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fixed some class definitions and typos (aimacode#1131)
* changed queue to set in AC3 Changed queue to set in AC3 (as in the pseudocode of the original algorithm) to reduce the number of consistency-check due to the redundancy of the same arcs in queue. For example, on the harder1 configuration of the Sudoku CSP the number consistency-check has been reduced from 40464 to 12562! * re-added test commented by mistake * added the mentioned AC4 algorithm for constraint propagation AC3 algorithm has non-optimal worst case time-complexity O(cd^3 ), while AC4 algorithm runs in O(cd^2) worst case time * added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference * removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py * added map coloring SAT problems * fixed typo errors and removed unnecessary brackets * reformulated the map coloring problem * Revert "reformulated the map coloring problem" This reverts commit 20ab0e5. * Revert "fixed typo errors and removed unnecessary brackets" This reverts commit f743146. * Revert "added map coloring SAT problems" This reverts commit 9e0fa55. * Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py" This reverts commit b3cd24c. * Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference" This reverts commit 6986247. * Revert "added the mentioned AC4 algorithm for constraint propagation" This reverts commit 03551fb. * added map coloring SAT problem * fixed build error * Revert "added map coloring SAT problem" This reverts commit 93af259. * Revert "fixed build error" This reverts commit 6641c2c. * added map coloring SAT problem * removed redundant parentheses * added Viterbi algorithm * added monkey & bananas planning problem * simplified condition in search.py * added tests for monkey & bananas planning problem * removed monkey & bananas planning problem * Revert "removed monkey & bananas planning problem" This reverts commit 9d37ae0. * Revert "added tests for monkey & bananas planning problem" This reverts commit 24041e9. * Revert "simplified condition in search.py" This reverts commit 6d229ce. * Revert "added monkey & bananas planning problem" This reverts commit c74933a. * defined the PlanningProblem as a specialization of a search.Problem & fixed typo errors * fixed doctest in logic.py * fixed doctest for cascade_distribution * added ForwardPlanner and tests * added __lt__ implementation for Expr * added more tests * renamed forward planner * Revert "renamed forward planner" This reverts commit c4139e5. * renamed forward planner class & added doc * added backward planner and tests * fixed mdp4e.py doctests * removed ignore_delete_lists_heuristic flag * fixed heuristic for forward and backward planners * added SATPlan and tests * fixed ignore delete lists heuristic in forward and backward planners * fixed backward planner and added tests * updated doc * added nary csp definition and examples * added CSPlan and tests * fixed CSPlan * added book's cryptarithmetic puzzle example * fixed typo errors in test_csp * fixed aimacode#1111 * added sortedcontainers to yml and doc to CSPlan * added tests for n-ary csp * fixed utils.extend * updated test_probability.py * converted static methods to functions * added AC3b and AC4 with heuristic and tests * added conflict-driven clause learning sat solver * added tests for cdcl and heuristics * fixed probability.py * fixed import * fixed kakuro * added Martelli and Montanari rule-based unification algorithm * removed duplicate standardize_variables * renamed variables known as built-in functions * fixed typos in learning.py * renamed some files and fixed typos * fixed typos * fixed typos * fixed tests * removed unify_mm * remove unnecessary brackets * fixed tests * moved utility functions to utils.py * fixed typos * moved utils function to utils.py, separated probability learning classes from learning.py, fixed typos and fixed imports in .ipynb files * added missing learners * fixed Travis build * fixed typos * fixed typos * fixed typos * fixed typos * fixed typos in agents files * fixed imports in agent files * fixed deep learning .ipynb imports * fixed typos * added .ipynb and fixed typos * adapted code for .ipynb * fixed typos * updated .ipynb * updated .ipynb * updated logic.py * updated .ipynb * updated .ipynb * updated planning.py * updated inf definition * fixed typos * fixed typos * fixed typos * fixed typos * Revert "fixed typos" This reverts commit 658309d. * Revert "fixed typos" This reverts commit 08ad660. * fixed typos * fixed typos * fixed typos * fixed typos * fixed typos and utils imports in *4e.py files * fixed typos
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‎csp.py‎

Lines changed: 17 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -1,18 +1,17 @@
11
"""CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6)"""
2+
3+
import itertools
4+
import random
5+
import re
26
import string
7+
from collections import defaultdict, Counter
8+
from functools import reduce
39
from operator import eq, neg
410

511
from sortedcontainers import SortedSet
612

7-
from utils import argmin_random_tie, count, first, extend
813
import search
9-
10-
from collections import defaultdict, Counter
11-
from functools import reduce
12-
13-
import itertools
14-
import re
15-
import random
14+
from utils import argmin_random_tie, count, first, extend
1615

1716

1817
class CSP(search.Problem):
@@ -54,12 +53,12 @@ class CSP(search.Problem):
5453

5554
def __init__(self, variables, domains, neighbors, constraints):
5655
"""Construct a CSP problem. If variables is empty, it becomes domains.keys()."""
56+
super().__init__(())
5757
variables = variables or list(domains.keys())
5858
self.variables = variables
5959
self.domains = domains
6060
self.neighbors = neighbors
6161
self.constraints = constraints
62-
self.initial = ()
6362
self.curr_domains = None
6463
self.nassigns = 0
6564

@@ -80,8 +79,7 @@ def nconflicts(self, var, val, assignment):
8079

8180
# Subclasses may implement this more efficiently
8281
def conflict(var2):
83-
return (var2 in assignment and
84-
not self.constraints(var, val, var2, assignment[var2]))
82+
return var2 in assignment and not self.constraints(var, val, var2, assignment[var2])
8583

8684
return count(conflict(v) for v in self.neighbors[var])
8785

@@ -552,7 +550,7 @@ def assign_value(Xj, Xk, csp, assignment):
552550

553551

554552
# ______________________________________________________________________________
555-
# Map Coloring Problems
553+
# Map Coloring CSP Problems
556554

557555

558556
class UniversalDict:
@@ -585,7 +583,7 @@ def MapColoringCSP(colors, neighbors):
585583
return CSP(list(neighbors.keys()), UniversalDict(colors), neighbors, different_values_constraint)
586584

587585

588-
def parse_neighbors(neighbors, variables=None):
586+
def parse_neighbors(neighbors):
589587
"""Convert a string of the form 'X: Y Z; Y: Z' into a dict mapping
590588
regions to neighbors. The syntax is a region name followed by a ':'
591589
followed by zero or more region names, followed by ';', repeated for
@@ -676,10 +674,10 @@ def nconflicts(self, var, val, assignment):
676674

677675
def assign(self, var, val, assignment):
678676
"""Assign var, and keep track of conflicts."""
679-
oldval = assignment.get(var, None)
680-
if val != oldval:
681-
if oldval is not None: # Remove old val if there was one
682-
self.record_conflict(assignment, var, oldval, -1)
677+
old_val = assignment.get(var, None)
678+
if val != old_val:
679+
if old_val is not None: # Remove old val if there was one
680+
self.record_conflict(assignment, var, old_val, -1)
683681
self.record_conflict(assignment, var, val, +1)
684682
CSP.assign(self, var, val, assignment)
685683

@@ -776,7 +774,7 @@ class Sudoku(CSP):
776774
>>> h = Sudoku(harder1)
777775
>>> backtracking_search(h, select_unassigned_variable=mrv, inference=forward_checking) is not None
778776
True
779-
""" # noqa
777+
"""
780778

781779
R3 = _R3
782780
Cell = _CELL
@@ -831,7 +829,7 @@ def Zebra():
831829
Spaniard: Dog; Kools: Yellow; Chesterfields: Fox;
832830
Norwegian: Blue; Winston: Snails; LuckyStrike: OJ;
833831
Ukranian: Tea; Japanese: Parliaments; Kools: Horse;
834-
Coffee: Green; Green: Ivory""", variables)
832+
Coffee: Green; Green: Ivory""")
835833
for type in [Colors, Pets, Drinks, Countries, Smokes]:
836834
for A in type:
837835
for B in type:

‎knowledge.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -300,7 +300,7 @@ def extend_example(self, example, literal):
300300

301301
def new_literals(self, clause):
302302
"""Generate new literals based on known predicate symbols.
303-
Generated literal must share atleast one variable with clause"""
303+
Generated literal must share at least one variable with clause"""
304304
share_vars = variables(clause[0])
305305
for l in clause[1]:
306306
share_vars.update(variables(l))

‎logic.py‎

Lines changed: 10 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -46,13 +46,10 @@
4646
issequence, Expr, expr, subexpressions, extend)
4747

4848

49-
# ______________________________________________________________________________
50-
51-
5249
class KB:
5350
"""A knowledge base to which you can tell and ask sentences.
5451
To create a KB, first subclass this class and implement
55-
tell, ask_generator, and retract. Why ask_generator instead of ask?
52+
tell, ask_generator, and retract. Why ask_generator instead of ask?
5653
The book is a bit vague on what ask means --
5754
For a Propositional Logic KB, ask(P & Q) returns True or False, but for an
5855
FOL KB, something like ask(Brother(x, y)) might return many substitutions
@@ -173,7 +170,7 @@ def variables(s):
173170

174171
def is_definite_clause(s):
175172
"""Returns True for exprs s of the form A & B & ... & C ==> D,
176-
where all literals are positive. In clause form, this is
173+
where all literals are positive. In clause form, this is
177174
~A | ~B | ... | ~C | D, where exactly one clause is positive.
178175
>>> is_definite_clause(expr('Farmer(Mac)'))
179176
True
@@ -602,7 +599,7 @@ def pl_fc_entails(kb, q):
602599

603600

604601
# ______________________________________________________________________________
605-
# DPLL-Satisfiable [Figure 7.17]
602+
# Heuristics for SAT Solvers
606603

607604

608605
def no_branching_heuristic(symbols, clauses):
@@ -707,6 +704,10 @@ def jw2(symbols, clauses):
707704
return P, True if scores[P] >= scores[~P] else False
708705

709706

707+
# ______________________________________________________________________________
708+
# DPLL-Satisfiable [Figure 7.17]
709+
710+
710711
def dpll_satisfiable(s, branching_heuristic=no_branching_heuristic):
711712
"""Check satisfiability of a propositional sentence.
712713
This differs from the book code in two ways: (1) it returns a model
@@ -1114,7 +1115,7 @@ def sat_count(sym):
11141115

11151116

11161117
# ______________________________________________________________________________
1117-
# Map Coloring Problems
1118+
# Map Coloring SAT Problems
11181119

11191120

11201121
def MapColoringSAT(colors, neighbors):
@@ -1803,7 +1804,7 @@ def cascade_substitution(s):
18031804
for x in s:
18041805
s[x] = subst(s, s.get(x))
18051806
if isinstance(s.get(x), Expr) and not is_variable(s.get(x)):
1806-
# Ensure Function Terms are correct updates by passing over them again.
1807+
# Ensure Function Terms are correct updates by passing over them again
18071808
s[x] = subst(s, s.get(x))
18081809

18091810

@@ -2055,7 +2056,7 @@ def fol_bc_and(kb, goals, theta):
20552056
# ______________________________________________________________________________
20562057

20572058
# Example application (not in the book).
2058-
# You can use the Expr class to do symbolic differentiation. This used to be
2059+
# You can use the Expr class to do symbolic differentiation. This used to be
20592060
# a part of AI; now it is considered a separate field, Symbolic Algebra.
20602061

20612062

‎making_simple_decision4e.py‎

Lines changed: 9 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,9 @@
1-
from utils4e import (
2-
argmax, element_wise_product, matrix_multiplication,
3-
vector_to_diagonal, vector_add, scalar_vector_product, inverse_matrix,
4-
weighted_sample_with_replacement, probability, normalize
5-
)
1+
import random
2+
63
from agents import Agent
74
from probability import BayesNet
8-
import random
5+
from utils4e import argmax, vector_add, weighted_sample_with_replacement
6+
97

108
# Making Simple Decisions (Chapter 15)
119

@@ -108,6 +106,7 @@ def vpi(self, variable):
108106
class MCLmap:
109107
"""Map which provides probability distributions and sensor readings.
110108
Consists of discrete cells which are either an obstacle or empty"""
109+
111110
def __init__(self, m):
112111
self.m = m
113112
self.nrows = len(m)
@@ -131,7 +130,7 @@ def ray_cast(self, sensor_num, kin_state):
131130
# 0
132131
# 3R1
133132
# 2
134-
delta = ((sensor_num % 2 == 0)*(sensor_num - 1), (sensor_num % 2 == 1)*(2 - sensor_num))
133+
delta = ((sensor_num % 2 == 0) * (sensor_num - 1), (sensor_num % 2 == 1) * (2 - sensor_num))
135134
# sensor direction changes based on orientation
136135
for _ in range(orient):
137136
delta = (delta[1], -delta[0])
@@ -149,9 +148,9 @@ def ray_cast(sensor_num, kin_state, m):
149148
return m.ray_cast(sensor_num, kin_state)
150149

151150
M = len(z)
152-
W = [0]*N
153-
S_ = [0]*N
154-
W_ = [0]*N
151+
W = [0] * N
152+
S_ = [0] * N
153+
W_ = [0] * N
155154
v = a['v']
156155
w = a['w']
157156

@@ -167,4 +166,3 @@ def ray_cast(sensor_num, kin_state, m):
167166

168167
S = weighted_sample_with_replacement(N, S_, W_)
169168
return S
170-

‎planning.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1047,8 +1047,8 @@ def orderlevel(self, level, planning_problem):
10471047
def execute(self):
10481048
"""Finds total-order solution for a planning graph"""
10491049

1050-
graphplan_solution = GraphPlan(self.planning_problem).execute()
1051-
filtered_solution = self.filter(graphplan_solution)
1050+
graphPlan_solution = GraphPlan(self.planning_problem).execute()
1051+
filtered_solution = self.filter(graphPlan_solution)
10521052
ordered_solution = []
10531053
planning_problem = self.planning_problem
10541054
for level in filtered_solution:
@@ -1635,7 +1635,7 @@ def angelic_search(self, hierarchy, initial_plan):
16351635
if guaranteed and RealWorldPlanningProblem.making_progress(plan, initial_plan):
16361636
final_state = guaranteed[0] # any element of guaranteed
16371637
return RealWorldPlanningProblem.decompose(hierarchy, final_state, pes_reachable_set)
1638-
# there should be at least one HLA/Angelic_HLA, otherwise plan would be primitive
1638+
# there should be at least one HLA/AngelicHLA, otherwise plan would be primitive
16391639
hla, index = RealWorldPlanningProblem.find_hla(plan, hierarchy)
16401640
prefix = plan.action[:index]
16411641
suffix = plan.action[index + 1:]

‎probability.py‎

Lines changed: 11 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -2,18 +2,16 @@
22
Probability models. (Chapter 13-15)
33
"""
44

5-
from utils import (product, argmax, element_wise_product, matrix_multiplication, vector_to_diagonal, vector_add,
6-
scalar_vector_product, inverse_matrix, weighted_sample_with_replacement, isclose, probability,
7-
normalize, extend)
8-
from agents import Agent
9-
105
import random
116
from collections import defaultdict
127
from functools import reduce
13-
import numpy as np
148

9+
import numpy as np
1510

16-
# ______________________________________________________________________________
11+
from agents import Agent
12+
from utils import (product, argmax, element_wise_product, matrix_multiplication, vector_to_diagonal, vector_add,
13+
scalar_vector_product, inverse_matrix, weighted_sample_with_replacement, isclose, probability,
14+
normalize, extend)
1715

1816

1917
def DTAgentProgram(belief_state):
@@ -106,7 +104,7 @@ def __getitem__(self, values):
106104
return ProbDist.__getitem__(self, values)
107105

108106
def __setitem__(self, values, p):
109-
"""Set P(values) = p. Values can be a tuple or a dict; it must
107+
"""Set P(values) = p. Values can be a tuple or a dict; it must
110108
have a value for each of the variables in the joint. Also keep track
111109
of the values we have seen so far for each variable."""
112110
values = event_values(values, self.variables)
@@ -307,7 +305,7 @@ class BayesNode:
307305

308306
def __init__(self, X, parents, cpt):
309307
"""X is a variable name, and parents a sequence of variable
310-
names or a space-separated string. cpt, the conditional
308+
names or a space-separated string. cpt, the conditional
311309
probability table, takes one of these forms:
312310
313311
* A number, the unconditional probability P(X=true). You can
@@ -541,8 +539,10 @@ def prior_sample(bn):
541539

542540

543541
def rejection_sampling(X, e, bn, N=10000):
544-
"""Estimate the probability distribution of variable X given
545-
evidence e in BayesNet bn, using N samples. [Figure 14.14]
542+
"""
543+
[Figure 14.14]
544+
Estimate the probability distribution of variable X given
545+
evidence e in BayesNet bn, using N samples.
546546
Raises a ZeroDivisionError if all the N samples are rejected,
547547
i.e., inconsistent with e.
548548
>>> random.seed(47)

‎probability4e.py‎

Lines changed: 8 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,12 @@
11
"""Probability models."""
22

3-
from utils4e import product, argmax, isclose, probability, extend
4-
from math import sqrt, pi, exp
53
import copy
64
import random
75
from collections import defaultdict
86
from functools import reduce
7+
from math import sqrt, pi, exp
8+
9+
from utils4e import product, argmax, isclose, probability, extend
910

1011

1112
# ______________________________________________________________________________
@@ -107,7 +108,7 @@ def __getitem__(self, values):
107108
return ProbDist.__getitem__(self, values)
108109

109110
def __setitem__(self, values, p):
110-
"""Set P(values) = p. Values can be a tuple or a dict; it must
111+
"""Set P(values) = p. Values can be a tuple or a dict; it must
111112
have a value for each of the variables in the joint. Also keep track
112113
of the values we have seen so far for each variable."""
113114
values = event_values(values, self.variables)
@@ -628,8 +629,9 @@ def prior_sample(bn):
628629

629630
def rejection_sampling(X, e, bn, N=10000):
630631
"""
632+
[Figure 13.16]
631633
Estimate the probability distribution of variable X given
632-
evidence e in BayesNet bn, using N samples. [Figure 13.16]
634+
evidence e in BayesNet bn, using N samples.
633635
Raises a ZeroDivisionError if all the N samples are rejected,
634636
i.e., inconsistent with e.
635637
>>> random.seed(47)
@@ -656,8 +658,9 @@ def consistent_with(event, evidence):
656658

657659
def likelihood_weighting(X, e, bn, N=10000):
658660
"""
661+
[Figure 13.17]
659662
Estimate the probability distribution of variable X given
660-
evidence e in BayesNet bn. [Figure 13.17]
663+
evidence e in BayesNet bn.
661664
>>> random.seed(1017)
662665
>>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),
663666
... burglary, 10000).show_approx()

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