATCH 25/28] Update test_learning.py (#376) Add DecisionTreeLearner, NeuralNetLearner and PerceptronLearner tests --- tests/test_learning.py | 42 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 36 insertions(+), 6 deletions(-) diff --git a/tests/test_learning.py b/tests/test_learning.py index 46ac8dd26..f216ad168 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,11 +1,13 @@ from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ - PluralityLearner, NaiveBayesLearner, NearestNeighborLearner + PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \ + NeuralNetLearner, PerceptronLearner, DecisionTreeLearner from utils import DataFile + def test_parse_csv(): Iris = DataFile('iris.csv').read() - assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa'] + assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa'] def test_weighted_mode(): @@ -20,18 +22,46 @@ def test_plurality_learner(): zoo = DataSet(name="zoo") pL = PluralityLearner(zoo) - assert pL([]) == "mammal" + assert pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == "mammal" def test_naive_bayes(): iris = DataSet(name="iris") nB = NaiveBayesLearner(iris) - assert nB([5, 3, 1, 0.1]) == "setosa" + assert nB([5,3,1,0.1]) == "setosa" def test_k_nearest_neighbors(): iris = DataSet(name="iris") - kNN = NearestNeighborLearner(iris, k=3) - assert kNN([5, 3, 1, 0.1]) == "setosa" + kNN = NearestNeighborLearner(iris,k=3) + assert kNN([5,3,1,0.1]) == "setosa" + +def test_decision_tree_learner(): + iris = DataSet(name="iris") + + dTL = DecisionTreeLearner(iris) + assert dTL([5,3,1,0.1]) == "setosa" + + +def test_neural_network_learner(): + iris = DataSet(name="iris") + classes = ["setosa","versicolor","virginica"] + + iris.classes_to_numbers() + + nNL = NeuralNetLearner(iris) + # NeuralNetLearner might be wrong. Just check if prediction is in range + assert nNL([5,3,1,0.1]) in range(len(classes)) + + +def test_perceptron(): + iris = DataSet(name="iris") + classes = ["setosa","versicolor","virginica"] + + iris.classes_to_numbers() + + perceptron = PerceptronLearner(iris) + # PerceptronLearner might be wrong. Just check if prediction is in range + assert perceptron([5,3,1,0.1]) in range(len(classes)) From 1bd46b96024f492afbb9f99c61684ad084de687b Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Thu, 23 Mar 2017 17:26:36 +0530 Subject: [PATCH 26/28] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 08e5e23fd..d1e972197 100644 --- a/README.md +++ b/README.md @@ -76,16 +76,16 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`][logic] | | 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`][logic] | | 9.8 | Append | | | -| 10.1 | Air-Cargo-problem | | -| 10.2 | Spare-Tire-Problem | | -| 10.3 | Three-Block-Tower | | -| 10.7 | Cake-Problem | | +| 10.1 | Air-Cargo-problem |air_cargo |[planning.py][planning]| +| 10.2 | Spare-Tire-Problem | spare_tire |[planning.py][planning]| +| 10.3 | Three-Block-Tower | three_block_tower |[planning.py][planning]| +| 10.7 | Cake-Problem | have_cake_and_eat_cake_too |[planning.py][planning]| | 10.9 | Graphplan | | | 10.13 | Partial-Order-Planner | | | 11.1 | Job-Shop-Problem-With-Resources | | | 11.5 | Hierarchical-Search | | | 11.8 | Angelic-Search | | -| 11.10 | Doubles-tennis | | +| 11.10 | Doubles-tennis | double_tennis_problem |[planning.py][planning]| | 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`][probability] | | 13.1 | DT-Agent | `DTAgent` | [`probability.py`][probability] | | 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`][probability] | From 6e3fb8eaed5d0d1d8619b429e58fbb57a2833c6e Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Thu, 23 Mar 2017 17:28:56 +0530 Subject: [PATCH 27/28] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d1e972197..7001c3b95 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 10.2 | Spare-Tire-Problem | spare_tire |[planning.py][planning]| | 10.3 | Three-Block-Tower | three_block_tower |[planning.py][planning]| | 10.7 | Cake-Problem | have_cake_and_eat_cake_too |[planning.py][planning]| -| 10.9 | Graphplan | | +| 10.9 | Graphplan | GraphPlan |[planning.py][planning]| | 10.13 | Partial-Order-Planner | | | 11.1 | Job-Shop-Problem-With-Resources | | | 11.5 | Hierarchical-Search | | From 3f3ef6dc47233ccede6fa309a715cbd65e9b6104 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Thu, 23 Mar 2017 17:30:29 +0530 Subject: [PATCH 28/28] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 7001c3b95..7cb796b02 100644 --- a/README.md +++ b/README.md @@ -76,16 +76,16 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`][logic] | | 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`][logic] | | 9.8 | Append | | | -| 10.1 | Air-Cargo-problem |air_cargo |[planning.py][planning]| -| 10.2 | Spare-Tire-Problem | spare_tire |[planning.py][planning]| -| 10.3 | Three-Block-Tower | three_block_tower |[planning.py][planning]| -| 10.7 | Cake-Problem | have_cake_and_eat_cake_too |[planning.py][planning]| -| 10.9 | Graphplan | GraphPlan |[planning.py][planning]| +| 10.1 | Air-Cargo-problem |`air_cargo` |[`planning.py`][planning]| +| 10.2 | Spare-Tire-Problem | `spare_tire` |[`planning.py`][planning]| +| 10.3 | Three-Block-Tower | `three_block_tower` |[`planning.py`][planning]| +| 10.7 | Cake-Problem | `have_cake_and_eat_cake_too` |[`planning.py`][planning]| +| 10.9 | Graphplan | `GraphPlan` |[`planning.py`][planning]| | 10.13 | Partial-Order-Planner | | | 11.1 | Job-Shop-Problem-With-Resources | | | 11.5 | Hierarchical-Search | | | 11.8 | Angelic-Search | | -| 11.10 | Doubles-tennis | double_tennis_problem |[planning.py][planning]| +| 11.10 | Doubles-tennis | `double_tennis_problem` |[`planning.py`][planning]| | 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`][probability] | | 13.1 | DT-Agent | `DTAgent` | [`probability.py`][probability] | | 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`][probability] |