modifies Fig. to Figure all over the repository (aimacode#223)

* modifies Fig. to Figure all over the repository

* fixed a small type in logic.py

Author: SnShine

agents.py

@@ -118,7 +118,7 @@ def TableDrivenAgentProgram(table):
     """This agent selects an action based on the percept sequence.
     It is practical only for tiny domains.
     To customize it, provide as table a dictionary of all
-    {percept_sequence:action} pairs. [Fig. 2.7]"""
+    {percept_sequence:action} pairs. [Figure 2.7]"""
     percepts = []
 
     def program(percept):
@@ -136,7 +136,7 @@ def RandomAgentProgram(actions):
 
 
 def SimpleReflexAgentProgram(rules, interpret_input):
-    "This agent takes action based solely on the percept. [Fig. 2.10]"
+    "This agent takes action based solely on the percept. [Figure 2.10]"
     def program(percept):
         state = interpret_input(percept)
         rule = rule_match(state, rules)
@@ -146,7 +146,7 @@ def program(percept):
 
 
 def ModelBasedReflexAgentProgram(rules, update_state):
-    "This agent takes action based on the percept and state. [Fig. 2.12]"
+    "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)
         rule = rule_match(program.state, rules)
@@ -173,7 +173,7 @@ def RandomVacuumAgent():
 
 
 def TableDrivenVacuumAgent():
-    "[Fig. 2.3]"
+    "[Figure 2.3]"
     table = {((loc_A, 'Clean'),): 'Right',
              ((loc_A, 'Dirty'),): 'Suck',
              ((loc_B, 'Clean'),): 'Left',
@@ -189,7 +189,7 @@ def TableDrivenVacuumAgent():
 
 
 def ReflexVacuumAgent():
-    "A reflex agent for the two-state vacuum environment. [Fig. 2.8]"
+    "A reflex agent for the two-state vacuum environment. [Figure 2.8]"
     def program(percept):
         location, status = percept
         if status == 'Dirty':

csp.py

@@ -157,7 +157,7 @@ def conflicted_vars(self, current):
 
 
 def AC3(csp, queue=None, removals=None):
-    """[Fig. 6.3]"""
+    """[Figure 6.3]"""
     if queue is None:
         queue = [(Xi, Xk) for Xi in csp.variables for Xk in csp.neighbors[Xi]]
     csp.support_pruning()
@@ -251,7 +251,7 @@ def backtracking_search(csp,
                         select_unassigned_variable=first_unassigned_variable,
                         order_domain_values=unordered_domain_values,
                         inference=no_inference):
-    """[Fig. 6.5]
+    """[Figure 6.5]
     """
 
     def backtrack(assignment):
@@ -306,7 +306,7 @@ def min_conflicts_value(csp, var, current):
 
 
 def tree_csp_solver(csp):
-    "[Fig. 6.11]"
+    "[Figure 6.11]"
     assignment = {}
     root = csp.variables[0]
     X, parent = topological_sort(csp.variables, root)

games.py

@@ -15,7 +15,7 @@
 
 def minimax_decision(state, game):
     """Given a state in a game, calculate the best move by searching
-    forward all the way to the terminal states. [Fig. 5.3]"""
+    forward all the way to the terminal states. [Figure 5.3]"""
 
     player = game.to_move(state)
 
@@ -44,7 +44,7 @@ def min_value(state):
 
 def alphabeta_full_search(state, game):
     """Search game to determine best action; use alpha-beta pruning.
-    As in [Fig. 5.7], this version searches all the way to the leaves."""
+    As in [Figure 5.7], this version searches all the way to the leaves."""
 
     player = game.to_move(state)
 
@@ -207,7 +207,7 @@ def __repr__(self):
 
 
 class Fig52Game(Game):
-    """The game represented in [Fig. 5.2]. Serves as a simple test case."""
+    """The game represented in [Figure 5.2]. Serves as a simple test case."""
 
     succs = dict(A=dict(a1='B', a2='C', a3='D'),
                  B=dict(b1='B1', b2='B2', b3='B3'),
@@ -334,12 +334,12 @@ def __init__(self, varname, player_1='human', player_2='random', id=None, width=
         self.players = (player_1, player_2)
         self.draw_board()
         self.font("Ariel 30px")
-        
+
     def mouse_click(self, x, y):
         player = self.players[self.turn]
         if self.ttt.terminal_test(self.state):
             return
-            
+
         if player == 'human':
             x, y = int(3*x/self.width) + 1, int(3*y/self.height) + 1
             if (x, y) not in self.ttt.actions(self.state):
@@ -379,7 +379,7 @@ def draw_board(self):
             self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), 0.1, 0.1)
 
         self.update()
-    
+
     def draw_x(self, position):
         self.stroke(0, 255, 0)
         x, y = [i-1 for i in position]

learning.py

@@ -328,7 +328,7 @@ def __repr__(self):
 
 
 def DecisionTreeLearner(dataset):
-    "[Fig. 18.5]"
+    "[Figure 18.5]"
 
     target, values = dataset.target, dataset.values
 
@@ -398,7 +398,7 @@ def information_content(values):
 
 
 def DecisionListLearner(dataset):
-    """[Fig. 18.11]"""
+    """[Figure 18.11]"""
 
     def decision_list_learning(examples):
         if not examples:
@@ -511,7 +511,7 @@ def network(input_units, hidden_layer_sizes, output_units):
 
 
 def BackPropagationLearner(dataset, net, learning_rate, epoches):
-    "[Fig. 18.23] The back-propagation algorithm for multilayer network"
+    "[Figure 18.23] The back-propagation algorithm for multilayer network"
     # Initialise weights
     for layer in net:
         for node in layer:
@@ -668,7 +668,7 @@ def predict(example):
 
 
 def AdaBoost(L, K):
-    """[Fig. 18.34]"""
+    """[Figure 18.34]"""
     def train(dataset):
         examples, target = dataset.examples, dataset.target
         N = len(examples)
@@ -868,11 +868,11 @@ def score(learner, size):
                attrnames="sepal-len sepal-width petal-len petal-width class")
 
 # ______________________________________________________________________________
-# The Restaurant example from Fig. 18.2
+# The Restaurant example from [Figure 18.2]
 
 
 def RestaurantDataSet(examples=None):
-    "Build a DataSet of Restaurant waiting examples. [Fig. 18.3]"
+    "Build a DataSet of Restaurant waiting examples. [Figure 18.3]"
     return DataSet(name='restaurant', target='Wait', examples=examples,
                    attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' +
                    'Raining Reservation Type WaitEstimate Wait')

logic.py

@@ -111,7 +111,7 @@ def retract(self, sentence):
 
 
 def KB_AgentProgram(KB):
-    """A generic logical knowledge-based agent program. [Fig. 7.1]"""
+    """A generic logical knowledge-based agent program. [Figure 7.1]"""
     steps = itertools.count()
 
     def program(percept):
@@ -191,7 +191,7 @@ def parse_definite_clause(s):
 
 def tt_entails(kb, alpha):
     """Does kb entail the sentence alpha? Use truth tables. For propositional
-    kb's and sentences. [Fig. 7.10]. Note that the 'kb' should be an
+    kb's and sentences. [Figure 7.10]. Note that the 'kb' should be an
     Expr which is a conjunction of clauses.
     >>> tt_entails(expr('P & Q'), expr('Q'))
     True
@@ -434,7 +434,7 @@ def disjuncts(s):
 
 
 def pl_resolution(KB, alpha):
-    "Propositional-logic resolution: say if alpha follows from KB. [Fig. 7.12]"
+    "Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12]"
     clauses = KB.clauses + conjuncts(to_cnf(~alpha))
     new = set()
     while True:
@@ -493,7 +493,7 @@ def clauses_with_premise(self, p):
 
 def pl_fc_entails(KB, q):
     """Use forward chaining to see if a PropDefiniteKB entails symbol q.
-    [Fig. 7.15]
+    [Figure 7.15]
     >>> pl_fc_entails(horn_clauses_KB, expr('Q'))
     True
     """
@@ -527,7 +527,7 @@ def pl_fc_entails(KB, q):
     horn_clauses_KB.tell(expr(s))
 
 # ______________________________________________________________________________
-# DPLL-Satisfiable [Fig. 7.17]
+# DPLL-Satisfiable [Figure 7.17]
 
 
 def dpll_satisfiable(s):
@@ -633,7 +633,7 @@ def inspect_literal(literal):
         return literal, True
 
 # ______________________________________________________________________________
-# Walk-SAT [Fig. 7.18]
+# Walk-SAT [Figure 7.18]
 
 
 def WalkSAT(clauses, p=0.5, max_flips=10000):
@@ -670,7 +670,7 @@ def sat_count(sym):
 
 class HybridWumpusAgent(agents.Agent):
 
-    "An agent for the wumpus world that does logical inference. [Fig. 7.20]"""
+    "An agent for the wumpus world that does logical inference. [Figure 7.20]"""
 
     def __init__(self):
         unimplemented()
@@ -684,7 +684,7 @@ def plan_route(current, goals, allowed):
 
 def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable):
     """Converts a planning problem to Satisfaction problem by translating it to a cnf sentence.
-    [Fig. 7.22]"""
+    [Figure 7.22]"""
 
     #Functions used by SAT_plan
     def translate_to_SAT(init, transition, goal, time):
@@ -767,7 +767,7 @@ def extract_solution(model):
 def unify(x, y, s):
     """Unify expressions x,y with substitution s; return a substitution that
     would make x,y equal, or None if x,y can not unify. x and y can be
-    variables (e.g. Expr('x')), constants, lists, or Exprs. [Fig. 9.1]"""
+    variables (e.g. Expr('x')), constants, lists, or Exprs. [Figure 9.1]"""
     if s is None:
         return None
     elif x == y:
@@ -933,7 +933,7 @@ def fetch_rules_for_goal(self, goal):
 
 
 def fol_bc_ask(KB, query):
-    """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6]
+    """A simple backward-chaining algorithm for first-order logic. [Figure 9.6]
     KB should be an instance of FolKB, and query an atomic sentence. """
     return fol_bc_or(KB, query, {})
 

mdp.py

@@ -109,7 +109,7 @@ def to_arrows(self, policy):
 
 
 def value_iteration(mdp, epsilon=0.001):
-    "Solving an MDP by value iteration. [Fig. 17.4]"
+    "Solving an MDP by value iteration. [Figure 17.4]"
     U1 = dict([(s, 0) for s in mdp.states])
     R, T, gamma = mdp.R, mdp.T, mdp.gamma
     while True:
@@ -140,7 +140,7 @@ def expected_utility(a, s, U, mdp):
 
 
 def policy_iteration(mdp):
-    "Solve an MDP by policy iteration [Fig. 17.7]"
+    "Solve an MDP by policy iteration [Figure 17.7]"
     U = dict([(s, 0) for s in mdp.states])
     pi = dict([(s, random.choice(mdp.actions(s))) for s in mdp.states])
     while True:

nlp.py

@@ -53,14 +53,14 @@ def __repr__(self):
         return '<Grammar %s>' % self.name
 
 E0 = Grammar('E0',
-             Rules(  # Grammar for E_0 [Fig. 22.4]
+             Rules(  # Grammar for E_0 [Figure 22.4]
                  S='NP VP | S Conjunction S',
                  NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause',  # noqa
                  VP='Verb | VP NP | VP Adjective | VP PP | VP Adverb',
                  PP='Preposition NP',
                  RelClause='That VP'),
 
-             Lexicon(  # Lexicon for E_0 [Fig. 22.3]
+             Lexicon(  # Lexicon for E_0 [Figure 22.3]
                  Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east",  # noqa
                  Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel",  # noqa
                  Adjective="right | left | east | south | back | smelly",
@@ -116,7 +116,7 @@ def rewrite(tokens, into):
 
 class Chart:
 
-    """Class for parsing sentences using a chart data structure. [Fig 22.7]
+    """Class for parsing sentences using a chart data structure. [Figure 22.7]
     >>> chart = Chart(E0);
     >>> len(chart.parses('the stench is in 2 2'))
     1