added double_tennis_problem to planning.py; and minor pep8 edits (aimacode#373)

* Update test_agents.py

pep8 changes, showed flake8 errors

* Update test_agents.py

* Update test_agents.py

* Update test_agents.py

* Update test_text.py

added missing whitespace after comma

* Update utils.py

added space after comma

* Update search.py

added space after comma

* Update probability.py

added space after comma

* Update learning.py

added space after comma

* Update planning.py

added double_tennis_problem

* 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).

* Update search.py

the 'uniform_cost_search' in notebook 'search-4e.ipynb' resembles more to the pseudocode in book.

* Update search.py

* Update search.py

* Update search.py

Author: sofmonk

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, [])

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)

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

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

tests/test_agents.py

@@ -1,21 +1,23 @@
 from agents import Direction
 from agents import ReflexVacuumAgent, ModelBasedVacuumAgent, TrivialVacuumEnvironment
 
+
 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)
@@ -37,7 +39,7 @@ 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
 
 def test_ReflexVacuumAgent() :
     # create an object of the ReflexVacuumAgent
@@ -62,3 +64,4 @@ def test_ModelBasedVacuumAgent() :
     environment.run()
     # check final status of the environment
     assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
+

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']
 

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."""