Fix flake8 warnings (aimacode#508)

* Fix flake8 warnings

* Remove unnecessary #noqa

* Fix doctest

Author: Chipe1

.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 100
-ignore = E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503,F405
+ignore = E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503,F405,F841
 exclude = tests

agents.py

@@ -86,9 +86,12 @@ def __init__(self, program=None):
         self.holding = []
         self.performance = 0
         if program is None or not isinstance(program, collections.Callable):
-            print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__))
+            print("Can't find a valid program for {}, falling back to default.".format(
+                self.__class__.__name__))
+
             def program(percept):
                 return eval(input('Percept={}; action? '.format(percept)))
+
         self.program = program
 
     def can_grab(self, thing):

canvas.py

@@ -121,6 +121,7 @@ def update(self):
         self.exec_list = []
         display_html(exec_code)
 
+
 def display_html(html_string):
     from IPython.display import HTML, display
     display(HTML(html_string))

csp.py

@@ -20,7 +20,7 @@ class CSP(search.Problem):
                     the other variables that participate in constraints.
         constraints A function f(A, a, B, b) that returns true if neighbors
                     A, B satisfy the constraint when they have values A=a, B=b
-    
+
     In the textbook and in most mathematical definitions, the
     constraints are specified as explicit pairs of allowable values,
     but the formulation here is easier to express and more compact for
@@ -347,6 +347,7 @@ def topological_sort(X, root):
     build_topological(root, None, neighbors, visited, stack, parents)
     return stack, parents
 
+
 def build_topological(node, parent, neighbors, visited, stack, parents):
     """Builds the topological sort and the parents of each node in the graph"""
     visited[node] = True
@@ -356,7 +357,7 @@ def build_topological(node, parent, neighbors, visited, stack, parents):
             build_topological(n, node, neighbors, visited, stack, parents)
 
     parents[node] = parent
-    stack.insert(0,node)
+    stack.insert(0, node)
 
 
 def make_arc_consistent(Xj, Xk, csp):
@@ -533,10 +534,12 @@ def display(self, assignment):
 # Sudoku
 
 
-def flatten(seqs): return sum(seqs, [])
+def flatten(seqs):
+    return sum(seqs, [])
+
 
-easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..'  # noqa
-harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......'  # noqa
+easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..'
+harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......'
 
 _R3 = list(range(3))
 _CELL = itertools.count().__next__

learning.py

@@ -184,8 +184,8 @@ def split_values_by_classes(self):
         target_names = self.values[self.target]
 
         for v in self.examples:
-            item = [a for a in v if a not in target_names] # Remove target from item
-            buckets[v[self.target]].append(item) # Add item to bucket of its class
+            item = [a for a in v if a not in target_names]  # Remove target from item
+            buckets[v[self.target]].append(item)  # Add item to bucket of its class
 
         return buckets
 
@@ -199,7 +199,7 @@ def find_means_and_deviations(self):
         feature_numbers = len(self.inputs)
 
         item_buckets = self.split_values_by_classes()
-        
+
         means = defaultdict(lambda: [0 for i in range(feature_numbers)])
         deviations = defaultdict(lambda: [0 for i in range(feature_numbers)])
 
@@ -216,7 +216,6 @@ def find_means_and_deviations(self):
 
         return means, deviations
 
-
     def __repr__(self):
         return '<DataSet({}): {:d} examples, {:d} attributes>'.format(
             self.name, len(self.examples), len(self.attrs))
@@ -760,7 +759,6 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100):
         for i in range(len(w)):
             w[i] = w[i] + learning_rate * (dotproduct(err, X_col[i]) / num_examples)
 
-
     def predict(example):
         x = [1] + example
         return dotproduct(w, x)

logic.py

@@ -845,23 +845,8 @@ def subst(s, x):
         return Expr(x.op, *[subst(s, arg) for arg in x.args])
 
 
-def fol_fc_ask(KB, alpha):
-    """A simple forward-chaining algorithm. [Figure 9.3]"""
-    while new is not None:
-        new = []
-        for rule in KB:
-            p, q = parse_definite_clause(standardize_variables(rule))
-            for p_ in random.KB.clauses:
-                if p != p_:
-                    for theta in (subst(theta, p) == subst(theta, p_)):
-                        q_ = subst(theta, q)
-                        if  not unify(q_,KB.sentence in KB) or not unify(q_, new):
-                            new.append(q_)
-                            phi = unify(q_,alpha)
-                            if phi is not None:
-                                return phi
-        KB.tell(new)
-    return None
+def fol_fc_ask(KB, alpha):  # TODO
+    raise NotImplementedError
 
 
 def standardize_variables(sentence, dic=None):
@@ -936,16 +921,15 @@ def fetch_rules_for_goal(self, goal):
                ]))
 
 crime_kb = FolKB(
-    map(expr,
-             ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)',  # noqa
-              'Owns(Nono, M1)',
-              'Missile(M1)',
-              '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)',
-              'Missile(x) ==> Weapon(x)',
-              'Enemy(x, America) ==> Hostile(x)',
-              'American(West)',
-              'Enemy(Nono, America)'
-              ]))
+    map(expr, ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)',
+               'Owns(Nono, M1)',
+               'Missile(M1)',
+               '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)',
+               'Missile(x) ==> Weapon(x)',
+               'Enemy(x, America) ==> Hostile(x)',
+               'American(West)',
+               'Enemy(Nono, America)'
+               ]))
 
 
 def fol_bc_ask(KB, query):

mdp.py

@@ -6,7 +6,7 @@
 dictionary of {state:number} pairs.  We then define the value_iteration
 and policy_iteration algorithms."""
 
-from utils import argmax, vector_add, print_table  # noqa
+from utils import argmax, vector_add
 from grid import orientations, turn_right, turn_left
 
 import random
@@ -173,6 +173,8 @@ def policy_evaluation(pi, U, mdp, k=20):
 >>> sequential_decision_environment.to_arrows(pi)
 [['>', '>', '>', '.'], ['^', None, '^', '.'], ['^', '>', '^', '<']]
 
+>>> from utils import print_table
+
 >>> print_table(sequential_decision_environment.to_arrows(pi))
 >   >      >   .
 ^   None   ^   .

nlp.py

@@ -58,16 +58,16 @@ def __repr__(self):
 E0 = Grammar('E0',
              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
+                 NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause',
                  VP='Verb | VP NP | VP Adjective | VP PP | VP Adverb',
                  PP='Preposition NP',
                  RelClause='That VP'),
 
              Lexicon(  # Lexicon for E_0 [Figure 22.3]
-                 Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east",  # noqa
+                 Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east",
                  Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel",  # noqa
                  Adjective="right | left | east | south | back | smelly",
-                 Adverb="here | there | nearby | ahead | right | left | east | south | back",  # noqa
+                 Adverb="here | there | nearby | ahead | right | left | east | south | back",
                  Pronoun="me | you | I | it",
                  Name="John | Mary | Boston | Aristotle",
                  Article="the | a | an",
@@ -166,7 +166,7 @@ def add_edge(self, edge):
                 self.predictor(edge)
 
     def scanner(self, j, word):
-        "For each edge expecting a word of this category here, extend the edge."  # noqa
+        "For each edge expecting a word of this category here, extend the edge."
         for (i, j, A, alpha, Bb) in self.chart[j]:
             if Bb and self.grammar.isa(word, Bb[0]):
                 self.add_edge([i, j+1, A, alpha + [(Bb[0], word)], Bb[1:]])
@@ -386,16 +386,18 @@ def __init__(self, address, hub=0, authority=0, inlinks=None, outlinks=None):
 
 def HITS(query):
     """The HITS algorithm for computing hubs and authorities with respect to a query."""
-    pages = expand_pages(relevant_pages(query))  # in order to 'map' faithfully to pseudocode we
-    for p in pages.values():                              # won't pass the list of pages as an argument
+    pages = expand_pages(relevant_pages(query))
+    for p in pages.values():
         p.authority = 1
         p.hub = 1
     while True:  # repeat until... convergence
         authority = {p: pages[p].authority for p in pages}
         hub = {p: pages[p].hub for p in pages}
         for p in pages:
-            pages[p].authority = sum(hub[x] for x in getInlinks(pages[p]))  # p.authority ← ∑i Inlinki(p).Hub
-            pages[p].hub = sum(authority[x] for x in getOutlinks(pages[p]))  # p.hub ← ∑i Outlinki(p).Authority
+            # p.authority ← ∑i Inlinki(p).Hub
+            pages[p].authority = sum(hub[x] for x in getInlinks(pages[p]))
+            # p.hub ← ∑i Outlinki(p).Authority
+            pages[p].hub = sum(authority[x] for x in getOutlinks(pages[p]))
         normalize(pages)
         if convergence():
             break

planning.py

@@ -663,9 +663,8 @@ def act(self, action):
         if list_action is None:
             raise Exception("Action '{}' not found".format(action.name))
         list_action.do_action(self.jobs, self.resources, self.kb, args)
-        # print(self.resources)
-    
-    def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA themselves ...
+
+    def refinements(hla, state, library):  # TODO - refinements may be (multiple) HLA themselves ...
         """
         state is a Problem, containing the current state kb
         library is a dictionary containing details for every possible refinement. eg:
@@ -709,39 +708,38 @@ def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA
         }
         """
         e = Expr(hla.name, hla.args)
-        indices = [i for i,x in enumerate(library["HLA"]) if expr(x).op == hla.name]
+        indices = [i for i, x in enumerate(library["HLA"]) if expr(x).op == hla.name]
         for i in indices:
-            action = HLA(expr(library["steps"][i][0]), [ # TODO multiple refinements
+            action = HLA(expr(library["steps"][i][0]), [  # TODO multiple refinements
                     [expr(x) for x in library["precond_pos"][i]],
                     [expr(x) for x in library["precond_neg"][i]]
-                ], 
+                ],
                 [
                     [expr(x) for x in library["effect_pos"][i]],
                     [expr(x) for x in library["effect_neg"][i]]
                 ])
             if action.check_precond(state.kb, action.args):
                 yield action
-    
+
     def hierarchical_search(problem, hierarchy):
         """
         [Figure 11.5] 'Hierarchical Search, a Breadth First Search implementation of Hierarchical
         Forward Planning Search'
-        
         The problem is a real-world prodlem defined by the problem class, and the hierarchy is
         a dictionary of HLA - refinements (see refinements generator for details)
         """
         act = Node(problem.actions[0])
         frontier = FIFOQueue()
         frontier.append(act)
         while(True):
-            if not frontier: #(len(frontier)==0):
+            if not frontier:
                 return None
             plan = frontier.pop()
             print(plan.state.name)
-            hla = plan.state #first_or_null(plan)
+            hla = plan.state  # first_or_null(plan)
             prefix = None
             if plan.parent:
-                prefix = plan.parent.state.action #prefix, suffix = subseq(plan.state, hla)
+                prefix = plan.parent.state.action  # prefix, suffix = subseq(plan.state, hla)
             outcome = Problem.result(problem, prefix)
             if hla is None:
                 if outcome.goal_test():
@@ -864,4 +862,3 @@ def goal_test(kb):
 
     return Problem(init, [add_engine1, add_engine2, add_wheels1, add_wheels2, inspect1, inspect2],
                    goal_test, [job_group1, job_group2], resources)
-

search.py

@@ -6,8 +6,7 @@
 
 from utils import (
     is_in, argmin, argmax, argmax_random_tie, probability, weighted_sampler,
-    weighted_sample_with_replacement, memoize, print_table, DataFile, Stack,
-    FIFOQueue, PriorityQueue, name
+    memoize, print_table, DataFile, Stack, FIFOQueue, PriorityQueue, name
 )
 from grid import distance
 
@@ -419,7 +418,7 @@ 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."""
         plan = {}
         for s in states:
             plan[s] = or_search(s, problem, path)
@@ -461,8 +460,8 @@ def __call__(self, percept):
                 if len(self.unbacktracked[s1]) == 0:
                     self.a = None
                 else:
-                    # else a <- an action b such that result[s', b] = POP(unbacktracked[s'])  # noqa
-                    unbacktracked_pop = self.unbacktracked[s1].pop(0)  # noqa
+                    # else a <- an action b such that result[s', b] = POP(unbacktracked[s'])
+                    unbacktracked_pop = self.unbacktracked[s1].pop(0)
                     for (s, b) in self.result.keys():
                         if self.result[(s, b)] == unbacktracked_pop:
                             self.a = b
@@ -546,7 +545,7 @@ def __call__(self, s1):     # as of now s1 is a state rather than a percept
 
             # an action b in problem.actions(s1) that minimizes costs
             self.a = argmin(self.problem.actions(s1),
-                            key=lambda b:self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H))
+                            key=lambda b: self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H))
 
             self.s = s1
             return self.a
@@ -573,17 +572,17 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
     """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."""
-    
+
     # NOTE: This is not tested and might not work.
     # TODO: Use this function to make Problems work with genetic_algorithm.
-    
+
     s = problem.initial_state
     states = [problem.result(s, a) for a in problem.actions(s)]
     random.shuffle(states)
     return genetic_algorithm(states[:n], problem.value, ngen, pmut)
 
 
-def genetic_algorithm(population, fitness_fn, gene_pool=['0', '1'], f_thres=None, ngen=1000, pmut=0.1):
+def genetic_algorithm(population, fitness_fn, gene_pool=['0', '1'], f_thres=None, ngen=1000, pmut=0.1):  # noqa
     """[Figure 4.8]"""
     for i in range(ngen):
         new_population = []
@@ -954,7 +953,7 @@ def print_boggle(board):
     print()
 
 
-def boggle_neighbors(n2, cache={}):  # noqa
+def boggle_neighbors(n2, cache={}):
     """Return a list of lists, where the i-th element is the list of indexes
     for the neighbors of square i."""
     if cache.get(n2):

tests/test_csp.py

@@ -1,5 +1,5 @@
 import pytest
-from csp import *   # noqa
+from csp import *
 
 
 def test_csp_assign():

tests/test_games.py

@@ -5,7 +5,7 @@
 
 import pytest
 
-from games import *  # noqa
+from games import *
 
 # Creating the game instances
 f52 = Fig52Game()

tests/test_grid.py

@@ -1,5 +1,5 @@
 import pytest
-from grid import *  # noqa
+from grid import *
 
 
 def compare_list(x, y):

tests/test_logic.py

@@ -1,5 +1,5 @@
 import pytest
-from logic import *  # noqa
+from logic import *
 from utils import expr_handle_infix_ops, count, Symbol
 
 

tests/test_mdp.py

@@ -1,4 +1,4 @@
-from mdp import *  # noqa
+from mdp import *
 
 
 def test_value_iteration():

tests/test_planning.py

@@ -1,4 +1,4 @@
-from planning import *  # noqa
+from planning import *
 from utils import expr
 from logic import FolKB
 

tests/test_probability.py

@@ -1,5 +1,5 @@
 import random
-from probability import *  # noqa
+from probability import *
 from utils import rounder
 
 
@@ -183,7 +183,7 @@ def test_particle_filtering():
 >>> P['rain']               #doctest:+ELLIPSIS
 0.2...
 
-# A Joint Probability Distribution is dealt with like this [Figure 13.3]:  # noqa
+# A Joint Probability Distribution is dealt with like this [Figure 13.3]:
 >>> P = JointProbDist(['Toothache', 'Cavity', 'Catch'])
 >>> T, F = True, False
 >>> P[T, T, T] = 0.108; P[T, T, F] = 0.012; P[F, T, T] = 0.072; P[F, T, F] = 0.008

tests/test_search.py

@@ -1,5 +1,5 @@
 import pytest
-from search import *  # noqa
+from search import *
 
 
 romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)

tests/test_text.py

@@ -2,7 +2,7 @@
 import os
 import random
 
-from text import *  # noqa
+from text import *
 from utils import isclose, DataFile
 
 
@@ -304,7 +304,7 @@ def test_bigrams():
 
 >>> P3.samples(20)
 'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle'
-"""  # noqa
+"""
 
 if __name__ == '__main__':
     pytest.main()

tests/test_utils.py

@@ -1,5 +1,5 @@
 import pytest
-from utils import *  # noqa
+from utils import *
 import random
 
 def test_removeall_list():

text.py

@@ -26,7 +26,6 @@ def samples(self, n):
         return ' '.join(self.sample() for i in range(n))
 
 
-
 class NgramTextModel(CountingProbDist):
 
     """This is a discrete probability distribution over n-tuples of words.
@@ -80,7 +79,7 @@ def samples(self, nwords):
 
 class NgramCharModel(NgramTextModel):
     def add_empty(self, words, n):
-        return  ' ' * (n - 1) + words
+        return ' ' * (n - 1) + words
 
     def add_sequence(self, words):
         for word in words:
@@ -362,14 +361,13 @@ def decode(self, ciphertext):
         solution.state[' '] = ' '
         return translate(self.ciphertext, lambda c: solution.state[c])
 
-
     def score(self, code):
         """Score is product of word scores, unigram scores, and bigram scores.
         This can get very small, so we use logs and exp."""
 
         # remake code dictionary to contain translation for all characters
         full_code = code.copy()
-        full_code.update({x:x for x in self.chardomain if x not in code})
+        full_code.update({x: x for x in self.chardomain if x not in code})
         full_code[' '] = ' '
         text = translate(self.ciphertext, lambda c: full_code[c])
 

utils.py

@@ -7,6 +7,7 @@
 import os.path
 import random
 import math
+import functools
 
 # ______________________________________________________________________________
 # Functions on Sequences and Iterables
@@ -258,6 +259,7 @@ def step(x):
     """Return activation value of x with sign function"""
     return 1 if x >= 0 else 0
 
+
 def gaussian(mean, st_dev, x):
     """Given the mean and standard deviation of a distribution, it returns the probability of x."""
     return 1/(math.sqrt(2*math.pi)*st_dev)*math.e**(-0.5*(float(x-mean)/st_dev)**2)
@@ -656,7 +658,7 @@ def extend(self, items):
     def pop(self):
         if len(self.queue) > 0:
             return self.queue.popleft()
-        else :
+        else:
             raise Exception('FIFOQueue is empty')
 
     def __len__(self):