removed inf and isclose definition from utils and replaced with np.in… · aaronsgithub/aima-python@c587f2c · GitHub

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Commit c587f2c

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dmeoli

authored and

antmarakis

committed

removed inf and isclose definition from utils and replaced with np.inf and np.isclose (aimacode#1141)

* 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 SVM * 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 * fixed typos * fixed typos * fixed typos * fixed import * fixed typos * fixed typos * fixd typos * fixed typos * fixed typos * updated SVM * added svm test * fixed SVM and tests * fixed some definitions and typos * fixed svm and tests * added SVMs also in learning4e.py * fixed inf definition * fixed .travis.yml * fixed .travis.yml * fixed import * fixed inf definition * replaced cvxopt with qpsolvers * replaced cvxopt with quadprog * fixed some definitions * fixed typos and removed unnecessary tests * replaced quadprog with qpsolvers * fixed extend in utils * specified error type in try-catch block * fixed extend in utils * fixed typos * fixed learning.py * fixed doctest errors * added comments * removed unnecessary if condition * updated learning.py * fixed imports * removed unnecessary imports * fixed keras imports * fixed typos * fixed learning_curve * added comments * fixed typos * removed inf and isclose definition from utils and replaced with numpy.inf and numpy.isclose * fixed doctests

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+172-225Lines changed: 172 additions & 225 deletions

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• agents.py
• agents4e.py
• deep_learning4e.py
• games.py
• games4e.py
• gui
  • romania_problem.py
• knowledge.py
• learning.py
• learning4e.py
• making_simple_decision4e.py
• mdp.py
• mdp4e.py
• nlp.py
• notebook.py
• notebook4e.py
• perception4e.py
• planning.py
• probability.py
• probability4e.py
• reinforcement_learning.py
• reinforcement_learning4e.py
• search.py
• tests
  • test_search.py
  • test_text.py
  • test_utils.py
• text.py
• utils.py
• utils4e.py

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‎agents.py‎

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+1-2Lines changed: 1 addition & 2 deletions

Original file line number	Diff line number	Diff line change
@@ -354,8 +354,7 @@ def list_things_at(self, location, tclass=Thing):
354	354	return [thing for thing in self.things
355	355	if thing.location == location and isinstance(thing, tclass)]
356	356	return [thing for thing in self.things
357		- if all(x==y for x,y in zip(thing.location, location))
358		- and isinstance(thing, tclass)]
	357	+ if all(x == y for x, y in zip(thing.location, location)) and isinstance(thing, tclass)]
359	358
360	359	def some_things_at(self, location, tclass=Thing):
361	360	"""Return true if at least one of the things at location

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‎agents4e.py‎

Copy file name to clipboardExpand all lines: agents4e.py

+1-2Lines changed: 1 addition & 2 deletions

Original file line number	Diff line number	Diff line change
@@ -354,8 +354,7 @@ def list_things_at(self, location, tclass=Thing):
354	354	return [thing for thing in self.things
355	355	if thing.location == location and isinstance(thing, tclass)]
356	356	return [thing for thing in self.things
357		- if all(x==y for x,y in zip(thing.location, location))
358		- and isinstance(thing, tclass)]
	357	+ if all(x == y for x, y in zip(thing.location, location)) and isinstance(thing, tclass)]
359	358
360	359	def some_things_at(self, location, tclass=Thing):
361	360	"""Return true if at least one of the things at location

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‎deep_learning4e.py‎

Copy file name to clipboardExpand all lines: deep_learning4e.py

+3-3Lines changed: 3 additions & 3 deletions

Original file line number	Diff line number	Diff line change
@@ -1,9 +1,9 @@
1	1	"""Deep learning. (Chapters 20)"""
2	2
3		- import math
4	3	import random
5	4	import statistics
6	5
	6	+ import numpy as np
7	7	from keras import Sequential, optimizers
8	8	from keras.layers import Embedding, SimpleRNN, Dense
9	9	from keras.preprocessing import sequence
@@ -249,7 +249,7 @@ def adam(dataset, net, loss, epochs=1000, rho=(0.9, 0.999), delta=1 / 10 ** 8,
249	249	r_hat = scalar_vector_product(1 / (1 - rho[1] ** t), r)
250	250
251	251	# rescale r_hat
252		- r_hat = map_vector(lambda x: 1 / (math.sqrt(x) + delta), r_hat)
	252	+ r_hat = map_vector(lambda x: 1 / (np.sqrt(x) + delta), r_hat)
253	253
254	254	# delta weights
255	255	delta_theta = scalar_vector_product(-l_rate, element_wise_product(s_hat, r_hat))
@@ -341,7 +341,7 @@ def forward(self, inputs):
341	341	res = []
342	342	# get normalized value of each input
343	343	for i in range(len(self.nodes)):
344		- val = [(inputs[i] - mu) * self.weights[0] / math.sqrt(self.epsilon + stderr ** 2) + self.weights[1]]
	344	+ val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.epsilon + stderr ** 2) + self.weights[1]]
345	345	res.append(val)
346	346	self.nodes[i].val = val
347	347	return res

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‎games.py‎

Copy file name to clipboardExpand all lines: games.py

+16-14Lines changed: 16 additions & 14 deletions

Original file line number	Diff line number	Diff line change
@@ -1,11 +1,13 @@
1		- """Games or Adversarial Search. (Chapter 5)"""
	1	+ """Games or Adversarial Search (Chapter 5)"""
2	2
3	3	import copy
4	4	import itertools
5	5	import random
6	6	from collections import namedtuple
7	7
8		- from utils import vector_add, inf
	8	+ import numpy as np
	9	+
	10	+ from utils import vector_add
9	11
10	12	GameState = namedtuple('GameState', 'to_move, utility, board, moves')
11	13	StochasticGameState = namedtuple('StochasticGameState', 'to_move, utility, board, moves, chance')
@@ -24,15 +26,15 @@ def minmax_decision(state, game):
24	26	def max_value(state):
25	27	if game.terminal_test(state):
26	28	return game.utility(state, player)
27		- v = -inf
	29	+ v = -np.inf
28	30	for a in game.actions(state):
29	31	v = max(v, min_value(game.result(state, a)))
30	32	return v
31	33
32	34	def min_value(state):
33	35	if game.terminal_test(state):
34	36	return game.utility(state, player)
35		- v = inf
	37	+ v = np.inf
36	38	for a in game.actions(state):
37	39	v = min(v, max_value(game.result(state, a)))
38	40	return v
@@ -53,13 +55,13 @@ def expect_minmax(state, game):
53	55	player = game.to_move(state)
54	56
55	57	def max_value(state):
56		- v = -inf
	58	+ v = -np.inf
57	59	for a in game.actions(state):
58	60	v = max(v, chance_node(state, a))
59	61	return v
60	62
61	63	def min_value(state):
62		- v = inf
	64	+ v = np.inf
63	65	for a in game.actions(state):
64	66	v = min(v, chance_node(state, a))
65	67	return v
@@ -94,7 +96,7 @@ def alpha_beta_search(state, game):
94	96	def max_value(state, alpha, beta):
95	97	if game.terminal_test(state):
96	98	return game.utility(state, player)
97		- v = -inf
	99	+ v = -np.inf
98	100	for a in game.actions(state):
99	101	v = max(v, min_value(game.result(state, a), alpha, beta))
100	102	if v >= beta:
@@ -105,7 +107,7 @@ def max_value(state, alpha, beta):
105	107	def min_value(state, alpha, beta):
106	108	if game.terminal_test(state):
107	109	return game.utility(state, player)
108		- v = inf
	110	+ v = np.inf
109	111	for a in game.actions(state):
110	112	v = min(v, max_value(game.result(state, a), alpha, beta))
111	113	if v <= alpha:
@@ -114,8 +116,8 @@ def min_value(state, alpha, beta):
114	116	return v
115	117
116	118	# Body of alpha_beta_search:
117		- best_score = -inf
118		- beta = inf
	119	+ best_score = -np.inf
	120	+ beta = np.inf
119	121	best_action = None
120	122	for a in game.actions(state):
121	123	v = min_value(game.result(state, a), best_score, beta)
@@ -135,7 +137,7 @@ def alpha_beta_cutoff_search(state, game, d=4, cutoff_test=None, eval_fn=None):
135	137	def max_value(state, alpha, beta, depth):
136	138	if cutoff_test(state, depth):
137	139	return eval_fn(state)
138		- v = -inf
	140	+ v = -np.inf
139	141	for a in game.actions(state):
140	142	v = max(v, min_value(game.result(state, a), alpha, beta, depth + 1))
141	143	if v >= beta:
@@ -146,7 +148,7 @@ def max_value(state, alpha, beta, depth):
146	148	def min_value(state, alpha, beta, depth):
147	149	if cutoff_test(state, depth):
148	150	return eval_fn(state)
149		- v = inf
	151	+ v = np.inf
150	152	for a in game.actions(state):
151	153	v = min(v, max_value(game.result(state, a), alpha, beta, depth + 1))
152	154	if v <= alpha:
@@ -158,8 +160,8 @@ def min_value(state, alpha, beta, depth):
158	160	# The default test cuts off at depth d or at a terminal state
159	161	cutoff_test = (cutoff_test or (lambda state, depth: depth > d or game.terminal_test(state)))
160	162	eval_fn = eval_fn or (lambda state: game.utility(state, player))
161		- best_score = -inf
162		- beta = inf
	163	+ best_score = -np.inf
	164	+ beta = np.inf
163	165	best_action = None
164	166	for a in game.actions(state):
165	167	v = min_value(game.result(state, a), best_score, beta, 1)

Collapse file

‎games4e.py‎

Copy file name to clipboardExpand all lines: games4e.py

+16-14Lines changed: 16 additions & 14 deletions

Original file line number	Diff line number	Diff line change
@@ -1,11 +1,13 @@
1		- """Games or Adversarial Search. (Chapter 5)"""
	1	+ """Games or Adversarial Search (Chapter 5)"""
2	2
3	3	import copy
4	4	import itertools
5	5	import random
6	6	from collections import namedtuple
7	7
8		- from utils4e import vector_add, MCT_Node, ucb, inf
	8	+ import numpy as np
	9	+
	10	+ from utils4e import vector_add, MCT_Node, ucb
9	11
10	12	GameState = namedtuple('GameState', 'to_move, utility, board, moves')
11	13	StochasticGameState = namedtuple('StochasticGameState', 'to_move, utility, board, moves, chance')
@@ -24,15 +26,15 @@ def minmax_decision(state, game):
24	26	def max_value(state):
25	27	if game.terminal_test(state):
26	28	return game.utility(state, player)
27		- v = -inf
	29	+ v = -np.inf
28	30	for a in game.actions(state):
29	31	v = max(v, min_value(game.result(state, a)))
30	32	return v
31	33
32	34	def min_value(state):
33	35	if game.terminal_test(state):
34	36	return game.utility(state, player)
35		- v = inf
	37	+ v = np.inf
36	38	for a in game.actions(state):
37	39	v = min(v, max_value(game.result(state, a)))
38	40	return v
@@ -53,13 +55,13 @@ def expect_minmax(state, game):
53	55	player = game.to_move(state)
54	56
55	57	def max_value(state):
56		- v = -inf
	58	+ v = -np.inf
57	59	for a in game.actions(state):
58	60	v = max(v, chance_node(state, a))
59	61	return v
60	62
61	63	def min_value(state):
62		- v = inf
	64	+ v = np.inf
63	65	for a in game.actions(state):
64	66	v = min(v, chance_node(state, a))
65	67	return v
@@ -94,7 +96,7 @@ def alpha_beta_search(state, game):
94	96	def max_value(state, alpha, beta):
95	97	if game.terminal_test(state):
96	98	return game.utility(state, player)
97		- v = -inf
	99	+ v = -np.inf
98	100	for a in game.actions(state):
99	101	v = max(v, min_value(game.result(state, a), alpha, beta))
100	102	if v >= beta:
@@ -105,7 +107,7 @@ def max_value(state, alpha, beta):
105	107	def min_value(state, alpha, beta):
106	108	if game.terminal_test(state):
107	109	return game.utility(state, player)
108		- v = inf
	110	+ v = np.inf
109	111	for a in game.actions(state):
110	112	v = min(v, max_value(game.result(state, a), alpha, beta))
111	113	if v <= alpha:
@@ -114,8 +116,8 @@ def min_value(state, alpha, beta):
114	116	return v
115	117
116	118	# Body of alpha_beta_search:
117		- best_score = -inf
118		- beta = inf
	119	+ best_score = -np.inf
	120	+ beta = np.inf
119	121	best_action = None
120	122	for a in game.actions(state):
121	123	v = min_value(game.result(state, a), best_score, beta)
@@ -135,7 +137,7 @@ def alpha_beta_cutoff_search(state, game, d=4, cutoff_test=None, eval_fn=None):
135	137	def max_value(state, alpha, beta, depth):
136	138	if cutoff_test(state, depth):
137	139	return eval_fn(state)
138		- v = -inf
	140	+ v = -np.inf
139	141	for a in game.actions(state):
140	142	v = max(v, min_value(game.result(state, a), alpha, beta, depth + 1))
141	143	if v >= beta:
@@ -146,7 +148,7 @@ def max_value(state, alpha, beta, depth):
146	148	def min_value(state, alpha, beta, depth):
147	149	if cutoff_test(state, depth):
148	150	return eval_fn(state)
149		- v = inf
	151	+ v = np.inf
150	152	for a in game.actions(state):
151	153	v = min(v, max_value(game.result(state, a), alpha, beta, depth + 1))
152	154	if v <= alpha:
@@ -158,8 +160,8 @@ def min_value(state, alpha, beta, depth):
158	160	# The default test cuts off at depth d or at a terminal state
159	161	cutoff_test = (cutoff_test or (lambda state, depth: depth > d or game.terminal_test(state)))
160	162	eval_fn = eval_fn or (lambda state: game.utility(state, player))
161		- best_score = -inf
162		- beta = inf
	163	+ best_score = -np.inf
	164	+ beta = np.inf
163	165	best_action = None
164	166	for a in game.actions(state):
165	167	v = min_value(game.result(state, a), best_score, beta, 1)

Collapse file

‎gui/romania_problem.py‎

Copy file name to clipboardExpand all lines: gui/romania_problem.py

+12-18Lines changed: 12 additions & 18 deletions

Original file line number	Diff line number	Diff line change
@@ -1,14 +1,10 @@
	1	+ from copy import deepcopy
1	2	from tkinter import *
2		- import sys
3		- import os.path
4		- import math
5		- sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
	3	+
6	4	from search import *
7		- from search import breadth_first_tree_search as bfts, depth_first_tree_search as dfts, \
8		- depth_first_graph_search as dfgs, breadth_first_graph_search as bfs, uniform_cost_search as ucs, \
9		- astar_search as asts
10	5	from utils import PriorityQueue
11		- from copy import deepcopy
	6	+
	7	+ sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
12	8
13	9	root = None
14	10	city_coord = {}
@@ -289,7 +285,6 @@ def make_rectangle(map, x0, y0, margin, city_name):
289	285
290	286
291	287	def make_legend(map):
292		-
293	288	rect1 = map.create_rectangle(600, 100, 610, 110, fill="white")
294	289	text1 = map.create_text(615, 105, anchor=W, text="Un-explored")
295	290
@@ -325,13 +320,11 @@ def tree_search(problem):
325	320	display_current(node)
326	321	if counter % 3 == 1 and counter >= 0:
327	322	if problem.goal_test(node.state):
328		-
329	323	return node
330	324	frontier.extend(node.expand(problem))
331	325
332	326	display_frontier(frontier)
333	327	if counter % 3 == 2 and counter >= 0:
334		-
335	328	display_explored(node)
336	329	return None
337	330
@@ -562,7 +555,7 @@ def astar_search(problem, h=None):
562	555
563	556	# TODO:
564	557	# Remove redundant code.
565		- # Make the interchangbility work between various algorithms at each step.
	558	+ # Make the interchangeability work between various algorithms at each step.
566	559	def on_click():
567	560	"""
568	561	This function defines the action of the 'Next' button.
@@ -572,47 +565,47 @@ def on_click():
572	565	if "Breadth-First Tree Search" == algo.get():
573	566	node = breadth_first_tree_search(romania_problem)
574	567	if node is not None:
575		- final_path = bfts(romania_problem).solution()
	568	+ final_path = breadth_first_tree_search(romania_problem).solution()
576	569	final_path.append(start.get())
577	570	display_final(final_path)
578	571	next_button.config(state="disabled")
579	572	counter += 1
580	573	elif "Depth-First Tree Search" == algo.get():
581	574	node = depth_first_tree_search(romania_problem)
582	575	if node is not None:
583		- final_path = dfts(romania_problem).solution()
	576	+ final_path = depth_first_tree_search(romania_problem).solution()
584	577	final_path.append(start.get())
585	578	display_final(final_path)
586	579	next_button.config(state="disabled")
587	580	counter += 1
588	581	elif "Breadth-First Graph Search" == algo.get():
589	582	node = breadth_first_graph_search(romania_problem)
590	583	if node is not None:
591		- final_path = bfs(romania_problem).solution()
	584	+ final_path = breadth_first_graph_search(romania_problem).solution()
592	585	final_path.append(start.get())
593	586	display_final(final_path)
594	587	next_button.config(state="disabled")
595	588	counter += 1
596	589	elif "Depth-First Graph Search" == algo.get():
597	590	node = depth_first_graph_search(romania_problem)
598	591	if node is not None:
599		- final_path = dfgs(romania_problem).solution()
	592	+ final_path = depth_first_graph_search(romania_problem).solution()
600	593	final_path.append(start.get())
601	594	display_final(final_path)
602	595	next_button.config(state="disabled")
603	596	counter += 1
604	597	elif "Uniform Cost Search" == algo.get():
605	598	node = uniform_cost_search(romania_problem)
606	599	if node is not None:
607		- final_path = ucs(romania_problem).solution()
	600	+ final_path = uniform_cost_search(romania_problem).solution()
608	601	final_path.append(start.get())
609	602	display_final(final_path)
610	603	next_button.config(state="disabled")
611	604	counter += 1
612	605	elif "A* - Search" == algo.get():
613	606	node = astar_search(romania_problem)
614	607	if node is not None:
615		- final_path = asts(romania_problem).solution()
	608	+ final_path = astar_search(romania_problem).solution()
616	609	final_path.append(start.get())
617	610	display_final(final_path)
618	611	next_button.config(state="disabled")
@@ -626,6 +619,7 @@ def reset_map():
626	619	city_map.itemconfig(city_coord[city], fill="white")
627	620	next_button.config(state="normal")
628	621
	622	+
629	623	# TODO: Add more search algorithms in the OptionMenu
630	624
631	625

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