From c587f2c429b9dec199f190c3453cd269b6b6bbd1 Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Sat, 14 Dec 2019 21:40:37 +0100 Subject: [PATCH 01/31] removed inf and isclose definition from utils and replaced with np.inf and np.isclose (#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 20ab0e5afa238a0556e68f173b07ad32d0779d3b. * Revert "fixed typo errors and removed unnecessary brackets" This reverts commit f743146c43b28e0525b0f0b332faebc78c15946f. * Revert "added map coloring SAT problems" This reverts commit 9e0fa550e85081cf5b92fb6a3418384ab5a9fdfd. * Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py" This reverts commit b3cd24c511a82275f5b43c9f176396e6ba05f67e. * Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference" This reverts commit 6986247481a05f1e558b93b2bf3cdae395f9c4ee. * Revert "added the mentioned AC4 algorithm for constraint propagation" This reverts commit 03551fbf2aa3980b915d4b6fefcbc70f24547b03. * added map coloring SAT problem * fixed build error * Revert "added map coloring SAT problem" This reverts commit 93af259e4811ddd775429f8a334111b9dd9e268c. * Revert "fixed build error" This reverts commit 6641c2c861728f3d43d3931ef201c6f7093cbc96. * 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 9d37ae0def15b9e058862cb465da13d2eb926968. * Revert "added tests for monkey & bananas planning problem" This reverts commit 24041e9a1a0ab936f7a2608e3662c8efec559382. * Revert "simplified condition in search.py" This reverts commit 6d229ce9bde5033802aca29ad3047f37ee6d870d. * Revert "added monkey & bananas planning problem" This reverts commit c74933a8905de7bb569bcaed7230930780560874. * 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 c4139e50e3a75a036607f4627717d70ad0919554. * 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 #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 658309d32a3baa0a6b8aac247c0d4ae39cf39ea4. * Revert "fixed typos" This reverts commit 08ad6603ce7b6a6442a28bc0a07c46fa25af3452. * 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 --- agents.py | 3 +-- agents4e.py | 3 +-- deep_learning4e.py | 6 +++--- games.py | 30 ++++++++++++++++-------------- games4e.py | 30 ++++++++++++++++-------------- gui/romania_problem.py | 30 ++++++++++++------------------ knowledge.py | 24 ++++++++++++------------ learning.py | 19 ++++++------------- learning4e.py | 16 +++++----------- making_simple_decision4e.py | 2 +- mdp.py | 2 +- mdp4e.py | 2 +- nlp.py | 2 +- notebook.py | 8 ++++---- notebook4e.py | 8 ++++---- perception4e.py | 10 +++++----- planning.py | 12 ++++++------ probability.py | 10 +++------- probability4e.py | 16 ++++++++-------- reinforcement_learning.py | 2 +- reinforcement_learning4e.py | 2 +- search.py | 36 ++++++++++++++++-------------------- tests/test_search.py | 14 +++++--------- tests/test_text.py | 9 +++++---- tests/test_utils.py | 6 +++--- text.py | 27 ++++++++++++++------------- utils.py | 31 +++++++++---------------------- utils4e.py | 37 ++++++++++++------------------------- 28 files changed, 172 insertions(+), 225 deletions(-) diff --git a/agents.py b/agents.py index 2e292948b..135711249 100644 --- a/agents.py +++ b/agents.py @@ -354,8 +354,7 @@ def list_things_at(self, location, tclass=Thing): return [thing for thing in self.things if thing.location == location and isinstance(thing, tclass)] return [thing for thing in self.things - if all(x==y for x,y in zip(thing.location, location)) - and isinstance(thing, tclass)] + if all(x == y for x, y in zip(thing.location, location)) and isinstance(thing, tclass)] def some_things_at(self, location, tclass=Thing): """Return true if at least one of the things at location diff --git a/agents4e.py b/agents4e.py index 7c66a6194..7308cbb59 100644 --- a/agents4e.py +++ b/agents4e.py @@ -354,8 +354,7 @@ def list_things_at(self, location, tclass=Thing): return [thing for thing in self.things if thing.location == location and isinstance(thing, tclass)] return [thing for thing in self.things - if all(x==y for x,y in zip(thing.location, location)) - and isinstance(thing, tclass)] + if all(x == y for x, y in zip(thing.location, location)) and isinstance(thing, tclass)] def some_things_at(self, location, tclass=Thing): """Return true if at least one of the things at location diff --git a/deep_learning4e.py b/deep_learning4e.py index 4f8f52ad9..bea9c8d2c 100644 --- a/deep_learning4e.py +++ b/deep_learning4e.py @@ -1,9 +1,9 @@ """Deep learning. (Chapters 20)""" -import math import random import statistics +import numpy as np from keras import Sequential, optimizers from keras.layers import Embedding, SimpleRNN, Dense 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, r_hat = scalar_vector_product(1 / (1 - rho[1] ** t), r) # rescale r_hat - r_hat = map_vector(lambda x: 1 / (math.sqrt(x) + delta), r_hat) + r_hat = map_vector(lambda x: 1 / (np.sqrt(x) + delta), r_hat) # delta weights delta_theta = scalar_vector_product(-l_rate, element_wise_product(s_hat, r_hat)) @@ -341,7 +341,7 @@ def forward(self, inputs): res = [] # get normalized value of each input for i in range(len(self.nodes)): - val = [(inputs[i] - mu) * self.weights[0] / math.sqrt(self.epsilon + stderr ** 2) + self.weights[1]] + val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.epsilon + stderr ** 2) + self.weights[1]] res.append(val) self.nodes[i].val = val return res diff --git a/games.py b/games.py index efc65cc67..97bceb198 100644 --- a/games.py +++ b/games.py @@ -1,11 +1,13 @@ -"""Games or Adversarial Search. (Chapter 5)""" +"""Games or Adversarial Search (Chapter 5)""" import copy import itertools import random from collections import namedtuple -from utils import vector_add, inf +import numpy as np + +from utils import vector_add GameState = namedtuple('GameState', 'to_move, utility, board, moves') StochasticGameState = namedtuple('StochasticGameState', 'to_move, utility, board, moves, chance') @@ -24,7 +26,7 @@ def minmax_decision(state, game): def max_value(state): if game.terminal_test(state): return game.utility(state, player) - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, min_value(game.result(state, a))) return v @@ -32,7 +34,7 @@ def max_value(state): def min_value(state): if game.terminal_test(state): return game.utility(state, player) - v = inf + v = np.inf for a in game.actions(state): v = min(v, max_value(game.result(state, a))) return v @@ -53,13 +55,13 @@ def expect_minmax(state, game): player = game.to_move(state) def max_value(state): - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, chance_node(state, a)) return v def min_value(state): - v = inf + v = np.inf for a in game.actions(state): v = min(v, chance_node(state, a)) return v @@ -94,7 +96,7 @@ def alpha_beta_search(state, game): def max_value(state, alpha, beta): if game.terminal_test(state): return game.utility(state, player) - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, min_value(game.result(state, a), alpha, beta)) if v >= beta: @@ -105,7 +107,7 @@ def max_value(state, alpha, beta): def min_value(state, alpha, beta): if game.terminal_test(state): return game.utility(state, player) - v = inf + v = np.inf for a in game.actions(state): v = min(v, max_value(game.result(state, a), alpha, beta)) if v <= alpha: @@ -114,8 +116,8 @@ def min_value(state, alpha, beta): return v # Body of alpha_beta_search: - best_score = -inf - beta = inf + best_score = -np.inf + beta = np.inf best_action = None for a in game.actions(state): 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): def max_value(state, alpha, beta, depth): if cutoff_test(state, depth): return eval_fn(state) - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, min_value(game.result(state, a), alpha, beta, depth + 1)) if v >= beta: @@ -146,7 +148,7 @@ def max_value(state, alpha, beta, depth): def min_value(state, alpha, beta, depth): if cutoff_test(state, depth): return eval_fn(state) - v = inf + v = np.inf for a in game.actions(state): v = min(v, max_value(game.result(state, a), alpha, beta, depth + 1)) if v <= alpha: @@ -158,8 +160,8 @@ def min_value(state, alpha, beta, depth): # The default test cuts off at depth d or at a terminal state cutoff_test = (cutoff_test or (lambda state, depth: depth > d or game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) - best_score = -inf - beta = inf + best_score = -np.inf + beta = np.inf best_action = None for a in game.actions(state): v = min_value(game.result(state, a), best_score, beta, 1) diff --git a/games4e.py b/games4e.py index 3fb000862..aba5b0eb3 100644 --- a/games4e.py +++ b/games4e.py @@ -1,11 +1,13 @@ -"""Games or Adversarial Search. (Chapter 5)""" +"""Games or Adversarial Search (Chapter 5)""" import copy import itertools import random from collections import namedtuple -from utils4e import vector_add, MCT_Node, ucb, inf +import numpy as np + +from utils4e import vector_add, MCT_Node, ucb GameState = namedtuple('GameState', 'to_move, utility, board, moves') StochasticGameState = namedtuple('StochasticGameState', 'to_move, utility, board, moves, chance') @@ -24,7 +26,7 @@ def minmax_decision(state, game): def max_value(state): if game.terminal_test(state): return game.utility(state, player) - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, min_value(game.result(state, a))) return v @@ -32,7 +34,7 @@ def max_value(state): def min_value(state): if game.terminal_test(state): return game.utility(state, player) - v = inf + v = np.inf for a in game.actions(state): v = min(v, max_value(game.result(state, a))) return v @@ -53,13 +55,13 @@ def expect_minmax(state, game): player = game.to_move(state) def max_value(state): - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, chance_node(state, a)) return v def min_value(state): - v = inf + v = np.inf for a in game.actions(state): v = min(v, chance_node(state, a)) return v @@ -94,7 +96,7 @@ def alpha_beta_search(state, game): def max_value(state, alpha, beta): if game.terminal_test(state): return game.utility(state, player) - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, min_value(game.result(state, a), alpha, beta)) if v >= beta: @@ -105,7 +107,7 @@ def max_value(state, alpha, beta): def min_value(state, alpha, beta): if game.terminal_test(state): return game.utility(state, player) - v = inf + v = np.inf for a in game.actions(state): v = min(v, max_value(game.result(state, a), alpha, beta)) if v <= alpha: @@ -114,8 +116,8 @@ def min_value(state, alpha, beta): return v # Body of alpha_beta_search: - best_score = -inf - beta = inf + best_score = -np.inf + beta = np.inf best_action = None for a in game.actions(state): 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): def max_value(state, alpha, beta, depth): if cutoff_test(state, depth): return eval_fn(state) - v = -inf + v = -np.inf for a in game.actions(state): v = max(v, min_value(game.result(state, a), alpha, beta, depth + 1)) if v >= beta: @@ -146,7 +148,7 @@ def max_value(state, alpha, beta, depth): def min_value(state, alpha, beta, depth): if cutoff_test(state, depth): return eval_fn(state) - v = inf + v = np.inf for a in game.actions(state): v = min(v, max_value(game.result(state, a), alpha, beta, depth + 1)) if v <= alpha: @@ -158,8 +160,8 @@ def min_value(state, alpha, beta, depth): # The default test cuts off at depth d or at a terminal state cutoff_test = (cutoff_test or (lambda state, depth: depth > d or game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) - best_score = -inf - beta = inf + best_score = -np.inf + beta = np.inf best_action = None for a in game.actions(state): v = min_value(game.result(state, a), best_score, beta, 1) diff --git a/gui/romania_problem.py b/gui/romania_problem.py index 55efa1837..08219bb55 100644 --- a/gui/romania_problem.py +++ b/gui/romania_problem.py @@ -1,14 +1,10 @@ +from copy import deepcopy from tkinter import * -import sys -import os.path -import math -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) + from search import * -from search import breadth_first_tree_search as bfts, depth_first_tree_search as dfts, \ - depth_first_graph_search as dfgs, breadth_first_graph_search as bfs, uniform_cost_search as ucs, \ - astar_search as asts from utils import PriorityQueue -from copy import deepcopy + +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) root = None city_coord = {} @@ -289,7 +285,6 @@ def make_rectangle(map, x0, y0, margin, city_name): def make_legend(map): - rect1 = map.create_rectangle(600, 100, 610, 110, fill="white") text1 = map.create_text(615, 105, anchor=W, text="Un-explored") @@ -325,13 +320,11 @@ def tree_search(problem): display_current(node) if counter % 3 == 1 and counter >= 0: if problem.goal_test(node.state): - return node frontier.extend(node.expand(problem)) display_frontier(frontier) if counter % 3 == 2 and counter >= 0: - display_explored(node) return None @@ -562,7 +555,7 @@ def astar_search(problem, h=None): # TODO: # Remove redundant code. -# Make the interchangbility work between various algorithms at each step. +# Make the interchangeability work between various algorithms at each step. def on_click(): """ This function defines the action of the 'Next' button. @@ -572,7 +565,7 @@ def on_click(): if "Breadth-First Tree Search" == algo.get(): node = breadth_first_tree_search(romania_problem) if node is not None: - final_path = bfts(romania_problem).solution() + final_path = breadth_first_tree_search(romania_problem).solution() final_path.append(start.get()) display_final(final_path) next_button.config(state="disabled") @@ -580,7 +573,7 @@ def on_click(): elif "Depth-First Tree Search" == algo.get(): node = depth_first_tree_search(romania_problem) if node is not None: - final_path = dfts(romania_problem).solution() + final_path = depth_first_tree_search(romania_problem).solution() final_path.append(start.get()) display_final(final_path) next_button.config(state="disabled") @@ -588,7 +581,7 @@ def on_click(): elif "Breadth-First Graph Search" == algo.get(): node = breadth_first_graph_search(romania_problem) if node is not None: - final_path = bfs(romania_problem).solution() + final_path = breadth_first_graph_search(romania_problem).solution() final_path.append(start.get()) display_final(final_path) next_button.config(state="disabled") @@ -596,7 +589,7 @@ def on_click(): elif "Depth-First Graph Search" == algo.get(): node = depth_first_graph_search(romania_problem) if node is not None: - final_path = dfgs(romania_problem).solution() + final_path = depth_first_graph_search(romania_problem).solution() final_path.append(start.get()) display_final(final_path) next_button.config(state="disabled") @@ -604,7 +597,7 @@ def on_click(): elif "Uniform Cost Search" == algo.get(): node = uniform_cost_search(romania_problem) if node is not None: - final_path = ucs(romania_problem).solution() + final_path = uniform_cost_search(romania_problem).solution() final_path.append(start.get()) display_final(final_path) next_button.config(state="disabled") @@ -612,7 +605,7 @@ def on_click(): elif "A* - Search" == algo.get(): node = astar_search(romania_problem) if node is not None: - final_path = asts(romania_problem).solution() + final_path = astar_search(romania_problem).solution() final_path.append(start.get()) display_final(final_path) next_button.config(state="disabled") @@ -626,6 +619,7 @@ def reset_map(): city_map.itemconfig(city_coord[city], fill="white") next_button.config(state="normal") + # TODO: Add more search algorithms in the OptionMenu diff --git a/knowledge.py b/knowledge.py index 945f27d3d..8c27c3eb8 100644 --- a/knowledge.py +++ b/knowledge.py @@ -1,23 +1,23 @@ """Knowledge in learning (Chapter 19)""" -from random import shuffle -from math import log -from utils import power_set from collections import defaultdict -from itertools import combinations, product -from logic import (FolKB, constant_symbols, predicate_symbols, standardize_variables, - variables, is_definite_clause, subst, expr, Expr) from functools import partial +from itertools import combinations, product +from random import shuffle +import numpy as np -# ______________________________________________________________________________ +from logic import (FolKB, constant_symbols, predicate_symbols, standardize_variables, + variables, is_definite_clause, subst, expr, Expr) +from utils import power_set def current_best_learning(examples, h, examples_so_far=None): """ [Figure 19.2] The hypothesis is a list of dictionaries, with each dictionary representing - a disjunction.""" + a disjunction. + """ if examples_so_far is None: examples_so_far = [] if not examples: @@ -128,7 +128,8 @@ def version_space_learning(examples): """ [Figure 19.3] The version space is a list of hypotheses, which in turn are a list - of dictionaries/disjunctions.""" + of dictionaries/disjunctions. + """ V = all_hypotheses(examples) for e in examples: if V: @@ -314,7 +315,6 @@ def new_literals(self, clause): def choose_literal(self, literals, examples): """Choose the best literal based on the information gain.""" - return max(literals, key=partial(self.gain, examples=examples)) def gain(self, l, examples): @@ -345,8 +345,8 @@ def gain(self, l, examples): represents = lambda d: all(d[x] == example[x] for x in example) if any(represents(l_) for l_ in post_pos): T += 1 - value = T * (log(len(post_pos) / (len(post_pos) + len(post_neg)) + 1e-12, 2) - - log(pre_pos / (pre_pos + pre_neg), 2)) + value = T * (np.log2(len(post_pos) / (len(post_pos) + len(post_neg)) + 1e-12) - + np.log2(pre_pos / (pre_pos + pre_neg))) return value def update_examples(self, target, examples, extended_examples): diff --git a/learning.py b/learning.py index 401729cb9..bcaf0961e 100644 --- a/learning.py +++ b/learning.py @@ -1,20 +1,13 @@ -"""Learning from examples. (Chapters 18)""" +"""Learning from examples (Chapters 18)""" import copy -import heapq -import math -import random from collections import defaultdict -from statistics import mean, stdev +from statistics import stdev -import numpy as np from qpsolvers import solve_qp from probabilistic_learning import NaiveBayesLearner -from utils import (remove_all, unique, mode, argmax_random_tie, isclose, dot_product, vector_add, clip, sigmoid, - scalar_vector_product, weighted_sample_with_replacement, num_or_str, normalize, print_table, - open_data, sigmoid_derivative, probability, relu, relu_derivative, tanh, tanh_derivative, leaky_relu, - leaky_relu_derivative, elu, elu_derivative, mean_boolean_error, random_weights, linear_kernel, inf) +from utils import * class DataSet: @@ -272,7 +265,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): while True: errT, errV = cross_validation(learner, dataset, size, k, trials) # check for convergence provided err_val is not empty - if errT and not isclose(errT[-1], errT, rel_tol=1e-6): + if errT and not np.isclose(errT[-1], errT, rel_tol=1e-6): best_size = 0 min_val = inf i = 0 @@ -462,7 +455,7 @@ def split_by(attr, examples): def information_content(values): """Number of bits to represent the probability distribution in values.""" probabilities = normalize(remove_all(0, values)) - return sum(-p * math.log2(p) for p in probabilities) + return sum(-p * np.log2(p) for p in probabilities) def DecisionListLearner(dataset): @@ -980,7 +973,7 @@ def ada_boost(dataset, L, K): if example[target] == h_k(example): w[j] *= error / (1 - error) w = normalize(w) - z.append(math.log((1 - error) / error)) + z.append(np.log((1 - error) / error)) return weighted_majority(h, z) diff --git a/learning4e.py b/learning4e.py index bd3bcf50a..01d9ea290 100644 --- a/learning4e.py +++ b/learning4e.py @@ -1,20 +1,14 @@ -"""Learning from examples. (Chapters 18)""" +"""Learning from examples (Chapters 18)""" import copy -import heapq -import math -import random from collections import defaultdict -from statistics import mean, stdev +from statistics import stdev -import numpy as np from qpsolvers import solve_qp from probabilistic_learning import NaiveBayesLearner from utils import sigmoid, sigmoid_derivative -from utils4e import (remove_all, unique, mode, argmax_random_tie, isclose, dot_product, num_or_str, normalize, clip, - weighted_sample_with_replacement, print_table, open_data, probability, random_weights, - mean_boolean_error, linear_kernel, inf) +from utils4e import * class DataSet: @@ -457,7 +451,7 @@ def split_by(attr, examples): def information_content(values): """Number of bits to represent the probability distribution in values.""" probabilities = normalize(remove_all(0, values)) - return sum(-p * math.log2(p) for p in probabilities) + return sum(-p * np.log2(p) for p in probabilities) def DecisionListLearner(dataset): @@ -754,7 +748,7 @@ def ada_boost(dataset, L, K): if example[target] == h_k(example): w[j] *= error / (1 - error) w = normalize(w) - z.append(math.log((1 - error) / error)) + z.append(np.log((1 - error) / error)) return weighted_majority(h, z) diff --git a/making_simple_decision4e.py b/making_simple_decision4e.py index a3b50e57c..4a35f94bd 100644 --- a/making_simple_decision4e.py +++ b/making_simple_decision4e.py @@ -1,4 +1,4 @@ -"""Making Simple Decisions. (Chapter 15)""" +"""Making Simple Decisions (Chapter 15)""" import random diff --git a/mdp.py b/mdp.py index f558c8d40..1003e26b5 100644 --- a/mdp.py +++ b/mdp.py @@ -1,5 +1,5 @@ """ -Markov Decision Processes. (Chapter 17) +Markov Decision Processes (Chapter 17) First we define an MDP, and the special case of a GridMDP, in which states are laid out in a 2-dimensional grid. We also represent a policy diff --git a/mdp4e.py b/mdp4e.py index afa87ea0a..f8871bdc9 100644 --- a/mdp4e.py +++ b/mdp4e.py @@ -1,5 +1,5 @@ """ -Markov Decision Processes. (Chapter 16) +Markov Decision Processes (Chapter 16) First we define an MDP, and the special case of a GridMDP, in which states are laid out in a 2-dimensional grid. We also represent a policy diff --git a/nlp.py b/nlp.py index d883f3566..03aabf54b 100644 --- a/nlp.py +++ b/nlp.py @@ -1,4 +1,4 @@ -"""Natural Language Processing; Chart Parsing and PageRanking. (Chapter 22-23)""" +"""Natural Language Processing; Chart Parsing and PageRanking (Chapter 22-23)""" from collections import defaultdict from utils import weighted_choice diff --git a/notebook.py b/notebook.py index b28e97230..507aec330 100644 --- a/notebook.py +++ b/notebook.py @@ -11,7 +11,7 @@ from PIL import Image from matplotlib import lines -from games import TicTacToe, alpha_beta_player, random_player, Fig52Extended, inf +from games import TicTacToe, alpha_beta_player, random_player, Fig52Extended from learning import DataSet from logic import parse_definite_clause, standardize_variables, unify_mm, subst from search import GraphProblem, romania_map @@ -642,7 +642,7 @@ def max_value(node, alpha, beta): self.change_list.append(('h',)) self.change_list.append(('p',)) return game.utility(node, player) - v = -inf + v = -np.inf self.change_list.append(('a', node)) self.change_list.append(('ab', node, v, beta)) self.change_list.append(('h',)) @@ -671,7 +671,7 @@ def min_value(node, alpha, beta): self.change_list.append(('h',)) self.change_list.append(('p',)) return game.utility(node, player) - v = inf + v = np.inf self.change_list.append(('a', node)) self.change_list.append(('ab', node, alpha, v)) self.change_list.append(('h',)) @@ -694,7 +694,7 @@ def min_value(node, alpha, beta): self.change_list.append(('h',)) return v - return max_value(node, -inf, inf) + return max_value(node, -np.inf, np.inf) def stack_manager_gen(self): self.alpha_beta_search(0) diff --git a/notebook4e.py b/notebook4e.py index 8a5d92cd6..fa19b12d2 100644 --- a/notebook4e.py +++ b/notebook4e.py @@ -12,7 +12,7 @@ from matplotlib import lines from matplotlib.colors import ListedColormap -from games import TicTacToe, alpha_beta_player, random_player, Fig52Extended, inf +from games import TicTacToe, alpha_beta_player, random_player, Fig52Extended from learning import DataSet from logic import parse_definite_clause, standardize_variables, unify_mm, subst from search import GraphProblem, romania_map @@ -678,7 +678,7 @@ def max_value(node, alpha, beta): self.change_list.append(('h',)) self.change_list.append(('p',)) return game.utility(node, player) - v = -inf + v = -np.inf self.change_list.append(('a', node)) self.change_list.append(('ab', node, v, beta)) self.change_list.append(('h',)) @@ -707,7 +707,7 @@ def min_value(node, alpha, beta): self.change_list.append(('h',)) self.change_list.append(('p',)) return game.utility(node, player) - v = inf + v = np.inf self.change_list.append(('a', node)) self.change_list.append(('ab', node, alpha, v)) self.change_list.append(('h',)) @@ -730,7 +730,7 @@ def min_value(node, alpha, beta): self.change_list.append(('h',)) return v - return max_value(node, -inf, inf) + return max_value(node, -np.inf, np.inf) def stack_manager_gen(self): self.alpha_beta_search(0) diff --git a/perception4e.py b/perception4e.py index a36461cf6..d5bc15718 100644 --- a/perception4e.py +++ b/perception4e.py @@ -1,4 +1,4 @@ -"""Perception. (Chapter 24)""" +"""Perception (Chapter 24)""" import cv2 import keras @@ -9,7 +9,7 @@ from keras.layers import Dense, Activation, Flatten, InputLayer, Conv2D, MaxPooling2D from keras.models import Sequential -from utils4e import gaussian_kernel_2D, inf +from utils4e import gaussian_kernel_2D # ____________________________________________________ @@ -86,8 +86,8 @@ def sum_squared_difference(pic1, pic2): pic1 = np.asarray(pic1) pic2 = np.asarray(pic2) assert pic1.shape == pic2.shape - min_ssd = inf - min_dxy = (inf, inf) + min_ssd = np.inf + min_dxy = (np.inf, np.inf) # consider picture shift from -30 to 30 for Dx in range(-30, 31): @@ -241,7 +241,7 @@ def min_cut(self, source, sink): max_flow = 0 while self.bfs(source, sink, parent): - path_flow = inf + path_flow = np.inf # find the minimum flow of s-t path for s, t in parent: path_flow = min(path_flow, self.flow[s][t]) diff --git a/planning.py b/planning.py index 5d57c3f55..1e4a19209 100644 --- a/planning.py +++ b/planning.py @@ -1,17 +1,17 @@ -""" -Planning (Chapters 10-11) -""" +"""Planning (Chapters 10-11)""" import copy import itertools from collections import deque, defaultdict from functools import reduce as _reduce +import numpy as np + import search from csp import sat_up, NaryCSP, Constraint, ac_search_solver, is_constraint from logic import FolKB, conjuncts, unify_mm, associate, SAT_plan, cdcl_satisfiable from search import Node -from utils import Expr, expr, first, inf +from utils import Expr, expr, first class PlanningProblem: @@ -593,7 +593,7 @@ def h(self, state): try: return len(linearize(GraphPlan(relaxed_planning_problem).execute())) except: - return inf + return np.inf class BackwardPlan(search.Problem): @@ -646,7 +646,7 @@ def h(self, subgoal): try: return len(linearize(GraphPlan(relaxed_planning_problem).execute())) except: - return inf + return np.inf def CSPlan(planning_problem, solution_length, CSP_solver=ac_search_solver, arc_heuristic=sat_up): diff --git a/probability.py b/probability.py index 9925079a2..e1e77d224 100644 --- a/probability.py +++ b/probability.py @@ -1,14 +1,10 @@ -"""Probability models. (Chapter 13-15)""" +"""Probability models (Chapter 13-15)""" -import random from collections import defaultdict from functools import reduce -import numpy as np - from agents import Agent -from utils import (product, element_wise_product, matrix_multiplication, vector_add, scalar_vector_product, - weighted_sample_with_replacement, isclose, probability, normalize, extend) +from utils import * def DTAgentProgram(belief_state): @@ -68,7 +64,7 @@ def normalize(self): Returns the normalized distribution. Raises a ZeroDivisionError if the sum of the values is 0.""" total = sum(self.prob.values()) - if not isclose(total, 1.0): + if not np.isclose(total, 1.0): for val in self.prob: self.prob[val] /= total return self diff --git a/probability4e.py b/probability4e.py index cd1ff2022..d413a55ae 100644 --- a/probability4e.py +++ b/probability4e.py @@ -1,12 +1,13 @@ -"""Probability models.""" +"""Probability models (Chapter 12-13)""" import copy import random from collections import defaultdict from functools import reduce -from math import sqrt, pi, exp -from utils4e import product, isclose, probability, extend +import numpy as np + +from utils4e import product, probability, extend # ______________________________________________________________________________ @@ -69,7 +70,7 @@ def normalize(self): Returns the normalized distribution. Raises a ZeroDivisionError if the sum of the values is 0.""" total = sum(self.prob.values()) - if not isclose(total, 1.0): + if not np.isclose(total, 1.0): for val in self.prob: self.prob[val] /= total return self @@ -385,7 +386,7 @@ def gaussian_probability(param, event, value): for k, v in event.items(): # buffer varianle to calculate h1*a_h1 + h2*a_h2 buff += param['a'][k] * v - res = 1 / (param['sigma'] * sqrt(2 * pi)) * exp(-0.5 * ((value - buff - param['b']) / param['sigma']) ** 2) + res = 1 / (param['sigma'] * np.sqrt(2 * np.pi)) * np.exp(-0.5 * ((value - buff - param['b']) / param['sigma']) ** 2) return res @@ -403,7 +404,7 @@ def logistic_probability(param, event, value): # buffer variable to calculate (value-mu)/sigma buff *= (v - param['mu']) / param['sigma'] - p = 1 - 1 / (1 + exp(-4 / sqrt(2 * pi) * buff)) + p = 1 - 1 / (1 + np.exp(-4 / np.sqrt(2 * np.pi) * buff)) return p if value else 1 - p @@ -456,8 +457,7 @@ def continuous_p(self, value, c_event, d_event): ('Cost', 'Subsidy', 'Harvest', {True: {'sigma': 0.5, 'b': 1, 'a': {'Harvest': 0.5}}, False: {'sigma': 0.6, 'b': 1, 'a': {'Harvest': 0.5}}}, 'c'), - ('Buys', '', 'Cost', {T: {'mu': 0.5, 'sigma': 0.5}, F: {'mu': 0.6, 'sigma': 0.6}}, 'd'), -]) + ('Buys', '', 'Cost', {T: {'mu': 0.5, 'sigma': 0.5}, F: {'mu': 0.6, 'sigma': 0.6}}, 'd')]) # ______________________________________________________________________________ diff --git a/reinforcement_learning.py b/reinforcement_learning.py index a640ac39a..4cb91af0f 100644 --- a/reinforcement_learning.py +++ b/reinforcement_learning.py @@ -1,4 +1,4 @@ -"""Reinforcement Learning. (Chapter 21)""" +"""Reinforcement Learning (Chapter 21)""" import random from collections import defaultdict diff --git a/reinforcement_learning4e.py b/reinforcement_learning4e.py index fecfdaa32..eaaba3e5a 100644 --- a/reinforcement_learning4e.py +++ b/reinforcement_learning4e.py @@ -1,4 +1,4 @@ -"""Reinforcement Learning. (Chapter 21)""" +"""Reinforcement Learning (Chapter 21)""" import random from collections import defaultdict diff --git a/search.py b/search.py index 999dc8f57..0104eb341 100644 --- a/search.py +++ b/search.py @@ -6,14 +6,10 @@ functions. """ -import bisect -import math -import random import sys from collections import deque -from utils import (is_in, argmax_random_tie, probability, weighted_sampler, memoize, print_table, open_data, - PriorityQueue, name, distance, vector_add, inf) +from utils import * class Problem: @@ -331,7 +327,7 @@ def bidirectional_search(problem): gF, gB = {problem.initial: 0}, {problem.goal: 0} openF, openB = [problem.initial], [problem.goal] closedF, closedB = [], [] - U = inf + U = np.inf def extend(U, open_dir, open_other, g_dir, g_other, closed_dir): """Extend search in given direction""" @@ -357,7 +353,7 @@ def extend(U, open_dir, open_other, g_dir, g_other, closed_dir): def find_min(open_dir, g): """Finds minimum priority, g and f values in open_dir""" - m, m_f = inf, inf + m, m_f = np.inf, np.inf for n in open_dir: f = g[n] + problem.h(n) pr = max(f, 2 * g[n]) @@ -369,7 +365,7 @@ def find_min(open_dir, g): def find_key(pr_min, open_dir, g): """Finds key in open_dir with value equal to pr_min and minimum g value.""" - m = inf + m = np.inf state = -1 for n in open_dir: pr = max(g[n] + problem.h(n), 2 * g[n]) @@ -395,7 +391,7 @@ def find_key(pr_min, open_dir, g): # Extend backward U, openB, closedB, gB = extend(U, openB, openF, gB, gF, closedB) - return inf + return np.inf # ______________________________________________________________________________ @@ -605,7 +601,7 @@ def RBFS(problem, node, flimit): return node, 0 # (The second value is immaterial) successors = node.expand(problem) if len(successors) == 0: - return None, inf + return None, np.inf for s in successors: s.f = max(s.path_cost + h(s), node.f) while True: @@ -617,14 +613,14 @@ def RBFS(problem, node, flimit): if len(successors) > 1: alternative = successors[1].f else: - alternative = inf + alternative = np.inf result, best.f = RBFS(problem, best, min(flimit, alternative)) if result is not None: return result, best.f node = Node(problem.initial) node.f = h(node) - result, bestf = RBFS(problem, node, inf) + result, bestf = RBFS(problem, node, np.inf) return result @@ -648,7 +644,7 @@ def hill_climbing(problem): def exp_schedule(k=20, lam=0.005, limit=100): """One possible schedule function for simulated annealing""" - return lambda t: (k * math.exp(-lam * t) if t < limit else 0) + return lambda t: (k * np.exp(-lam * t) if t < limit else 0) def simulated_annealing(problem, schedule=exp_schedule()): @@ -664,7 +660,7 @@ def simulated_annealing(problem, schedule=exp_schedule()): return current.state next_choice = random.choice(neighbors) delta_e = problem.value(next_choice.state) - problem.value(current.state) - if delta_e > 0 or probability(math.exp(delta_e / T)): + if delta_e > 0 or probability(np.exp(delta_e / T)): current = next_choice @@ -683,7 +679,7 @@ def simulated_annealing_full(problem, schedule=exp_schedule()): return current.state next_choice = random.choice(neighbors) delta_e = problem.value(next_choice.state) - problem.value(current.state) - if delta_e > 0 or probability(math.exp(delta_e / T)): + if delta_e > 0 or probability(np.exp(delta_e / T)): current = next_choice @@ -1080,7 +1076,7 @@ def RandomGraph(nodes=list(range(10)), min_links=2, width=400, height=300, def distance_to_node(n): if n is node or g.get(node, n): - return inf + return np.inf return distance(g.locations[n], here) neighbor = min(nodes, key=distance_to_node) @@ -1188,11 +1184,11 @@ def result(self, state, action): return action def path_cost(self, cost_so_far, A, action, B): - return cost_so_far + (self.graph.get(A, B) or inf) + return cost_so_far + (self.graph.get(A, B) or np.inf) def find_min_edge(self): """Find minimum value of edges.""" - m = inf + m = np.inf for d in self.graph.graph_dict.values(): local_min = min(d.values()) m = min(m, local_min) @@ -1208,7 +1204,7 @@ def h(self, node): return int(distance(locs[node.state], locs[self.goal])) else: - return inf + return np.inf class GraphProblemStochastic(GraphProblem): @@ -1368,7 +1364,7 @@ def boggle_neighbors(n2, cache={}): def exact_sqrt(n2): """If n2 is a perfect square, return its square root, else raise error.""" - n = int(math.sqrt(n2)) + n = int(np.sqrt(n2)) assert n * n == n2 return n diff --git a/tests/test_search.py b/tests/test_search.py index 978894fa3..d37f8fa38 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -156,15 +156,13 @@ def test_recursive_best_first_search(): romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] assert recursive_best_first_search( EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0))).solution() == [ - 'UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN' - ] + 'UP', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT', 'DOWN'] def manhattan(node): state = node.state index_goal = {0: [2, 2], 1: [0, 0], 2: [0, 1], 3: [0, 2], 4: [1, 0], 5: [1, 1], 6: [1, 2], 7: [2, 0], 8: [2, 1]} index_state = {} index = [[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]] - x, y = 0, 0 for i in range(len(state)): index_state[state[i]] = index[i] @@ -260,12 +258,10 @@ def test_LRTAStarAgent(): def test_genetic_algorithm(): # Graph coloring - edges = { - 'A': [0, 1], - 'B': [0, 3], - 'C': [1, 2], - 'D': [2, 3] - } + edges = {'A': [0, 1], + 'B': [0, 3], + 'C': [1, 2], + 'D': [2, 3]} def fitness(c): return sum(c[n1] != c[n2] for (n1, n2) in edges.values()) diff --git a/tests/test_text.py b/tests/test_text.py index 0d8e3b6ab..3aaa007f6 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -1,9 +1,10 @@ import random +import numpy as np import pytest from text import * -from utils import isclose, open_data +from utils import open_data random.seed("aima-python") @@ -31,9 +32,9 @@ def test_text_models(): (13, ('as', 'well', 'as'))] # Test isclose - assert isclose(P1['the'], 0.0611, rel_tol=0.001) - assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01) - assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001) + assert np.isclose(P1['the'], 0.0611, rtol=0.001) + assert np.isclose(P2['of', 'the'], 0.0108, rtol=0.01) + assert np.isclose(P3['so', 'as', 'to'], 0.000323, rtol=0.001) # Test cond_prob.get assert P2.cond_prob.get(('went',)) is None diff --git a/tests/test_utils.py b/tests/test_utils.py index e7a22b562..31b5848f0 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -116,10 +116,10 @@ def test_cross_entropy(): def test_rms_error(): assert rms_error([2, 2], [2, 2]) == 0 - assert rms_error((0, 0), (0, 1)) == math.sqrt(0.5) + assert rms_error((0, 0), (0, 1)) == np.sqrt(0.5) assert rms_error((1, 0), (0, 1)) == 1 - assert rms_error((0, 0), (0, -1)) == math.sqrt(0.5) - assert rms_error((0, 0.5), (0, -0.5)) == math.sqrt(0.5) + assert rms_error((0, 0), (0, -1)) == np.sqrt(0.5) + assert rms_error((0, 0.5), (0, -0.5)) == np.sqrt(0.5) def test_manhattan_distance(): diff --git a/text.py b/text.py index 58918bb4d..11a5731f1 100644 --- a/text.py +++ b/text.py @@ -1,5 +1,5 @@ """ -Statistical Language Processing tools. (Chapter 22) +Statistical Language Processing tools (Chapter 22) We define Unigram and Ngram text models, use them to generate random text, and show the Viterbi algorithm for segmentation of letters into words. @@ -7,15 +7,16 @@ working on a tiny sample of Unix manual pages. """ -from utils import hashabledict -from probabilistic_learning import CountingProbDist -import search - -from math import log, exp -from collections import defaultdict import heapq -import re import os +import re +from collections import defaultdict + +import numpy as np + +import search +from probabilistic_learning import CountingProbDist +from utils import hashabledict class UnigramWordModel(CountingProbDist): @@ -184,7 +185,7 @@ def query(self, query_text, n=10): def score(self, word, docid): """Compute a score for this word on the document with this docid.""" # There are many options; here we take a very simple approach - return log(1 + self.index[word][docid]) / log(1 + self.documents[docid].nwords) + return np.log(1 + self.index[word][docid]) / np.log(1 + self.documents[docid].nwords) def total_score(self, words, docid): """Compute the sum of the scores of these words on the document with this docid.""" @@ -385,10 +386,10 @@ def score(self, code): # add small positive value to prevent computing log(0) # TODO: Modify the values to make score more accurate - logP = (sum(log(self.Pwords[word] + 1e-20) for word in words(text)) + - sum(log(self.P1[c] + 1e-5) for c in text) + - sum(log(self.P2[b] + 1e-10) for b in bigrams(text))) - return -exp(logP) + logP = (sum(np.log(self.Pwords[word] + 1e-20) for word in words(text)) + + sum(np.log(self.P1[c] + 1e-5) for c in text) + + sum(np.log(self.P2[b] + 1e-10) for b in bigrams(text))) + return -np.exp(logP) class PermutationDecoderProblem(search.Problem): diff --git a/utils.py b/utils.py index 04fbd303c..1d7f1e4f5 100644 --- a/utils.py +++ b/utils.py @@ -1,11 +1,10 @@ -"""Provides some utilities widely used by other modules.""" +"""Provides some utilities widely used by other modules""" import bisect import collections import collections.abc import functools import heapq -import math import operator import os.path import random @@ -14,11 +13,6 @@ import numpy as np -try: # math.inf was added in Python 3.5 - from math import inf -except ImportError: # Python 3.4 - inf = float('inf') - # ______________________________________________________________________________ # Functions on Sequences and Iterables @@ -236,15 +230,15 @@ def num_or_str(x): # TODO: rename as `atom` def euclidean_distance(x, y): - return math.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y))) + return np.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y))) def cross_entropy_loss(x, y): - return (-1.0 / len(x)) * sum(x * math.log(y) + (1 - x) * math.log(1 - y) for x, y in zip(x, y)) + return (-1.0 / len(x)) * sum(x * np.log(y) + (1 - x) * np.log(1 - y) for x, y in zip(x, y)) def rms_error(x, y): - return math.sqrt(ms_error(x, y)) + return np.sqrt(ms_error(x, y)) def ms_error(x, y): @@ -299,15 +293,15 @@ def sigmoid_derivative(value): def sigmoid(x): """Return activation value of x with sigmoid function.""" - return 1 / (1 + math.exp(-x)) + return 1 / (1 + np.exp(-x)) def elu(x, alpha=0.01): - return x if x > 0 else alpha * (math.exp(x) - 1) + return x if x > 0 else alpha * (np.exp(x) - 1) def elu_derivative(value, alpha=0.01): - return 1 if value > 0 else alpha * math.exp(value) + return 1 if value > 0 else alpha * np.exp(value) def tanh(x): @@ -341,7 +335,7 @@ def step(x): 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) + return 1 / (np.sqrt(2 * np.pi) * st_dev) * np.e ** (-0.5 * (float(x - mean) / st_dev) ** 2) def linear_kernel(x, y=None): @@ -366,13 +360,6 @@ def rbf_kernel(x, y=None, gamma=None): np.sum(x * x, axis=1).reshape((-1, 1)) + np.sum(y * y, axis=1).reshape((1, -1)))) -try: # math.isclose was added in Python 3.5 - from math import isclose -except ImportError: # Python 3.4 - def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - """Return true if numbers a and b are close to each other.""" - return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) - # ______________________________________________________________________________ # Grid Functions @@ -397,7 +384,7 @@ def distance(a, b): """The distance between two (x, y) points.""" xA, yA = a xB, yB = b - return math.hypot((xA - xB), (yA - yB)) + return np.hypot((xA - xB), (yA - yB)) def distance_squared(a, b): diff --git a/utils4e.py b/utils4e.py index 3aec273f8..6ed4a7f79 100644 --- a/utils4e.py +++ b/utils4e.py @@ -1,11 +1,10 @@ -"""Provides some utilities widely used by other modules.""" +"""Provides some utilities widely used by other modules""" import bisect import collections import collections.abc import functools import heapq -import math import os.path import random from itertools import chain, combinations @@ -13,11 +12,6 @@ import numpy as np -try: # math.inf was added in Python 3.5 - from math import inf -except ImportError: # Python 3.4 - inf = float('inf') - # part1. General data structures and their functions # ______________________________________________________________________________ @@ -318,11 +312,11 @@ def num_or_str(x): # TODO: rename as `atom` def euclidean_distance(x, y): - return math.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y))) + return np.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y))) def rms_error(x, y): - return math.sqrt(ms_error(x, y)) + return np.sqrt(ms_error(x, y)) def ms_error(x, y): @@ -350,7 +344,7 @@ def hamming_distance(x, y): def cross_entropy_loss(x, y): """Example of cross entropy loss. x and y are 1D iterable objects.""" - return (-1.0 / len(x)) * sum(x * math.log(y) + (1 - x) * math.log(1 - y) for x, y in zip(x, y)) + return (-1.0 / len(x)) * sum(x * np.log(y) + (1 - x) * np.log(1 - y) for x, y in zip(x, y)) def mse_loss(x, y): @@ -419,7 +413,7 @@ def clip(x, lowest, highest): def softmax1D(x): """Return the softmax vector of input vector x.""" - exps = [math.exp(_x) for _x in x] + exps = [np.exp(_x) for _x in x] sum_exps = sum(exps) return [exp / sum_exps for exp in exps] @@ -431,7 +425,7 @@ def f(self, x): return 1 if x <= -100: return 0 - return 1 / (1 + math.exp(-x)) + return 1 / (1 + np.exp(-x)) def derivative(self, value): return value * (1 - value) @@ -449,10 +443,10 @@ def derivative(self, value): class elu(Activation): def f(self, x, alpha=0.01): - return x if x > 0 else alpha * (math.exp(x) - 1) + return x if x > 0 else alpha * (np.exp(x) - 1) def derivative(self, value, alpha=0.01): - return 1 if value > 0 else alpha * math.exp(value) + return 1 if value > 0 else alpha * np.exp(value) class tanh(Activation): @@ -480,7 +474,7 @@ def step(x): 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.exp(-0.5 * (float(x - mean) / st_dev) ** 2) + return 1 / (np.sqrt(2 * np.pi) * st_dev) * np.exp(-0.5 * (float(x - mean) / st_dev) ** 2) def gaussian_2D(means, sigma, point): @@ -489,7 +483,7 @@ def gaussian_2D(means, sigma, point): assert det != 0 x_u = vector_add(point, scalar_vector_product(-1, means)) buff = matrix_multiplication(matrix_multiplication([x_u], inverse), np.array(x_u).T) - return 1 / (math.sqrt(det) * 2 * math.pi) * math.exp(-0.5 * buff[0][0]) + return 1 / (np.sqrt(det) * 2 * np.pi) * np.exp(-0.5 * buff[0][0]) def linear_kernel(x, y=None): @@ -514,13 +508,6 @@ def rbf_kernel(x, y=None, gamma=None): np.sum(x * x, axis=1).reshape((-1, 1)) + np.sum(y * y, axis=1).reshape((1, -1)))) -try: # math.isclose was added in Python 3.5 - from math import isclose -except ImportError: # Python 3.4 - def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - """Return true if numbers a and b are close to each other.""" - return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) - # part4. Self defined data structures # ______________________________________________________________________________ # Grid Functions @@ -546,7 +533,7 @@ def distance(a, b): """The distance between two (x, y) points.""" xA, yA = a xB, yB = b - return math.hypot((xA - xB), (yA - yB)) + return np.hypot((xA - xB), (yA - yB)) def distance_squared(a, b): @@ -907,7 +894,7 @@ def __init__(self, parent=None, state=None, U=0, N=0): def ucb(n, C=1.4): - return inf if n.N == 0 else n.U / n.N + C * math.sqrt(math.log(n.parent.N) / n.N) + return np.inf if n.N == 0 else n.U / n.N + C * np.sqrt(np.log(n.parent.N) / n.N) # ______________________________________________________________________________ From 04b332646c6043fd842d62e426ec97278a77dc12 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Wed, 18 Dec 2019 00:23:48 +0530 Subject: [PATCH 02/31] [MRG] ENH: Small improvements for agents.py (#1139) * ENH: Small improvements for agents.py * FIXUP: fix `add_thing` to pass the tests * [MRG] ENH: Add small chnages to agents.py * [MRG] FIX: `default_location` now returns a valid location * FIXUP: fix `default_location` in agents4e.py and modify tests --- agents.py | 36 ++++++++++++++++++++++-------------- agents4e.py | 36 ++++++++++++++++++++++-------------- tests/test_agents.py | 11 +++++++++-- tests/test_agents4e.py | 13 ++++++++++--- 4 files changed, 63 insertions(+), 33 deletions(-) diff --git a/agents.py b/agents.py index 135711249..084a752e1 100644 --- a/agents.py +++ b/agents.py @@ -37,7 +37,7 @@ from utils import distance_squared, turn_heading from statistics import mean from ipythonblocks import BlockGrid -from IPython.display import HTML, display +from IPython.display import HTML, display, clear_output from time import sleep import random @@ -89,7 +89,7 @@ def __init__(self, program=None): self.bump = False self.holding = [] self.performance = 0 - if program is None or not isinstance(program, collections.Callable): + if program is None or not isinstance(program, collections.abc.Callable): print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__)) def program(percept): @@ -455,15 +455,17 @@ def move_forward(self, from_location): >>> l1 (1, 0) """ + # get the iterable class to return + iclass = from_location.__class__ x, y = from_location if self.direction == self.R: - return x + 1, y + return iclass((x + 1, y)) elif self.direction == self.L: - return x - 1, y + return iclass((x - 1, y)) elif self.direction == self.U: - return x, y - 1 + return iclass((x, y - 1)) elif self.direction == self.D: - return x, y + 1 + return iclass((x, y + 1)) class XYEnvironment(Environment): @@ -518,7 +520,11 @@ def execute_action(self, agent, action): agent.holding.pop() def default_location(self, thing): - return random.choice(self.width), random.choice(self.height) + location = self.random_location_inbounds() + while self.some_things_at(location, Obstacle): + # we will find a random location with no obstacles + location = self.random_location_inbounds() + return location def move_to(self, thing, destination): """Move a thing to a new location. Returns True on success or False if there is an Obstacle. @@ -534,10 +540,12 @@ def move_to(self, thing, destination): t.location = destination return thing.bump - def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): + def add_thing(self, thing, location=None, exclude_duplicate_class_items=False): """Add things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class.""" - if self.is_inbounds(location): + if location is None: + super().add_thing(thing) + elif self.is_inbounds(location): if (exclude_duplicate_class_items and any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): return @@ -666,16 +674,16 @@ def run(self, steps=1000, delay=1): def update(self, delay=1): sleep(delay) - if self.visible: - self.conceal() - self.reveal() - else: - self.reveal() + self.reveal() def reveal(self): """Display the BlockGrid for this world - the last thing to be added at a location defines the location color.""" self.draw_world() + # wait for the world to update and + # apply changes to the same grid instead + # of making a new one. + clear_output(1) self.grid.show() self.visible = True diff --git a/agents4e.py b/agents4e.py index 7308cbb59..9408afb8a 100644 --- a/agents4e.py +++ b/agents4e.py @@ -37,7 +37,7 @@ from utils4e import distance_squared, turn_heading from statistics import mean from ipythonblocks import BlockGrid -from IPython.display import HTML, display +from IPython.display import HTML, display, clear_output from time import sleep import random @@ -89,7 +89,7 @@ def __init__(self, program=None): self.bump = False self.holding = [] self.performance = 0 - if program is None or not isinstance(program, collections.Callable): + if program is None or not isinstance(program, collections.abc.Callable): print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__)) def program(percept): @@ -455,15 +455,17 @@ def move_forward(self, from_location): >>> l1 (1, 0) """ + # get the iterable class to return + iclass = from_location.__class__ x, y = from_location if self.direction == self.R: - return x + 1, y + return iclass((x + 1, y)) elif self.direction == self.L: - return x - 1, y + return iclass((x - 1, y)) elif self.direction == self.U: - return x, y - 1 + return iclass((x, y - 1)) elif self.direction == self.D: - return x, y + 1 + return iclass((x, y + 1)) class XYEnvironment(Environment): @@ -518,7 +520,11 @@ def execute_action(self, agent, action): agent.holding.pop() def default_location(self, thing): - return random.choice(self.width), random.choice(self.height) + location = self.random_location_inbounds() + while self.some_things_at(location, Obstacle): + # we will find a random location with no obstacles + location = self.random_location_inbounds() + return location def move_to(self, thing, destination): """Move a thing to a new location. Returns True on success or False if there is an Obstacle. @@ -534,10 +540,12 @@ def move_to(self, thing, destination): t.location = destination return thing.bump - def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): + def add_thing(self, thing, location=None, exclude_duplicate_class_items=False): """Add things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class.""" - if self.is_inbounds(location): + if location is None: + super().add_thing(thing) + elif self.is_inbounds(location): if (exclude_duplicate_class_items and any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): return @@ -666,16 +674,16 @@ def run(self, steps=1000, delay=1): def update(self, delay=1): sleep(delay) - if self.visible: - self.conceal() - self.reveal() - else: - self.reveal() + self.reveal() def reveal(self): """Display the BlockGrid for this world - the last thing to be added at a location defines the location color.""" self.draw_world() + # wait for the world to update and + # apply changes to the same grid instead + # of making a new one. + clear_output(1) self.grid.show() self.visible = True diff --git a/tests/test_agents.py b/tests/test_agents.py index 39d9b9262..d1a669486 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -7,8 +7,13 @@ SimpleReflexAgentProgram, ModelBasedReflexAgentProgram, Wall, Gold, Explorer, Thing, Bump, Glitter, WumpusEnvironment, Pit, VacuumEnvironment, Dirt, Direction, Agent) -random.seed("aima-python") - +# random seed may affect the placement +# of things in the environment which may +# lead to failure of tests. Please change +# the seed if the tests are failing with +# current changes in any stochastic method +# function or variable. +random.seed(9) def test_move_forward(): d = Direction("up") @@ -88,6 +93,7 @@ def test_RandomVacuumAgent(): def test_TableDrivenAgent(): + random.seed(10) loc_A, loc_B = (0, 0), (1, 0) # table defining all the possible states of the agent table = {((loc_A, 'Clean'),): 'Right', @@ -346,6 +352,7 @@ def constant_prog(percept): def test_WumpusEnvironmentActions(): + random.seed(9) def constant_prog(percept): return percept diff --git a/tests/test_agents4e.py b/tests/test_agents4e.py index 2c6759c22..295a1ee47 100644 --- a/tests/test_agents4e.py +++ b/tests/test_agents4e.py @@ -7,8 +7,13 @@ SimpleReflexAgentProgram, ModelBasedReflexAgentProgram, Wall, Gold, Explorer, Thing, Bump, Glitter, WumpusEnvironment, Pit, VacuumEnvironment, Dirt, Direction, Agent) -random.seed("aima-python") - +# random seed may affect the placement +# of things in the environment which may +# lead to failure of tests. Please change +# the seed if the tests are failing with +# current changes in any stochastic method +# function or variable. +random.seed(9) def test_move_forward(): d = Direction("up") @@ -88,6 +93,7 @@ def test_RandomVacuumAgent(): def test_TableDrivenAgent(): + random.seed(10) loc_A, loc_B = (0, 0), (1, 0) # table defining all the possible states of the agent table = {((loc_A, 'Clean'),): 'Right', @@ -271,7 +277,7 @@ def test_VacuumEnvironment(): # get an agent agent = ModelBasedVacuumAgent() agent.direction = Direction(Direction.R) - v.add_thing(agent) + v.add_thing(agent, location=(1, 1)) v.add_thing(Dirt(), location=(2, 1)) # check if things are added properly @@ -345,6 +351,7 @@ def constant_prog(percept): def test_WumpusEnvironmentActions(): + random.seed(9) def constant_prog(percept): return percept From df33d47be72bc94daeaeb4a35c9b352b2062379b Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Thu, 2 Jan 2020 22:54:26 +0100 Subject: [PATCH 03/31] fixed numpy imports (#1145) * 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 20ab0e5afa238a0556e68f173b07ad32d0779d3b. * Revert "fixed typo errors and removed unnecessary brackets" This reverts commit f743146c43b28e0525b0f0b332faebc78c15946f. * Revert "added map coloring SAT problems" This reverts commit 9e0fa550e85081cf5b92fb6a3418384ab5a9fdfd. * Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py" This reverts commit b3cd24c511a82275f5b43c9f176396e6ba05f67e. * Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference" This reverts commit 6986247481a05f1e558b93b2bf3cdae395f9c4ee. * Revert "added the mentioned AC4 algorithm for constraint propagation" This reverts commit 03551fbf2aa3980b915d4b6fefcbc70f24547b03. * added map coloring SAT problem * fixed build error * Revert "added map coloring SAT problem" This reverts commit 93af259e4811ddd775429f8a334111b9dd9e268c. * Revert "fixed build error" This reverts commit 6641c2c861728f3d43d3931ef201c6f7093cbc96. * 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 9d37ae0def15b9e058862cb465da13d2eb926968. * Revert "added tests for monkey & bananas planning problem" This reverts commit 24041e9a1a0ab936f7a2608e3662c8efec559382. * Revert "simplified condition in search.py" This reverts commit 6d229ce9bde5033802aca29ad3047f37ee6d870d. * Revert "added monkey & bananas planning problem" This reverts commit c74933a8905de7bb569bcaed7230930780560874. * 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 c4139e50e3a75a036607f4627717d70ad0919554. * 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 #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 658309d32a3baa0a6b8aac247c0d4ae39cf39ea4. * Revert "fixed typos" This reverts commit 08ad6603ce7b6a6442a28bc0a07c46fa25af3452. * 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 * fixed numpy imports * fixed superclass call * removed utils import from 4e py file * removed unnecessary norm function in utils and fixed Activation definition --- deep_learning4e.py | 18 +++++++++--------- learning.py | 4 ++-- learning4e.py | 11 +++++------ tests/test_deep_learning4e.py | 1 - utils.py | 5 ----- utils4e.py | 24 +++++++++++++----------- 6 files changed, 29 insertions(+), 34 deletions(-) diff --git a/deep_learning4e.py b/deep_learning4e.py index bea9c8d2c..64aa49e90 100644 --- a/deep_learning4e.py +++ b/deep_learning4e.py @@ -8,7 +8,7 @@ from keras.layers import Embedding, SimpleRNN, Dense from keras.preprocessing import sequence -from utils4e import (sigmoid, dot_product, softmax1D, conv1D, gaussian_kernel, element_wise_product, vector_add, +from utils4e import (Sigmoid, dot_product, softmax1D, conv1D, gaussian_kernel, element_wise_product, vector_add, random_weights, scalar_vector_product, matrix_multiplication, map_vector, mse_loss) @@ -37,7 +37,7 @@ class NNUnit(Node): """ def __init__(self, weights=None, value=None): - super(NNUnit, self).__init__(value) + super().__init__(value) self.weights = weights or [] @@ -59,7 +59,7 @@ class OutputLayer(Layer): """1D softmax output layer in 19.3.2""" def __init__(self, size=3): - super(OutputLayer, self).__init__(size) + super().__init__(size) def forward(self, inputs): assert len(self.nodes) == len(inputs) @@ -73,7 +73,7 @@ class InputLayer(Layer): """1D input layer. Layer size is the same as input vector size.""" def __init__(self, size=3): - super(InputLayer, self).__init__(size) + super().__init__(size) def forward(self, inputs): """Take each value of the inputs to each unit in the layer.""" @@ -92,10 +92,10 @@ class DenseLayer(Layer): """ def __init__(self, in_size=3, out_size=3, activation=None): - super(DenseLayer, self).__init__(out_size) + super().__init__(out_size) self.out_size = out_size self.inputs = None - self.activation = sigmoid() if not activation else activation + self.activation = Sigmoid() if not activation else activation # initialize weights for node in self.nodes: node.weights = random_weights(-0.5, 0.5, in_size) @@ -118,7 +118,7 @@ class ConvLayer1D(Layer): """ def __init__(self, size=3, kernel_size=3): - super(ConvLayer1D, self).__init__(size) + super().__init__(size) # init convolution kernel as gaussian kernel for node in self.nodes: node.weights = gaussian_kernel(kernel_size) @@ -142,7 +142,7 @@ class MaxPoolingLayer1D(Layer): """ def __init__(self, size=3, kernel_size=3): - super(MaxPoolingLayer1D, self).__init__(size) + super().__init__(size) self.kernel_size = kernel_size self.inputs = None @@ -326,7 +326,7 @@ class BatchNormalizationLayer(Layer): """Batch normalization layer.""" def __init__(self, size, epsilon=0.001): - super(BatchNormalizationLayer, self).__init__(size) + super().__init__(size) self.epsilon = epsilon # self.weights = [beta, gamma] self.weights = [0, 0] diff --git a/learning.py b/learning.py index bcaf0961e..99ef8abc2 100644 --- a/learning.py +++ b/learning.py @@ -265,9 +265,9 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): while True: errT, errV = cross_validation(learner, dataset, size, k, trials) # check for convergence provided err_val is not empty - if errT and not np.isclose(errT[-1], errT, rel_tol=1e-6): + if errT and not np.isclose(errT[-1], errT, rtol=1e-6): best_size = 0 - min_val = inf + min_val = np.inf i = 0 while i < size: if errs[i] < min_val: diff --git a/learning4e.py b/learning4e.py index 01d9ea290..f581b9ec1 100644 --- a/learning4e.py +++ b/learning4e.py @@ -7,7 +7,6 @@ from qpsolvers import solve_qp from probabilistic_learning import NaiveBayesLearner -from utils import sigmoid, sigmoid_derivative from utils4e import * @@ -265,9 +264,9 @@ def model_selection(learner, dataset, k=10, trials=1): while True: err = cross_validation(learner, dataset, size, k, trials) # check for convergence provided err_val is not empty - if err and not isclose(err[-1], err, rel_tol=1e-6): + if err and not np.isclose(err[-1], err, rtol=1e-6): best_size = 0 - min_val = inf + min_val = np.inf i = 0 while i < size: if errs[i] < min_val: @@ -569,8 +568,8 @@ def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): # pass over all examples for example in examples: x = [1] + example - y = sigmoid(dot_product(w, x)) - h.append(sigmoid_derivative(y)) + y = Sigmoid().f(dot_product(w, x)) + h.append(Sigmoid().derivative(y)) t = example[idx_t] err.append(t - y) @@ -581,7 +580,7 @@ def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): def predict(example): x = [1] + example - return sigmoid(dot_product(w, x)) + return Sigmoid().f(dot_product(w, x)) return predict diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index 92d73e96e..ed8979a0a 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -1,4 +1,3 @@ -import numpy as np import pytest from keras.datasets import imdb diff --git a/utils.py b/utils.py index 1d7f1e4f5..4bf29a9a3 100644 --- a/utils.py +++ b/utils.py @@ -273,11 +273,6 @@ def normalize(dist): return [(n / total) for n in dist] -def norm(x, ord=2): - """Return the n-norm of vector x.""" - return np.linalg.norm(x, ord) - - def random_weights(min_value, max_value, num_weights): return [random.uniform(min_value, max_value) for _ in range(num_weights)] diff --git a/utils4e.py b/utils4e.py index 6ed4a7f79..1c376066e 100644 --- a/utils4e.py +++ b/utils4e.py @@ -92,6 +92,10 @@ def remove_all(item, seq): """Return a copy of seq (or string) with all occurrences of item removed.""" if isinstance(seq, str): return seq.replace(item, '') + elif isinstance(seq, set): + rest = seq.copy() + rest.remove(item) + return rest else: return [x for x in seq if x != item] @@ -368,11 +372,6 @@ def normalize(dist): return [(n / total) for n in dist] -def norm(x, ord=2): - """Return the n-norm of vector x.""" - return np.linalg.norm(x, ord) - - def random_weights(min_value, max_value, num_weights): return [random.uniform(min_value, max_value) for _ in range(num_weights)] @@ -402,7 +401,10 @@ def gaussian_kernel_2D(size=3, sigma=0.5): class Activation: - def derivative(self, value): + def f(self, x): + pass + + def derivative(self, x): pass @@ -418,7 +420,7 @@ def softmax1D(x): return [exp / sum_exps for exp in exps] -class sigmoid(Activation): +class Sigmoid(Activation): def f(self, x): if x >= 100: @@ -431,7 +433,7 @@ def derivative(self, value): return value * (1 - value) -class relu(Activation): +class Relu(Activation): def f(self, x): return max(0, x) @@ -440,7 +442,7 @@ def derivative(self, value): return 1 if value > 0 else 0 -class elu(Activation): +class Elu(Activation): def f(self, x, alpha=0.01): return x if x > 0 else alpha * (np.exp(x) - 1) @@ -449,7 +451,7 @@ def derivative(self, value, alpha=0.01): return 1 if value > 0 else alpha * np.exp(value) -class tanh(Activation): +class Tanh(Activation): def f(self, x): return np.tanh(x) @@ -458,7 +460,7 @@ def derivative(self, value): return 1 - (value ** 2) -class leaky_relu(Activation): +class LeakyRelu(Activation): def f(self, x, alpha=0.01): return x if x > 0 else alpha * x From 4363ddb135b12f9b35d9ca80980510711c208995 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Fri, 3 Jan 2020 03:25:17 +0530 Subject: [PATCH 04/31] MAINT: Add documentation and descriptive variable names in search.py (#1142) * DOC: Add docstring to __hash__ method in Node * MAINT: Add documenation and descriptive variable names * FIXUP: Revert to previos names --- search.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/search.py b/search.py index 0104eb341..689671769 100644 --- a/search.py +++ b/search.py @@ -123,6 +123,10 @@ def __eq__(self, other): return isinstance(other, Node) and self.state == other.state def __hash__(self): + # We use the hash value of the state + # stored in the node instead of the node + # object itself to quickly search a node + # with the same state in a Hash Table return hash(self.state) @@ -353,14 +357,16 @@ def extend(U, open_dir, open_other, g_dir, g_other, closed_dir): def find_min(open_dir, g): """Finds minimum priority, g and f values in open_dir""" - m, m_f = np.inf, np.inf + # pr_min_f isn't forward pr_min instead it's the f-value + # of node with priority pr_min. + pr_min, pr_min_f = np.inf, np.inf for n in open_dir: f = g[n] + problem.h(n) pr = max(f, 2 * g[n]) - m = min(m, pr) - m_f = min(m_f, f) + pr_min = min(pr_min, pr) + pr_min_f = min(pr_min_f, f) - return m, m_f, min(g.values()) + return pr_min, pr_min_f, min(g.values()) def find_key(pr_min, open_dir, g): """Finds key in open_dir with value equal to pr_min From 22dd82cbc1f6281713e1cae6ca94fb3fc59adade Mon Sep 17 00:00:00 2001 From: Angelino Date: Sat, 4 Jan 2020 15:57:59 +0100 Subject: [PATCH 05/31] cd into aima folder before installing requirements (#1143) --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 563f0b50e..ce4af7372 100644 --- a/README.md +++ b/README.md @@ -35,12 +35,14 @@ To download the repository: Then you need to install the basic dependencies to run the project on your system: -`pip install -r requirements.txt` +``` +cd aima-python +pip install -r requirements.txt +``` You also need to fetch the datasets from the [`aima-data`](https://github.com/aimacode/aima-data) repository: ``` -cd aima-python git submodule init git submodule update ``` From ec2111a5962ac416dfca760fa2c087aa1fb9c20f Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Sat, 4 Jan 2020 17:50:42 +0100 Subject: [PATCH 06/31] removed unnecessary imports and substituted clip function with np.clip (#1146) --- deep_learning4e.py | 70 ++++++++++++++++++----------------- learning.py | 2 +- learning4e.py | 2 +- tests/test_deep_learning4e.py | 15 +++++--- tests/test_utils.py | 8 ---- utils.py | 45 +++++++++------------- utils4e.py | 50 +++++++++---------------- 7 files changed, 82 insertions(+), 110 deletions(-) diff --git a/deep_learning4e.py b/deep_learning4e.py index 64aa49e90..734a9307c 100644 --- a/deep_learning4e.py +++ b/deep_learning4e.py @@ -9,14 +9,14 @@ from keras.preprocessing import sequence from utils4e import (Sigmoid, dot_product, softmax1D, conv1D, gaussian_kernel, element_wise_product, vector_add, - random_weights, scalar_vector_product, matrix_multiplication, map_vector, mse_loss) + random_weights, scalar_vector_product, matrix_multiplication, map_vector, mean_squared_error_loss) class Node: """ A node in a computational graph contains the pointer to all its parents. - :param val: value of current node. - :param parents: a container of all parents of current node. + :param val: value of current node + :param parents: a container of all parents of current node """ def __init__(self, val=None, parents=None): @@ -55,40 +55,40 @@ def forward(self, inputs): raise NotImplementedError -class OutputLayer(Layer): - """1D softmax output layer in 19.3.2""" +class InputLayer(Layer): + """1D input layer. Layer size is the same as input vector size.""" def __init__(self, size=3): super().__init__(size) def forward(self, inputs): + """Take each value of the inputs to each unit in the layer.""" assert len(self.nodes) == len(inputs) - res = softmax1D(inputs) - for node, val in zip(self.nodes, res): - node.val = val - return res + for node, inp in zip(self.nodes, inputs): + node.val = inp + return inputs -class InputLayer(Layer): - """1D input layer. Layer size is the same as input vector size.""" +class OutputLayer(Layer): + """1D softmax output layer in 19.3.2.""" def __init__(self, size=3): super().__init__(size) def forward(self, inputs): - """Take each value of the inputs to each unit in the layer.""" assert len(self.nodes) == len(inputs) - for node, inp in zip(self.nodes, inputs): - node.val = inp - return inputs + res = softmax1D(inputs) + for node, val in zip(self.nodes, res): + node.val = val + return res class DenseLayer(Layer): """ 1D dense layer in a neural network. - :param in_size: input vector size, int. - :param out_size: output vector size, int. - :param activation: activation function, Activation object. + :param in_size: (int) input vector size + :param out_size: (int) output vector size + :param activation: (Activation object) activation function """ def __init__(self, in_size=3, out_size=3, activation=None): @@ -124,7 +124,7 @@ def __init__(self, size=3, kernel_size=3): node.weights = gaussian_kernel(kernel_size) def forward(self, features): - # each node in layer takes a channel in the features. + # each node in layer takes a channel in the features assert len(self.nodes) == len(features) res = [] # compute the convolution output of each channel, store it in node.val @@ -154,7 +154,8 @@ def forward(self, features): for i in range(len(self.nodes)): feature = features[i] # get the max value in a kernel_size * kernel_size area - out = [max(feature[i:i + self.kernel_size]) for i in range(len(feature) - self.kernel_size + 1)] + out = [max(feature[i:i + self.kernel_size]) + for i in range(len(feature) - self.kernel_size + 1)] res.append(out) self.nodes[i].val = out return res @@ -270,13 +271,13 @@ def adam(dataset, net, loss, epochs=1000, rho=(0.9, 0.999), delta=1 / 10 ** 8, def BackPropagation(inputs, targets, theta, net, loss): """ - The back-propagation algorithm for multilayer networks in only one epoch, to calculate gradients of theta - :param inputs: a batch of inputs in an array. Each input is an iterable object. - :param targets: a batch of targets in an array. Each target is an iterable object. - :param theta: parameters to be updated. - :param net: a list of predefined layer objects representing their linear sequence. - :param loss: a predefined loss function taking array of inputs and targets. - :return: gradients of theta, loss of the input batch. + The back-propagation algorithm for multilayer networks in only one epoch, to calculate gradients of theta. + :param inputs: a batch of inputs in an array. Each input is an iterable object + :param targets: a batch of targets in an array. Each target is an iterable object + :param theta: parameters to be updated + :param net: a list of predefined layer objects representing their linear sequence + :param loss: a predefined loss function taking array of inputs and targets + :return: gradients of theta, loss of the input batch """ assert len(inputs) == len(targets) @@ -325,9 +326,9 @@ def BackPropagation(inputs, targets, theta, net, loss): class BatchNormalizationLayer(Layer): """Batch normalization layer.""" - def __init__(self, size, epsilon=0.001): + def __init__(self, size, eps=0.001): super().__init__(size) - self.epsilon = epsilon + self.eps = eps # self.weights = [beta, gamma] self.weights = [0, 0] self.inputs = None @@ -341,7 +342,7 @@ def forward(self, inputs): res = [] # get normalized value of each input for i in range(len(self.nodes)): - val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.epsilon + stderr ** 2) + self.weights[1]] + val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.eps + stderr ** 2) + self.weights[1]] res.append(val) self.nodes[i].val = val return res @@ -375,7 +376,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=None, learning_rate=0.01, epoch raw_net.append(DenseLayer(hidden_input_size, output_size)) # update parameters of the network - learned_net = optimizer(dataset, raw_net, mse_loss, epochs, l_rate=learning_rate, + learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=learning_rate, batch_size=batch_size, verbose=verbose) def predict(example): @@ -394,7 +395,7 @@ def predict(example): return predict -def PerceptronLearner(dataset, learning_rate=0.01, epochs=100, verbose=None): +def PerceptronLearner(dataset, learning_rate=0.01, epochs=100, optimizer=gradient_descent, batch_size=1, verbose=None): """ Simple perceptron neural network. """ @@ -405,7 +406,8 @@ def PerceptronLearner(dataset, learning_rate=0.01, epochs=100, verbose=None): raw_net = [InputLayer(input_size), DenseLayer(input_size, output_size)] # update the network - learned_net = gradient_descent(dataset, raw_net, mse_loss, epochs, l_rate=learning_rate, verbose=verbose) + learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=learning_rate, + batch_size=batch_size, verbose=verbose) def predict(example): layer_out = learned_net[1].forward(example) @@ -419,7 +421,7 @@ def SimpleRNNLearner(train_data, val_data, epochs=2): RNN example for text sentimental analysis. :param train_data: a tuple of (training data, targets) Training data: ndarray taking training examples, while each example is coded by embedding - Targets: ndarray taking targets of each example. Each target is mapped to an integer. + Targets: ndarray taking targets of each example. Each target is mapped to an integer :param val_data: a tuple of (validation data, targets) :param epochs: number of epochs :return: a keras model diff --git a/learning.py b/learning.py index 99ef8abc2..764392c7d 100644 --- a/learning.py +++ b/learning.py @@ -968,7 +968,7 @@ def ada_boost(dataset, L, K): h.append(h_k) error = sum(weight for example, weight in zip(examples, w) if example[target] != h_k(example)) # avoid divide-by-0 from either 0% or 100% error rates - error = clip(error, eps, 1 - eps) + error = np.clip(error, eps, 1 - eps) for j, example in enumerate(examples): if example[target] == h_k(example): w[j] *= error / (1 - error) diff --git a/learning4e.py b/learning4e.py index f581b9ec1..7dba31cfa 100644 --- a/learning4e.py +++ b/learning4e.py @@ -742,7 +742,7 @@ def ada_boost(dataset, L, K): h.append(h_k) error = sum(weight for example, weight in zip(examples, w) if example[target] != h_k(example)) # avoid divide-by-0 from either 0% or 100% error rates - error = clip(error, eps, 1 - eps) + error = np.clip(error, eps, 1 - eps) for j, example in enumerate(examples): if example[target] == h_k(example): w[j] *= error / (1 - error) diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index ed8979a0a..305c2e65c 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -11,8 +11,8 @@ def test_neural_net(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - nnl_adam = NeuralNetLearner(iris, [4], learning_rate=0.001, epochs=200, optimizer=adam) nnl_gd = NeuralNetLearner(iris, [4], learning_rate=0.15, epochs=100, optimizer=gradient_descent) + nnl_adam = NeuralNetLearner(iris, [4], learning_rate=0.001, epochs=200, optimizer=adam) tests = [([5.0, 3.1, 0.9, 0.1], 0), ([5.1, 3.5, 1.0, 0.0], 0), ([4.9, 3.3, 1.1, 0.1], 0), @@ -22,25 +22,28 @@ def test_neural_net(): ([7.5, 4.1, 6.2, 2.3], 2), ([7.3, 4.0, 6.1, 2.4], 2), ([7.0, 3.3, 6.1, 2.5], 2)] - assert grade_learner(nnl_adam, tests) >= 1 / 3 assert grade_learner(nnl_gd, tests) >= 1 / 3 - assert err_ratio(nnl_adam, iris) < 0.21 assert err_ratio(nnl_gd, iris) < 0.21 + assert grade_learner(nnl_adam, tests) >= 1 / 3 + assert err_ratio(nnl_adam, iris) < 0.21 def test_perceptron(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - pl = PerceptronLearner(iris, learning_rate=0.01, epochs=100) + pl_gd = PerceptronLearner(iris, learning_rate=0.01, epochs=100, optimizer=gradient_descent) + pl_adam = PerceptronLearner(iris, learning_rate=0.01, epochs=100, optimizer=adam) tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1), ([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)] - assert grade_learner(pl, tests) > 1 / 2 - assert err_ratio(pl, iris) < 0.4 + assert grade_learner(pl_gd, tests) > 1 / 2 + assert err_ratio(pl_gd, iris) < 0.4 + assert grade_learner(pl_adam, tests) > 1 / 2 + assert err_ratio(pl_adam, iris) < 0.4 def test_rnn(): diff --git a/tests/test_utils.py b/tests/test_utils.py index 31b5848f0..6c2a50808 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -173,10 +173,6 @@ def test_normalize(): assert normalize([1, 2, 1]) == [0.25, 0.5, 0.25] -def test_clip(): - assert [clip(x, 0, 1) for x in [-1, 0.5, 10]] == [0, 0.5, 1] - - def test_gaussian(): assert gaussian(1, 0.5, 0.7) == 0.6664492057835993 assert gaussian(5, 2, 4.5) == 0.19333405840142462 @@ -201,10 +197,6 @@ def test_distance_squared(): assert distance_squared((1, 2), (5, 5)) == 25.0 -def test_vector_clip(): - assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) - - def test_turn_heading(): assert turn_heading((0, 1), 1) == (-1, 0) assert turn_heading((0, 1), -1) == (1, 0) diff --git a/utils.py b/utils.py index 4bf29a9a3..fd683d34a 100644 --- a/utils.py +++ b/utils.py @@ -233,8 +233,20 @@ def euclidean_distance(x, y): return np.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y))) +def manhattan_distance(x, y): + return sum(abs(_x - _y) for _x, _y in zip(x, y)) + + +def hamming_distance(x, y): + return sum(_x != _y for _x, _y in zip(x, y)) + + def cross_entropy_loss(x, y): - return (-1.0 / len(x)) * sum(x * np.log(y) + (1 - x) * np.log(1 - y) for x, y in zip(x, y)) + return (-1.0 / len(x)) * sum(_x * np.log(_y) + (1 - _x) * np.log(1 - _y) for _x, _y in zip(x, y)) + + +def mean_squared_error_loss(x, y): + return (1.0 / len(x)) * sum((_x - _y) ** 2 for _x, _y in zip(x, y)) def rms_error(x, y): @@ -242,25 +254,17 @@ def rms_error(x, y): def ms_error(x, y): - return mean((x - y) ** 2 for x, y in zip(x, y)) + return mean((_x - _y) ** 2 for _x, _y in zip(x, y)) def mean_error(x, y): - return mean(abs(x - y) for x, y in zip(x, y)) - - -def manhattan_distance(x, y): - return sum(abs(_x - _y) for _x, _y in zip(x, y)) + return mean(abs(_x - _y) for _x, _y in zip(x, y)) def mean_boolean_error(x, y): return mean(_x != _y for _x, _y in zip(x, y)) -def hamming_distance(x, y): - return sum(_x != _y for _x, _y in zip(x, y)) - - def normalize(dist): """Multiply each number by a constant such that the sum is 1.0""" if isinstance(dist, dict): @@ -277,20 +281,15 @@ def random_weights(min_value, max_value, num_weights): return [random.uniform(min_value, max_value) for _ in range(num_weights)] -def clip(x, lowest, highest): - """Return x clipped to the range [lowest..highest].""" - return max(lowest, min(x, highest)) +def sigmoid(x): + """Return activation value of x with sigmoid function.""" + return 1 / (1 + np.exp(-x)) def sigmoid_derivative(value): return value * (1 - value) -def sigmoid(x): - """Return activation value of x with sigmoid function.""" - return 1 / (1 + np.exp(-x)) - - def elu(x, alpha=0.01): return x if x > 0 else alpha * (np.exp(x) - 1) @@ -389,13 +388,6 @@ def distance_squared(a, b): return (xA - xB) ** 2 + (yA - yB) ** 2 -def vector_clip(vector, lowest, highest): - """Return vector, except if any element is less than the corresponding - value of lowest or more than the corresponding value of highest, clip to - those values.""" - return type(vector)(map(clip, vector, lowest, highest)) - - # ______________________________________________________________________________ # Misc Functions @@ -484,7 +476,6 @@ def failure_test(algorithm, tests): to check for correctness. On the other hand, a lot of algorithms output something particular on fail (for example, False, or None). tests is a list with each element in the form: (values, failure_output).""" - from statistics import mean return mean(int(algorithm(x) != y) for x, y in tests) diff --git a/utils4e.py b/utils4e.py index 1c376066e..b0fbf8df8 100644 --- a/utils4e.py +++ b/utils4e.py @@ -319,6 +319,14 @@ def euclidean_distance(x, y): return np.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y))) +def manhattan_distance(x, y): + return sum(abs(_x - _y) for _x, _y in zip(x, y)) + + +def hamming_distance(x, y): + return sum(_x != _y for _x, _y in zip(x, y)) + + def rms_error(x, y): return np.sqrt(ms_error(x, y)) @@ -331,28 +339,20 @@ def mean_error(x, y): return mean(abs(x - y) for x, y in zip(x, y)) -def manhattan_distance(x, y): - return sum(abs(_x - _y) for _x, _y in zip(x, y)) - - def mean_boolean_error(x, y): return mean(_x != _y for _x, _y in zip(x, y)) -def hamming_distance(x, y): - return sum(_x != _y for _x, _y in zip(x, y)) - - -# 19.2 Common Loss Functions +# loss functions def cross_entropy_loss(x, y): - """Example of cross entropy loss. x and y are 1D iterable objects.""" - return (-1.0 / len(x)) * sum(x * np.log(y) + (1 - x) * np.log(1 - y) for x, y in zip(x, y)) + """Cross entropy loss function. x and y are 1D iterable objects.""" + return (-1.0 / len(x)) * sum(x * np.log(_y) + (1 - _x) * np.log(1 - _y) for _x, _y in zip(x, y)) -def mse_loss(x, y): - """Example of min square loss. x and y are 1D iterable objects.""" +def mean_squared_error_loss(x, y): + """Min square loss function. x and y are 1D iterable objects.""" return (1.0 / len(x)) * sum((_x - _y) ** 2 for _x, _y in zip(x, y)) @@ -395,29 +395,21 @@ def gaussian_kernel_2D(size=3, sigma=0.5): return g / g.sum() -# ______________________________________________________________________________ -# loss and activation functions +# activation functions class Activation: def f(self, x): - pass + return NotImplementedError def derivative(self, x): - pass - - -def clip(x, lowest, highest): - """Return x clipped to the range [lowest..highest].""" - return max(lowest, min(x, highest)) + return NotImplementedError def softmax1D(x): """Return the softmax vector of input vector x.""" - exps = [np.exp(_x) for _x in x] - sum_exps = sum(exps) - return [exp / sum_exps for exp in exps] + return np.exp(x) / sum(np.exp(x)) class Sigmoid(Activation): @@ -545,13 +537,6 @@ def distance_squared(a, b): return (xA - xB) ** 2 + (yA - yB) ** 2 -def vector_clip(vector, lowest, highest): - """Return vector, except if any element is less than the corresponding - value of lowest or more than the corresponding value of highest, clip to - those values.""" - return type(vector)(map(clip, vector, lowest, highest)) - - # ______________________________________________________________________________ # Misc Functions @@ -642,7 +627,6 @@ def failure_test(algorithm, tests): to check for correctness. On the other hand, a lot of algorithms output something particular on fail (for example, False, or None). tests is a list with each element in the form: (values, failure_output).""" - from statistics import mean return mean(int(algorithm(x) != y) for x, y in tests) From 69b6a46b816248a273f259ab8d374f14bdaa62f7 Mon Sep 17 00:00:00 2001 From: Tirth Patel Date: Wed, 8 Jan 2020 15:27:06 +0530 Subject: [PATCH 07/31] [WIP] ENH: add support for all types of problems in Bidirectional Search (#1147) * ENH: all problems can now use BS * TST: add test for all types of problems for BS --- search.py | 20 +++++++++++--------- tests/test_search.py | 2 ++ 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/search.py b/search.py index 689671769..89f872079 100644 --- a/search.py +++ b/search.py @@ -327,9 +327,11 @@ def iterative_deepening_search(problem): # Pseudocode from https://webdocs.cs.ualberta.ca/%7Eholte/Publications/MM-AAAI2016.pdf def bidirectional_search(problem): - e = problem.find_min_edge() - gF, gB = {problem.initial: 0}, {problem.goal: 0} - openF, openB = [problem.initial], [problem.goal] + e = 0 + if isinstance(problem, GraphProblem): + e = problem.find_min_edge() + gF, gB = {Node(problem.initial): 0}, {Node(problem.goal): 0} + openF, openB = [Node(problem.initial)], [Node(problem.goal)] closedF, closedB = [], [] U = np.inf @@ -340,14 +342,14 @@ def extend(U, open_dir, open_other, g_dir, g_other, closed_dir): open_dir.remove(n) closed_dir.append(n) - for c in problem.actions(n): + for c in n.expand(problem): if c in open_dir or c in closed_dir: - if g_dir[c] <= problem.path_cost(g_dir[n], n, None, c): + if g_dir[c] <= problem.path_cost(g_dir[n], n.state, None, c.state): continue open_dir.remove(c) - g_dir[c] = problem.path_cost(g_dir[n], n, None, c) + g_dir[c] = problem.path_cost(g_dir[n], n.state, None, c.state) open_dir.append(c) if c in open_other: @@ -372,15 +374,15 @@ def find_key(pr_min, open_dir, g): """Finds key in open_dir with value equal to pr_min and minimum g value.""" m = np.inf - state = -1 + node = Node(-1) for n in open_dir: pr = max(g[n] + problem.h(n), 2 * g[n]) if pr == pr_min: if g[n] < m: m = g[n] - state = n + node = n - return state + return node while openF and openB: pr_min_f, f_min_f, g_min_f = find_min(openF, gF) diff --git a/tests/test_search.py b/tests/test_search.py index d37f8fa38..075a57312 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -71,6 +71,8 @@ def test_depth_limited_search(): def test_bidirectional_search(): assert bidirectional_search(romania_problem) == 418 + assert bidirectional_search(eight_puzzle) == 12 + assert bidirectional_search(EightPuzzle((1, 2, 3, 4, 5, 6, 0, 7, 8))) == 2 def test_astar_search(): From 2ebdc4144cbea0bd38837ff69d32e8f1a0e5b64b Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Jan 2020 20:44:15 +0100 Subject: [PATCH 08/31] type in ga section --- search.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.ipynb b/search.ipynb index aeb035902..0d9fa5e72 100644 --- a/search.ipynb +++ b/search.ipynb @@ -3676,7 +3676,7 @@ "\n", " * Random chance to mutate individuals.\n", "\n", - "5) Repeat from step 2) until an individual is fit enough or the maximum number of iterations was reached." + "5) Repeat from step 2) until an individual is fit enough or the maximum number of iterations is reached." ] }, { From 1b24e0d7a492968c111bd0c87aa185b77a7d9a64 Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Sat, 25 Jan 2020 09:49:41 +0100 Subject: [PATCH 09/31] fixed typos in gui folder (#1150) --- deep_learning4e.py | 60 +- gui/eight_puzzle.py | 221 +++--- gui/genetic_algorithm_example.py | 179 ++--- gui/grid_mdp.py | 1115 +++++++++++++++--------------- gui/romania_problem.py | 8 +- gui/tic-tac-toe.py | 16 +- gui/tsp.py | 100 ++- gui/vacuum_agent.py | 18 +- gui/xy_vacuum_environment.py | 28 +- learning4e.py | 4 +- pytest.ini | 3 +- tests/test_deep_learning4e.py | 8 +- utils4e.py | 12 +- 13 files changed, 890 insertions(+), 882 deletions(-) diff --git a/deep_learning4e.py b/deep_learning4e.py index 734a9307c..0a0387afc 100644 --- a/deep_learning4e.py +++ b/deep_learning4e.py @@ -13,23 +13,6 @@ class Node: - """ - A node in a computational graph contains the pointer to all its parents. - :param val: value of current node - :param parents: a container of all parents of current node - """ - - def __init__(self, val=None, parents=None): - if parents is None: - parents = [] - self.val = val - self.parents = parents - - def __repr__(self): - return "".format(self.val) - - -class NNUnit(Node): """ A single unit of a layer in a neural network :param weights: weights between parent nodes and current node @@ -37,7 +20,7 @@ class NNUnit(Node): """ def __init__(self, weights=None, value=None): - super().__init__(value) + self.value = value self.weights = weights or [] @@ -47,8 +30,8 @@ class Layer: :param size: number of units in the current layer """ - def __init__(self, size=3): - self.nodes = [NNUnit() for _ in range(size)] + def __init__(self, size): + self.nodes = [Node() for _ in range(size)] def forward(self, inputs): """Define the operation to get the output of this layer""" @@ -65,7 +48,7 @@ def forward(self, inputs): """Take each value of the inputs to each unit in the layer.""" assert len(self.nodes) == len(inputs) for node, inp in zip(self.nodes, inputs): - node.val = inp + node.value = inp return inputs @@ -79,7 +62,7 @@ def forward(self, inputs): assert len(self.nodes) == len(inputs) res = softmax1D(inputs) for node, val in zip(self.nodes, res): - node.val = val + node.value = val return res @@ -91,11 +74,11 @@ class DenseLayer(Layer): :param activation: (Activation object) activation function """ - def __init__(self, in_size=3, out_size=3, activation=None): + def __init__(self, in_size=3, out_size=3, activation=Sigmoid): super().__init__(out_size) self.out_size = out_size self.inputs = None - self.activation = Sigmoid() if not activation else activation + self.activation = activation() # initialize weights for node in self.nodes: node.weights = random_weights(-0.5, 0.5, in_size) @@ -105,8 +88,8 @@ def forward(self, inputs): res = [] # get the output value of each unit for unit in self.nodes: - val = self.activation.f(dot_product(unit.weights, inputs)) - unit.val = val + val = self.activation.function(dot_product(unit.weights, inputs)) + unit.value = val res.append(val) return res @@ -131,7 +114,7 @@ def forward(self, features): for node, feature in zip(self.nodes, features): out = conv1D(feature, node.weights) res.append(out) - node.val = out + node.value = out return res @@ -157,7 +140,7 @@ def forward(self, features): out = [max(feature[i:i + self.kernel_size]) for i in range(len(feature) - self.kernel_size + 1)] res.append(out) - self.nodes[i].val = out + self.nodes[i].value = out return res @@ -181,7 +164,7 @@ def init_examples(examples, idx_i, idx_t, o_units): return inputs, targets -def gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, batch_size=1, verbose=None): +def stochastic_gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, batch_size=1, verbose=None): """ Gradient descent algorithm to update the learnable parameters of a network. :return: the updated network @@ -200,6 +183,7 @@ def gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, batch_size=1, # update weights with gradient descent weights = vector_add(weights, scalar_vector_product(-l_rate, gs)) total_loss += batch_loss + # update the weights of network each batch for i in range(len(net)): if weights[i]: @@ -310,7 +294,7 @@ def BackPropagation(inputs, targets, theta, net, loss): # backward pass for i in range(h_layers, 0, -1): layer = net[i] - derivative = [layer.activation.derivative(node.val) for node in layer.nodes] + derivative = [layer.activation.derivative(node.value) for node in layer.nodes] delta[i] = element_wise_product(previous, derivative) # pass to layer i-1 in the next iteration previous = matrix_multiplication([delta[i]], theta[i])[0] @@ -344,7 +328,7 @@ def forward(self, inputs): for i in range(len(self.nodes)): val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.eps + stderr ** 2) + self.weights[1]] res.append(val) - self.nodes[i].val = val + self.nodes[i].value = val return res @@ -354,15 +338,12 @@ def get_batch(examples, batch_size=1): yield examples[i: i + batch_size] -def NeuralNetLearner(dataset, hidden_layer_sizes=None, learning_rate=0.01, epochs=100, - optimizer=gradient_descent, batch_size=1, verbose=None): +def NeuralNetLearner(dataset, hidden_layer_sizes, l_rate=0.01, epochs=1000, batch_size=1, + optimizer=stochastic_gradient_descent, verbose=None): """ Simple dense multilayer neural network. :param hidden_layer_sizes: size of hidden layers in the form of a list """ - - if hidden_layer_sizes is None: - hidden_layer_sizes = [4] input_size = len(dataset.inputs) output_size = len(dataset.values[dataset.target]) @@ -376,7 +357,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=None, learning_rate=0.01, epoch raw_net.append(DenseLayer(hidden_input_size, output_size)) # update parameters of the network - learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=learning_rate, + learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=l_rate, batch_size=batch_size, verbose=verbose) def predict(example): @@ -395,7 +376,8 @@ def predict(example): return predict -def PerceptronLearner(dataset, learning_rate=0.01, epochs=100, optimizer=gradient_descent, batch_size=1, verbose=None): +def PerceptronLearner(dataset, l_rate=0.01, epochs=1000, batch_size=1, + optimizer=stochastic_gradient_descent, verbose=None): """ Simple perceptron neural network. """ @@ -406,7 +388,7 @@ def PerceptronLearner(dataset, learning_rate=0.01, epochs=100, optimizer=gradien raw_net = [InputLayer(input_size), DenseLayer(input_size, output_size)] # update the network - learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=learning_rate, + learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=l_rate, batch_size=batch_size, verbose=verbose) def predict(example): diff --git a/gui/eight_puzzle.py b/gui/eight_puzzle.py index 82acced03..5733228d7 100644 --- a/gui/eight_puzzle.py +++ b/gui/eight_puzzle.py @@ -1,138 +1,151 @@ -# author ad71 -from tkinter import * +import os.path +import random +import time from functools import partial +from tkinter import * -import time -import random -import numpy as np +from search import astar_search, EightPuzzle -import sys -import os.path sys.path.append(os.path.join(os.path.dirname(__file__), '..')) -from search import astar_search, EightPuzzle -import utils - root = Tk() state = [1, 2, 3, 4, 5, 6, 7, 8, 0] puzzle = EightPuzzle(tuple(state)) solution = None -b = [None]*9 +b = [None] * 9 + # TODO: refactor into OOP, remove global variables def scramble(): - """ Scrambles the puzzle starting from the goal state """ + """Scrambles the puzzle starting from the goal state""" + + global state + global puzzle + possible_actions = ['UP', 'DOWN', 'LEFT', 'RIGHT'] + scramble = [] + for _ in range(60): + scramble.append(random.choice(possible_actions)) - global state - global puzzle - possible_actions = ['UP', 'DOWN', 'LEFT', 'RIGHT'] - scramble = [] - for _ in range(60): - scramble.append(random.choice(possible_actions)) + for move in scramble: + if move in puzzle.actions(state): + state = list(puzzle.result(state, move)) + puzzle = EightPuzzle(tuple(state)) + create_buttons() - for move in scramble: - if move in puzzle.actions(state): - state = list(puzzle.result(state, move)) - puzzle = EightPuzzle(tuple(state)) - create_buttons() def solve(): - """ Solves the puzzle using astar_search """ + """Solves the puzzle using astar_search""" + + return astar_search(puzzle).solution() - return astar_search(puzzle).solution() def solve_steps(): - """ Solves the puzzle step by step """ - - global puzzle - global solution - global state - solution = solve() - print(solution) - - for move in solution: - state = puzzle.result(state, move) - create_buttons() - root.update() - root.after(1, time.sleep(0.75)) + """Solves the puzzle step by step""" + + global puzzle + global solution + global state + solution = solve() + print(solution) + + for move in solution: + state = puzzle.result(state, move) + create_buttons() + root.update() + root.after(1, time.sleep(0.75)) + def exchange(index): - """ Interchanges the position of the selected tile with the zero tile under certain conditions """ - - global state - global solution - global puzzle - zero_ix = list(state).index(0) - actions = puzzle.actions(state) - current_action = '' - i_diff = index//3 - zero_ix//3 - j_diff = index%3 - zero_ix%3 - if i_diff == 1: - current_action += 'DOWN' - elif i_diff == -1: - current_action += 'UP' - - if j_diff == 1: - current_action += 'RIGHT' - elif j_diff == -1: - current_action += 'LEFT' - - if abs(i_diff) + abs(j_diff) != 1: - current_action = '' - - if current_action in actions: - b[zero_ix].grid_forget() - b[zero_ix] = Button(root, text=f'{state[index]}', width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, zero_ix)) - b[zero_ix].grid(row=zero_ix//3, column=zero_ix%3, ipady=40) - b[index].grid_forget() - b[index] = Button(root, text=None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, index)) - b[index].grid(row=index//3, column=index%3, ipady=40) - state[zero_ix], state[index] = state[index], state[zero_ix] - puzzle = EightPuzzle(tuple(state)) + """Interchanges the position of the selected tile with the zero tile under certain conditions""" + + global state + global solution + global puzzle + zero_ix = list(state).index(0) + actions = puzzle.actions(state) + current_action = '' + i_diff = index // 3 - zero_ix // 3 + j_diff = index % 3 - zero_ix % 3 + if i_diff == 1: + current_action += 'DOWN' + elif i_diff == -1: + current_action += 'UP' + + if j_diff == 1: + current_action += 'RIGHT' + elif j_diff == -1: + current_action += 'LEFT' + + if abs(i_diff) + abs(j_diff) != 1: + current_action = '' + + if current_action in actions: + b[zero_ix].grid_forget() + b[zero_ix] = Button(root, text=f'{state[index]}', width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, zero_ix)) + b[zero_ix].grid(row=zero_ix // 3, column=zero_ix % 3, ipady=40) + b[index].grid_forget() + b[index] = Button(root, text=None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, index)) + b[index].grid(row=index // 3, column=index % 3, ipady=40) + state[zero_ix], state[index] = state[index], state[zero_ix] + puzzle = EightPuzzle(tuple(state)) + def create_buttons(): - """ Creates dynamic buttons """ - - # TODO: Find a way to use grid_forget() with a for loop for initialization - b[0] = Button(root, text=f'{state[0]}' if state[0] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 0)) - b[0].grid(row=0, column=0, ipady=40) - b[1] = Button(root, text=f'{state[1]}' if state[1] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 1)) - b[1].grid(row=0, column=1, ipady=40) - b[2] = Button(root, text=f'{state[2]}' if state[2] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 2)) - b[2].grid(row=0, column=2, ipady=40) - b[3] = Button(root, text=f'{state[3]}' if state[3] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 3)) - b[3].grid(row=1, column=0, ipady=40) - b[4] = Button(root, text=f'{state[4]}' if state[4] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 4)) - b[4].grid(row=1, column=1, ipady=40) - b[5] = Button(root, text=f'{state[5]}' if state[5] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 5)) - b[5].grid(row=1, column=2, ipady=40) - b[6] = Button(root, text=f'{state[6]}' if state[6] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 6)) - b[6].grid(row=2, column=0, ipady=40) - b[7] = Button(root, text=f'{state[7]}' if state[7] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 7)) - b[7].grid(row=2, column=1, ipady=40) - b[8] = Button(root, text=f'{state[8]}' if state[8] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), command=partial(exchange, 8)) - b[8].grid(row=2, column=2, ipady=40) + """Creates dynamic buttons""" + + # TODO: Find a way to use grid_forget() with a for loop for initialization + b[0] = Button(root, text=f'{state[0]}' if state[0] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 0)) + b[0].grid(row=0, column=0, ipady=40) + b[1] = Button(root, text=f'{state[1]}' if state[1] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 1)) + b[1].grid(row=0, column=1, ipady=40) + b[2] = Button(root, text=f'{state[2]}' if state[2] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 2)) + b[2].grid(row=0, column=2, ipady=40) + b[3] = Button(root, text=f'{state[3]}' if state[3] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 3)) + b[3].grid(row=1, column=0, ipady=40) + b[4] = Button(root, text=f'{state[4]}' if state[4] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 4)) + b[4].grid(row=1, column=1, ipady=40) + b[5] = Button(root, text=f'{state[5]}' if state[5] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 5)) + b[5].grid(row=1, column=2, ipady=40) + b[6] = Button(root, text=f'{state[6]}' if state[6] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 6)) + b[6].grid(row=2, column=0, ipady=40) + b[7] = Button(root, text=f'{state[7]}' if state[7] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 7)) + b[7].grid(row=2, column=1, ipady=40) + b[8] = Button(root, text=f'{state[8]}' if state[8] != 0 else None, width=6, font=('Helvetica', 40, 'bold'), + command=partial(exchange, 8)) + b[8].grid(row=2, column=2, ipady=40) + def create_static_buttons(): - """ Creates scramble and solve buttons """ + """Creates scramble and solve buttons""" + + scramble_btn = Button(root, text='Scramble', font=('Helvetica', 30, 'bold'), width=8, command=partial(init)) + scramble_btn.grid(row=3, column=0, ipady=10) + solve_btn = Button(root, text='Solve', font=('Helvetica', 30, 'bold'), width=8, command=partial(solve_steps)) + solve_btn.grid(row=3, column=2, ipady=10) - scramble_btn = Button(root, text='Scramble', font=('Helvetica', 30, 'bold'), width=8, command=partial(init)) - scramble_btn.grid(row=3, column=0, ipady=10) - solve_btn = Button(root, text='Solve', font=('Helvetica', 30, 'bold'), width=8, command=partial(solve_steps)) - solve_btn.grid(row=3, column=2, ipady=10) def init(): - """ Calls necessary functions """ - - global state - global solution - state = [1, 2, 3, 4, 5, 6, 7, 8, 0] - scramble() - create_buttons() - create_static_buttons() + """Calls necessary functions""" + + global state + global solution + state = [1, 2, 3, 4, 5, 6, 7, 8, 0] + scramble() + create_buttons() + create_static_buttons() + init() root.mainloop() diff --git a/gui/genetic_algorithm_example.py b/gui/genetic_algorithm_example.py index 418da02e9..c987151c8 100644 --- a/gui/genetic_algorithm_example.py +++ b/gui/genetic_algorithm_example.py @@ -1,4 +1,3 @@ -# author: ad71 # A simple program that implements the solution to the phrase generation problem using # genetic algorithms as given in the search.ipynb notebook. # @@ -9,17 +8,13 @@ # Displays a progress bar that indicates the amount of completion of the algorithm # Displays the first few individuals of the current generation -import sys -import time -import random import os.path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) - from tkinter import * from tkinter import ttk import search -from utils import argmax + +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) LARGE_FONT = ('Verdana', 12) EXTRA_LARGE_FONT = ('Consolas', 36, 'bold') @@ -34,20 +29,20 @@ # genetic algorithm variables # feel free to play around with these -target = 'Genetic Algorithm' # the phrase to be generated -max_population = 100 # number of samples in each population -mutation_rate = 0.1 # probability of mutation -f_thres = len(target) # fitness threshold -ngen = 1200 # max number of generations to run the genetic algorithm +target = 'Genetic Algorithm' # the phrase to be generated +max_population = 100 # number of samples in each population +mutation_rate = 0.1 # probability of mutation +f_thres = len(target) # fitness threshold +ngen = 1200 # max number of generations to run the genetic algorithm -generation = 0 # counter to keep track of generation number +generation = 0 # counter to keep track of generation number -u_case = [chr(x) for x in range(65, 91)] # list containing all uppercase characters -l_case = [chr(x) for x in range(97, 123)] # list containing all lowercase characters -punctuations1 = [chr(x) for x in range(33, 48)] # lists containing punctuation symbols +u_case = [chr(x) for x in range(65, 91)] # list containing all uppercase characters +l_case = [chr(x) for x in range(97, 123)] # list containing all lowercase characters +punctuations1 = [chr(x) for x in range(33, 48)] # lists containing punctuation symbols punctuations2 = [chr(x) for x in range(58, 65)] punctuations3 = [chr(x) for x in range(91, 97)] -numerals = [chr(x) for x in range(48, 58)] # list containing numbers +numerals = [chr(x) for x in range(48, 58)] # list containing numbers # extend the gene pool with the required lists and append the space character gene_pool = [] @@ -55,44 +50,51 @@ gene_pool.extend(l_case) gene_pool.append(' ') + # callbacks to update global variables from the slider values def update_max_population(slider_value): - global max_population - max_population = slider_value + global max_population + max_population = slider_value + def update_mutation_rate(slider_value): - global mutation_rate - mutation_rate = slider_value + global mutation_rate + mutation_rate = slider_value + def update_f_thres(slider_value): - global f_thres - f_thres = slider_value + global f_thres + f_thres = slider_value + def update_ngen(slider_value): - global ngen - ngen = slider_value + global ngen + ngen = slider_value + # fitness function def fitness_fn(_list): - fitness = 0 - # create string from list of characters - phrase = ''.join(_list) - # add 1 to fitness value for every matching character - for i in range(len(phrase)): - if target[i] == phrase[i]: - fitness += 1 - return fitness + fitness = 0 + # create string from list of characters + phrase = ''.join(_list) + # add 1 to fitness value for every matching character + for i in range(len(phrase)): + if target[i] == phrase[i]: + fitness += 1 + return fitness + # function to bring a new frame on top def raise_frame(frame, init=False, update_target=False, target_entry=None, f_thres_slider=None): - frame.tkraise() - global target - if update_target and target_entry is not None: - target = target_entry.get() - f_thres_slider.config(to=len(target)) - if init: - population = search.init_population(max_population, gene_pool, len(target)) - genetic_algorithm_stepwise(population) + frame.tkraise() + global target + if update_target and target_entry is not None: + target = target_entry.get() + f_thres_slider.config(to=len(target)) + if init: + population = search.init_population(max_population, gene_pool, len(target)) + genetic_algorithm_stepwise(population) + # defining root and child frames root = Tk() @@ -101,7 +103,7 @@ def raise_frame(frame, init=False, update_target=False, target_entry=None, f_thr # pack frames on top of one another for frame in (f1, f2): - frame.grid(row=0, column=0, sticky='news') + frame.grid(row=0, column=0, sticky='news') # Home Screen (f1) widgets target_entry = Entry(f1, font=('Consolas 46 bold'), exportselection=0, foreground=p_blue, justify=CENTER) @@ -109,64 +111,79 @@ def raise_frame(frame, init=False, update_target=False, target_entry=None, f_thr target_entry.pack(expand=YES, side=TOP, fill=X, padx=50) target_entry.focus_force() -max_population_slider = Scale(f1, from_=3, to=1000, orient=HORIZONTAL, label='Max population', command=lambda value: update_max_population(int(value))) +max_population_slider = Scale(f1, from_=3, to=1000, orient=HORIZONTAL, label='Max population', + command=lambda value: update_max_population(int(value))) max_population_slider.set(max_population) max_population_slider.pack(expand=YES, side=TOP, fill=X, padx=40) -mutation_rate_slider = Scale(f1, from_=0, to=1, orient=HORIZONTAL, label='Mutation rate', resolution=0.0001, command=lambda value: update_mutation_rate(float(value))) +mutation_rate_slider = Scale(f1, from_=0, to=1, orient=HORIZONTAL, label='Mutation rate', resolution=0.0001, + command=lambda value: update_mutation_rate(float(value))) mutation_rate_slider.set(mutation_rate) mutation_rate_slider.pack(expand=YES, side=TOP, fill=X, padx=40) -f_thres_slider = Scale(f1, from_=0, to=len(target), orient=HORIZONTAL, label='Fitness threshold', command=lambda value: update_f_thres(int(value))) +f_thres_slider = Scale(f1, from_=0, to=len(target), orient=HORIZONTAL, label='Fitness threshold', + command=lambda value: update_f_thres(int(value))) f_thres_slider.set(f_thres) f_thres_slider.pack(expand=YES, side=TOP, fill=X, padx=40) -ngen_slider = Scale(f1, from_=1, to=5000, orient=HORIZONTAL, label='Max number of generations', command=lambda value: update_ngen(int(value))) +ngen_slider = Scale(f1, from_=1, to=5000, orient=HORIZONTAL, label='Max number of generations', + command=lambda value: update_ngen(int(value))) ngen_slider.set(ngen) ngen_slider.pack(expand=YES, side=TOP, fill=X, padx=40) -button = ttk.Button(f1, text='RUN', command=lambda: raise_frame(f2, init=True, update_target=True, target_entry=target_entry, f_thres_slider=f_thres_slider)).pack(side=BOTTOM, pady=50) +button = ttk.Button(f1, text='RUN', + command=lambda: raise_frame(f2, init=True, update_target=True, target_entry=target_entry, + f_thres_slider=f_thres_slider)).pack(side=BOTTOM, pady=50) # f2 widgets canvas = Canvas(f2, width=canvas_width, height=canvas_height) canvas.pack(expand=YES, fill=BOTH, padx=20, pady=15) button = ttk.Button(f2, text='EXIT', command=lambda: raise_frame(f1)).pack(side=BOTTOM, pady=15) + # function to run the genetic algorithm and update text on the canvas def genetic_algorithm_stepwise(population): - root.title('Genetic Algorithm') - for generation in range(ngen): - # generating new population after selecting, recombining and mutating the existing population - population = [search.mutate(search.recombine(*search.select(2, population, fitness_fn)), gene_pool, mutation_rate) for i in range(len(population))] - # genome with the highest fitness in the current generation - current_best = ''.join(argmax(population, key=fitness_fn)) - # collecting first few examples from the current population - members = [''.join(x) for x in population][:48] - - # clear the canvas - canvas.delete('all') - # displays current best on top of the screen - canvas.create_text(canvas_width / 2, 40, fill=p_blue, font='Consolas 46 bold', text=current_best) - - # displaying a part of the population on the screen - for i in range(len(members) // 3): - canvas.create_text((canvas_width * .175), (canvas_height * .25 + (25 * i)), fill=lp_blue, font='Consolas 16', text=members[3 * i]) - canvas.create_text((canvas_width * .500), (canvas_height * .25 + (25 * i)), fill=lp_blue, font='Consolas 16', text=members[3 * i + 1]) - canvas.create_text((canvas_width * .825), (canvas_height * .25 + (25 * i)), fill=lp_blue, font='Consolas 16', text=members[3 * i + 2]) - - # displays current generation number - canvas.create_text((canvas_width * .5), (canvas_height * 0.95), fill=p_blue, font='Consolas 18 bold', text=f'Generation {generation}') - - # displays blue bar that indicates current maximum fitness compared to maximum possible fitness - scaling_factor = fitness_fn(current_best) / len(target) - canvas.create_rectangle(canvas_width * 0.1, 90, canvas_width * 0.9, 100, outline=p_blue) - canvas.create_rectangle(canvas_width * 0.1, 90, canvas_width * 0.1 + scaling_factor * canvas_width * 0.8, 100, fill=lp_blue) - canvas.update() - - # checks for completion - fittest_individual = search.fitness_threshold(fitness_fn, f_thres, population) - if fittest_individual: - break + root.title('Genetic Algorithm') + for generation in range(ngen): + # generating new population after selecting, recombining and mutating the existing population + population = [ + search.mutate(search.recombine(*search.select(2, population, fitness_fn)), gene_pool, mutation_rate) for i + in range(len(population))] + # genome with the highest fitness in the current generation + current_best = ''.join(max(population, key=fitness_fn)) + # collecting first few examples from the current population + members = [''.join(x) for x in population][:48] + + # clear the canvas + canvas.delete('all') + # displays current best on top of the screen + canvas.create_text(canvas_width / 2, 40, fill=p_blue, font='Consolas 46 bold', text=current_best) + + # displaying a part of the population on the screen + for i in range(len(members) // 3): + canvas.create_text((canvas_width * .175), (canvas_height * .25 + (25 * i)), fill=lp_blue, + font='Consolas 16', text=members[3 * i]) + canvas.create_text((canvas_width * .500), (canvas_height * .25 + (25 * i)), fill=lp_blue, + font='Consolas 16', text=members[3 * i + 1]) + canvas.create_text((canvas_width * .825), (canvas_height * .25 + (25 * i)), fill=lp_blue, + font='Consolas 16', text=members[3 * i + 2]) + + # displays current generation number + canvas.create_text((canvas_width * .5), (canvas_height * 0.95), fill=p_blue, font='Consolas 18 bold', + text=f'Generation {generation}') + + # displays blue bar that indicates current maximum fitness compared to maximum possible fitness + scaling_factor = fitness_fn(current_best) / len(target) + canvas.create_rectangle(canvas_width * 0.1, 90, canvas_width * 0.9, 100, outline=p_blue) + canvas.create_rectangle(canvas_width * 0.1, 90, canvas_width * 0.1 + scaling_factor * canvas_width * 0.8, 100, + fill=lp_blue) + canvas.update() + + # checks for completion + fittest_individual = search.fitness_threshold(fitness_fn, f_thres, population) + if fittest_individual: + break + raise_frame(f1) -root.mainloop() \ No newline at end of file +root.mainloop() diff --git a/gui/grid_mdp.py b/gui/grid_mdp.py index 540bc2611..cb04c54b9 100644 --- a/gui/grid_mdp.py +++ b/gui/grid_mdp.py @@ -1,26 +1,22 @@ -# author: ad71 +import os.path +import sys import tkinter as tk import tkinter.messagebox -from tkinter import ttk - from functools import partial - -import sys -import os.path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) - -from mdp import * -import utils -import numpy as np -import time +from tkinter import ttk import matplotlib import matplotlib.animation as animation +from matplotlib import pyplot as plt +from matplotlib import style from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg -from matplotlib.ticker import MaxNLocator from matplotlib.figure import Figure -from matplotlib import style -from matplotlib import pyplot as plt +from matplotlib.ticker import MaxNLocator + +from mdp import * + +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) + matplotlib.use('TkAgg') style.use('ggplot') @@ -41,617 +37,640 @@ green8 = '#008080' green4 = '#004040' -cell_window_mantainer=None +cell_window_mantainer = None + def extents(f): - ''' adjusts axis markers for heatmap ''' + """adjusts axis markers for heatmap""" + + delta = f[1] - f[0] + return [f[0] - delta / 2, f[-1] + delta / 2] - delta = f[1] - f[0] - return [f[0] - delta/2, f[-1] + delta/2] def display(gridmdp, _height, _width): - ''' displays matrix ''' + """displays matrix""" - dialog = tk.Toplevel() - dialog.wm_title('Values') + dialog = tk.Toplevel() + dialog.wm_title('Values') - container = tk.Frame(dialog) - container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) + container = tk.Frame(dialog) + container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) - for i in range(max(1, _height)): - for j in range(max(1, _width)): - label = ttk.Label(container, text=f'{gridmdp[_height - i - 1][j]:.3f}', font=('Helvetica', 12)) - label.grid(row=i + 1, column=j + 1, padx=3, pady=3) + for i in range(max(1, _height)): + for j in range(max(1, _width)): + label = ttk.Label(container, text=f'{gridmdp[_height - i - 1][j]:.3f}', font=('Helvetica', 12)) + label.grid(row=i + 1, column=j + 1, padx=3, pady=3) + + dialog.mainloop() - dialog.mainloop() def display_best_policy(_best_policy, _height, _width): - ''' displays best policy ''' + """displays best policy""" + dialog = tk.Toplevel() + dialog.wm_title('Best Policy') - dialog = tk.Toplevel() - dialog.wm_title('Best Policy') + container = tk.Frame(dialog) + container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) - container = tk.Frame(dialog) - container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) + for i in range(max(1, _height)): + for j in range(max(1, _width)): + label = ttk.Label(container, text=_best_policy[i][j], font=('Helvetica', 12, 'bold')) + label.grid(row=i + 1, column=j + 1, padx=3, pady=3) - for i in range(max(1, _height)): - for j in range(max(1, _width)): - label = ttk.Label(container, text=_best_policy[i][j], font=('Helvetica', 12, 'bold')) - label.grid(row=i + 1, column=j + 1, padx=3, pady=3) + dialog.mainloop() - dialog.mainloop() def initialize_dialogbox(_width, _height, gridmdp, terminals, buttons): - ''' creates dialogbox for initialization ''' - - dialog = tk.Toplevel() - dialog.wm_title('Initialize') - - container = tk.Frame(dialog) - container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) - container.grid_rowconfigure(0, weight=1) - container.grid_columnconfigure(0, weight=1) - - wall = tk.IntVar() - wall.set(0) - term = tk.IntVar() - term.set(0) - reward = tk.DoubleVar() - reward.set(0.0) - - label = ttk.Label(container, text='Initialize', font=('Helvetica', 12), anchor=tk.N) - label.grid(row=0, column=0, columnspan=3, sticky='new', pady=15, padx=5) - label_reward = ttk.Label(container, text='Reward', font=('Helvetica', 10), anchor=tk.N) - label_reward.grid(row=1, column=0, columnspan=3, sticky='new', pady=1, padx=5) - entry_reward = ttk.Entry(container, font=('Helvetica', 10), justify=tk.CENTER, exportselection=0, textvariable=reward) - entry_reward.grid(row=2, column=0, columnspan=3, sticky='new', pady=5, padx=50) - - rbtn_term = ttk.Radiobutton(container, text='Terminal', variable=term, value=TERM_VALUE) - rbtn_term.grid(row=3, column=0, columnspan=3, sticky='nsew', padx=160, pady=5) - rbtn_wall = ttk.Radiobutton(container, text='Wall', variable=wall, value=WALL_VALUE) - rbtn_wall.grid(row=4, column=0, columnspan=3, sticky='nsew', padx=172, pady=5) - - initialize_widget_disability_checks(_width, _height, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, rbtn_term) - - btn_apply = ttk.Button(container, text='Apply', command=partial(initialize_update_table, _width, _height, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, rbtn_wall)) - btn_apply.grid(row=5, column=0, sticky='nsew', pady=5, padx=5) - btn_reset = ttk.Button(container, text='Reset', command=partial(initialize_reset_all, _width, _height, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_wall, rbtn_term)) - btn_reset.grid(row=5, column=1, sticky='nsew', pady=5, padx=5) - btn_ok = ttk.Button(container, text='Ok', command=dialog.destroy) - btn_ok.grid(row=5, column=2, sticky='nsew', pady=5, padx=5) - - dialog.geometry('400x200') - dialog.mainloop() - -def update_table(i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, rbtn_wall): - ''' functionality for 'apply' button ''' - - if wall.get() == WALL_VALUE: - buttons[i][j].configure(style='wall.TButton') - buttons[i][j].config(text='Wall') - label_reward.config(foreground='#999') - entry_reward.config(state=tk.DISABLED) - rbtn_term.state(['!focus', '!selected']) - rbtn_term.config(state=tk.DISABLED) - gridmdp[i][j] = WALL_VALUE - - elif wall.get() != WALL_VALUE: - if reward.get() != 0.0: - gridmdp[i][j] = reward.get() - buttons[i][j].configure(style='reward.TButton') - buttons[i][j].config(text=f'R = {reward.get()}') - - if term.get() == TERM_VALUE: - if (i, j) not in terminals: - terminals.append((i, j)) - rbtn_wall.state(['!focus', '!selected']) - rbtn_wall.config(state=tk.DISABLED) - - if gridmdp[i][j] < 0: - buttons[i][j].configure(style='-term.TButton') - - elif gridmdp[i][j] > 0: - buttons[i][j].configure(style='+term.TButton') - - elif gridmdp[i][j] == 0.0: - buttons[i][j].configure(style='=term.TButton') - -def initialize_update_table(_width, _height, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, rbtn_wall): - ''' runs update_table for all cells ''' - - for i in range(max(1, _height)): - for j in range(max(1, _width)): - update_table(i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, rbtn_wall) - -def reset_all(_height, i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_wall, rbtn_term): - ''' functionality for reset button ''' - - reward.set(0.0) - term.set(0) - wall.set(0) - gridmdp[i][j] = 0.0 - buttons[i][j].configure(style='TButton') - buttons[i][j].config(text=f'({_height - i - 1}, {j})') - - if (i, j) in terminals: - terminals.remove((i, j)) - - label_reward.config(foreground='#000') - entry_reward.config(state=tk.NORMAL) - rbtn_term.config(state=tk.NORMAL) - rbtn_wall.config(state=tk.NORMAL) - rbtn_wall.state(['!focus', '!selected']) - rbtn_term.state(['!focus', '!selected']) - -def initialize_reset_all(_width, _height, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_wall, rbtn_term): - ''' runs reset_all for all cells ''' - - for i in range(max(1, _height)): - for j in range(max(1, _width)): - reset_all(_height, i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_wall, rbtn_term) + """creates dialogbox for initialization""" + + dialog = tk.Toplevel() + dialog.wm_title('Initialize') + + container = tk.Frame(dialog) + container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) + container.grid_rowconfigure(0, weight=1) + container.grid_columnconfigure(0, weight=1) + + wall = tk.IntVar() + wall.set(0) + term = tk.IntVar() + term.set(0) + reward = tk.DoubleVar() + reward.set(0.0) + + label = ttk.Label(container, text='Initialize', font=('Helvetica', 12), anchor=tk.N) + label.grid(row=0, column=0, columnspan=3, sticky='new', pady=15, padx=5) + label_reward = ttk.Label(container, text='Reward', font=('Helvetica', 10), anchor=tk.N) + label_reward.grid(row=1, column=0, columnspan=3, sticky='new', pady=1, padx=5) + entry_reward = ttk.Entry(container, font=('Helvetica', 10), justify=tk.CENTER, exportselection=0, + textvariable=reward) + entry_reward.grid(row=2, column=0, columnspan=3, sticky='new', pady=5, padx=50) + + rbtn_term = ttk.Radiobutton(container, text='Terminal', variable=term, value=TERM_VALUE) + rbtn_term.grid(row=3, column=0, columnspan=3, sticky='nsew', padx=160, pady=5) + rbtn_wall = ttk.Radiobutton(container, text='Wall', variable=wall, value=WALL_VALUE) + rbtn_wall.grid(row=4, column=0, columnspan=3, sticky='nsew', padx=172, pady=5) + + initialize_widget_disability_checks(_width, _height, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, + rbtn_term) + + btn_apply = ttk.Button(container, text='Apply', + command=partial(initialize_update_table, _width, _height, gridmdp, terminals, buttons, + reward, term, wall, label_reward, entry_reward, rbtn_term, rbtn_wall)) + btn_apply.grid(row=5, column=0, sticky='nsew', pady=5, padx=5) + btn_reset = ttk.Button(container, text='Reset', + command=partial(initialize_reset_all, _width, _height, gridmdp, terminals, buttons, reward, + term, wall, label_reward, entry_reward, rbtn_wall, rbtn_term)) + btn_reset.grid(row=5, column=1, sticky='nsew', pady=5, padx=5) + btn_ok = ttk.Button(container, text='Ok', command=dialog.destroy) + btn_ok.grid(row=5, column=2, sticky='nsew', pady=5, padx=5) + + dialog.geometry('400x200') + dialog.mainloop() + + +def update_table(i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, + rbtn_wall): + """functionality for 'apply' button""" + if wall.get() == WALL_VALUE: + buttons[i][j].configure(style='wall.TButton') + buttons[i][j].config(text='Wall') + label_reward.config(foreground='#999') + entry_reward.config(state=tk.DISABLED) + rbtn_term.state(['!focus', '!selected']) + rbtn_term.config(state=tk.DISABLED) + gridmdp[i][j] = WALL_VALUE + + elif wall.get() != WALL_VALUE: + if reward.get() != 0.0: + gridmdp[i][j] = reward.get() + buttons[i][j].configure(style='reward.TButton') + buttons[i][j].config(text=f'R = {reward.get()}') + + if term.get() == TERM_VALUE: + if (i, j) not in terminals: + terminals.append((i, j)) + rbtn_wall.state(['!focus', '!selected']) + rbtn_wall.config(state=tk.DISABLED) + + if gridmdp[i][j] < 0: + buttons[i][j].configure(style='-term.TButton') + + elif gridmdp[i][j] > 0: + buttons[i][j].configure(style='+term.TButton') + + elif gridmdp[i][j] == 0.0: + buttons[i][j].configure(style='=term.TButton') + + +def initialize_update_table(_width, _height, gridmdp, terminals, buttons, reward, term, wall, label_reward, + entry_reward, rbtn_term, rbtn_wall): + """runs update_table for all cells""" + + for i in range(max(1, _height)): + for j in range(max(1, _width)): + update_table(i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, + rbtn_wall) + + +def reset_all(_height, i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_wall, + rbtn_term): + """functionality for reset button""" + reward.set(0.0) + term.set(0) + wall.set(0) + gridmdp[i][j] = 0.0 + buttons[i][j].configure(style='TButton') + buttons[i][j].config(text=f'({_height - i - 1}, {j})') + + if (i, j) in terminals: + terminals.remove((i, j)) + + label_reward.config(foreground='#000') + entry_reward.config(state=tk.NORMAL) + rbtn_term.config(state=tk.NORMAL) + rbtn_wall.config(state=tk.NORMAL) + rbtn_wall.state(['!focus', '!selected']) + rbtn_term.state(['!focus', '!selected']) + + +def initialize_reset_all(_width, _height, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, + rbtn_wall, rbtn_term): + """runs reset_all for all cells""" + + for i in range(max(1, _height)): + for j in range(max(1, _width)): + reset_all(_height, i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, + rbtn_wall, rbtn_term) + def external_reset(_width, _height, gridmdp, terminals, buttons): - ''' reset from edit menu ''' + """reset from edit menu""" + for i in range(max(1, _height)): + for j in range(max(1, _width)): + gridmdp[i][j] = 0.0 + buttons[i][j].configure(style='TButton') + buttons[i][j].config(text=f'({_height - i - 1}, {j})') - terminals = [] - for i in range(max(1, _height)): - for j in range(max(1, _width)): - gridmdp[i][j] = 0.0 - buttons[i][j].configure(style='TButton') - buttons[i][j].config(text=f'({_height - i - 1}, {j})') def widget_disability_checks(i, j, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, rbtn_term): - ''' checks for required state of widgets in dialogboxes ''' + """checks for required state of widgets in dialog boxes""" - if gridmdp[i][j] == WALL_VALUE: - label_reward.config(foreground='#999') - entry_reward.config(state=tk.DISABLED) - rbtn_term.config(state=tk.DISABLED) - rbtn_wall.state(['!focus', 'selected']) - rbtn_term.state(['!focus', '!selected']) + if gridmdp[i][j] == WALL_VALUE: + label_reward.config(foreground='#999') + entry_reward.config(state=tk.DISABLED) + rbtn_term.config(state=tk.DISABLED) + rbtn_wall.state(['!focus', 'selected']) + rbtn_term.state(['!focus', '!selected']) - if (i, j) in terminals: - rbtn_wall.config(state=tk.DISABLED) - rbtn_wall.state(['!focus', '!selected']) + if (i, j) in terminals: + rbtn_wall.config(state=tk.DISABLED) + rbtn_wall.state(['!focus', '!selected']) -def flatten_list(_list): - ''' returns a flattened list ''' - - return sum(_list, []) - -def initialize_widget_disability_checks(_width, _height, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, rbtn_term): - ''' checks for required state of widgets when cells are initialized ''' - - bool_walls = [['False']*max(1, _width) for _ in range(max(1, _height))] - bool_terms = [['False']*max(1, _width) for _ in range(max(1, _height))] - - for i in range(max(1, _height)): - for j in range(max(1, _width)): - if gridmdp[i][j] == WALL_VALUE: - bool_walls[i][j] = 'True' - - if (i, j) in terminals: - bool_terms[i][j] = 'True' - - bool_walls_fl = flatten_list(bool_walls) - bool_terms_fl = flatten_list(bool_terms) - - if bool_walls_fl.count('True') == len(bool_walls_fl): - print('`') - label_reward.config(foreground='#999') - entry_reward.config(state=tk.DISABLED) - rbtn_term.config(state=tk.DISABLED) - rbtn_wall.state(['!focus', 'selected']) - rbtn_term.state(['!focus', '!selected']) - - if bool_terms_fl.count('True') == len(bool_terms_fl): - rbtn_wall.config(state=tk.DISABLED) - rbtn_wall.state(['!focus', '!selected']) - rbtn_term.state(['!focus', 'selected']) - -def dialogbox(i, j, gridmdp, terminals, buttons, _height): - ''' creates dialogbox for each cell ''' - - global cell_window_mantainer - if(cell_window_mantainer!=None): - cell_window_mantainer.destroy() - - dialog = tk.Toplevel() - cell_window_mantainer=dialog - dialog.wm_title(f'{_height - i - 1}, {j}') - - container = tk.Frame(dialog) - container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) - container.grid_rowconfigure(0, weight=1) - container.grid_columnconfigure(0, weight=1) - - wall = tk.IntVar() - wall.set(gridmdp[i][j]) - term = tk.IntVar() - term.set(TERM_VALUE if (i, j) in terminals else 0.0) - reward = tk.DoubleVar() - reward.set(gridmdp[i][j] if gridmdp[i][j] != WALL_VALUE else 0.0) - - label = ttk.Label(container, text=f'Configure cell {_height - i - 1}, {j}', font=('Helvetica', 12), anchor=tk.N) - label.grid(row=0, column=0, columnspan=3, sticky='new', pady=15, padx=5) - label_reward = ttk.Label(container, text='Reward', font=('Helvetica', 10), anchor=tk.N) - label_reward.grid(row=1, column=0, columnspan=3, sticky='new', pady=1, padx=5) - entry_reward = ttk.Entry(container, font=('Helvetica', 10), justify=tk.CENTER, exportselection=0, textvariable=reward) - entry_reward.grid(row=2, column=0, columnspan=3, sticky='new', pady=5, padx=50) - - rbtn_term = ttk.Radiobutton(container, text='Terminal', variable=term, value=TERM_VALUE) - rbtn_term.grid(row=3, column=0, columnspan=3, sticky='nsew', padx=160, pady=5) - rbtn_wall = ttk.Radiobutton(container, text='Wall', variable=wall, value=WALL_VALUE) - rbtn_wall.grid(row=4, column=0, columnspan=3, sticky='nsew', padx=172, pady=5) - - widget_disability_checks(i, j, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, rbtn_term) - - btn_apply = ttk.Button(container, text='Apply', command=partial(update_table, i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_term, rbtn_wall)) - btn_apply.grid(row=5, column=0, sticky='nsew', pady=5, padx=5) - btn_reset = ttk.Button(container, text='Reset', command=partial(reset_all, _height, i, j, gridmdp, terminals, buttons, reward, term, wall, label_reward, entry_reward, rbtn_wall, rbtn_term)) - btn_reset.grid(row=5, column=1, sticky='nsew', pady=5, padx=5) - btn_ok = ttk.Button(container, text='Ok', command=dialog.destroy) - btn_ok.grid(row=5, column=2, sticky='nsew', pady=5, padx=5) - - dialog.geometry('400x200') - dialog.mainloop() +def flatten_list(_list): + """returns a flattened list""" + return sum(_list, []) -class MDPapp(tk.Tk): - - def __init__(self, *args, **kwargs): - - tk.Tk.__init__(self, *args, **kwargs) - tk.Tk.wm_title(self, 'Grid MDP') - self.shared_data = { - 'height': tk.IntVar(), - 'width': tk.IntVar() - } - self.shared_data['height'].set(1) - self.shared_data['width'].set(1) - self.container = tk.Frame(self) - self.container.pack(side='top', fill='both', expand=True) - self.container.grid_rowconfigure(0, weight=1) - self.container.grid_columnconfigure(0, weight=1) - - self.frames = {} - - self.menu_bar = tk.Menu(self.container) - self.file_menu = tk.Menu(self.menu_bar, tearoff=0) - self.file_menu.add_command(label='Exit', command=self.exit) - self.menu_bar.add_cascade(label='File', menu=self.file_menu) - - self.edit_menu = tk.Menu(self.menu_bar, tearoff=1) - self.edit_menu.add_command(label='Reset', command=self.master_reset) - self.edit_menu.add_command(label='Initialize', command=self.initialize) - self.edit_menu.add_separator() - self.edit_menu.add_command(label='View matrix', command=self.view_matrix) - self.edit_menu.add_command(label='View terminals', command=self.view_terminals) - self.menu_bar.add_cascade(label='Edit', menu=self.edit_menu) - self.menu_bar.entryconfig('Edit', state=tk.DISABLED) - - self.build_menu = tk.Menu(self.menu_bar, tearoff=1) - self.build_menu.add_command(label='Build and Run', command=self.build) - self.menu_bar.add_cascade(label='Build', menu=self.build_menu) - self.menu_bar.entryconfig('Build', state=tk.DISABLED) - tk.Tk.config(self, menu=self.menu_bar) - - for F in (HomePage, BuildMDP, SolveMDP): - frame = F(self.container, self) - self.frames[F] = frame - frame.grid(row=0, column=0, sticky='nsew') - - self.show_frame(HomePage) - - def placeholder_function(self): - ''' placeholder function ''' - - print('Not supported yet!') - - def exit(self): - ''' function to exit ''' - - if tkinter.messagebox.askokcancel('Exit?', 'All changes will be lost'): - quit() - - def new(self): - ''' function to create new GridMDP ''' - - self.master_reset() - build_page = self.get_page(BuildMDP) - build_page.gridmdp = None - build_page.terminals = None - build_page.buttons = None - self.show_frame(HomePage) - - def get_page(self, page_class): - ''' returns pages from stored frames ''' - - return self.frames[page_class] - def view_matrix(self): - ''' prints current matrix to console ''' +def initialize_widget_disability_checks(_width, _height, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, + rbtn_term): + """checks for required state of widgets when cells are initialized""" - build_page = self.get_page(BuildMDP) - _height = self.shared_data['height'].get() - _width = self.shared_data['width'].get() - print(build_page.gridmdp) - display(build_page.gridmdp, _height, _width) + bool_walls = [['False'] * max(1, _width) for _ in range(max(1, _height))] + bool_terms = [['False'] * max(1, _width) for _ in range(max(1, _height))] - def view_terminals(self): - ''' prints current terminals to console ''' + for i in range(max(1, _height)): + for j in range(max(1, _width)): + if gridmdp[i][j] == WALL_VALUE: + bool_walls[i][j] = 'True' - build_page = self.get_page(BuildMDP) - print('Terminals', build_page.terminals) + if (i, j) in terminals: + bool_terms[i][j] = 'True' - def initialize(self): - ''' calls initialize from BuildMDP ''' + bool_walls_fl = flatten_list(bool_walls) + bool_terms_fl = flatten_list(bool_terms) - build_page = self.get_page(BuildMDP) - build_page.initialize() + if bool_walls_fl.count('True') == len(bool_walls_fl): + print('`') + label_reward.config(foreground='#999') + entry_reward.config(state=tk.DISABLED) + rbtn_term.config(state=tk.DISABLED) + rbtn_wall.state(['!focus', 'selected']) + rbtn_term.state(['!focus', '!selected']) - def master_reset(self): - ''' calls master_reset from BuildMDP ''' + if bool_terms_fl.count('True') == len(bool_terms_fl): + rbtn_wall.config(state=tk.DISABLED) + rbtn_wall.state(['!focus', '!selected']) + rbtn_term.state(['!focus', 'selected']) - build_page = self.get_page(BuildMDP) - build_page.master_reset() - def build(self): - ''' runs specified mdp solving algorithm ''' +def dialogbox(i, j, gridmdp, terminals, buttons, _height): + """creates dialogbox for each cell""" + global cell_window_mantainer + if (cell_window_mantainer != None): + cell_window_mantainer.destroy() + + dialog = tk.Toplevel() + cell_window_mantainer = dialog + dialog.wm_title(f'{_height - i - 1}, {j}') + + container = tk.Frame(dialog) + container.pack(side=tk.TOP, fill=tk.BOTH, expand=True) + container.grid_rowconfigure(0, weight=1) + container.grid_columnconfigure(0, weight=1) + + wall = tk.IntVar() + wall.set(gridmdp[i][j]) + term = tk.IntVar() + term.set(TERM_VALUE if (i, j) in terminals else 0.0) + reward = tk.DoubleVar() + reward.set(gridmdp[i][j] if gridmdp[i][j] != WALL_VALUE else 0.0) + + label = ttk.Label(container, text=f'Configure cell {_height - i - 1}, {j}', font=('Helvetica', 12), anchor=tk.N) + label.grid(row=0, column=0, columnspan=3, sticky='new', pady=15, padx=5) + label_reward = ttk.Label(container, text='Reward', font=('Helvetica', 10), anchor=tk.N) + label_reward.grid(row=1, column=0, columnspan=3, sticky='new', pady=1, padx=5) + entry_reward = ttk.Entry(container, font=('Helvetica', 10), justify=tk.CENTER, exportselection=0, + textvariable=reward) + entry_reward.grid(row=2, column=0, columnspan=3, sticky='new', pady=5, padx=50) + + rbtn_term = ttk.Radiobutton(container, text='Terminal', variable=term, value=TERM_VALUE) + rbtn_term.grid(row=3, column=0, columnspan=3, sticky='nsew', padx=160, pady=5) + rbtn_wall = ttk.Radiobutton(container, text='Wall', variable=wall, value=WALL_VALUE) + rbtn_wall.grid(row=4, column=0, columnspan=3, sticky='nsew', padx=172, pady=5) + + widget_disability_checks(i, j, gridmdp, terminals, label_reward, entry_reward, rbtn_wall, rbtn_term) + + btn_apply = ttk.Button(container, text='Apply', + command=partial(update_table, i, j, gridmdp, terminals, buttons, reward, term, wall, + label_reward, entry_reward, rbtn_term, rbtn_wall)) + btn_apply.grid(row=5, column=0, sticky='nsew', pady=5, padx=5) + btn_reset = ttk.Button(container, text='Reset', + command=partial(reset_all, _height, i, j, gridmdp, terminals, buttons, reward, term, wall, + label_reward, entry_reward, rbtn_wall, rbtn_term)) + btn_reset.grid(row=5, column=1, sticky='nsew', pady=5, padx=5) + btn_ok = ttk.Button(container, text='Ok', command=dialog.destroy) + btn_ok.grid(row=5, column=2, sticky='nsew', pady=5, padx=5) + + dialog.geometry('400x200') + dialog.mainloop() - frame = SolveMDP(self.container, self) - self.frames[SolveMDP] = frame - frame.grid(row=0, column=0, sticky='nsew') - self.show_frame(SolveMDP) - build_page = self.get_page(BuildMDP) - gridmdp = build_page.gridmdp - terminals = build_page.terminals - solve_page = self.get_page(SolveMDP) - _height = self.shared_data['height'].get() - _width = self.shared_data['width'].get() - solve_page.create_graph(gridmdp, terminals, _height, _width) - def show_frame(self, controller, cb=False): - ''' shows specified frame and optionally runs create_buttons ''' +class MDPapp(tk.Tk): - if cb: - build_page = self.get_page(BuildMDP) - build_page.create_buttons() - frame = self.frames[controller] - frame.tkraise() + def __init__(self, *args, **kwargs): + + tk.Tk.__init__(self, *args, **kwargs) + tk.Tk.wm_title(self, 'Grid MDP') + self.shared_data = { + 'height': tk.IntVar(), + 'width': tk.IntVar()} + self.shared_data['height'].set(1) + self.shared_data['width'].set(1) + self.container = tk.Frame(self) + self.container.pack(side='top', fill='both', expand=True) + self.container.grid_rowconfigure(0, weight=1) + self.container.grid_columnconfigure(0, weight=1) + + self.frames = {} + + self.menu_bar = tk.Menu(self.container) + self.file_menu = tk.Menu(self.menu_bar, tearoff=0) + self.file_menu.add_command(label='Exit', command=self.exit) + self.menu_bar.add_cascade(label='File', menu=self.file_menu) + + self.edit_menu = tk.Menu(self.menu_bar, tearoff=1) + self.edit_menu.add_command(label='Reset', command=self.master_reset) + self.edit_menu.add_command(label='Initialize', command=self.initialize) + self.edit_menu.add_separator() + self.edit_menu.add_command(label='View matrix', command=self.view_matrix) + self.edit_menu.add_command(label='View terminals', command=self.view_terminals) + self.menu_bar.add_cascade(label='Edit', menu=self.edit_menu) + self.menu_bar.entryconfig('Edit', state=tk.DISABLED) + + self.build_menu = tk.Menu(self.menu_bar, tearoff=1) + self.build_menu.add_command(label='Build and Run', command=self.build) + self.menu_bar.add_cascade(label='Build', menu=self.build_menu) + self.menu_bar.entryconfig('Build', state=tk.DISABLED) + tk.Tk.config(self, menu=self.menu_bar) + + for F in (HomePage, BuildMDP, SolveMDP): + frame = F(self.container, self) + self.frames[F] = frame + frame.grid(row=0, column=0, sticky='nsew') + + self.show_frame(HomePage) + + def placeholder_function(self): + """placeholder function""" + + print('Not supported yet!') + + def exit(self): + """function to exit""" + if tkinter.messagebox.askokcancel('Exit?', 'All changes will be lost'): + quit() + + def new(self): + """function to create new GridMDP""" + + self.master_reset() + build_page = self.get_page(BuildMDP) + build_page.gridmdp = None + build_page.terminals = None + build_page.buttons = None + self.show_frame(HomePage) + + def get_page(self, page_class): + """returns pages from stored frames""" + return self.frames[page_class] + + def view_matrix(self): + """prints current matrix to console""" + + build_page = self.get_page(BuildMDP) + _height = self.shared_data['height'].get() + _width = self.shared_data['width'].get() + print(build_page.gridmdp) + display(build_page.gridmdp, _height, _width) + + def view_terminals(self): + """prints current terminals to console""" + build_page = self.get_page(BuildMDP) + print('Terminals', build_page.terminals) + + def initialize(self): + """calls initialize from BuildMDP""" + + build_page = self.get_page(BuildMDP) + build_page.initialize() + + def master_reset(self): + """calls master_reset from BuildMDP""" + build_page = self.get_page(BuildMDP) + build_page.master_reset() + + def build(self): + """runs specified mdp solving algorithm""" + + frame = SolveMDP(self.container, self) + self.frames[SolveMDP] = frame + frame.grid(row=0, column=0, sticky='nsew') + self.show_frame(SolveMDP) + build_page = self.get_page(BuildMDP) + gridmdp = build_page.gridmdp + terminals = build_page.terminals + solve_page = self.get_page(SolveMDP) + _height = self.shared_data['height'].get() + _width = self.shared_data['width'].get() + solve_page.create_graph(gridmdp, terminals, _height, _width) + + def show_frame(self, controller, cb=False): + """shows specified frame and optionally runs create_buttons""" + if cb: + build_page = self.get_page(BuildMDP) + build_page.create_buttons() + frame = self.frames[controller] + frame.tkraise() class HomePage(tk.Frame): - def __init__(self, parent, controller): - ''' HomePage constructor ''' - - tk.Frame.__init__(self, parent) - self.controller = controller - frame1 = tk.Frame(self) - frame1.pack(side=tk.TOP) - frame3 = tk.Frame(self) - frame3.pack(side=tk.TOP) - frame4 = tk.Frame(self) - frame4.pack(side=tk.TOP) - frame2 = tk.Frame(self) - frame2.pack(side=tk.TOP) - - s = ttk.Style() - s.theme_use('clam') - s.configure('TButton', background=grayd, padding=0) - s.configure('wall.TButton', background=gray2, foreground=white) - s.configure('reward.TButton', background=gray9) - s.configure('+term.TButton', background=green8) - s.configure('-term.TButton', background=pblue, foreground=white) - s.configure('=term.TButton', background=green4) - - label = ttk.Label(frame1, text='GridMDP builder', font=('Helvetica', 18, 'bold'), background=grayef) - label.pack(pady=75, padx=50, side=tk.TOP) - - ec_btn = ttk.Button(frame3, text='Empty cells', width=20) - ec_btn.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) - ec_btn.configure(style='TButton') - - w_btn = ttk.Button(frame3, text='Walls', width=20) - w_btn.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) - w_btn.configure(style='wall.TButton') - - r_btn = ttk.Button(frame3, text='Rewards', width=20) - r_btn.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) - r_btn.configure(style='reward.TButton') - - term_p = ttk.Button(frame3, text='Positive terminals', width=20) - term_p.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) - term_p.configure(style='+term.TButton') - - term_z = ttk.Button(frame3, text='Neutral terminals', width=20) - term_z.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) - term_z.configure(style='=term.TButton') - - term_n = ttk.Button(frame3, text='Negative terminals', width=20) - term_n.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) - term_n.configure(style='-term.TButton') - - label = ttk.Label(frame4, text='Dimensions', font=('Verdana', 14), background=grayef) - label.pack(pady=15, padx=10, side=tk.TOP) - entry_h = tk.Entry(frame2, textvariable=self.controller.shared_data['height'], font=('Verdana', 10), width=3, justify=tk.CENTER) - entry_h.pack(pady=10, padx=10, side=tk.LEFT) - label_x = ttk.Label(frame2, text='X', font=('Verdana', 10), background=grayef) - label_x.pack(pady=10, padx=4, side=tk.LEFT) - entry_w = tk.Entry(frame2, textvariable=self.controller.shared_data['width'], font=('Verdana', 10), width=3, justify=tk.CENTER) - entry_w.pack(pady=10, padx=10, side=tk.LEFT) - button = ttk.Button(self, text='Build a GridMDP', command=lambda: controller.show_frame(BuildMDP, cb=True)) - button.pack(pady=10, padx=10, side=tk.TOP, ipadx=20, ipady=10) - button.configure(style='reward.TButton') + def __init__(self, parent, controller): + """HomePage constructor""" + + tk.Frame.__init__(self, parent) + self.controller = controller + frame1 = tk.Frame(self) + frame1.pack(side=tk.TOP) + frame3 = tk.Frame(self) + frame3.pack(side=tk.TOP) + frame4 = tk.Frame(self) + frame4.pack(side=tk.TOP) + frame2 = tk.Frame(self) + frame2.pack(side=tk.TOP) + + s = ttk.Style() + s.theme_use('clam') + s.configure('TButton', background=grayd, padding=0) + s.configure('wall.TButton', background=gray2, foreground=white) + s.configure('reward.TButton', background=gray9) + s.configure('+term.TButton', background=green8) + s.configure('-term.TButton', background=pblue, foreground=white) + s.configure('=term.TButton', background=green4) + + label = ttk.Label(frame1, text='GridMDP builder', font=('Helvetica', 18, 'bold'), background=grayef) + label.pack(pady=75, padx=50, side=tk.TOP) + + ec_btn = ttk.Button(frame3, text='Empty cells', width=20) + ec_btn.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) + ec_btn.configure(style='TButton') + + w_btn = ttk.Button(frame3, text='Walls', width=20) + w_btn.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) + w_btn.configure(style='wall.TButton') + + r_btn = ttk.Button(frame3, text='Rewards', width=20) + r_btn.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) + r_btn.configure(style='reward.TButton') + + term_p = ttk.Button(frame3, text='Positive terminals', width=20) + term_p.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) + term_p.configure(style='+term.TButton') + + term_z = ttk.Button(frame3, text='Neutral terminals', width=20) + term_z.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) + term_z.configure(style='=term.TButton') + + term_n = ttk.Button(frame3, text='Negative terminals', width=20) + term_n.pack(pady=0, padx=0, side=tk.LEFT, ipady=10) + term_n.configure(style='-term.TButton') + + label = ttk.Label(frame4, text='Dimensions', font=('Verdana', 14), background=grayef) + label.pack(pady=15, padx=10, side=tk.TOP) + entry_h = tk.Entry(frame2, textvariable=self.controller.shared_data['height'], font=('Verdana', 10), width=3, + justify=tk.CENTER) + entry_h.pack(pady=10, padx=10, side=tk.LEFT) + label_x = ttk.Label(frame2, text='X', font=('Verdana', 10), background=grayef) + label_x.pack(pady=10, padx=4, side=tk.LEFT) + entry_w = tk.Entry(frame2, textvariable=self.controller.shared_data['width'], font=('Verdana', 10), width=3, + justify=tk.CENTER) + entry_w.pack(pady=10, padx=10, side=tk.LEFT) + button = ttk.Button(self, text='Build a GridMDP', command=lambda: controller.show_frame(BuildMDP, cb=True)) + button.pack(pady=10, padx=10, side=tk.TOP, ipadx=20, ipady=10) + button.configure(style='reward.TButton') class BuildMDP(tk.Frame): - def __init__(self, parent, controller): - - tk.Frame.__init__(self, parent) - self.grid_rowconfigure(0, weight=1) - self.grid_columnconfigure(0, weight=1) - self.frame = tk.Frame(self) - self.frame.pack() - self.controller = controller - - def create_buttons(self): - ''' creates interactive cells to build MDP ''' - - _height = self.controller.shared_data['height'].get() - _width = self.controller.shared_data['width'].get() - self.controller.menu_bar.entryconfig('Edit', state=tk.NORMAL) - self.controller.menu_bar.entryconfig('Build', state=tk.NORMAL) - self.gridmdp = [[0.0]*max(1, _width) for _ in range(max(1, _height))] - self.buttons = [[None]*max(1, _width) for _ in range(max(1, _height))] - self.terminals = [] - - s = ttk.Style() - s.theme_use('clam') - s.configure('TButton', background=grayd, padding=0) - s.configure('wall.TButton', background=gray2, foreground=white) - s.configure('reward.TButton', background=gray9) - s.configure('+term.TButton', background=green8) - s.configure('-term.TButton', background=pblue, foreground=white) - s.configure('=term.TButton', background=green4) - - for i in range(max(1, _height)): - for j in range(max(1, _width)): - self.buttons[i][j] = ttk.Button(self.frame, text=f'({_height - i - 1}, {j})', width=int(196/max(1, _width)), command=partial(dialogbox, i, j, self.gridmdp, self.terminals, self.buttons, _height)) - self.buttons[i][j].grid(row=i, column=j, ipady=int(336/max(1, _height)) - 12) - - def initialize(self): - ''' runs initialize_dialogbox ''' - - _height = self.controller.shared_data['height'].get() - _width = self.controller.shared_data['width'].get() - initialize_dialogbox(_width, _height, self.gridmdp, self.terminals, self.buttons) - - def master_reset(self): - ''' runs external reset ''' - - _height = self.controller.shared_data['height'].get() - _width = self.controller.shared_data['width'].get() - if tkinter.messagebox.askokcancel('Reset', 'Are you sure you want to reset all cells?'): - external_reset(_width, _height, self.gridmdp, self.terminals, self.buttons) + def __init__(self, parent, controller): + + tk.Frame.__init__(self, parent) + self.grid_rowconfigure(0, weight=1) + self.grid_columnconfigure(0, weight=1) + self.frame = tk.Frame(self) + self.frame.pack() + self.controller = controller + + def create_buttons(self): + """creates interactive cells to build MDP""" + _height = self.controller.shared_data['height'].get() + _width = self.controller.shared_data['width'].get() + self.controller.menu_bar.entryconfig('Edit', state=tk.NORMAL) + self.controller.menu_bar.entryconfig('Build', state=tk.NORMAL) + self.gridmdp = [[0.0] * max(1, _width) for _ in range(max(1, _height))] + self.buttons = [[None] * max(1, _width) for _ in range(max(1, _height))] + self.terminals = [] + + s = ttk.Style() + s.theme_use('clam') + s.configure('TButton', background=grayd, padding=0) + s.configure('wall.TButton', background=gray2, foreground=white) + s.configure('reward.TButton', background=gray9) + s.configure('+term.TButton', background=green8) + s.configure('-term.TButton', background=pblue, foreground=white) + s.configure('=term.TButton', background=green4) + + for i in range(max(1, _height)): + for j in range(max(1, _width)): + self.buttons[i][j] = ttk.Button(self.frame, text=f'({_height - i - 1}, {j})', + width=int(196 / max(1, _width)), + command=partial(dialogbox, i, j, self.gridmdp, self.terminals, + self.buttons, _height)) + self.buttons[i][j].grid(row=i, column=j, ipady=int(336 / max(1, _height)) - 12) + + def initialize(self): + """runs initialize_dialogbox""" + + _height = self.controller.shared_data['height'].get() + _width = self.controller.shared_data['width'].get() + initialize_dialogbox(_width, _height, self.gridmdp, self.terminals, self.buttons) + + def master_reset(self): + """runs external reset""" + _height = self.controller.shared_data['height'].get() + _width = self.controller.shared_data['width'].get() + if tkinter.messagebox.askokcancel('Reset', 'Are you sure you want to reset all cells?'): + external_reset(_width, _height, self.gridmdp, self.terminals, self.buttons) class SolveMDP(tk.Frame): - def __init__(self, parent, controller): - - tk.Frame.__init__(self, parent) - self.grid_rowconfigure(0, weight=1) - self.grid_columnconfigure(0, weight=1) - self.frame = tk.Frame(self) - self.frame.pack() - self.controller = controller - self.terminated = False - self.iterations = 0 - self.epsilon = 0.001 - self.delta = 0 + def __init__(self, parent, controller): - def process_data(self, terminals, _height, _width, gridmdp): - ''' preprocess variables ''' + tk.Frame.__init__(self, parent) + self.grid_rowconfigure(0, weight=1) + self.grid_columnconfigure(0, weight=1) + self.frame = tk.Frame(self) + self.frame.pack() + self.controller = controller + self.terminated = False + self.iterations = 0 + self.epsilon = 0.001 + self.delta = 0 - flipped_terminals = [] + def process_data(self, terminals, _height, _width, gridmdp): + """preprocess variables""" - for terminal in terminals: - flipped_terminals.append((terminal[1], _height - terminal[0] - 1)) + flipped_terminals = [] - grid_to_solve = [[0.0]*max(1, _width) for _ in range(max(1, _height))] - grid_to_show = [[0.0]*max(1, _width) for _ in range(max(1, _height))] + for terminal in terminals: + flipped_terminals.append((terminal[1], _height - terminal[0] - 1)) - for i in range(max(1, _height)): - for j in range(max(1, _width)): - if gridmdp[i][j] == WALL_VALUE: - grid_to_show[i][j] = 0.0 - grid_to_solve[i][j] = None + grid_to_solve = [[0.0] * max(1, _width) for _ in range(max(1, _height))] + grid_to_show = [[0.0] * max(1, _width) for _ in range(max(1, _height))] - else: - grid_to_show[i][j] = grid_to_solve[i][j] = gridmdp[i][j] + for i in range(max(1, _height)): + for j in range(max(1, _width)): + if gridmdp[i][j] == WALL_VALUE: + grid_to_show[i][j] = 0.0 + grid_to_solve[i][j] = None - return flipped_terminals, grid_to_solve, np.flipud(grid_to_show) + else: + grid_to_show[i][j] = grid_to_solve[i][j] = gridmdp[i][j] - def create_graph(self, gridmdp, terminals, _height, _width): - ''' creates canvas and initializes value_iteration_paramteres ''' + return flipped_terminals, grid_to_solve, np.flipud(grid_to_show) - self._height = _height - self._width = _width - self.controller.menu_bar.entryconfig('Edit', state=tk.DISABLED) - self.controller.menu_bar.entryconfig('Build', state=tk.DISABLED) + def create_graph(self, gridmdp, terminals, _height, _width): + """creates canvas and initializes value_iteration_parameters""" + self._height = _height + self._width = _width + self.controller.menu_bar.entryconfig('Edit', state=tk.DISABLED) + self.controller.menu_bar.entryconfig('Build', state=tk.DISABLED) - self.terminals, self.gridmdp, self.grid_to_show = self.process_data(terminals, _height, _width, gridmdp) - self.sequential_decision_environment = GridMDP(self.gridmdp, terminals=self.terminals) + self.terminals, self.gridmdp, self.grid_to_show = self.process_data(terminals, _height, _width, gridmdp) + self.sequential_decision_environment = GridMDP(self.gridmdp, terminals=self.terminals) - self.initialize_value_iteration_parameters(self.sequential_decision_environment) + self.initialize_value_iteration_parameters(self.sequential_decision_environment) - self.canvas = FigureCanvasTkAgg(fig, self.frame) - self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True) - self.anim = animation.FuncAnimation(fig, self.animate_graph, interval=50) - self.canvas.show() + self.canvas = FigureCanvasTkAgg(fig, self.frame) + self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True) + self.anim = animation.FuncAnimation(fig, self.animate_graph, interval=50) + self.canvas.show() - def animate_graph(self, i): - ''' performs value iteration and animates graph ''' + def animate_graph(self, i): + """performs value iteration and animates graph""" - # cmaps to use: bone_r, Oranges, inferno, BrBG, copper - self.iterations += 1 - x_interval = max(2, len(self.gridmdp[0])) - y_interval = max(2, len(self.gridmdp)) - x = np.linspace(0, len(self.gridmdp[0]) - 1, x_interval) - y = np.linspace(0, len(self.gridmdp) - 1, y_interval) + # cmaps to use: bone_r, Oranges, inferno, BrBG, copper + self.iterations += 1 + x_interval = max(2, len(self.gridmdp[0])) + y_interval = max(2, len(self.gridmdp)) + x = np.linspace(0, len(self.gridmdp[0]) - 1, x_interval) + y = np.linspace(0, len(self.gridmdp) - 1, y_interval) - sub.clear() - sub.imshow(self.grid_to_show, cmap='BrBG', aspect='auto', interpolation='none', extent=extents(x) + extents(y), origin='lower') - fig.tight_layout() + sub.clear() + sub.imshow(self.grid_to_show, cmap='BrBG', aspect='auto', interpolation='none', extent=extents(x) + extents(y), + origin='lower') + fig.tight_layout() - U = self.U1.copy() + U = self.U1.copy() - for s in self.sequential_decision_environment.states: - self.U1[s] = self.R(s) + self.gamma * max([sum([p * U[s1] for (p, s1) in self.T(s, a)]) for a in self.sequential_decision_environment.actions(s)]) - self.delta = max(self.delta, abs(self.U1[s] - U[s])) + for s in self.sequential_decision_environment.states: + self.U1[s] = self.R(s) + self.gamma * max( + [sum([p * U[s1] for (p, s1) in self.T(s, a)]) for a in self.sequential_decision_environment.actions(s)]) + self.delta = max(self.delta, abs(self.U1[s] - U[s])) - self.grid_to_show = grid_to_show = [[0.0]*max(1, self._width) for _ in range(max(1, self._height))] - for k, v in U.items(): - self.grid_to_show[k[1]][k[0]] = v + self.grid_to_show = grid_to_show = [[0.0] * max(1, self._width) for _ in range(max(1, self._height))] + for k, v in U.items(): + self.grid_to_show[k[1]][k[0]] = v - if (self.delta < self.epsilon * (1 - self.gamma) / self.gamma) or (self.iterations > 60) and self.terminated == False: - self.terminated = True - display(self.grid_to_show, self._height, self._width) + if (self.delta < self.epsilon * (1 - self.gamma) / self.gamma) or ( + self.iterations > 60) and self.terminated == False: + self.terminated = True + display(self.grid_to_show, self._height, self._width) - pi = best_policy(self.sequential_decision_environment, value_iteration(self.sequential_decision_environment, .01)) - display_best_policy(self.sequential_decision_environment.to_arrows(pi), self._height, self._width) - - ax = fig.gca() - ax.xaxis.set_major_locator(MaxNLocator(integer=True)) - ax.yaxis.set_major_locator(MaxNLocator(integer=True)) + pi = best_policy(self.sequential_decision_environment, + value_iteration(self.sequential_decision_environment, .01)) + display_best_policy(self.sequential_decision_environment.to_arrows(pi), self._height, self._width) - def initialize_value_iteration_parameters(self, mdp): - ''' initializes value_iteration parameters ''' + ax = fig.gca() + ax.xaxis.set_major_locator(MaxNLocator(integer=True)) + ax.yaxis.set_major_locator(MaxNLocator(integer=True)) - self.U1 = {s: 0 for s in mdp.states} - self.R, self.T, self.gamma = mdp.R, mdp.T, mdp.gamma + def initialize_value_iteration_parameters(self, mdp): + """initializes value_iteration parameters""" + self.U1 = {s: 0 for s in mdp.states} + self.R, self.T, self.gamma = mdp.R, mdp.T, mdp.gamma - def value_iteration_metastep(self, mdp, iterations=20): - ''' runs value_iteration ''' + def value_iteration_metastep(self, mdp, iterations=20): + """runs value_iteration""" - U_over_time = [] - U1 = {s: 0 for s in mdp.states} - R, T, gamma = mdp.R, mdp.T, mdp.gamma + U_over_time = [] + U1 = {s: 0 for s in mdp.states} + R, T, gamma = mdp.R, mdp.T, mdp.gamma - for _ in range(iterations): - U = U1.copy() + for _ in range(iterations): + U = U1.copy() - for s in mdp.states: - U1[s] = R(s) + gamma * max([sum([p * U[s1] for (p, s1) in T(s, a)]) for a in mdp.actions(s)]) + for s in mdp.states: + U1[s] = R(s) + gamma * max([sum([p * U[s1] for (p, s1) in T(s, a)]) for a in mdp.actions(s)]) - U_over_time.append(U) - return U_over_time + U_over_time.append(U) + return U_over_time if __name__ == '__main__': - app = MDPapp() - app.geometry('1280x720') - app.mainloop() \ No newline at end of file + app = MDPapp() + app.geometry('1280x720') + app.mainloop() diff --git a/gui/romania_problem.py b/gui/romania_problem.py index 08219bb55..9ec94099d 100644 --- a/gui/romania_problem.py +++ b/gui/romania_problem.py @@ -621,9 +621,7 @@ def reset_map(): # TODO: Add more search algorithms in the OptionMenu - - -def main(): +if __name__ == "__main__": global algo, start, goal, next_button root = Tk() root.title("Road Map of Romania") @@ -672,7 +670,3 @@ def main(): frame1.pack(side=BOTTOM) create_map(root) root.mainloop() - - -if __name__ == "__main__": - main() diff --git a/gui/tic-tac-toe.py b/gui/tic-tac-toe.py index 4f51425c1..66d9d6e75 100644 --- a/gui/tic-tac-toe.py +++ b/gui/tic-tac-toe.py @@ -1,11 +1,12 @@ -from tkinter import * -import sys import os.path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from tkinter import * + from games import minmax_decision, alpha_beta_player, random_player, TicTacToe # "gen_state" can be used to generate a game state to apply the algorithm from tests.test_games import gen_state +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) + ttt = TicTacToe() root = None buttons = [] @@ -152,8 +153,7 @@ def check_victory(button): return True # check if previous move was on the secondary diagonal and caused a win - if x + y \ - == 2 and buttons[0][2]['text'] == buttons[1][1]['text'] == buttons[2][0]['text'] != " ": + if x + y == 2 and buttons[0][2]['text'] == buttons[1][1]['text'] == buttons[2][0]['text'] != " ": buttons[0][2].config(text="/" + tt + "/") buttons[1][1].config(text="/" + tt + "/") buttons[2][0].config(text="/" + tt + "/") @@ -213,7 +213,7 @@ def exit_game(root): root.destroy() -def main(): +if __name__ == "__main__": global result, choices root = Tk() @@ -230,7 +230,3 @@ def main(): menu = OptionMenu(root, choices, "Vs Random", "Vs Pro", "Vs Legend") menu.pack() root.mainloop() - - -if __name__ == "__main__": - main() diff --git a/gui/tsp.py b/gui/tsp.py index 1830cba23..590fff354 100644 --- a/gui/tsp.py +++ b/gui/tsp.py @@ -1,21 +1,19 @@ from tkinter import * from tkinter import messagebox -import sys -import os.path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) -from search import * + import utils -import numpy as np +from search import * -distances = {} +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +distances = {} -class TSP_problem(Problem): - """ subclass of Problem to define various functions """ +class TSProblem(Problem): + """subclass of Problem to define various functions""" def two_opt(self, state): - """ Neighbour generating function for Traveling Salesman Problem """ + """Neighbour generating function for Traveling Salesman Problem""" neighbour_state = state[:] left = random.randint(0, len(neighbour_state) - 1) right = random.randint(0, len(neighbour_state) - 1) @@ -25,15 +23,15 @@ def two_opt(self, state): return neighbour_state def actions(self, state): - """ action that can be excuted in given state """ + """action that can be executed in given state""" return [self.two_opt] def result(self, state, action): - """ result after applying the given action on the given state """ + """result after applying the given action on the given state""" return action(state) def path_cost(self, c, state1, action, state2): - """ total distance for the Traveling Salesman to be covered if in state2 """ + """total distance for the Traveling Salesman to be covered if in state2""" cost = 0 for i in range(len(state2) - 1): cost += distances[state2[i]][state2[i + 1]] @@ -41,12 +39,12 @@ def path_cost(self, c, state1, action, state2): return cost def value(self, state): - """ value of path cost given negative for the given state """ + """value of path cost given negative for the given state""" return -1 * self.path_cost(None, None, None, state) -class TSP_Gui(): - """ Class to create gui of Traveling Salesman using simulated annealing where one can +class TSPGui(): + """Class to create gui of Traveling Salesman using simulated annealing where one can select cities, change speed and temperature. Distances between cities are euclidean distances between them. """ @@ -67,7 +65,7 @@ def __init__(self, root, all_cities): Label(self.root, text="Map of Romania", font="Times 13 bold").grid(row=0, columnspan=10) def create_checkboxes(self, side=LEFT, anchor=W): - """ To select cities which are to be a part of Traveling Salesman Problem """ + """To select cities which are to be a part of Traveling Salesman Problem""" row_number = 0 column_number = 0 @@ -85,7 +83,7 @@ def create_checkboxes(self, side=LEFT, anchor=W): row_number += 1 def create_buttons(self): - """ Create start and quit button """ + """Create start and quit button""" Button(self.frame_select_cities, textvariable=self.button_text, command=self.run_traveling_salesman).grid(row=5, column=4, sticky=E + W) @@ -93,7 +91,7 @@ def create_buttons(self): row=5, column=5, sticky=E + W) def create_dropdown_menu(self): - """ Create dropdown menu for algorithm selection """ + """Create dropdown menu for algorithm selection""" choices = {'Simulated Annealing', 'Genetic Algorithm', 'Hill Climbing'} self.algo_var.set('Simulated Annealing') @@ -102,19 +100,19 @@ def create_dropdown_menu(self): dropdown_menu.config(width=19) def run_traveling_salesman(self): - """ Choose selected citites """ + """Choose selected cities""" cities = [] for i in range(len(self.vars)): if self.vars[i].get() == 1: cities.append(self.all_cities[i]) - tsp_problem = TSP_problem(cities) + tsp_problem = TSProblem(cities) self.button_text.set("Reset") self.create_canvas(tsp_problem) def calculate_canvas_size(self): - """ Width and height for canvas """ + """Width and height for canvas""" minx, maxx = sys.maxsize, -1 * sys.maxsize miny, maxy = sys.maxsize, -1 * sys.maxsize @@ -137,7 +135,7 @@ def calculate_canvas_size(self): self.canvas_height = canvas_height def create_canvas(self, problem): - """ creating map with cities """ + """creating map with cities""" map_canvas = Canvas(self.frame_canvas, width=self.canvas_width, height=self.canvas_height) map_canvas.grid(row=3, columnspan=10) @@ -163,18 +161,18 @@ def create_canvas(self, problem): variable=self.speed, label="Speed ----> ", showvalue=0, font="Times 11", relief="sunken", cursor="gumby") speed_scale.grid(row=1, columnspan=5, sticky=N + S + E + W) - + if self.algo_var.get() == 'Simulated Annealing': self.temperature = IntVar() temperature_scale = Scale(self.frame_canvas, from_=100, to=0, orient=HORIZONTAL, - length=200, variable=self.temperature, label="Temperature ---->", - font="Times 11", relief="sunken", showvalue=0, cursor="gumby") + length=200, variable=self.temperature, label="Temperature ---->", + font="Times 11", relief="sunken", showvalue=0, cursor="gumby") temperature_scale.grid(row=1, column=5, columnspan=5, sticky=N + S + E + W) self.simulated_annealing_with_tunable_T(problem, map_canvas) elif self.algo_var.get() == 'Genetic Algorithm': self.mutation_rate = DoubleVar() self.mutation_rate.set(0.05) - mutation_rate_scale = Scale(self.frame_canvas, from_=0, to=1, orient=HORIZONTAL, + mutation_rate_scale = Scale(self.frame_canvas, from_=0, to=1, orient=HORIZONTAL, length=200, variable=self.mutation_rate, label='Mutation Rate ---->', font='Times 11', relief='sunken', showvalue=0, cursor='gumby', resolution=0.001) mutation_rate_scale.grid(row=1, column=5, columnspan=5, sticky='nsew') @@ -182,23 +180,23 @@ def create_canvas(self, problem): elif self.algo_var.get() == 'Hill Climbing': self.no_of_neighbors = IntVar() self.no_of_neighbors.set(100) - no_of_neighbors_scale = Scale(self.frame_canvas, from_=10, to=1000, orient=HORIZONTAL, + no_of_neighbors_scale = Scale(self.frame_canvas, from_=10, to=1000, orient=HORIZONTAL, length=200, variable=self.no_of_neighbors, label='Number of neighbors ---->', - font='Times 11',relief='sunken', showvalue=0, cursor='gumby') + font='Times 11', relief='sunken', showvalue=0, cursor='gumby') no_of_neighbors_scale.grid(row=1, column=5, columnspan=5, sticky='nsew') self.hill_climbing(problem, map_canvas) def exp_schedule(k=100, lam=0.03, limit=1000): - """ One possible schedule function for simulated annealing """ + """One possible schedule function for simulated annealing""" - return lambda t: (k * math.exp(-lam * t) if t < limit else 0) + return lambda t: (k * np.exp(-lam * t) if t < limit else 0) def simulated_annealing_with_tunable_T(self, problem, map_canvas, schedule=exp_schedule()): - """ Simulated annealing where temperature is taken as user input """ + """Simulated annealing where temperature is taken as user input""" current = Node(problem.initial) - while(1): + while True: T = schedule(self.temperature.get()) if T == 0: return current.state @@ -207,7 +205,7 @@ def simulated_annealing_with_tunable_T(self, problem, map_canvas, schedule=exp_s return current.state next = random.choice(neighbors) delta_e = problem.value(next.state) - problem.value(current.state) - if delta_e > 0 or probability(math.exp(delta_e / T)): + if delta_e > 0 or probability(np.exp(delta_e / T)): map_canvas.delete("poly") current = next @@ -221,10 +219,10 @@ def simulated_annealing_with_tunable_T(self, problem, map_canvas, schedule=exp_s map_canvas.after(self.speed.get()) def genetic_algorithm(self, problem, map_canvas): - """ Genetic Algorithm modified for the given problem """ + """Genetic Algorithm modified for the given problem""" def init_population(pop_number, gene_pool, state_length): - """ initialize population """ + """initialize population""" population = [] for i in range(pop_number): @@ -232,7 +230,7 @@ def init_population(pop_number, gene_pool, state_length): return population def recombine(state_a, state_b): - """ recombine two problem states """ + """recombine two problem states""" start = random.randint(0, len(state_a) - 1) end = random.randint(start + 1, len(state_a)) @@ -243,7 +241,7 @@ def recombine(state_a, state_b): return new_state def mutate(state, mutation_rate): - """ mutate problem states """ + """mutate problem states""" if random.uniform(0, 1) < mutation_rate: sample = random.sample(range(len(state)), 2) @@ -251,17 +249,18 @@ def mutate(state, mutation_rate): return state def fitness_fn(state): - """ calculate fitness of a particular state """ - + """calculate fitness of a particular state""" + fitness = problem.value(state) return int((5600 + fitness) ** 2) current = Node(problem.initial) population = init_population(100, current.state, len(current.state)) all_time_best = current.state - while(1): - population = [mutate(recombine(*select(2, population, fitness_fn)), self.mutation_rate.get()) for i in range(len(population))] - current_best = utils.argmax(population, key=fitness_fn) + while True: + population = [mutate(recombine(*select(2, population, fitness_fn)), self.mutation_rate.get()) + for _ in range(len(population))] + current_best = np.argmax(population, key=fitness_fn) if fitness_fn(current_best) > fitness_fn(all_time_best): all_time_best = current_best self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(all_time_best)))) @@ -280,10 +279,10 @@ def fitness_fn(state): map_canvas.after(self.speed.get()) def hill_climbing(self, problem, map_canvas): - """ hill climbing where number of neighbors is taken as user input """ + """hill climbing where number of neighbors is taken as user input""" def find_neighbors(state, number_of_neighbors=100): - """ finds neighbors using two_opt method """ + """finds neighbors using two_opt method""" neighbors = [] for i in range(number_of_neighbors): @@ -293,9 +292,9 @@ def find_neighbors(state, number_of_neighbors=100): return neighbors current = Node(problem.initial) - while(1): + while True: neighbors = find_neighbors(current.state, self.no_of_neighbors.get()) - neighbor = utils.argmax_random_tie(neighbors, key=lambda node: problem.value(node.state)) + neighbor = np.argmax_random_tie(neighbors, key=lambda node: problem.value(node.state)) map_canvas.delete('poly') points = [] for city in current.state: @@ -317,7 +316,8 @@ def on_closing(self): if messagebox.askokcancel('Quit', 'Do you want to quit?'): self.root.destroy() -def main(): + +if __name__ == '__main__': all_cities = [] for city in romania_map.locations.keys(): distances[city] = {} @@ -334,13 +334,9 @@ def main(): root = Tk() root.title("Traveling Salesman Problem") - cities_selection_panel = TSP_Gui(root, all_cities) + cities_selection_panel = TSPGui(root, all_cities) cities_selection_panel.create_checkboxes() cities_selection_panel.create_buttons() cities_selection_panel.create_dropdown_menu() root.protocol('WM_DELETE_WINDOW', cities_selection_panel.on_closing) root.mainloop() - - -if __name__ == '__main__': - main() diff --git a/gui/vacuum_agent.py b/gui/vacuum_agent.py index 23292efb3..b07dab282 100644 --- a/gui/vacuum_agent.py +++ b/gui/vacuum_agent.py @@ -1,15 +1,14 @@ -from tkinter import * -import random -import sys import os.path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from tkinter import * + from agents import * +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) + loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world class Gui(Environment): - """This GUI environment has two locations, A and B. Each can be Dirty or Clean. The agent perceives its location and the location's status.""" @@ -33,7 +32,7 @@ def thing_classes(self): def percept(self, agent): """Returns the agent's location, and the location status (Dirty/Clean).""" - return (agent.location, self.status[agent.location]) + return agent.location, self.status[agent.location] def execute_action(self, agent, action): """Change the location status (Dirty/Clean); track performance. @@ -137,8 +136,7 @@ def move_agent(env, agent, before_step): # TODO: Add more agents to the environment. # TODO: Expand the environment to XYEnvironment. -def main(): - """The main function of the program.""" +if __name__ == "__main__": root = Tk() root.title("Vacuum Environment") root.geometry("420x380") @@ -154,7 +152,3 @@ def main(): create_agent(env, agent) next_button.config(command=lambda: env.update_env(agent)) root.mainloop() - - -if __name__ == "__main__": - main() diff --git a/gui/xy_vacuum_environment.py b/gui/xy_vacuum_environment.py index 4ba4497ea..093abc6c3 100644 --- a/gui/xy_vacuum_environment.py +++ b/gui/xy_vacuum_environment.py @@ -1,10 +1,10 @@ -from tkinter import * -import random -import sys import os.path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from tkinter import * + from agents import * +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) + class Gui(VacuumEnvironment): """This is a two-dimensional GUI environment. Each location may be @@ -13,8 +13,10 @@ class Gui(VacuumEnvironment): xi, yi = (0, 0) perceptible_distance = 1 - def __init__(self, root, width=7, height=7, elements=['D', 'W']): + def __init__(self, root, width=7, height=7, elements=None): super().__init__(width, height) + if elements is None: + elements = ['D', 'W'] self.root = root self.create_frames() self.create_buttons() @@ -71,10 +73,10 @@ def display_element(self, button): def execute_action(self, agent, action): """Determines the action the agent performs.""" - xi, yi = ((self.xi, self.yi)) + xi, yi = (self.xi, self.yi) if action == 'Suck': dirt_list = self.list_things_at(agent.location, Dirt) - if dirt_list != []: + if dirt_list: dirt = dirt_list[0] agent.performance += 100 self.delete_thing(dirt) @@ -166,11 +168,9 @@ def __init__(self, program=None): self.direction = Direction("up") -# TODO: -# Check the coordinate system. -# Give manual choice for agent's location. -def main(): - """The main function.""" +# TODO: Check the coordinate system. +# TODO: Give manual choice for agent's location. +if __name__ == "__main__": root = Tk() root.title("Vacuum Environment") root.geometry("420x440") @@ -189,7 +189,3 @@ def main(): next_button.config(command=env.update_env) reset_button.config(command=lambda: env.reset_env(agt)) root.mainloop() - - -if __name__ == "__main__": - main() diff --git a/learning4e.py b/learning4e.py index 7dba31cfa..3cf41ad1e 100644 --- a/learning4e.py +++ b/learning4e.py @@ -568,7 +568,7 @@ def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): # pass over all examples for example in examples: x = [1] + example - y = Sigmoid().f(dot_product(w, x)) + y = Sigmoid().function(dot_product(w, x)) h.append(Sigmoid().derivative(y)) t = example[idx_t] err.append(t - y) @@ -580,7 +580,7 @@ def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): def predict(example): x = [1] + example - return Sigmoid().f(dot_product(w, x)) + return Sigmoid().function(dot_product(w, x)) return predict diff --git a/pytest.ini b/pytest.ini index 7d983c3fc..5b9f41dbc 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,3 +1,4 @@ [pytest] filterwarnings = - ignore::ResourceWarning + ignore::DeprecationWarning + ignore::RuntimeWarning diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index 305c2e65c..060e55788 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -11,8 +11,8 @@ def test_neural_net(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - nnl_gd = NeuralNetLearner(iris, [4], learning_rate=0.15, epochs=100, optimizer=gradient_descent) - nnl_adam = NeuralNetLearner(iris, [4], learning_rate=0.001, epochs=200, optimizer=adam) + nnl_gd = NeuralNetLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent) + nnl_adam = NeuralNetLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam) tests = [([5.0, 3.1, 0.9, 0.1], 0), ([5.1, 3.5, 1.0, 0.0], 0), ([4.9, 3.3, 1.1, 0.1], 0), @@ -32,8 +32,8 @@ def test_perceptron(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - pl_gd = PerceptronLearner(iris, learning_rate=0.01, epochs=100, optimizer=gradient_descent) - pl_adam = PerceptronLearner(iris, learning_rate=0.01, epochs=100, optimizer=adam) + pl_gd = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=stochastic_gradient_descent) + pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam) tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1), diff --git a/utils4e.py b/utils4e.py index b0fbf8df8..777a88e4a 100644 --- a/utils4e.py +++ b/utils4e.py @@ -400,7 +400,7 @@ def gaussian_kernel_2D(size=3, sigma=0.5): class Activation: - def f(self, x): + def function(self, x): return NotImplementedError def derivative(self, x): @@ -414,7 +414,7 @@ def softmax1D(x): class Sigmoid(Activation): - def f(self, x): + def function(self, x): if x >= 100: return 1 if x <= -100: @@ -427,7 +427,7 @@ def derivative(self, value): class Relu(Activation): - def f(self, x): + def function(self, x): return max(0, x) def derivative(self, value): @@ -436,7 +436,7 @@ def derivative(self, value): class Elu(Activation): - def f(self, x, alpha=0.01): + def function(self, x, alpha=0.01): return x if x > 0 else alpha * (np.exp(x) - 1) def derivative(self, value, alpha=0.01): @@ -445,7 +445,7 @@ def derivative(self, value, alpha=0.01): class Tanh(Activation): - def f(self, x): + def function(self, x): return np.tanh(x) def derivative(self, value): @@ -454,7 +454,7 @@ def derivative(self, value): class LeakyRelu(Activation): - def f(self, x, alpha=0.01): + def function(self, x, alpha=0.01): return x if x > 0 else alpha * x def derivative(self, value, alpha=0.01): From 7e5c1d6a33f1b245cd020f6ed0695b33016ed4c8 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 30 Jan 2020 16:17:05 +0100 Subject: [PATCH 10/31] removed apostrophe --- search.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.ipynb b/search.ipynb index 0d9fa5e72..a8e8fe83b 100644 --- a/search.ipynb +++ b/search.ipynb @@ -4156,7 +4156,7 @@ "source": [ "We pick a gene in `x` to mutate and a gene from the gene pool to replace it with.\n", "\n", - "To help initializing the population we have the helper function `init_population`\":" + "To help initializing the population we have the helper function `init_population`:" ] }, { From 076556a090fe649223583b0126d414347bd06cad Mon Sep 17 00:00:00 2001 From: Soham Das <47505306+So-ham@users.noreply.github.com> Date: Sun, 16 Feb 2020 18:56:33 +0530 Subject: [PATCH 11/31] Update Optimizer and Backpropagation.ipynb (#1168) --- notebooks/chapter19/Optimizer and Backpropagation.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/chapter19/Optimizer and Backpropagation.ipynb b/notebooks/chapter19/Optimizer and Backpropagation.ipynb index e1c0a4db7..6a67e36ce 100644 --- a/notebooks/chapter19/Optimizer and Backpropagation.ipynb +++ b/notebooks/chapter19/Optimizer and Backpropagation.ipynb @@ -10,7 +10,7 @@ "\n", "## Stochastic Gradient Descent\n", "\n", - "The goal of an optimization algorithm is to nd the value of the parameter to make loss function very low. For some types of models, an optimization algorithm might find the global minimum value of loss function, but for neural network, the most efficient way to converge loss function to a local minimum is to minimize loss function according to each example.\n", + "The goal of an optimization algorithm is to find the value of the parameter to make loss function very low. For some types of models, an optimization algorithm might find the global minimum value of loss function, but for neural network, the most efficient way to converge loss function to a local minimum is to minimize loss function according to each example.\n", "\n", "Gradient descent uses the following update rule to minimize loss function:" ] From 70f4e82f8415b542b756ea565d0e6ac6bb528259 Mon Sep 17 00:00:00 2001 From: Soham Das <47505306+So-ham@users.noreply.github.com> Date: Sun, 16 Feb 2020 18:57:20 +0530 Subject: [PATCH 12/31] Search.ipynb (#1167) * Update search.ipynb * Update search.ipynb --- search.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/search.ipynb b/search.ipynb index a8e8fe83b..d3dc3cca7 100644 --- a/search.ipynb +++ b/search.ipynb @@ -2853,6 +2853,7 @@ " neighbor = argmax_random_tie(neighbors,\n", " key=lambda node: problem.value(node.state))\n", " if problem.value(neighbor.state) <= problem.value(current.state):\n", + " \"\"\"Note that it is based on negative path cost method\"\"\"\n", " current.state = neighbor.state\n", " iterations -= 1\n", " \n", From 918168cd1c8edf81ec6fbbfc75fc511bffdc9da5 Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Sun, 16 Feb 2020 14:33:06 +0100 Subject: [PATCH 13/31] added LinearRegressionLearner, LogisticRegressionLearner with tests and fixed NeuralNetLearner and PerceptronLearner (#1163) --- deep_learning4e.py | 263 ++++++++++++++-------- learning.py | 6 +- learning4e.py | 396 +++++++++++++++++++++------------- perception4e.py | 2 +- pytest.ini | 1 + tests/test_deep_learning4e.py | 59 ++--- tests/test_learning.py | 2 +- tests/test_learning4e.py | 69 ++++-- utils4e.py | 107 ++------- 9 files changed, 506 insertions(+), 399 deletions(-) diff --git a/deep_learning4e.py b/deep_learning4e.py index 0a0387afc..0e2aec242 100644 --- a/deep_learning4e.py +++ b/deep_learning4e.py @@ -8,8 +8,8 @@ from keras.layers import Embedding, SimpleRNN, Dense from keras.preprocessing import sequence -from utils4e import (Sigmoid, dot_product, softmax1D, conv1D, gaussian_kernel, element_wise_product, vector_add, - random_weights, scalar_vector_product, matrix_multiplication, map_vector, mean_squared_error_loss) +from utils4e import (softmax1D, conv1D, gaussian_kernel, element_wise_product, vector_add, random_weights, + scalar_vector_product, map_vector, mean_squared_error_loss) class Node: @@ -31,13 +31,67 @@ class Layer: """ def __init__(self, size): - self.nodes = [Node() for _ in range(size)] + self.nodes = np.array([Node() for _ in range(size)]) def forward(self, inputs): """Define the operation to get the output of this layer""" raise NotImplementedError +class Activation: + + def function(self, x): + return NotImplementedError + + def derivative(self, x): + return NotImplementedError + + +class Sigmoid(Activation): + + def function(self, x): + return 1 / (1 + np.exp(-x)) + + def derivative(self, value): + return value * (1 - value) + + +class Relu(Activation): + + def function(self, x): + return max(0, x) + + def derivative(self, value): + return 1 if value > 0 else 0 + + +class Elu(Activation): + + def function(self, x, alpha=0.01): + return x if x > 0 else alpha * (np.exp(x) - 1) + + def derivative(self, value, alpha=0.01): + return 1 if value > 0 else alpha * np.exp(value) + + +class Tanh(Activation): + + def function(self, x): + return np.tanh(x) + + def derivative(self, value): + return 1 - (value ** 2) + + +class LeakyRelu(Activation): + + def function(self, x, alpha=0.01): + return x if x > 0 else alpha * x + + def derivative(self, value, alpha=0.01): + return 1 if value > 0 else alpha + + class InputLayer(Layer): """1D input layer. Layer size is the same as input vector size.""" @@ -88,7 +142,7 @@ def forward(self, inputs): res = [] # get the output value of each unit for unit in self.nodes: - val = self.activation.function(dot_product(unit.weights, inputs)) + val = self.activation.function(np.dot(unit.weights, inputs)) unit.value = val res.append(val) return res @@ -144,6 +198,31 @@ def forward(self, features): return res +class BatchNormalizationLayer(Layer): + """Batch normalization layer.""" + + def __init__(self, size, eps=0.001): + super().__init__(size) + self.eps = eps + # self.weights = [beta, gamma] + self.weights = [0, 0] + self.inputs = None + + def forward(self, inputs): + # mean value of inputs + mu = sum(inputs) / len(inputs) + # standard error of inputs + stderr = statistics.stdev(inputs) + self.inputs = inputs + res = [] + # get normalized value of each input + for i in range(len(self.nodes)): + val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.eps + stderr ** 2) + self.weights[1]] + res.append(val) + self.nodes[i].value = val + return res + + def init_examples(examples, idx_i, idx_t, o_units): """Init examples from dataset.examples.""" @@ -164,7 +243,7 @@ def init_examples(examples, idx_i, idx_t, o_units): return inputs, targets -def stochastic_gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, batch_size=1, verbose=None): +def stochastic_gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, batch_size=1, verbose=False): """ Gradient descent algorithm to update the learnable parameters of a network. :return: the updated network @@ -181,23 +260,23 @@ def stochastic_gradient_descent(dataset, net, loss, epochs=1000, l_rate=0.01, ba # compute gradients of weights gs, batch_loss = BackPropagation(inputs, targets, weights, net, loss) # update weights with gradient descent - weights = vector_add(weights, scalar_vector_product(-l_rate, gs)) + weights = [x + y for x, y in zip(weights, [np.array(tg) * -l_rate for tg in gs])] total_loss += batch_loss # update the weights of network each batch for i in range(len(net)): - if weights[i]: + if weights[i].size != 0: for j in range(len(weights[i])): net[i].nodes[j].weights = weights[i][j] - if verbose and (e + 1) % verbose == 0: + if verbose: print("epoch:{}, total_loss:{}".format(e + 1, total_loss)) return net def adam(dataset, net, loss, epochs=1000, rho=(0.9, 0.999), delta=1 / 10 ** 8, - l_rate=0.001, batch_size=1, verbose=None): + l_rate=0.001, batch_size=1, verbose=False): """ [Figure 19.6] Adam optimizer to update the learnable parameters of a network. @@ -247,7 +326,7 @@ def adam(dataset, net, loss, epochs=1000, rho=(0.9, 0.999), delta=1 / 10 ** 8, for j in range(len(weights[i])): net[i].nodes[j].weights = weights[i][j] - if verbose and (e + 1) % verbose == 0: + if verbose: print("epoch:{}, total_loss:{}".format(e + 1, total_loss)) return net @@ -288,16 +367,16 @@ def BackPropagation(inputs, targets, theta, net, loss): # initialize delta delta = [[] for _ in range(n_layers)] - previous = [layer_out[i] - t_val[i] for i in range(o_units)] + previous = np.array([layer_out[i] - t_val[i] for i in range(o_units)]) h_layers = n_layers - 1 # backward pass for i in range(h_layers, 0, -1): layer = net[i] - derivative = [layer.activation.derivative(node.value) for node in layer.nodes] - delta[i] = element_wise_product(previous, derivative) + derivative = np.array([layer.activation.derivative(node.value) for node in layer.nodes]) + delta[i] = previous * derivative # pass to layer i-1 in the next iteration - previous = matrix_multiplication([delta[i]], theta[i])[0] + previous = np.matmul([delta[i]], theta[i])[0] # compute gradient of layer i gradients[i] = [scalar_vector_product(d, net[i].inputs) for d in delta[i]] @@ -307,98 +386,108 @@ def BackPropagation(inputs, targets, theta, net, loss): return total_gradients, batch_loss -class BatchNormalizationLayer(Layer): - """Batch normalization layer.""" - - def __init__(self, size, eps=0.001): - super().__init__(size) - self.eps = eps - # self.weights = [beta, gamma] - self.weights = [0, 0] - self.inputs = None - - def forward(self, inputs): - # mean value of inputs - mu = sum(inputs) / len(inputs) - # standard error of inputs - stderr = statistics.stdev(inputs) - self.inputs = inputs - res = [] - # get normalized value of each input - for i in range(len(self.nodes)): - val = [(inputs[i] - mu) * self.weights[0] / np.sqrt(self.eps + stderr ** 2) + self.weights[1]] - res.append(val) - self.nodes[i].value = val - return res - - def get_batch(examples, batch_size=1): """Split examples into multiple batches""" for i in range(0, len(examples), batch_size): yield examples[i: i + batch_size] -def NeuralNetLearner(dataset, hidden_layer_sizes, l_rate=0.01, epochs=1000, batch_size=1, - optimizer=stochastic_gradient_descent, verbose=None): +class NeuralNetworkLearner: """ Simple dense multilayer neural network. :param hidden_layer_sizes: size of hidden layers in the form of a list """ - input_size = len(dataset.inputs) - output_size = len(dataset.values[dataset.target]) - # initialize the network - raw_net = [InputLayer(input_size)] - # add hidden layers - hidden_input_size = input_size - for h_size in hidden_layer_sizes: - raw_net.append(DenseLayer(hidden_input_size, h_size)) - hidden_input_size = h_size - raw_net.append(DenseLayer(hidden_input_size, output_size)) - - # update parameters of the network - learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=l_rate, - batch_size=batch_size, verbose=verbose) - - def predict(example): - n_layers = len(learned_net) + def __init__(self, dataset, hidden_layer_sizes, l_rate=0.01, epochs=1000, batch_size=10, + optimizer=stochastic_gradient_descent, loss=mean_squared_error_loss, verbose=False, plot=False): + self.dataset = dataset + self.l_rate = l_rate + self.epochs = epochs + self.batch_size = batch_size + self.optimizer = optimizer + self.loss = loss + self.verbose = verbose + self.plot = plot + + input_size = len(dataset.inputs) + output_size = len(dataset.values[dataset.target]) + + # initialize the network + raw_net = [InputLayer(input_size)] + # add hidden layers + hidden_input_size = input_size + for h_size in hidden_layer_sizes: + raw_net.append(DenseLayer(hidden_input_size, h_size)) + hidden_input_size = h_size + raw_net.append(DenseLayer(hidden_input_size, output_size)) + self.raw_net = raw_net + + def fit(self, X, y): + self.learned_net = self.optimizer(self.dataset, self.raw_net, loss=self.loss, epochs=self.epochs, + l_rate=self.l_rate, batch_size=self.batch_size, verbose=self.verbose) + return self + + def predict(self, example): + n_layers = len(self.learned_net) layer_input = example layer_out = example # get the output of each layer by forward passing for i in range(1, n_layers): - layer_out = learned_net[i].forward(layer_input) + layer_out = self.learned_net[i].forward(np.array(layer_input).reshape((-1, 1))) layer_input = layer_out return layer_out.index(max(layer_out)) - return predict - -def PerceptronLearner(dataset, l_rate=0.01, epochs=1000, batch_size=1, - optimizer=stochastic_gradient_descent, verbose=None): +class PerceptronLearner: """ Simple perceptron neural network. """ - input_size = len(dataset.inputs) - output_size = len(dataset.values[dataset.target]) - # initialize the network, add dense layer - raw_net = [InputLayer(input_size), DenseLayer(input_size, output_size)] - - # update the network - learned_net = optimizer(dataset, raw_net, mean_squared_error_loss, epochs, l_rate=l_rate, - batch_size=batch_size, verbose=verbose) - - def predict(example): - layer_out = learned_net[1].forward(example) + def __init__(self, dataset, l_rate=0.01, epochs=1000, batch_size=10, optimizer=stochastic_gradient_descent, + loss=mean_squared_error_loss, verbose=False, plot=False): + self.dataset = dataset + self.l_rate = l_rate + self.epochs = epochs + self.batch_size = batch_size + self.optimizer = optimizer + self.loss = loss + self.verbose = verbose + self.plot = plot + + input_size = len(dataset.inputs) + output_size = len(dataset.values[dataset.target]) + + # initialize the network, add dense layer + self.raw_net = [InputLayer(input_size), DenseLayer(input_size, output_size)] + + def fit(self, X, y): + self.learned_net = self.optimizer(self.dataset, self.raw_net, loss=self.loss, epochs=self.epochs, + l_rate=self.l_rate, batch_size=self.batch_size, verbose=self.verbose) + return self + + def predict(self, example): + layer_out = self.learned_net[1].forward(np.array(example).reshape((-1, 1))) return layer_out.index(max(layer_out)) - return predict + +def keras_dataset_loader(dataset, max_length=500): + """ + Helper function to load keras datasets. + :param dataset: keras data set type + :param max_length: max length of each input sequence + """ + # init dataset + (X_train, y_train), (X_val, y_val) = dataset + if max_length > 0: + X_train = sequence.pad_sequences(X_train, maxlen=max_length) + X_val = sequence.pad_sequences(X_val, maxlen=max_length) + return (X_train[10:], y_train[10:]), (X_val, y_val), (X_train[:10], y_train[:10]) -def SimpleRNNLearner(train_data, val_data, epochs=2): +def SimpleRNNLearner(train_data, val_data, epochs=2, verbose=False): """ RNN example for text sentimental analysis. :param train_data: a tuple of (training data, targets) @@ -406,6 +495,7 @@ def SimpleRNNLearner(train_data, val_data, epochs=2): Targets: ndarray taking targets of each example. Each target is mapped to an integer :param val_data: a tuple of (validation data, targets) :param epochs: number of epochs + :param verbose: verbosity mode :return: a keras model """ @@ -424,31 +514,18 @@ def SimpleRNNLearner(train_data, val_data, epochs=2): model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # train the model - model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=epochs, batch_size=128, verbose=2) + model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=epochs, batch_size=128, verbose=verbose) return model -def keras_dataset_loader(dataset, max_length=500): - """ - Helper function to load keras datasets. - :param dataset: keras data set type - :param max_length: max length of each input sequence - """ - # init dataset - (X_train, y_train), (X_val, y_val) = dataset - if max_length > 0: - X_train = sequence.pad_sequences(X_train, maxlen=max_length) - X_val = sequence.pad_sequences(X_val, maxlen=max_length) - return (X_train[10:], y_train[10:]), (X_val, y_val), (X_train[:10], y_train[:10]) - - -def AutoencoderLearner(inputs, encoding_size, epochs=200): +def AutoencoderLearner(inputs, encoding_size, epochs=200, verbose=False): """ Simple example of linear auto encoder learning producing the input itself. :param inputs: a batch of input data in np.ndarray type :param encoding_size: int, the size of encoding layer :param epochs: number of epochs + :param verbose: verbosity mode :return: a keras model """ @@ -466,6 +543,6 @@ def AutoencoderLearner(inputs, encoding_size, epochs=200): model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy']) # train the model - model.fit(inputs, inputs, epochs=epochs, batch_size=10, verbose=2) + model.fit(inputs, inputs, epochs=epochs, batch_size=10, verbose=verbose) return model diff --git a/learning.py b/learning.py index 764392c7d..e83467c43 100644 --- a/learning.py +++ b/learning.py @@ -201,7 +201,7 @@ def parse_csv(input, delim=','): return [list(map(num_or_str, line.split(delim))) for line in lines] -def err_ratio(predict, dataset, examples=None, verbose=0): +def err_ratio(predict, dataset, examples=None): """ Return the proportion of the examples that are NOT correctly predicted. verbose - 0: No output; 1: Output wrong; 2 (or greater): Output correct @@ -215,10 +215,6 @@ def err_ratio(predict, dataset, examples=None, verbose=0): output = predict(dataset.sanitize(example)) if output == desired: right += 1 - if verbose >= 2: - print(' OK: got {} for {}'.format(desired, example)) - elif verbose: - print('WRONG: got {}, expected {} for {}'.format(output, desired, example)) return 1 - (right / len(examples)) diff --git a/learning4e.py b/learning4e.py index 3cf41ad1e..4ef022e83 100644 --- a/learning4e.py +++ b/learning4e.py @@ -5,7 +5,9 @@ from statistics import stdev from qpsolvers import solve_qp +from scipy.optimize import minimize +from deep_learning4e import Sigmoid from probabilistic_learning import NaiveBayesLearner from utils4e import * @@ -128,7 +130,7 @@ def update_values(self): def sanitize(self, example): """Return a copy of example, with non-input attributes replaced by None.""" - return [attr_i if i in self.inputs else None for i, attr_i in enumerate(example)] + return [attr_i if i in self.inputs else None for i, attr_i in enumerate(example)][:-1] def classes_to_numbers(self, classes=None): """Converts class names to numbers.""" @@ -201,7 +203,7 @@ def parse_csv(input, delim=','): return [list(map(num_or_str, line.split(delim))) for line in lines] -def err_ratio(predict, dataset, examples=None, verbose=0): +def err_ratio(learner, dataset, examples=None): """ Return the proportion of the examples that are NOT correctly predicted. verbose - 0: No output; 1: Output wrong; 2 (or greater): Output correct @@ -212,22 +214,18 @@ def err_ratio(predict, dataset, examples=None, verbose=0): right = 0 for example in examples: desired = example[dataset.target] - output = predict(dataset.sanitize(example)) - if output == desired: + output = learner.predict(dataset.sanitize(example)) + if np.allclose(output, desired): right += 1 - if verbose >= 2: - print(' OK: got {} for {}'.format(desired, example)) - elif verbose: - print('WRONG: got {}, expected {} for {}'.format(output, desired, example)) return 1 - (right / len(examples)) -def grade_learner(predict, tests): +def grade_learner(learner, tests): """ Grades the given learner based on how many tests it passes. tests is a list with each element in the form: (values, output). """ - return mean(int(predict(X) == y) for X, y in tests) + return mean(int(learner.predict(X) == y) for X, y in tests) def train_test_split(dataset, start=None, end=None, test_split=None): @@ -323,18 +321,18 @@ def score(learner, size): return [(size, mean([score(learner, size) for _ in range(trials)])) for size in sizes] -def PluralityLearner(dataset): +class PluralityLearner: """ A very dumb algorithm: always pick the result that was most popular in the training data. Makes a baseline for comparison. """ - most_popular = mode([e[dataset.target] for e in dataset.examples]) - def predict(example): - """Always return same result: the most popular from the training set.""" - return most_popular + def __init__(self, dataset): + self.most_popular = mode([e[dataset.target] for e in dataset.examples]) - return predict + def predict(self, example): + """Always return same result: the most popular from the training set.""" + return self.most_popular class DecisionFork: @@ -390,61 +388,67 @@ def __repr__(self): return repr(self.result) -def DecisionTreeLearner(dataset): +class DecisionTreeLearner: """[Figure 18.5]""" - target, values = dataset.target, dataset.values + def __init__(self, dataset): + self.dataset = dataset + self.tree = self.decision_tree_learning(dataset.examples, dataset.inputs) - def decision_tree_learning(examples, attrs, parent_examples=()): + def decision_tree_learning(self, examples, attrs, parent_examples=()): if len(examples) == 0: - return plurality_value(parent_examples) - if all_same_class(examples): - return DecisionLeaf(examples[0][target]) + return self.plurality_value(parent_examples) + if self.all_same_class(examples): + return DecisionLeaf(examples[0][self.dataset.target]) if len(attrs) == 0: - return plurality_value(examples) - A = choose_attribute(attrs, examples) - tree = DecisionFork(A, dataset.attr_names[A], plurality_value(examples)) - for (v_k, exs) in split_by(A, examples): - subtree = decision_tree_learning(exs, remove_all(A, attrs), examples) + return self.plurality_value(examples) + A = self.choose_attribute(attrs, examples) + tree = DecisionFork(A, self.dataset.attr_names[A], self.plurality_value(examples)) + for (v_k, exs) in self.split_by(A, examples): + subtree = self.decision_tree_learning(exs, remove_all(A, attrs), examples) tree.add(v_k, subtree) return tree - def plurality_value(examples): + def plurality_value(self, examples): """ Return the most popular target value for this set of examples. (If target is binary, this is the majority; otherwise plurality). """ - popular = argmax_random_tie(values[target], key=lambda v: count(target, v, examples)) + popular = argmax_random_tie(self.dataset.values[self.dataset.target], + key=lambda v: self.count(self.dataset.target, v, examples)) return DecisionLeaf(popular) - def count(attr, val, examples): + def count(self, attr, val, examples): """Count the number of examples that have example[attr] = val.""" return sum(e[attr] == val for e in examples) - def all_same_class(examples): + def all_same_class(self, examples): """Are all these examples in the same target class?""" - class0 = examples[0][target] - return all(e[target] == class0 for e in examples) + class0 = examples[0][self.dataset.target] + return all(e[self.dataset.target] == class0 for e in examples) - def choose_attribute(attrs, examples): + def choose_attribute(self, attrs, examples): """Choose the attribute with the highest information gain.""" - return argmax_random_tie(attrs, key=lambda a: information_gain(a, examples)) + return argmax_random_tie(attrs, key=lambda a: self.information_gain(a, examples)) - def information_gain(attr, examples): + def information_gain(self, attr, examples): """Return the expected reduction in entropy from splitting by attr.""" def I(examples): - return information_content([count(target, v, examples) for v in values[target]]) + return information_content([self.count(self.dataset.target, v, examples) + for v in self.dataset.values[self.dataset.target]]) n = len(examples) - remainder = sum((len(examples_i) / n) * I(examples_i) for (v, examples_i) in split_by(attr, examples)) + remainder = sum((len(examples_i) / n) * I(examples_i) + for (v, examples_i) in self.split_by(attr, examples)) return I(examples) - remainder - def split_by(attr, examples): + def split_by(self, attr, examples): """Return a list of (val, examples) pairs for each val of attr.""" - return [(v, [e for e in examples if e[attr] == v]) for v in values[attr]] + return [(v, [e for e in examples if e[attr] == v]) for v in self.dataset.values[attr]] - return decision_tree_learning(dataset.examples, dataset.inputs) + def predict(self, x): + return self.tree(x) def information_content(values): @@ -453,136 +457,213 @@ def information_content(values): return sum(-p * np.log2(p) for p in probabilities) -def DecisionListLearner(dataset): +class DecisionListLearner: """ [Figure 18.11] A decision list implemented as a list of (test, value) pairs. """ - def decision_list_learning(examples): + def __init__(self, dataset): + self.predict.decision_list = self.decision_list_learning(set(dataset.examples)) + + def decision_list_learning(self, examples): if not examples: return [(True, False)] - t, o, examples_t = find_examples(examples) + t, o, examples_t = self.find_examples(examples) if not t: raise Exception - return [(t, o)] + decision_list_learning(examples - examples_t) + return [(t, o)] + self.decision_list_learning(examples - examples_t) - def find_examples(examples): + def find_examples(self, examples): """ Find a set of examples that all have the same outcome under some test. Return a tuple of the test, outcome, and examples. """ raise NotImplementedError - def passes(example, test): + def passes(self, example, test): """Does the example pass the test?""" raise NotImplementedError - def predict(example): + def predict(self, example): """Predict the outcome for the first passing test.""" - for test, outcome in predict.decision_list: - if passes(example, test): + for test, outcome in self.predict.decision_list: + if self.passes(example, test): return outcome - predict.decision_list = decision_list_learning(set(dataset.examples)) - - return predict - -def NearestNeighborLearner(dataset, k=1): +class NearestNeighborLearner: """k-NearestNeighbor: the k nearest neighbors vote.""" - def predict(example): + def __init__(self, dataset, k=1): + self.dataset = dataset + self.k = k + + def predict(self, example): """Find the k closest items, and have them vote for the best.""" - best = heapq.nsmallest(k, ((dataset.distance(e, example), e) for e in dataset.examples)) - return mode(e[dataset.target] for (d, e) in best) + best = heapq.nsmallest(self.k, ((self.dataset.distance(e, example), e) for e in self.dataset.examples)) + return mode(e[self.dataset.target] for (d, e) in best) - return predict +class LossFunction: + def __init__(self, X, y): + self.X = X + self.y = y.flatten() -def LinearLearner(dataset, learning_rate=0.01, epochs=100): - """ - [Section 18.6.4] - Linear classifier with hard threshold. - """ - idx_i = dataset.inputs - idx_t = dataset.target - examples = dataset.examples - num_examples = len(examples) + @staticmethod + def predict(X, theta): + return NotImplementedError + + def function(self, theta): + return NotImplementedError - # X transpose - X_col = [dataset.values[i] for i in idx_i] # vertical columns of X + def jacobian(self, theta): + return NotImplementedError - # add dummy - ones = [1 for _ in range(len(examples))] - X_col = [ones] + X_col - # initialize random weights - num_weights = len(idx_i) + 1 - w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights) +class MeanSquaredError(LossFunction): + def __init__(self, X, y): + super().__init__(X, y) + self.x_star = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y) # or np.linalg.lstsq(X, y)[0] - for epoch in range(epochs): - err = [] - # pass over all examples - for example in examples: - x = [1] + example - y = dot_product(w, x) - t = example[idx_t] - err.append(t - y) + @staticmethod + def predict(X, theta): + return np.dot(X, theta) + + def function(self, theta): + return (1 / 2 * self.X.shape[0]) * np.sum(np.square(self.predict(self.X, theta) - self.y)) + + def jacobian(self, theta): + return (1 / self.X.shape[0]) * np.dot(self.X.T, self.predict(self.X, theta) - self.y) + + +class CrossEntropy(LossFunction): + def __init__(self, X, y): + super().__init__(X, y) + + @staticmethod + def predict(X, theta): + return Sigmoid().function(np.dot(X, theta)) + + def function(self, theta): + pred = self.predict(self.X, theta) + return -(1 / self.X.shape[0]) * np.sum(self.y * np.log(pred) + (1 - self.y) * np.log(1 - pred)) + + def jacobian(self, theta): + return (1 / self.X.shape[0]) * np.dot(self.X.T, self.predict(self.X, theta) - self.y) + + +class LinearRegressionLearner: + """ + [Section 18.6.4] + Linear Regressor + """ - # update weights - for i in range(len(w)): - w[i] = w[i] + learning_rate * (dot_product(err, X_col[i]) / num_examples) + def __init__(self, l_rate=0.01, epochs=1000, optimizer='bfgs'): + self.l_rate = l_rate + self.epochs = epochs + self.optimizer = optimizer - def predict(example): - x = [1] + example - return dot_product(w, x) + def fit(self, X, y): + loss = MeanSquaredError(X, y) + self.w = minimize(fun=loss.function, x0=np.zeros((X.shape[1], 1)), method=self.optimizer, jac=loss.jacobian).x + return self - return predict + def predict(self, example): + return np.dot(example, self.w) -def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): +class BinaryLogisticRegressionLearner: """ [Section 18.6.5] - Linear classifier with logistic regression. + Logistic Regression Classifier """ - idx_i = dataset.inputs - idx_t = dataset.target - examples = dataset.examples - num_examples = len(examples) - # X transpose - X_col = [dataset.values[i] for i in idx_i] # vertical columns of X + def __init__(self, l_rate=0.01, epochs=1000, optimizer='bfgs'): + self.l_rate = l_rate + self.epochs = epochs + self.optimizer = optimizer - # add dummy - ones = [1 for _ in range(len(examples))] - X_col = [ones] + X_col + def fit(self, X, y): + self.labels = np.unique(y) + y = np.where(y == self.labels[0], 0, 1) + loss = CrossEntropy(X, y) + self.w = minimize(fun=loss.function, x0=np.zeros((X.shape[1], 1)), method=self.optimizer, jac=loss.jacobian).x + return self + + def predict_score(self, x): + return CrossEntropy.predict(x, self.w) - # initialize random weights - num_weights = len(idx_i) + 1 - w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights) + def predict(self, x): + return np.where(self.predict_score(x) >= 0.5, self.labels[1], self.labels[0]).astype(int) - for epoch in range(epochs): - err = [] - h = [] - # pass over all examples - for example in examples: - x = [1] + example - y = Sigmoid().function(dot_product(w, x)) - h.append(Sigmoid().derivative(y)) - t = example[idx_t] - err.append(t - y) - # update weights - for i in range(len(w)): - buffer = [x * y for x, y in zip(err, h)] - w[i] = w[i] + learning_rate * (dot_product(buffer, X_col[i]) / num_examples) +class MultiLogisticRegressionLearner: + def __init__(self, l_rate=0.01, epochs=1000, optimizer='bfgs', decision_function='ovr'): + self.l_rate = l_rate + self.epochs = epochs + self.optimizer = optimizer + self.decision_function = decision_function + self.n_class, self.classifiers = 0, [] - def predict(example): - x = [1] + example - return Sigmoid().function(dot_product(w, x)) + def fit(self, X, y): + """ + Trains n_class or n_class * (n_class - 1) / 2 classifiers + according to the training method, ovr or ovo respectively. + :param X: array of size [n_samples, n_features] holding the training samples + :param y: array of size [n_samples] holding the class labels + :return: array of classifiers + """ + labels = np.unique(y) + self.n_class = len(labels) + if self.decision_function == 'ovr': # one-vs-rest method + for label in labels: + y1 = np.array(y) + y1[y1 != label] = -1.0 + y1[y1 == label] = 1.0 + clf = BinaryLogisticRegressionLearner(self.l_rate, self.epochs, self.optimizer) + clf.fit(X, y1) + self.classifiers.append(copy.deepcopy(clf)) + elif self.decision_function == 'ovo': # use one-vs-one method + n_labels = len(labels) + for i in range(n_labels): + for j in range(i + 1, n_labels): + neg_id, pos_id = y == labels[i], y == labels[j] + x1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]] + y1[y1 == labels[i]] = -1.0 + y1[y1 == labels[j]] = 1.0 + clf = BinaryLogisticRegressionLearner(self.l_rate, self.epochs, self.optimizer) + clf.fit(x1, y1) + self.classifiers.append(copy.deepcopy(clf)) + else: + return ValueError("Decision function must be either 'ovr' or 'ovo'.") + return self - return predict + def predict(self, x): + """ + Predicts the class of a given example according to the training method. + """ + n_samples = len(x) + if self.decision_function == 'ovr': # one-vs-rest method + assert len(self.classifiers) == self.n_class + score = np.zeros((n_samples, self.n_class)) + for i in range(self.n_class): + clf = self.classifiers[i] + score[:, i] = clf.predict_score(x) + return np.argmax(score, axis=1) + elif self.decision_function == 'ovo': # use one-vs-one method + assert len(self.classifiers) == self.n_class * (self.n_class - 1) / 2 + vote = np.zeros((n_samples, self.n_class)) + clf_id = 0 + for i in range(self.n_class): + for j in range(i + 1, self.n_class): + res = self.classifiers[clf_id].predict(x) + vote[res < 0, i] += 1.0 # negative sample: class i + vote[res > 0, j] += 1.0 # positive sample: class j + clf_id += 1 + return np.argmax(vote, axis=1) + else: + return ValueError("Decision function must be either 'ovr' or 'ovo'.") class BinarySVM: @@ -613,6 +694,7 @@ def fit(self, X, y): sv_boundary = self.alphas < self.C - self.eps self.b = np.mean(self.sv_y[sv_boundary] - np.dot(self.alphas * self.sv_y, self.kernel(self.sv_x, self.sv_x[sv_boundary]))) + return self def QP(self, X, y): """ @@ -687,6 +769,7 @@ def fit(self, X, y): self.classifiers.append(copy.deepcopy(clf)) else: return ValueError("Decision function must be either 'ovr' or 'ovo'.") + return self def predict(self, x): """ @@ -715,18 +798,17 @@ def predict(self, x): return ValueError("Decision function must be either 'ovr' or 'ovo'.") -def EnsembleLearner(learners): +class EnsembleLearner: """Given a list of learning algorithms, have them vote.""" - def train(dataset): - predictors = [learner(dataset) for learner in learners] + def __init__(self, learners): + self.learners = learners - def predict(example): - return mode(predictor(example) for predictor in predictors) + def train(self, dataset): + self.predictors = [learner(dataset) for learner in self.learners] - return predict - - return train + def predict(self, example): + return mode(predictor.predict(example) for predictor in self.predictors) def ada_boost(dataset, L, K): @@ -740,24 +822,26 @@ def ada_boost(dataset, L, K): for k in range(K): h_k = L(dataset, w) h.append(h_k) - error = sum(weight for example, weight in zip(examples, w) if example[target] != h_k(example)) + error = sum(weight for example, weight in zip(examples, w) if example[target] != h_k.predict(example[:-1])) # avoid divide-by-0 from either 0% or 100% error rates error = np.clip(error, eps, 1 - eps) for j, example in enumerate(examples): - if example[target] == h_k(example): + if example[target] == h_k.predict(example[:-1]): w[j] *= error / (1 - error) w = normalize(w) z.append(np.log((1 - error) / error)) return weighted_majority(h, z) -def weighted_majority(predictors, weights): +class weighted_majority: """Return a predictor that takes a weighted vote.""" - def predict(example): - return weighted_mode((predictor(example) for predictor in predictors), weights) + def __init__(self, predictors, weights): + self.predictors = predictors + self.weights = weights - return predict + def predict(self, example): + return weighted_mode((predictor.predict(example) for predictor in self.predictors), self.weights) def weighted_mode(values, weights): @@ -772,28 +856,28 @@ def weighted_mode(values, weights): return max(totals, key=totals.__getitem__) -def RandomForest(dataset, n=5): +class RandomForest: """An ensemble of Decision Trees trained using bagging and feature bagging.""" - def data_bagging(dataset, m=0): + def __init__(self, dataset, n=5): + self.dataset = dataset + self.n = n + self.predictors = [DecisionTreeLearner(DataSet(examples=self.data_bagging(), attrs=self.dataset.attrs, + attr_names=self.dataset.attr_names, target=self.dataset.target, + inputs=self.feature_bagging())) for _ in range(self.n)] + + def data_bagging(self, m=0): """Sample m examples with replacement""" - n = len(dataset.examples) - return weighted_sample_with_replacement(m or n, dataset.examples, [1] * n) + n = len(self.dataset.examples) + return weighted_sample_with_replacement(m or n, self.dataset.examples, [1] * n) - def feature_bagging(dataset, p=0.7): + def feature_bagging(self, p=0.7): """Feature bagging with probability p to retain an attribute""" - inputs = [i for i in dataset.inputs if probability(p)] - return inputs or dataset.inputs - - def predict(example): - print([predictor(example) for predictor in predictors]) - return mode(predictor(example) for predictor in predictors) - - predictors = [DecisionTreeLearner(DataSet(examples=data_bagging(dataset), attrs=dataset.attrs, - attr_names=dataset.attr_names, target=dataset.target, - inputs=feature_bagging(dataset))) for _ in range(n)] + inputs = [i for i in self.dataset.inputs if probability(p)] + return inputs or self.dataset.inputs - return predict + def predict(self, example): + return mode(predictor.predict(example) for predictor in self.predictors) def WeightedLearner(unweighted_learner): @@ -804,7 +888,11 @@ def WeightedLearner(unweighted_learner): """ def train(dataset, weights): - return unweighted_learner(replicated_dataset(dataset, weights)) + dataset = replicated_dataset(dataset, weights) + n_samples, n_features = len(dataset.examples), dataset.target + X, y = np.array([x[:n_features] for x in dataset.examples]), \ + np.array([x[n_features] for x in dataset.examples]) + return unweighted_learner.fit(X, y) return train diff --git a/perception4e.py b/perception4e.py index d5bc15718..2cb4b3891 100644 --- a/perception4e.py +++ b/perception4e.py @@ -392,7 +392,7 @@ def selective_search(image): # faster RCNN def pool_rois(feature_map, rois, pooled_height, pooled_width): """ - Applies ROI pooling for a single image and varios ROIs + Applies ROI pooling for a single image and various ROIs :param feature_map: ndarray, in shape of (width, height, channel) :param rois: list of roi :param pooled_height: height of pooled area diff --git a/pytest.ini b/pytest.ini index 5b9f41dbc..1561b6fe6 100644 --- a/pytest.ini +++ b/pytest.ini @@ -1,4 +1,5 @@ [pytest] filterwarnings = ignore::DeprecationWarning + ignore::UserWarning ignore::RuntimeWarning diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index 060e55788..b23f8bcfa 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -6,44 +6,45 @@ random.seed("aima-python") +iris_tests = [([5.0, 3.1, 0.9, 0.1], 0), + ([5.1, 3.5, 1.0, 0.0], 0), + ([4.9, 3.3, 1.1, 0.1], 0), + ([6.0, 3.0, 4.0, 1.1], 1), + ([6.1, 2.2, 3.5, 1.0], 1), + ([5.9, 2.5, 3.3, 1.1], 1), + ([7.5, 4.1, 6.2, 2.3], 2), + ([7.3, 4.0, 6.1, 2.4], 2), + ([7.0, 3.3, 6.1, 2.5], 2)] + def test_neural_net(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - nnl_gd = NeuralNetLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent) - nnl_adam = NeuralNetLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam) - tests = [([5.0, 3.1, 0.9, 0.1], 0), - ([5.1, 3.5, 1.0, 0.0], 0), - ([4.9, 3.3, 1.1, 0.1], 0), - ([6.0, 3.0, 4.0, 1.1], 1), - ([6.1, 2.2, 3.5, 1.0], 1), - ([5.9, 2.5, 3.3, 1.1], 1), - ([7.5, 4.1, 6.2, 2.3], 2), - ([7.3, 4.0, 6.1, 2.4], 2), - ([7.0, 3.3, 6.1, 2.5], 2)] - assert grade_learner(nnl_gd, tests) >= 1 / 3 - assert err_ratio(nnl_gd, iris) < 0.21 - assert grade_learner(nnl_adam, tests) >= 1 / 3 - assert err_ratio(nnl_adam, iris) < 0.21 + n_samples, n_features = len(iris.examples), iris.target + X, y = np.array([x[:n_features] for x in iris.examples]), \ + np.array([x[n_features] for x in iris.examples]) + nnl_gd = NeuralNetworkLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) + assert grade_learner(nnl_gd, iris_tests) > 0.7 + assert err_ratio(nnl_gd, iris) < 0.08 + nnl_adam = NeuralNetworkLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam).fit(X, y) + assert grade_learner(nnl_adam, iris_tests) == 1 + assert err_ratio(nnl_adam, iris) < 0.08 def test_perceptron(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - pl_gd = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=stochastic_gradient_descent) - pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam) - tests = [([5, 3, 1, 0.1], 0), - ([5, 3.5, 1, 0], 0), - ([6, 3, 4, 1.1], 1), - ([6, 2, 3.5, 1], 1), - ([7.5, 4, 6, 2], 2), - ([7, 3, 6, 2.5], 2)] - assert grade_learner(pl_gd, tests) > 1 / 2 - assert err_ratio(pl_gd, iris) < 0.4 - assert grade_learner(pl_adam, tests) > 1 / 2 - assert err_ratio(pl_adam, iris) < 0.4 + n_samples, n_features = len(iris.examples), iris.target + X, y = np.array([x[:n_features] for x in iris.examples]), \ + np.array([x[n_features] for x in iris.examples]) + pl_gd = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) + assert grade_learner(pl_gd, iris_tests) == 1 + assert err_ratio(pl_gd, iris) < 0.2 + pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam).fit(X, y) + assert grade_learner(pl_adam, iris_tests) == 1 + assert err_ratio(pl_adam, iris) < 0.2 def test_rnn(): @@ -52,8 +53,8 @@ def test_rnn(): train = (train[0][:1000], train[1][:1000]) val = (val[0][:200], val[1][:200]) rnn = SimpleRNNLearner(train, val) - score = rnn.evaluate(test[0][:200], test[1][:200], verbose=0) - assert score[1] >= 0.3 + score = rnn.evaluate(test[0][:200], test[1][:200], verbose=False) + assert score[1] >= 0.2 def test_autoencoder(): diff --git a/tests/test_learning.py b/tests/test_learning.py index fd84d74ed..57d603b86 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -149,7 +149,7 @@ def test_ada_boost(): ([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)] - assert grade_learner(ab, tests) > 4 / 6 + assert grade_learner(ab, tests) > 2 / 3 assert err_ratio(ab, iris) < 0.25 diff --git a/tests/test_learning4e.py b/tests/test_learning4e.py index 3913443b1..f0fc50493 100644 --- a/tests/test_learning4e.py +++ b/tests/test_learning4e.py @@ -38,42 +38,68 @@ def test_means_and_deviation(): def test_plurality_learner(): zoo = DataSet(name='zoo') pl = PluralityLearner(zoo) - assert pl([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == 'mammal' + assert pl.predict([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == 'mammal' def test_k_nearest_neighbors(): iris = DataSet(name='iris') knn = NearestNeighborLearner(iris, k=3) - assert knn([5, 3, 1, 0.1]) == 'setosa' - assert knn([6, 5, 3, 1.5]) == 'versicolor' - assert knn([7.5, 4, 6, 2]) == 'virginica' + assert knn.predict([5, 3, 1, 0.1]) == 'setosa' + assert knn.predict([6, 5, 3, 1.5]) == 'versicolor' + assert knn.predict([7.5, 4, 6, 2]) == 'virginica' def test_decision_tree_learner(): iris = DataSet(name='iris') dtl = DecisionTreeLearner(iris) - assert dtl([5, 3, 1, 0.1]) == 'setosa' - assert dtl([6, 5, 3, 1.5]) == 'versicolor' - assert dtl([7.5, 4, 6, 2]) == 'virginica' + assert dtl.predict([5, 3, 1, 0.1]) == 'setosa' + assert dtl.predict([6, 5, 3, 1.5]) == 'versicolor' + assert dtl.predict([7.5, 4, 6, 2]) == 'virginica' + + +def test_linear_learner(): + iris = DataSet(name='iris') + classes = ['setosa', 'versicolor', 'virginica'] + iris.classes_to_numbers(classes) + n_samples, n_features = len(iris.examples), iris.target + X, y = np.array([x[:n_features] for x in iris.examples]), \ + np.array([x[n_features] for x in iris.examples]) + ll = LinearRegressionLearner().fit(X, y) + assert np.allclose(ll.w, MeanSquaredError(X, y).x_star) + + +iris_tests = [([[5.0, 3.1, 0.9, 0.1]], 0), + ([[5.1, 3.5, 1.0, 0.0]], 0), + ([[4.9, 3.3, 1.1, 0.1]], 0), + ([[6.0, 3.0, 4.0, 1.1]], 1), + ([[6.1, 2.2, 3.5, 1.0]], 1), + ([[5.9, 2.5, 3.3, 1.1]], 1), + ([[7.5, 4.1, 6.2, 2.3]], 2), + ([[7.3, 4.0, 6.1, 2.4]], 2), + ([[7.0, 3.3, 6.1, 2.5]], 2)] + + +def test_logistic_learner(): + iris = DataSet(name='iris') + classes = ['setosa', 'versicolor', 'virginica'] + iris.classes_to_numbers(classes) + n_samples, n_features = len(iris.examples), iris.target + X, y = np.array([x[:n_features] for x in iris.examples]), \ + np.array([x[n_features] for x in iris.examples]) + ll = MultiLogisticRegressionLearner().fit(X, y) + assert grade_learner(ll, iris_tests) == 1 + assert np.allclose(err_ratio(ll, iris), 0.04) def test_svm(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - svm = MultiSVM() n_samples, n_features = len(iris.examples), iris.target X, y = np.array([x[:n_features] for x in iris.examples]), np.array([x[n_features] for x in iris.examples]) - svm.fit(X, y) - assert svm.predict([[5.0, 3.1, 0.9, 0.1]]) == 0 - assert svm.predict([[5.1, 3.5, 1.0, 0.0]]) == 0 - assert svm.predict([[4.9, 3.3, 1.1, 0.1]]) == 0 - assert svm.predict([[6.0, 3.0, 4.0, 1.1]]) == 1 - assert svm.predict([[6.1, 2.2, 3.5, 1.0]]) == 1 - assert svm.predict([[5.9, 2.5, 3.3, 1.1]]) == 1 - assert svm.predict([[7.5, 4.1, 6.2, 2.3]]) == 2 - assert svm.predict([[7.3, 4.0, 6.1, 2.4]]) == 2 - assert svm.predict([[7.0, 3.3, 6.1, 2.5]]) == 2 + svm = MultiSVM().fit(X, y) + assert grade_learner(svm, iris_tests) == 1 + assert np.isclose(err_ratio(svm, iris), 0.04) def test_information_content(): @@ -109,8 +135,9 @@ def test_random_weights(): def test_ada_boost(): iris = DataSet(name='iris') - iris.classes_to_numbers() - wl = WeightedLearner(PerceptronLearner) + classes = ['setosa', 'versicolor', 'virginica'] + iris.classes_to_numbers(classes) + wl = WeightedLearner(PerceptronLearner(iris)) ab = ada_boost(iris, wl, 5) tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), @@ -118,7 +145,7 @@ def test_ada_boost(): ([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)] - assert grade_learner(ab, tests) > 4 / 6 + assert grade_learner(ab, tests) > 2 / 3 assert err_ratio(ab, iris) < 0.25 diff --git a/utils4e.py b/utils4e.py index 777a88e4a..178e887b4 100644 --- a/utils4e.py +++ b/utils4e.py @@ -168,6 +168,7 @@ def extend(s, var, val): # ______________________________________________________________________________ # argmin and argmax + identity = lambda x: x @@ -209,11 +210,6 @@ def histogram(values, mode=0, bin_function=None): return sorted(bins.items()) -def dot_product(x, y): - """Return the sum of the element-wise product of vectors x and y.""" - return sum(_x * _y for _x, _y in zip(x, y)) - - def element_wise_product(x, y): if hasattr(x, '__iter__') and hasattr(y, '__iter__'): assert len(x) == len(y) @@ -224,16 +220,6 @@ def element_wise_product(x, y): raise Exception('Inputs must be in the same size!') -def matrix_multiplication(x, *y): - """Return a matrix as a matrix-multiplication of x and arbitrary number of matrices *y.""" - - result = x - for _y in y: - result = np.matmul(result, _y) - - return result - - def vector_add(a, b): """Component-wise addition of two vectors.""" if not (a and b): @@ -343,7 +329,8 @@ def mean_boolean_error(x, y): return mean(_x != _y for _x, _y in zip(x, y)) -# loss functions +# part3. Neural network util functions +# ______________________________________________________________________________ def cross_entropy_loss(x, y): @@ -356,10 +343,6 @@ def mean_squared_error_loss(x, y): return (1.0 / len(x)) * sum((_x - _y) ** 2 for _x, _y in zip(x, y)) -# part3. Neural network util functions -# ______________________________________________________________________________ - - def normalize(dist): """Multiply each number by a constant such that the sum is 1.0""" if isinstance(dist, dict): @@ -376,6 +359,11 @@ def random_weights(min_value, max_value, num_weights): return [random.uniform(min_value, max_value) for _ in range(num_weights)] +def softmax1D(x): + """Return the softmax vector of input vector x.""" + return np.exp(x) / np.sum(np.exp(x)) + + def conv1D(x, k): """1D convolution. x: input vector; K: kernel vector.""" return np.convolve(x, k, mode='same') @@ -395,72 +383,6 @@ def gaussian_kernel_2D(size=3, sigma=0.5): return g / g.sum() -# activation functions - - -class Activation: - - def function(self, x): - return NotImplementedError - - def derivative(self, x): - return NotImplementedError - - -def softmax1D(x): - """Return the softmax vector of input vector x.""" - return np.exp(x) / sum(np.exp(x)) - - -class Sigmoid(Activation): - - def function(self, x): - if x >= 100: - return 1 - if x <= -100: - return 0 - return 1 / (1 + np.exp(-x)) - - def derivative(self, value): - return value * (1 - value) - - -class Relu(Activation): - - def function(self, x): - return max(0, x) - - def derivative(self, value): - return 1 if value > 0 else 0 - - -class Elu(Activation): - - def function(self, x, alpha=0.01): - return x if x > 0 else alpha * (np.exp(x) - 1) - - def derivative(self, value, alpha=0.01): - return 1 if value > 0 else alpha * np.exp(value) - - -class Tanh(Activation): - - def function(self, x): - return np.tanh(x) - - def derivative(self, value): - return 1 - (value ** 2) - - -class LeakyRelu(Activation): - - def function(self, x, alpha=0.01): - return x if x > 0 else alpha * x - - def derivative(self, value, alpha=0.01): - return 1 if value > 0 else alpha - - def step(x): """Return activation value of x with sign function.""" return 1 if x >= 0 else 0 @@ -471,15 +393,6 @@ def gaussian(mean, st_dev, x): return 1 / (np.sqrt(2 * np.pi) * st_dev) * np.exp(-0.5 * (float(x - mean) / st_dev) ** 2) -def gaussian_2D(means, sigma, point): - det = sigma[0][0] * sigma[1][1] - sigma[0][1] * sigma[1][0] - inverse = np.linalg.inv(sigma) - assert det != 0 - x_u = vector_add(point, scalar_vector_product(-1, means)) - buff = matrix_multiplication(matrix_multiplication([x_u], inverse), np.array(x_u).T) - return 1 / (np.sqrt(det) * 2 * np.pi) * np.exp(-0.5 * buff[0][0]) - - def linear_kernel(x, y=None): if y is None: y = x @@ -540,6 +453,7 @@ def distance_squared(a, b): # ______________________________________________________________________________ # Misc Functions + class injection: """Dependency injection of temporary values for global functions/classes/etc. E.g., `with injection(DataBase=MockDataBase): ...`""" @@ -636,6 +550,7 @@ def failure_test(algorithm, tests): # See https://docs.python.org/3/reference/expressions.html#operator-precedence # See https://docs.python.org/3/reference/datamodel.html#special-method-names + class Expr: """A mathematical expression with an operator and 0 or more arguments. op is a str like '+' or 'sin'; args are Expressions. @@ -870,6 +785,8 @@ def __hash__(self): # ______________________________________________________________________________ # Monte Carlo tree node and ucb function + + class MCT_Node: """Node in the Monte Carlo search tree, keeps track of the children states.""" From c431efe2be73b51e8f95a3ad8211a3fb8ba725f9 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 20 Feb 2020 13:36:30 +0100 Subject: [PATCH 14/31] trying to fix keras issue --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index dc4ed0d05..12cebb35b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,7 +1,6 @@ language: python python: - - 3.4 - 3.5 - 3.6 - 3.7 From e5663e4a173ba0dba2c1a760ecd3c39071ab5d17 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 20 Feb 2020 14:23:08 +0100 Subject: [PATCH 15/31] dropping the acceptable error rate values --- tests/test_deep_learning4e.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index b23f8bcfa..fe4a8d194 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -26,10 +26,10 @@ def test_neural_net(): np.array([x[n_features] for x in iris.examples]) nnl_gd = NeuralNetworkLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) assert grade_learner(nnl_gd, iris_tests) > 0.7 - assert err_ratio(nnl_gd, iris) < 0.08 + assert err_ratio(nnl_gd, iris) < 0.1 nnl_adam = NeuralNetworkLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam).fit(X, y) assert grade_learner(nnl_adam, iris_tests) == 1 - assert err_ratio(nnl_adam, iris) < 0.08 + assert err_ratio(nnl_adam, iris) < 0.1 def test_perceptron(): From d2d3f31a861f2bfc28259213b5a04db2e4a76f6f Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 20 Feb 2020 14:31:24 +0100 Subject: [PATCH 16/31] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ce4af7372..a94d6fd21 100644 --- a/README.md +++ b/README.md @@ -19,9 +19,9 @@ When complete, this project will have Python implementations for all the pseudoc - `nlp_apps.ipynb`: A Jupyter notebook that gives example applications of the code. -## Python 3.4 and up +## Python 3.5 and up -This code requires Python 3.4 or later, and does not run in Python 2. You can [install Python](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code requires Python 3.5 or later, and does not run in Python 2. You can [install Python](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). You can run the code in an IDE, or from the command line with `python -i filename.py` where the `-i` option puts you in an interactive loop where you can run Python functions. All notebooks are available in a [binder environment](http://mybinder.org/repo/aimacode/aima-python). Alternatively, visit [jupyter.org](http://jupyter.org/) for instructions on setting up your own Jupyter notebook environment. There is a sibling [aima-docker](https://github.com/rajatjain1997/aima-docker) project that shows you how to use docker containers to run more complex problems in more complex software environments. From dcaa8808a8a776115b330ebe75b1a44c32c35e19 Mon Sep 17 00:00:00 2001 From: Aman Kumar Date: Thu, 20 Feb 2020 20:58:59 +0530 Subject: [PATCH 17/31] Image Rendering problem resolved (#1178) --- notebooks/chapter19/Learners.ipynb | 4 ++-- notebooks/chapter19/Loss Functions and Layers.ipynb | 6 +++--- .../chapter19/Optimizer and Backpropagation.ipynb | 6 +++--- notebooks/chapter19/RNN.ipynb | 12 ++++++------ notebooks/chapter24/Image Edge Detection.ipynb | 12 ++++++------ notebooks/chapter24/Objects in Images.ipynb | 6 +++--- 6 files changed, 23 insertions(+), 23 deletions(-) diff --git a/notebooks/chapter19/Learners.ipynb b/notebooks/chapter19/Learners.ipynb index 9997cfbcc..c6f3d1e4f 100644 --- a/notebooks/chapter19/Learners.ipynb +++ b/notebooks/chapter19/Learners.ipynb @@ -318,7 +318,7 @@ "\n", "By default we use dense networks with two hidden layers, which has the architecture as the following:\n", "\n", - "\n", + "\n", "\n", "In our code, we implemented it as:" ] @@ -500,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/chapter19/Loss Functions and Layers.ipynb b/notebooks/chapter19/Loss Functions and Layers.ipynb index cccad7a88..25676e899 100644 --- a/notebooks/chapter19/Loss Functions and Layers.ipynb +++ b/notebooks/chapter19/Loss Functions and Layers.ipynb @@ -40,7 +40,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -88,7 +88,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -390,7 +390,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/chapter19/Optimizer and Backpropagation.ipynb b/notebooks/chapter19/Optimizer and Backpropagation.ipynb index 6a67e36ce..5194adc7a 100644 --- a/notebooks/chapter19/Optimizer and Backpropagation.ipynb +++ b/notebooks/chapter19/Optimizer and Backpropagation.ipynb @@ -251,7 +251,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -260,7 +260,7 @@ "source": [ "Applying optimizers and back-propagation algorithm together, we can update the weights of a neural network to minimize the loss function with alternatively doing forward and back-propagation process. Here is a figure form [here](https://medium.com/datathings/neural-networks-and-backpropagation-explained-in-a-simple-way-f540a3611f5e) describing how a neural network updates its weights:\n", "\n", - "" + "" ] }, { @@ -303,7 +303,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/chapter19/RNN.ipynb b/notebooks/chapter19/RNN.ipynb index 16d4928df..b6971b36a 100644 --- a/notebooks/chapter19/RNN.ipynb +++ b/notebooks/chapter19/RNN.ipynb @@ -12,7 +12,7 @@ "\n", "Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.\n", "\n", - "" + "" ] }, { @@ -21,7 +21,7 @@ "source": [ "A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the above loop:\n", " \n", - "" + "" ] }, { @@ -30,7 +30,7 @@ "source": [ "As demonstrated in the book, recurrent neural networks may be connected in many different ways: sequences in the input, the output, or in the most general case both.\n", "\n", - "" + "" ] }, { @@ -303,7 +303,7 @@ "\n", "Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning. It works by compressing the input into a latent-space representation, to do transformations on the data. \n", "\n", - "" + "" ] }, { @@ -314,7 +314,7 @@ "\n", "Autoencoders have different architectures for different kinds of data. Here we only provide a simple example of a vanilla encoder, which means they're only one hidden layer in the network:\n", "\n", - "\n", + "\n", "\n", "You can view the source code by:" ] @@ -479,7 +479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/chapter24/Image Edge Detection.ipynb b/notebooks/chapter24/Image Edge Detection.ipynb index cc1672e51..6429943a1 100644 --- a/notebooks/chapter24/Image Edge Detection.ipynb +++ b/notebooks/chapter24/Image Edge Detection.ipynb @@ -69,7 +69,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -105,7 +105,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "We will use `matplotlib` to read the image as a numpy ndarray:" ] @@ -226,7 +226,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -318,7 +318,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -334,7 +334,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "" ] }, { @@ -400,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/chapter24/Objects in Images.ipynb b/notebooks/chapter24/Objects in Images.ipynb index 9ffe6e957..03fc92235 100644 --- a/notebooks/chapter24/Objects in Images.ipynb +++ b/notebooks/chapter24/Objects in Images.ipynb @@ -306,7 +306,7 @@ "source": [ "The bounding boxes are drawn on the original picture showed in the following:\n", "\n", - "" + "" ] }, { @@ -324,7 +324,7 @@ "\n", "[Ross Girshick et al.](https://arxiv.org/pdf/1311.2524.pdf) proposed a method where they use selective search to extract just 2000 regions from the image. Then the regions in bounding boxes are feed into a convolutional neural network to perform classification. The brief architecture can be shown as:\n", "\n", - "" + "" ] }, { @@ -446,7 +446,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.6.9" } }, "nbformat": 4, From dae3e4d6e571c484e52212d42bd852a9d831942f Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Fri, 21 Feb 2020 12:47:14 +0100 Subject: [PATCH 18/31] relaxing test thresholds --- tests/test_deep_learning4e.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index fe4a8d194..54bb70055 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -26,10 +26,10 @@ def test_neural_net(): np.array([x[n_features] for x in iris.examples]) nnl_gd = NeuralNetworkLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) assert grade_learner(nnl_gd, iris_tests) > 0.7 - assert err_ratio(nnl_gd, iris) < 0.1 + assert err_ratio(nnl_gd, iris) < 0.15 nnl_adam = NeuralNetworkLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam).fit(X, y) assert grade_learner(nnl_adam, iris_tests) == 1 - assert err_ratio(nnl_adam, iris) < 0.1 + assert err_ratio(nnl_adam, iris) < 0.15 def test_perceptron(): From 43b5cb9e479f650dfce796709f697858368dcf14 Mon Sep 17 00:00:00 2001 From: W0s0 <37555653+W0s0@users.noreply.github.com> Date: Fri, 21 Feb 2020 15:36:16 +0200 Subject: [PATCH 19/31] Typos at search.ipynb (#1179) --- search.ipynb | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/search.ipynb b/search.ipynb index d3dc3cca7..72300557e 100644 --- a/search.ipynb +++ b/search.ipynb @@ -1623,7 +1623,7 @@ " elif limit >= 0:\n", " cutoff_occurred = True\n", " limit += 1\n", - " all_node_color.pop()\n", + " all_node_colors.pop()\n", " iterations -= 1\n", " node_colors[node.state] = \"gray\"\n", "\n", @@ -2162,6 +2162,8 @@ "outputs": [], "source": [ "# Heuristics for 8 Puzzle Problem\n", + "import math\n", + "\n", "def linear(node):\n", " return sum([1 if node.state[i] != goal[i] else 0 for i in range(8)])\n", "\n", @@ -2853,7 +2855,7 @@ " neighbor = argmax_random_tie(neighbors,\n", " key=lambda node: problem.value(node.state))\n", " if problem.value(neighbor.state) <= problem.value(current.state):\n", - " \"\"\"Note that it is based on negative path cost method\"\"\"\n", + " \"\"\"Note that it is based on negative path cost method\"\"\"\n", " current.state = neighbor.state\n", " iterations -= 1\n", " \n", @@ -6527,7 +6529,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.6" }, "widgets": { "state": { @@ -6561,8 +6563,17 @@ } }, "version": "1.2.0" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file From 677308e4d16c8e636138110edf9f6d7008e991b8 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Fri, 21 Feb 2020 14:52:48 +0100 Subject: [PATCH 20/31] relaxing tests some more... --- tests/test_deep_learning4e.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index 54bb70055..ca1f061f0 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -22,13 +22,16 @@ def test_neural_net(): classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) n_samples, n_features = len(iris.examples), iris.target + X, y = np.array([x[:n_features] for x in iris.examples]), \ np.array([x[n_features] for x in iris.examples]) + nnl_gd = NeuralNetworkLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) assert grade_learner(nnl_gd, iris_tests) > 0.7 assert err_ratio(nnl_gd, iris) < 0.15 + nnl_adam = NeuralNetworkLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam).fit(X, y) - assert grade_learner(nnl_adam, iris_tests) == 1 + assert grade_learner(nnl_adam, iris_tests) > 0.7 assert err_ratio(nnl_adam, iris) < 0.15 @@ -37,11 +40,14 @@ def test_perceptron(): classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) n_samples, n_features = len(iris.examples), iris.target + X, y = np.array([x[:n_features] for x in iris.examples]), \ np.array([x[n_features] for x in iris.examples]) + pl_gd = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) assert grade_learner(pl_gd, iris_tests) == 1 assert err_ratio(pl_gd, iris) < 0.2 + pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam).fit(X, y) assert grade_learner(pl_adam, iris_tests) == 1 assert err_ratio(pl_adam, iris) < 0.2 @@ -49,9 +55,11 @@ def test_perceptron(): def test_rnn(): data = imdb.load_data(num_words=5000) + train, val, test = keras_dataset_loader(data) train = (train[0][:1000], train[1][:1000]) val = (val[0][:200], val[1][:200]) + rnn = SimpleRNNLearner(train, val) score = rnn.evaluate(test[0][:200], test[1][:200], verbose=False) assert score[1] >= 0.2 @@ -62,6 +70,7 @@ def test_autoencoder(): classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) inputs = np.asarray(iris.examples) + al = AutoencoderLearner(inputs, 100) print(inputs[0]) print(al.predict(inputs[:1])) From f502be974dae001a4e3af4d6cdf876abcb8f121e Mon Sep 17 00:00:00 2001 From: Omar Date: Wed, 18 Mar 2020 14:52:27 +0200 Subject: [PATCH 21/31] fixed grabbing behaviour in agent (#1148) * fixed grabbing behaviour in agent * fixed the grabbing issues and itegrated into wumpus environment * cleaned the code a bit * fixing the code space formatting * fixing format --- agents.py | 45 +++++++++++++-------------------------------- 1 file changed, 13 insertions(+), 32 deletions(-) diff --git a/agents.py b/agents.py index 084a752e1..6ab9ea814 100644 --- a/agents.py +++ b/agents.py @@ -27,11 +27,6 @@ """ # TODO -# Implement grabbing correctly. -# When an object is grabbed, does it still have a location? -# What if it is released? -# What if the grabbed or the grabber is deleted? -# What if the grabber moves? # Speed control in GUI does not have any effect -- fix it. from utils import distance_squared, turn_heading @@ -510,14 +505,17 @@ def execute_action(self, agent, action): agent.direction += Direction.L elif action == 'Forward': agent.bump = self.move_to(agent, agent.direction.move_forward(agent.location)) - # elif action == 'Grab': - # things = [thing for thing in self.list_things_at(agent.location) - # if agent.can_grab(thing)] - # if things: - # agent.holding.append(things[0]) + elif action == 'Grab': + things = [thing for thing in self.list_things_at(agent.location) if agent.can_grab(thing)] + if things: + agent.holding.append(things[0]) + print("Grabbing ", things[0].__class__.__name__) + self.delete_thing(things[0]) elif action == 'Release': if agent.holding: - agent.holding.pop() + dropped = agent.holding.pop() + print("Dropping ", dropped.__class__.__name__) + self.add_thing(dropped, location=agent.location) def default_location(self, thing): location = self.random_location_inbounds() @@ -569,10 +567,7 @@ def random_location_inbounds(self, exclude=None): def delete_thing(self, thing): """Deletes thing, and everything it is holding (if thing is an agent)""" if isinstance(thing, Agent): - for obj in thing.holding: - super().delete_thing(obj) - for obs in self.observers: - obs.thing_deleted(obj) + del thing.holding super().delete_thing(thing) for obs in self.observers: @@ -964,24 +959,10 @@ def execute_action(self, agent, action): if isinstance(agent, Explorer) and self.in_danger(agent): return - + agent.bump = False - if action == 'TurnRight': - agent.direction += Direction.R - agent.performance -= 1 - elif action == 'TurnLeft': - agent.direction += Direction.L - agent.performance -= 1 - elif action == 'Forward': - agent.bump = self.move_to(agent, agent.direction.move_forward(agent.location)) - agent.performance -= 1 - elif action == 'Grab': - things = [thing for thing in self.list_things_at(agent.location) - if agent.can_grab(thing)] - if len(things): - print("Grabbing", things[0].__class__.__name__) - if len(things): - agent.holding.append(things[0]) + if action in ['TurnRight', 'TurnLeft', 'Forward', 'Grab']: + super().execute_action(agent, action) agent.performance -= 1 elif action == 'Climb': if agent.location == (1, 1): # Agent can only climb out of (1,1) From 746477a99cb8dc8cb65dda2858d43c77e6bde081 Mon Sep 17 00:00:00 2001 From: darius Date: Sun, 7 Jun 2020 23:19:42 -0500 Subject: [PATCH 22/31] Fix misspelled variable. --- agents4e.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/agents4e.py b/agents4e.py index 9408afb8a..75369a69a 100644 --- a/agents4e.py +++ b/agents4e.py @@ -170,14 +170,14 @@ def program(percept): return program -def ModelBasedReflexAgentProgram(rules, update_state, trainsition_model, sensor_model): +def ModelBasedReflexAgentProgram(rules, update_state, transition_model, sensor_model): """ [Figure 2.12] This agent takes action based on the percept and state. """ def program(percept): - program.state = update_state(program.state, program.action, percept, trainsition_model, sensor_model) + program.state = update_state(program.state, program.action, percept, transition_model, sensor_model) rule = rule_match(program.state, rules) action = rule.action return action From 82da1c3f350d506cae33f7a1e8ce4725bda78039 Mon Sep 17 00:00:00 2001 From: Hamed Rezayat <43059508+Ewindar@users.noreply.github.com> Date: Thu, 11 Jun 2020 04:34:58 +0430 Subject: [PATCH 23/31] update doc-string of Agent class (#1187) make it clear that the word slot refers to instance attribute, so it won't be confused with __slots__ magic. --- agents.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/agents.py b/agents.py index 6ab9ea814..d29b0c382 100644 --- a/agents.py +++ b/agents.py @@ -67,17 +67,17 @@ def display(self, canvas, x, y, width, height): class Agent(Thing): - """An Agent is a subclass of Thing with one required slot, - .program, which should hold a function that takes one argument, the - percept, and returns an action. (What counts as a percept or action + """An Agent is a subclass of Thing with one required instance attribute + (aka slot), .program, which should hold a function that takes one argument, + the percept, and returns an action. (What counts as a percept or action will depend on the specific environment in which the agent exists.) - Note that 'program' is a slot, not a method. If it were a method, - then the program could 'cheat' and look at aspects of the agent. - It's not supposed to do that: the program can only look at the - percepts. An agent program that needs a model of the world (and of - the agent itself) will have to build and maintain its own model. - There is an optional slot, .performance, which is a number giving - the performance measure of the agent in its environment.""" + Note that 'program' is a slot, not a method. If it were a method, then the + program could 'cheat' and look at aspects of the agent. It's not supposed + to do that: the program can only look at the percepts. An agent program + that needs a model of the world (and of the agent itself) will have to + build and maintain its own model. There is an optional slot, .performance, + which is a number giving the performance measure of the agent in its + environment.""" def __init__(self, program=None): self.alive = True From 62a5a30930c0be54de86fb6ae8db2dec50af0391 Mon Sep 17 00:00:00 2001 From: tianqiyang Date: Wed, 10 Jun 2020 20:08:04 -0400 Subject: [PATCH 24/31] add chapter 7-10 (#1096) --- logic4e.py | 1654 +++++++++++++++++++++++++++++++++++++++++ tests/test_logic4e.py | 347 +++++++++ 2 files changed, 2001 insertions(+) create mode 100644 logic4e.py create mode 100644 tests/test_logic4e.py diff --git a/logic4e.py b/logic4e.py new file mode 100644 index 000000000..f05634436 --- /dev/null +++ b/logic4e.py @@ -0,0 +1,1654 @@ +"""Representations and Inference for Logic (Chapters 7-10) + +Covers both Propositional and First-Order Logic. First we have four +important data types: + + KB Abstract class holds a knowledge base of logical expressions + KB_Agent Abstract class subclasses agents.Agent + Expr A logical expression, imported from utils.py + substitution Implemented as a dictionary of var:value pairs, {x:1, y:x} + +Be careful: some functions take an Expr as argument, and some take a KB. + +Logical expressions can be created with Expr or expr, imported from utils, TODO +or with expr, which adds the capability to write a string that uses +the connectives ==>, <==, <=>, or <=/=>. But be careful: these have the +operator precedence of commas; you may need to add parents to make precedence work. +See logic.ipynb for examples. + +Then we implement various functions for doing logical inference: + + pl_true Evaluate a propositional logical sentence in a model + tt_entails Say if a statement is entailed by a KB + pl_resolution Do resolution on propositional sentences + dpll_satisfiable See if a propositional sentence is satisfiable + WalkSAT Try to find a solution for a set of clauses + +And a few other functions: + + to_cnf Convert to conjunctive normal form + unify Do unification of two FOL sentences + diff, simp Symbolic differentiation and simplification +""" + +from utils import ( + removeall, unique, first, argmax, probability, + isnumber, issequence, Expr, expr, subexpressions +) +from agents import Agent, Glitter, Bump, Stench, Breeze, Scream +from search import astar_search, PlanRoute + +import itertools +import random +from collections import defaultdict + +# ______________________________________________________________________________ +# Chapter 7 Logical Agents +# 7.1 Knowledge Based Agents + + +class KB: + + """ + A knowledge base to which you can tell and ask sentences. + To create a KB, subclass this class and implement tell, ask_generator, and retract. + Ask_generator： + For a Propositional Logic KB, ask(P & Q) returns True or False, but for an + FOL KB, something like ask(Brother(x, y)) might return many substitutions + such as {x: Cain, y: Abel}, {x: Abel, y: Cain}, {x: George, y: Jeb}, etc. + So ask_generator generates these one at a time, and ask either returns the + first one or returns False. + """ + + def __init__(self, sentence=None): + raise NotImplementedError + + def tell(self, sentence): + """Add the sentence to the KB.""" + raise NotImplementedError + + def ask(self, query): + """Return a substitution that makes the query true, or, failing that, return False.""" + return first(self.ask_generator(query), default=False) + + def ask_generator(self, query): + """Yield all the substitutions that make query true.""" + raise NotImplementedError + + def retract(self, sentence): + """Remove sentence from the KB.""" + raise NotImplementedError + + +class PropKB(KB): + """A KB for propositional logic. Inefficient, with no indexing.""" + + def __init__(self, sentence=None): + self.clauses = [] + if sentence: + self.tell(sentence) + + def tell(self, sentence): + """Add the sentence's clauses to the KB.""" + self.clauses.extend(conjuncts(to_cnf(sentence))) + + def ask_generator(self, query): + """Yield the empty substitution {} if KB entails query; else no results.""" + if tt_entails(Expr('&', *self.clauses), query): + yield {} + + def ask_if_true(self, query): + """Return True if the KB entails query, else return False.""" + for _ in self.ask_generator(query): + return True + return False + + def retract(self, sentence): + """Remove the sentence's clauses from the KB.""" + for c in conjuncts(to_cnf(sentence)): + if c in self.clauses: + self.clauses.remove(c) + + +def KB_AgentProgram(KB): + """A generic logical knowledge-based agent program. [Figure 7.1]""" + steps = itertools.count() + + def program(percept): + t = next(steps) + KB.tell(make_percept_sentence(percept, t)) + action = KB.ask(make_action_query(t)) + KB.tell(make_action_sentence(action, t)) + return action + + def make_percept_sentence(percept, t): + return Expr("Percept")(percept, t) + + def make_action_query(t): + return expr("ShouldDo(action, {})".format(t)) + + def make_action_sentence(action, t): + return Expr("Did")(action[expr('action')], t) + + return program + +# _____________________________________________________________________________ +# 7.2 The Wumpus World + + +# Expr functions for WumpusKB and HybridWumpusAgent + + +def facing_east(time): + return Expr('FacingEast', time) + + +def facing_west (time): + return Expr('FacingWest', time) + + +def facing_north (time): + return Expr('FacingNorth', time) + + +def facing_south (time): + return Expr('FacingSouth', time) + + +def wumpus (x, y): + return Expr('W', x, y) + + +def pit(x, y): + return Expr('P', x, y) + + +def breeze(x, y): + return Expr('B', x, y) + + +def stench(x, y): + return Expr('S', x, y) + + +def wumpus_alive(time): + return Expr('WumpusAlive', time) + + +def have_arrow(time): + return Expr('HaveArrow', time) + + +def percept_stench(time): + return Expr('Stench', time) + + +def percept_breeze(time): + return Expr('Breeze', time) + + +def percept_glitter(time): + return Expr('Glitter', time) + + +def percept_bump(time): + return Expr('Bump', time) + + +def percept_scream(time): + return Expr('Scream', time) + + +def move_forward(time): + return Expr('Forward', time) + + +def shoot(time): + return Expr('Shoot', time) + + +def turn_left(time): + return Expr('TurnLeft', time) + + +def turn_right(time): + return Expr('TurnRight', time) + + +def ok_to_move(x, y, time): + return Expr('OK', x, y, time) + + +def location(x, y, time = None): + if time is None: + return Expr('L', x, y) + else: + return Expr('L', x, y, time) + +# Symbols + + +def implies(lhs, rhs): + return Expr('==>', lhs, rhs) + + +def equiv(lhs, rhs): + return Expr('<=>', lhs, rhs) + +# Helper Function + + +def new_disjunction(sentences): + t = sentences[0] + for i in range(1,len(sentences)): + t |= sentences[i] + return t + +# ______________________________________________________________________________ +# 7.4 Propositional Logic + + +def is_symbol(s): + """A string s is a symbol if it starts with an alphabetic char. + >>> is_symbol('R2D2') + True + """ + return isinstance(s, str) and s[:1].isalpha() + + +def is_var_symbol(s): + """A logic variable symbol is an initial-lowercase string. + >>> is_var_symbol('EXE') + False + """ + return is_symbol(s) and s[0].islower() + + +def is_prop_symbol(s): + """A proposition logic symbol is an initial-uppercase string. + >>> is_prop_symbol('exe') + False + """ + return is_symbol(s) and s[0].isupper() + + +def variables(s): + """Return a set of the variables in expression s. + >>> variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, 2)')) == {x, y, z} + True + """ + return {x for x in subexpressions(s) if is_variable(x)} + + +def is_definite_clause(s): + """ + Returns True for exprs s of the form A & B & ... & C ==> D, + where all literals are positive. In clause form, this is + ~A | ~B | ... | ~C | D, where exactly one clause is positive. + >>> is_definite_clause(expr('Farmer(Mac)')) + True + """ + if is_symbol(s.op): + return True + elif s.op == '==>': + antecedent, consequent = s.args + return (is_symbol(consequent.op) and + all(is_symbol(arg.op) for arg in conjuncts(antecedent))) + else: + return False + + +def parse_definite_clause(s): + """Return the antecedents and the consequent of a definite clause.""" + assert is_definite_clause(s) + if is_symbol(s.op): + return [], s + else: + antecedent, consequent = s.args + return conjuncts(antecedent), consequent + + +# Useful constant Exprs used in examples and code: +A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz') + + +# ______________________________________________________________________________ +# 7.4.4 A simple inference procedure + + +def tt_entails(kb, alpha): + """ + Does kb entail the sentence alpha? Use truth tables. For propositional + 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 + """ + assert not variables(alpha) + symbols = list(prop_symbols(kb & alpha)) + return tt_check_all(kb, alpha, symbols, {}) + + +def tt_check_all(kb, alpha, symbols, model): + """Auxiliary routine to implement tt_entails.""" + if not symbols: + if pl_true(kb, model): + result = pl_true(alpha, model) + assert result in (True, False) + return result + else: + return True + else: + P, rest = symbols[0], symbols[1:] + return (tt_check_all(kb, alpha, rest, extend(model, P, True)) and + tt_check_all(kb, alpha, rest, extend(model, P, False))) + + +def prop_symbols(x): + """Return the set of all propositional symbols in x.""" + if not isinstance(x, Expr): + return set() + elif is_prop_symbol(x.op): + return {x} + else: + return {symbol for arg in x.args for symbol in prop_symbols(arg)} + + +def constant_symbols(x): + """Return the set of all constant symbols in x.""" + if not isinstance(x, Expr): + return set() + elif is_prop_symbol(x.op) and not x.args: + return {x} + else: + return {symbol for arg in x.args for symbol in constant_symbols(arg)} + + +def predicate_symbols(x): + """ + Return a set of (symbol_name, arity) in x. + All symbols (even functional) with arity > 0 are considered. + """ + if not isinstance(x, Expr) or not x.args: + return set() + pred_set = {(x.op, len(x.args))} if is_prop_symbol(x.op) else set() + pred_set.update({symbol for arg in x.args for symbol in predicate_symbols(arg)}) + return pred_set + + +def tt_true(s): + """Is a propositional sentence a tautology? + >>> tt_true('P | ~P') + True + """ + s = expr(s) + return tt_entails(True, s) + + +def pl_true(exp, model={}): + """ + Return True if the propositional logic expression is true in the model, + and False if it is false. If the model does not specify the value for + every proposition, this may return None to indicate 'not obvious'; + this may happen even when the expression is tautological. + >>> pl_true(P, {}) is None + True + """ + if exp in (True, False): + return exp + op, args = exp.op, exp.args + if is_prop_symbol(op): + return model.get(exp) + elif op == '~': + p = pl_true(args[0], model) + if p is None: + return None + else: + return not p + elif op == '|': + result = False + for arg in args: + p = pl_true(arg, model) + if p is True: + return True + if p is None: + result = None + return result + elif op == '&': + result = True + for arg in args: + p = pl_true(arg, model) + if p is False: + return False + if p is None: + result = None + return result + p, q = args + if op == '==>': + return pl_true(~p | q, model) + elif op == '<==': + return pl_true(p | ~q, model) + pt = pl_true(p, model) + if pt is None: + return None + qt = pl_true(q, model) + if qt is None: + return None + if op == '<=>': + return pt == qt + elif op == '^': # xor or 'not equivalent' + return pt != qt + else: + raise ValueError("illegal operator in logic expression" + str(exp)) + +# ______________________________________________________________________________ +# 7.5 Propositional Theorem Proving + + +def to_cnf(s): + """Convert a propositional logical sentence to conjunctive normal form. + That is, to the form ((A | ~B | ...) & (B | C | ...) & ...) [p. 253] + >>> to_cnf('~(B | C)') + (~B & ~C) + """ + s = expr(s) + if isinstance(s, str): + s = expr(s) + s = eliminate_implications(s) # Steps 1, 2 from p. 253 + s = move_not_inwards(s) # Step 3 + return distribute_and_over_or(s) # Step 4 + + +def eliminate_implications(s): + """Change implications into equivalent form with only &, |, and ~ as logical operators.""" + s = expr(s) + if not s.args or is_symbol(s.op): + return s # Atoms are unchanged. + args = list(map(eliminate_implications, s.args)) + a, b = args[0], args[-1] + if s.op == '==>': + return b | ~a + elif s.op == '<==': + return a | ~b + elif s.op == '<=>': + return (a | ~b) & (b | ~a) + elif s.op == '^': + assert len(args) == 2 # TODO: relax this restriction + return (a & ~b) | (~a & b) + else: + assert s.op in ('&', '|', '~') + return Expr(s.op, *args) + + +def move_not_inwards(s): + """Rewrite sentence s by moving negation sign inward. + >>> move_not_inwards(~(A | B)) + (~A & ~B) + """ + s = expr(s) + if s.op == '~': + def NOT(b): + return move_not_inwards(~b) + a = s.args[0] + if a.op == '~': + return move_not_inwards(a.args[0]) # ~~A ==> A + if a.op == '&': + return associate('|', list(map(NOT, a.args))) + if a.op == '|': + return associate('&', list(map(NOT, a.args))) + return s + elif is_symbol(s.op) or not s.args: + return s + else: + return Expr(s.op, *list(map(move_not_inwards, s.args))) + + +def distribute_and_over_or(s): + """Given a sentence s consisting of conjunctions and disjunctions + of literals, return an equivalent sentence in CNF. + >>> distribute_and_over_or((A & B) | C) + ((A | C) & (B | C)) + """ + s = expr(s) + if s.op == '|': + s = associate('|', s.args) + if s.op != '|': + return distribute_and_over_or(s) + if len(s.args) == 0: + return False + if len(s.args) == 1: + return distribute_and_over_or(s.args[0]) + conj = first(arg for arg in s.args if arg.op == '&') + if not conj: + return s + others = [a for a in s.args if a is not conj] + rest = associate('|', others) + return associate('&', [distribute_and_over_or(c | rest) + for c in conj.args]) + elif s.op == '&': + return associate('&', list(map(distribute_and_over_or, s.args))) + else: + return s + + +def associate(op, args): + """Given an associative op, return an expression with the same + meaning as Expr(op, *args), but flattened -- that is, with nested + instances of the same op promoted to the top level. + >>> associate('&', [(A&B),(B|C),(B&C)]) + (A & B & (B | C) & B & C) + >>> associate('|', [A|(B|(C|(A&B)))]) + (A | B | C | (A & B)) + """ + args = dissociate(op, args) + if len(args) == 0: + return _op_identity[op] + elif len(args) == 1: + return args[0] + else: + return Expr(op, *args) + + +_op_identity = {'&': True, '|': False, '+': 0, '*': 1} + + +def dissociate(op, args): + """Given an associative op, return a flattened list result such + that Expr(op, *result) means the same as Expr(op, *args). + >>> dissociate('&', [A & B]) + [A, B] + """ + result = [] + + def collect(subargs): + for arg in subargs: + if arg.op == op: + collect(arg.args) + else: + result.append(arg) + collect(args) + return result + + +def conjuncts(s): + """Return a list of the conjuncts in the sentence s. + >>> conjuncts(A & B) + [A, B] + >>> conjuncts(A | B) + [(A | B)] + """ + return dissociate('&', [s]) + + +def disjuncts(s): + """Return a list of the disjuncts in the sentence s. + >>> disjuncts(A | B) + [A, B] + >>> disjuncts(A & B) + [(A & B)] + """ + return dissociate('|', [s]) + +# ______________________________________________________________________________ + + +def pl_resolution(KB, alpha): + """ + Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12] + >>> pl_resolution(horn_clauses_KB, A) + True + """ + clauses = KB.clauses + conjuncts(to_cnf(~alpha)) + new = set() + while True: + n = len(clauses) + pairs = [(clauses[i], clauses[j]) + for i in range(n) for j in range(i+1, n)] + for (ci, cj) in pairs: + resolvents = pl_resolve(ci, cj) + if False in resolvents: + return True + new = new.union(set(resolvents)) + if new.issubset(set(clauses)): + return False + for c in new: + if c not in clauses: + clauses.append(c) + + +def pl_resolve(ci, cj): + """Return all clauses that can be obtained by resolving clauses ci and cj.""" + clauses = [] + for di in disjuncts(ci): + for dj in disjuncts(cj): + if di == ~dj or ~di == dj: + dnew = unique(removeall(di, disjuncts(ci)) + + removeall(dj, disjuncts(cj))) + clauses.append(associate('|', dnew)) + return clauses + +# ______________________________________________________________________________ +# 7.5.4 Forward and backward chaining + + +class PropDefiniteKB(PropKB): + """A KB of propositional definite clauses.""" + + def tell(self, sentence): + """Add a definite clause to this KB.""" + assert is_definite_clause(sentence), "Must be definite clause" + self.clauses.append(sentence) + + def ask_generator(self, query): + """Yield the empty substitution if KB implies query; else nothing.""" + if pl_fc_entails(self.clauses, query): + yield {} + + def retract(self, sentence): + self.clauses.remove(sentence) + + def clauses_with_premise(self, p): + """Return a list of the clauses in KB that have p in their premise. + This could be cached away for O(1) speed, but we'll recompute it.""" + return [c for c in self.clauses + if c.op == '==>' and p in conjuncts(c.args[0])] + + +def pl_fc_entails(KB, q): + """Use forward chaining to see if a PropDefiniteKB entails symbol q. + [Figure 7.15] + >>> pl_fc_entails(horn_clauses_KB, expr('Q')) + True + """ + count = {c: len(conjuncts(c.args[0])) + for c in KB.clauses + if c.op == '==>'} + inferred = defaultdict(bool) + agenda = [s for s in KB.clauses if is_prop_symbol(s.op)] + while agenda: + p = agenda.pop() + if p == q: + return True + if not inferred[p]: + inferred[p] = True + for c in KB.clauses_with_premise(p): + count[c] -= 1 + if count[c] == 0: + agenda.append(c.args[1]) + return False + + +""" [Figure 7.13] +Simple inference in a wumpus world example +""" +wumpus_world_inference = expr("(B11 <=> (P12 | P21)) & ~B11") + + +""" [Figure 7.16] +Propositional Logic Forward Chaining example +""" +horn_clauses_KB = PropDefiniteKB() +for s in "P==>Q; (L&M)==>P; (B&L)==>M; (A&P)==>L; (A&B)==>L; A;B".split(';'): + horn_clauses_KB.tell(expr(s)) + +""" +Definite clauses KB example +""" +definite_clauses_KB = PropDefiniteKB() +for clause in ['(B & F)==>E', '(A & E & F)==>G', '(B & C)==>F', '(A & B)==>D', '(E & F)==>H', '(H & I)==>J', 'A', 'B', 'C']: + definite_clauses_KB.tell(expr(clause)) + +# ______________________________________________________________________________ +# 7.6 Effective Propositional Model Checking +# DPLL-Satisfiable [Figure 7.17] + + +def dpll_satisfiable(s): + """Check satisfiability of a propositional sentence. + This differs from the book code in two ways: (1) it returns a model + rather than True when it succeeds; this is more useful. (2) The + function find_pure_symbol is passed a list of unknown clauses, rather + than a list of all clauses and the model; this is more efficient. + >>> dpll_satisfiable(A |'<=>'| B) == {A: True, B: True} + True + """ + clauses = conjuncts(to_cnf(s)) + symbols = list(prop_symbols(s)) + return dpll(clauses, symbols, {}) + + +def dpll(clauses, symbols, model): + """See if the clauses are true in a partial model.""" + unknown_clauses = [] # clauses with an unknown truth value + for c in clauses: + val = pl_true(c, model) + if val is False: + return False + if val is not True: + unknown_clauses.append(c) + if not unknown_clauses: + return model + P, value = find_pure_symbol(symbols, unknown_clauses) + if P: + return dpll(clauses, removeall(P, symbols), extend(model, P, value)) + P, value = find_unit_clause(clauses, model) + if P: + return dpll(clauses, removeall(P, symbols), extend(model, P, value)) + if not symbols: + raise TypeError("Argument should be of the type Expr.") + P, symbols = symbols[0], symbols[1:] + return (dpll(clauses, symbols, extend(model, P, True)) or + dpll(clauses, symbols, extend(model, P, False))) + + +def find_pure_symbol(symbols, clauses): + """ + Find a symbol and its value if it appears only as a positive literal + (or only as a negative) in clauses. + >>> find_pure_symbol([A, B, C], [A|~B,~B|~C,C|A]) + (A, True) + """ + for s in symbols: + found_pos, found_neg = False, False + for c in clauses: + if not found_pos and s in disjuncts(c): + found_pos = True + if not found_neg and ~s in disjuncts(c): + found_neg = True + if found_pos != found_neg: + return s, found_pos + return None, None + + +def find_unit_clause(clauses, model): + """ + Find a forced assignment if possible from a clause with only 1 + variable not bound in the model. + >>> find_unit_clause([A|B|C, B|~C, ~A|~B], {A:True}) + (B, False) + """ + for clause in clauses: + P, value = unit_clause_assign(clause, model) + if P: + return P, value + return None, None + + +def unit_clause_assign(clause, model): + """Return a single variable/value pair that makes clause true in + the model, if possible. + >>> unit_clause_assign(A|B|C, {A:True}) + (None, None) + >>> unit_clause_assign(B|~C, {A:True}) + (None, None) + >>> unit_clause_assign(~A|~B, {A:True}) + (B, False) + """ + P, value = None, None + for literal in disjuncts(clause): + sym, positive = inspect_literal(literal) + if sym in model: + if model[sym] == positive: + return None, None # clause already True + elif P: + return None, None # more than 1 unbound variable + else: + P, value = sym, positive + return P, value + + +def inspect_literal(literal): + """The symbol in this literal, and the value it should take to + make the literal true. + >>> inspect_literal(P) + (P, True) + >>> inspect_literal(~P) + (P, False) + """ + if literal.op == '~': + return literal.args[0], False + else: + return literal, True + +# ______________________________________________________________________________ +# 7.6.2 Local search algorithms +# Walk-SAT [Figure 7.18] + + +def WalkSAT(clauses, p=0.5, max_flips=10000): + """ + Checks for satisfiability of all clauses by randomly flipping values of variables + >>> WalkSAT([A & ~A], 0.5, 100) is None + True + """ + # Set of all symbols in all clauses + symbols = {sym for clause in clauses for sym in prop_symbols(clause)} + # model is a random assignment of true/false to the symbols in clauses + model = {s: random.choice([True, False]) for s in symbols} + for i in range(max_flips): + satisfied, unsatisfied = [], [] + for clause in clauses: + (satisfied if pl_true(clause, model) else unsatisfied).append(clause) + if not unsatisfied: # if model satisfies all the clauses + return model + clause = random.choice(unsatisfied) + if probability(p): + sym = random.choice(list(prop_symbols(clause))) + else: + # Flip the symbol in clause that maximizes number of sat. clauses + def sat_count(sym): + # Return the the number of clauses satisfied after flipping the symbol. + model[sym] = not model[sym] + count = len([clause for clause in clauses if pl_true(clause, model)]) + model[sym] = not model[sym] + return count + sym = argmax(prop_symbols(clause), key=sat_count) + model[sym] = not model[sym] + # If no solution is found within the flip limit, we return failure + return None + +# ______________________________________________________________________________ +# 7.7 Agents Based on Propositional Logic +# 7.7.1 The current state of the world + + +class WumpusKB(PropKB): + """ + Create a Knowledge Base that contains the atemporal "Wumpus physics" and temporal rules with time zero. + """ + + def __init__(self,dimrow): + super().__init__() + self.dimrow = dimrow + self.tell( ~wumpus(1, 1) ) + self.tell( ~pit(1, 1) ) + + for y in range(1, dimrow+1): + for x in range(1, dimrow+1): + + pits_in = list() + wumpus_in = list() + + if x > 1: # West room exists + pits_in.append(pit(x - 1, y)) + wumpus_in.append(wumpus(x - 1, y)) + + if y < dimrow: # North room exists + pits_in.append(pit(x, y + 1)) + wumpus_in.append(wumpus(x, y + 1)) + + if x < dimrow: # East room exists + pits_in.append(pit(x + 1, y)) + wumpus_in.append(wumpus(x + 1, y)) + + if y > 1: # South room exists + pits_in.append(pit(x, y - 1)) + wumpus_in.append(wumpus(x, y - 1)) + + self.tell(equiv(breeze(x, y), new_disjunction(pits_in))) + self.tell(equiv(stench(x, y), new_disjunction(wumpus_in))) + + # Rule that describes existence of at least one Wumpus + wumpus_at_least = list() + for x in range(1, dimrow+1): + for y in range(1, dimrow + 1): + wumpus_at_least.append(wumpus(x, y)) + + self.tell(new_disjunction(wumpus_at_least)) + + # Rule that describes existence of at most one Wumpus + for i in range(1, dimrow+1): + for j in range(1, dimrow+1): + for u in range(1, dimrow+1): + for v in range(1, dimrow+1): + if i!=u or j!=v: + self.tell(~wumpus(i, j) | ~wumpus(u, v)) + + # Temporal rules at time zero + self.tell(location(1, 1, 0)) + for i in range(1, dimrow+1): + for j in range(1, dimrow + 1): + self.tell(implies(location(i, j, 0), equiv(percept_breeze(0), breeze(i, j)))) + self.tell(implies(location(i, j, 0), equiv(percept_stench(0), stench(i, j)))) + if i != 1 or j != 1: + self.tell(~location(i, j, 0)) + + self.tell(wumpus_alive(0)) + self.tell(have_arrow(0)) + self.tell(facing_east(0)) + self.tell(~facing_north(0)) + self.tell(~facing_south(0)) + self.tell(~facing_west(0)) + + def make_action_sentence(self, action, time): + actions = [move_forward(time), shoot(time), turn_left(time), turn_right(time)] + + for a in actions: + if action is a: + self.tell(action) + else: + self.tell(~a) + + def make_percept_sentence(self, percept, time): + # Glitter, Bump, Stench, Breeze, Scream + flags = [0, 0, 0, 0, 0] + + # Things perceived + if isinstance(percept, Glitter): + flags[0] = 1 + self.tell(percept_glitter(time)) + elif isinstance(percept, Bump): + flags[1] = 1 + self.tell(percept_bump(time)) + elif isinstance(percept, Stench): + flags[2] = 1 + self.tell(percept_stench(time)) + elif isinstance(percept, Breeze): + flags[3] = 1 + self.tell(percept_breeze(time)) + elif isinstance(percept, Scream): + flags[4] = 1 + self.tell(percept_scream(time)) + + # Things not perceived + for i in range(len(flags)): + if flags[i] == 0: + if i == 0: + self.tell(~percept_glitter(time)) + elif i == 1: + self.tell(~percept_bump(time)) + elif i == 2: + self.tell(~percept_stench(time)) + elif i == 3: + self.tell(~percept_breeze(time)) + elif i == 4: + self.tell(~percept_scream(time)) + + def add_temporal_sentences(self, time): + if time == 0: + return + t = time - 1 + + # current location rules + for i in range(1, self.dimrow+1): + for j in range(1, self.dimrow+1): + self.tell(implies(location(i, j, time), equiv(percept_breeze(time), breeze(i, j)))) + self.tell(implies(location(i, j, time), equiv(percept_stench(time), stench(i, j)))) + + s = list() + + s.append( + equiv( + location(i, j, time), location(i, j, time) & ~move_forward(time) | percept_bump(time))) + + if i != 1: + s.append(location(i - 1, j, t) & facing_east(t) & move_forward(t)) + + if i != self.dimrow: + s.append(location(i + 1, j, t) & facing_west(t) & move_forward(t)) + + if j != 1: + s.append(location(i, j - 1, t) & facing_north(t) & move_forward(t)) + + if j != self.dimrow: + s.append(location(i, j + 1, t) & facing_south(t) & move_forward(t)) + + # add sentence about location i,j + self.tell(new_disjunction(s)) + + # add sentence about safety of location i,j + self.tell( + equiv(ok_to_move(i, j, time), ~pit(i, j) & ~wumpus(i, j) & wumpus_alive(time)) + ) + + # Rules about current orientation + + a = facing_north(t) & turn_right(t) + b = facing_south(t) & turn_left(t) + c = facing_east(t) & ~turn_left(t) & ~turn_right(t) + s = equiv(facing_east(time), a | b | c) + self.tell(s) + + a = facing_north(t) & turn_left(t) + b = facing_south(t) & turn_right(t) + c = facing_west(t) & ~turn_left(t) & ~turn_right(t) + s = equiv(facing_west(time), a | b | c) + self.tell(s) + + a = facing_east(t) & turn_left(t) + b = facing_west(t) & turn_right(t) + c = facing_north(t) & ~turn_left(t) & ~turn_right(t) + s = equiv(facing_north(time), a | b | c) + self.tell(s) + + a = facing_west(t) & turn_left(t) + b = facing_east(t) & turn_right(t) + c = facing_south(t) & ~turn_left(t) & ~turn_right(t) + s = equiv(facing_south(time), a | b | c) + self.tell(s) + + # Rules about last action + self.tell(equiv(move_forward(t), ~turn_right(t) & ~turn_left(t))) + + # Rule about the arrow + self.tell(equiv(have_arrow(time), have_arrow(t) & ~shoot(t))) + + # Rule about Wumpus (dead or alive) + self.tell(equiv(wumpus_alive(time), wumpus_alive(t) & ~percept_scream(time))) + + def ask_if_true(self, query): + return pl_resolution(self, query) + + +# ______________________________________________________________________________ + + +class WumpusPosition(): + def __init__(self, x, y, orientation): + self.X = x + self.Y = y + self.orientation = orientation + + def get_location(self): + return self.X, self.Y + + def set_location(self, x, y): + self.X = x + self.Y = y + + def get_orientation(self): + return self.orientation + + def set_orientation(self, orientation): + self.orientation = orientation + + def __eq__(self, other): + if other.get_location() == self.get_location() and \ + other.get_orientation()==self.get_orientation(): + return True + else: + return False + +# ______________________________________________________________________________ +# 7.7.2 A hybrid agent + + +class HybridWumpusAgent(Agent): + """An agent for the wumpus world that does logical inference. [Figure 7.20]""" + + def __init__(self,dimentions): + self.dimrow = dimentions + self.kb = WumpusKB(self.dimrow) + self.t = 0 + self.plan = list() + self.current_position = WumpusPosition(1, 1, 'UP') + super().__init__(self.execute) + + def execute(self, percept): + self.kb.make_percept_sentence(percept, self.t) + self.kb.add_temporal_sentences(self.t) + + temp = list() + + for i in range(1, self.dimrow+1): + for j in range(1, self.dimrow+1): + if self.kb.ask_if_true(location(i, j, self.t)): + temp.append(i) + temp.append(j) + + if self.kb.ask_if_true(facing_north(self.t)): + self.current_position = WumpusPosition(temp[0], temp[1], 'UP') + elif self.kb.ask_if_true(facing_south(self.t)): + self.current_position = WumpusPosition(temp[0], temp[1], 'DOWN') + elif self.kb.ask_if_true(facing_west(self.t)): + self.current_position = WumpusPosition(temp[0], temp[1], 'LEFT') + elif self.kb.ask_if_true(facing_east(self.t)): + self.current_position = WumpusPosition(temp[0], temp[1], 'RIGHT') + + safe_points = list() + for i in range(1, self.dimrow+1): + for j in range(1, self.dimrow+1): + if self.kb.ask_if_true(ok_to_move(i, j, self.t)): + safe_points.append([i, j]) + + if self.kb.ask_if_true(percept_glitter(self.t)): + goals = list() + goals.append([1, 1]) + self.plan.append('Grab') + actions = self.plan_route(self.current_position,goals,safe_points) + self.plan.extend(actions) + self.plan.append('Climb') + + if len(self.plan) == 0: + unvisited = list() + for i in range(1, self.dimrow+1): + for j in range(1, self.dimrow+1): + for k in range(self.t): + if self.kb.ask_if_true(location(i, j, k)): + unvisited.append([i, j]) + unvisited_and_safe = list() + for u in unvisited: + for s in safe_points: + if u not in unvisited_and_safe and s == u: + unvisited_and_safe.append(u) + + temp = self.plan_route(self.current_position,unvisited_and_safe,safe_points) + self.plan.extend(temp) + + if len(self.plan) == 0 and self.kb.ask_if_true(have_arrow(self.t)): + possible_wumpus = list() + for i in range(1, self.dimrow+1): + for j in range(1, self.dimrow+1): + if not self.kb.ask_if_true(wumpus(i, j)): + possible_wumpus.append([i, j]) + + temp = self.plan_shot(self.current_position, possible_wumpus, safe_points) + self.plan.extend(temp) + + if len(self.plan) == 0: + not_unsafe = list() + for i in range(1, self.dimrow+1): + for j in range(1, self.dimrow+1): + if not self.kb.ask_if_true(ok_to_move(i, j, self.t)): + not_unsafe.append([i, j]) + temp = self.plan_route(self.current_position, not_unsafe, safe_points) + self.plan.extend(temp) + + if len(self.plan) == 0: + start = list() + start.append([1, 1]) + temp = self.plan_route(self.current_position, start, safe_points) + self.plan.extend(temp) + self.plan.append('Climb') + + action = self.plan[0] + self.plan = self.plan[1:] + self.kb.make_action_sentence(action, self.t) + self.t += 1 + + return action + + def plan_route(self, current, goals, allowed): + problem = PlanRoute(current, goals, allowed, self.dimrow) + return astar_search(problem).solution() + + def plan_shot(self, current, goals, allowed): + shooting_positions = set() + + for loc in goals: + x = loc[0] + y = loc[1] + for i in range(1, self.dimrow+1): + if i < x: + shooting_positions.add(WumpusPosition(i, y, 'EAST')) + if i > x: + shooting_positions.add(WumpusPosition(i, y, 'WEST')) + if i < y: + shooting_positions.add(WumpusPosition(x, i, 'NORTH')) + if i > y: + shooting_positions.add(WumpusPosition(x, i, 'SOUTH')) + + # Can't have a shooting position from any of the rooms the Wumpus could reside + orientations = ['EAST', 'WEST', 'NORTH', 'SOUTH'] + for loc in goals: + for orientation in orientations: + shooting_positions.remove(WumpusPosition(loc[0], loc[1], orientation)) + + actions = list() + actions.extend(self.plan_route(current, shooting_positions, allowed)) + actions.append('Shoot') + return actions + + +# ______________________________________________________________________________ +# 7.7.4 Making plans by propositional inference + + +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. + [Figure 7.22] + >>> transition = {'A': {'Left': 'A', 'Right': 'B'}, 'B': {'Left': 'A', 'Right': 'C'}, 'C': {'Left': 'B', 'Right': 'C'}} + >>> SAT_plan('A', transition, 'C', 2) is None + True + """ + + # Functions used by SAT_plan + def translate_to_SAT(init, transition, goal, time): + clauses = [] + states = [state for state in transition] + + # Symbol claiming state s at time t + state_counter = itertools.count() + for s in states: + for t in range(time+1): + state_sym[s, t] = Expr("State_{}".format(next(state_counter))) + + # Add initial state axiom + clauses.append(state_sym[init, 0]) + + # Add goal state axiom + clauses.append(state_sym[goal, time]) + + # All possible transitions + transition_counter = itertools.count() + for s in states: + for action in transition[s]: + s_ = transition[s][action] + for t in range(time): + # Action 'action' taken from state 's' at time 't' to reach 's_' + action_sym[s, action, t] = Expr( + "Transition_{}".format(next(transition_counter))) + + # Change the state from s to s_ + clauses.append(action_sym[s, action, t] |'==>'| state_sym[s, t]) + clauses.append(action_sym[s, action, t] |'==>'| state_sym[s_, t + 1]) + + # Allow only one state at any time + for t in range(time+1): + # must be a state at any time + clauses.append(associate('|', [state_sym[s, t] for s in states])) + + for s in states: + for s_ in states[states.index(s) + 1:]: + # for each pair of states s, s_ only one is possible at time t + clauses.append((~state_sym[s, t]) | (~state_sym[s_, t])) + + # Restrict to one transition per timestep + for t in range(time): + # list of possible transitions at time t + transitions_t = [tr for tr in action_sym if tr[2] == t] + + # make sure at least one of the transitions happens + clauses.append(associate('|', [action_sym[tr] for tr in transitions_t])) + + for tr in transitions_t: + for tr_ in transitions_t[transitions_t.index(tr) + 1:]: + # there cannot be two transitions tr and tr_ at time t + clauses.append(~action_sym[tr] | ~action_sym[tr_]) + + # Combine the clauses to form the cnf + return associate('&', clauses) + + def extract_solution(model): + true_transitions = [t for t in action_sym if model[action_sym[t]]] + # Sort transitions based on time, which is the 3rd element of the tuple + true_transitions.sort(key=lambda x: x[2]) + return [action for s, action, time in true_transitions] + + # Body of SAT_plan algorithm + for t in range(t_max): + # dictionaries to help extract the solution from model + state_sym = {} + action_sym = {} + + cnf = translate_to_SAT(init, transition, goal, t) + model = SAT_solver(cnf) + if model is not False: + return extract_solution(model) + return None + +# ______________________________________________________________________________ +# Chapter 9 Inference in First Order Logic +# 9.2 Unification and First Order Inference +# 9.2.1 Unification + + +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. [Figure 9.1] + >>> unify(x, 3, {}) + {x: 3} + """ + if s is None: + return None + elif x == y: + return s + elif is_variable(x): + return unify_var(x, y, s) + elif is_variable(y): + return unify_var(y, x, s) + elif isinstance(x, Expr) and isinstance(y, Expr): + return unify(x.args, y.args, unify(x.op, y.op, s)) + elif isinstance(x, str) or isinstance(y, str): + return None + elif issequence(x) and issequence(y) and len(x) == len(y): + if not x: + return s + return unify(x[1:], y[1:], unify(x[0], y[0], s)) + else: + return None + + +def is_variable(x): + """A variable is an Expr with no args and a lowercase symbol as the op.""" + return isinstance(x, Expr) and not x.args and x.op[0].islower() + + +def unify_var(var, x, s): + if var in s: + return unify(s[var], x, s) + elif x in s: + return unify(var, s[x], s) + elif occur_check(var, x, s): + return None + else: + return extend(s, var, x) + + +def occur_check(var, x, s): + """Return true if variable var occurs anywhere in x + (or in subst(s, x), if s has a binding for x).""" + if var == x: + return True + elif is_variable(x) and x in s: + return occur_check(var, s[x], s) + elif isinstance(x, Expr): + return (occur_check(var, x.op, s) or + occur_check(var, x.args, s)) + elif isinstance(x, (list, tuple)): + return first(e for e in x if occur_check(var, e, s)) + else: + return False + + +def extend(s, var, val): + """Copy the substitution s and extend it by setting var to val; return copy. + >>> extend({x: 1}, y, 2) == {x: 1, y: 2} + True + """ + s2 = s.copy() + s2[var] = val + return s2 + + +# 9.2.2 Storage and retrieval + + +class FolKB(KB): + """A knowledge base consisting of first-order definite clauses. + >>> kb0 = FolKB([expr('Farmer(Mac)'), expr('Rabbit(Pete)'), + ... expr('(Rabbit(r) & Farmer(f)) ==> Hates(f, r)')]) + >>> kb0.tell(expr('Rabbit(Flopsie)')) + >>> kb0.retract(expr('Rabbit(Pete)')) + >>> kb0.ask(expr('Hates(Mac, x)'))[x] + Flopsie + >>> kb0.ask(expr('Wife(Pete, x)')) + False + """ + + def __init__(self, initial_clauses=None): + self.clauses = [] # inefficient: no indexing + if initial_clauses: + for clause in initial_clauses: + self.tell(clause) + + def tell(self, sentence): + if is_definite_clause(sentence): + self.clauses.append(sentence) + else: + raise Exception("Not a definite clause: {}".format(sentence)) + + def ask_generator(self, query): + return fol_bc_ask(self, query) + + def retract(self, sentence): + self.clauses.remove(sentence) + + def fetch_rules_for_goal(self, goal): + return self.clauses + + +# ______________________________________________________________________________ +# 9.3 Forward Chaining +# 9.3.2 A simple forward-chaining algorithm + + +def fol_fc_ask(KB, alpha): + """A simple forward-chaining algorithm. [Figure 9.3]""" + kb_consts = list({c for clause in KB.clauses for c in constant_symbols(clause)}) + + def enum_subst(p): + query_vars = list({v for clause in p for v in variables(clause)}) + for assignment_list in itertools.product(kb_consts, repeat=len(query_vars)): + theta = {x: y for x, y in zip(query_vars, assignment_list)} + yield theta + + # check if we can answer without new inferences + for q in KB.clauses: + phi = unify(q, alpha, {}) + if phi is not None: + yield phi + + while True: + new = [] + for rule in KB.clauses: + p, q = parse_definite_clause(rule) + for theta in enum_subst(p): + if set(subst(theta, p)).issubset(set(KB.clauses)): + q_ = subst(theta, q) + if all([unify(x, q_, {}) is None for x in KB.clauses + new]): + new.append(q_) + phi = unify(q_, alpha, {}) + if phi is not None: + yield phi + if not new: + break + for clause in new: + KB.tell(clause) + return None + + +def subst(s, x): + """Substitute the substitution s into the expression x. + >>> subst({x: 42, y:0}, F(x) + y) + (F(42) + 0) + """ + if isinstance(x, list): + return [subst(s, xi) for xi in x] + elif isinstance(x, tuple): + return tuple([subst(s, xi) for xi in x]) + elif not isinstance(x, Expr): + return x + elif is_var_symbol(x.op): + return s.get(x, x) + else: + return Expr(x.op, *[subst(s, arg) for arg in x.args]) + + +def standardize_variables(sentence, dic=None): + """Replace all the variables in sentence with new variables.""" + if dic is None: + dic = {} + if not isinstance(sentence, Expr): + return sentence + elif is_var_symbol(sentence.op): + if sentence in dic: + return dic[sentence] + else: + v = Expr('v_{}'.format(next(standardize_variables.counter))) + dic[sentence] = v + return v + else: + return Expr(sentence.op, + *[standardize_variables(a, dic) for a in sentence.args]) + + +standardize_variables.counter = itertools.count() + + +# __________________________________________________________________ +# 9.4 Backward Chaining + + +def fol_bc_ask(KB, query): + """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, {}) + + +def fol_bc_or(KB, goal, theta): + for rule in KB.fetch_rules_for_goal(goal): + lhs, rhs = parse_definite_clause(standardize_variables(rule)) + for theta1 in fol_bc_and(KB, lhs, unify(rhs, goal, theta)): + yield theta1 + + +def fol_bc_and(KB, goals, theta): + if theta is None: + pass + elif not goals: + yield theta + else: + first, rest = goals[0], goals[1:] + for theta1 in fol_bc_or(KB, subst(theta, first), theta): + for theta2 in fol_bc_and(KB, rest, theta1): + yield theta2 + +# ______________________________________________________________________________ +# A simple KB that defines the relevant conditions of the Wumpus World as in Fig 7.4. +# See Sec. 7.4.3 +wumpus_kb = PropKB() + +P11, P12, P21, P22, P31, B11, B21 = expr('P11, P12, P21, P22, P31, B11, B21') +wumpus_kb.tell(~P11) +wumpus_kb.tell(B11 | '<=>' | ((P12 | P21))) +wumpus_kb.tell(B21 | '<=>' | ((P11 | P22 | P31))) +wumpus_kb.tell(~B11) +wumpus_kb.tell(B21) + +test_kb = FolKB( + map(expr, ['Farmer(Mac)', + 'Rabbit(Pete)', + 'Mother(MrsMac, Mac)', + 'Mother(MrsRabbit, Pete)', + '(Rabbit(r) & Farmer(f)) ==> Hates(f, r)', + '(Mother(m, c)) ==> Loves(m, c)', + '(Mother(m, r) & Rabbit(r)) ==> Rabbit(m)', + '(Farmer(f)) ==> Human(f)', + # Note that this order of conjuncts + # would result in infinite recursion: + # '(Human(h) & Mother(m, h)) ==> Human(m)' + '(Mother(m, h) & Human(h)) ==> Human(m)' + ])) + +crime_kb = FolKB( + 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)' + ])) + +# ______________________________________________________________________________ + +# Example application (not in the book). +# You can use the Expr class to do symbolic differentiation. This used to be +# a part of AI; now it is considered a separate field, Symbolic Algebra. + + +def diff(y, x): + """Return the symbolic derivative, dy/dx, as an Expr. + However, you probably want to simplify the results with simp. + >>> diff(x * x, x) + ((x * 1) + (x * 1)) + """ + if y == x: + return 1 + elif not y.args: + return 0 + else: + u, op, v = y.args[0], y.op, y.args[-1] + if op == '+': + return diff(u, x) + diff(v, x) + elif op == '-' and len(y.args) == 1: + return -diff(u, x) + elif op == '-': + return diff(u, x) - diff(v, x) + elif op == '*': + return u * diff(v, x) + v * diff(u, x) + elif op == '/': + return (v * diff(u, x) - u * diff(v, x)) / (v * v) + elif op == '**' and isnumber(x.op): + return (v * u ** (v - 1) * diff(u, x)) + elif op == '**': + return (v * u ** (v - 1) * diff(u, x) + + u ** v * Expr('log')(u) * diff(v, x)) + elif op == 'log': + return diff(u, x) / u + else: + raise ValueError("Unknown op: {} in diff({}, {})".format(op, y, x)) + + +def simp(x): + """Simplify the expression x.""" + if isnumber(x) or not x.args: + return x + args = list(map(simp, x.args)) + u, op, v = args[0], x.op, args[-1] + if op == '+': + if v == 0: + return u + if u == 0: + return v + if u == v: + return 2 * u + if u == -v or v == -u: + return 0 + elif op == '-' and len(args) == 1: + if u.op == '-' and len(u.args) == 1: + return u.args[0] # --y ==> y + elif op == '-': + if v == 0: + return u + if u == 0: + return -v + if u == v: + return 0 + if u == -v or v == -u: + return 0 + elif op == '*': + if u == 0 or v == 0: + return 0 + if u == 1: + return v + if v == 1: + return u + if u == v: + return u ** 2 + elif op == '/': + if u == 0: + return 0 + if v == 0: + return Expr('Undefined') + if u == v: + return 1 + if u == -v or v == -u: + return 0 + elif op == '**': + if u == 0: + return 0 + if v == 0: + return 1 + if u == 1: + return 1 + if v == 1: + return u + elif op == 'log': + if u == 1: + return 0 + else: + raise ValueError("Unknown op: " + op) + # If we fall through to here, we can not simplify further + return Expr(op, *args) + + +def d(y, x): + """Differentiate and then simplify. + >>> d(x * x - x, x) + ((2 * x) - 1) + """ + return simp(diff(y, x)) diff --git a/tests/test_logic4e.py b/tests/test_logic4e.py new file mode 100644 index 000000000..f8ed203d6 --- /dev/null +++ b/tests/test_logic4e.py @@ -0,0 +1,347 @@ +import pytest +from logic4e import * +from utils4e import expr_handle_infix_ops, count, Symbol + +definite_clauses_KB = PropDefiniteKB() +for clause in ['(B & F)==>E', '(A & E & F)==>G', '(B & C)==>F', '(A & B)==>D', '(E & F)==>H', '(H & I)==>J', 'A', 'B', 'C']: + definite_clauses_KB.tell(expr(clause)) + + +def test_is_symbol(): + assert is_symbol('x') + assert is_symbol('X') + assert is_symbol('N245') + assert not is_symbol('') + assert not is_symbol('1L') + assert not is_symbol([1, 2, 3]) + + +def test_is_var_symbol(): + assert is_var_symbol('xt') + assert not is_var_symbol('Txt') + assert not is_var_symbol('') + assert not is_var_symbol('52') + + +def test_is_prop_symbol(): + assert not is_prop_symbol('xt') + assert is_prop_symbol('Txt') + assert not is_prop_symbol('') + assert not is_prop_symbol('52') + + +def test_variables(): + assert variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, 2)')) == {x, y, z} + assert variables(expr('(x ==> y) & B(x, y) & A')) == {x, y} + + +def test_expr(): + assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' + assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' + assert (expr_handle_infix_ops('P & Q ==> R & ~S') + == "P & Q |'==>'| R & ~S") + + +def test_extend(): + assert extend({x: 1}, y, 2) == {x: 1, y: 2} + + +def test_subst(): + assert subst({x: 42, y:0}, F(x) + y) == (F(42) + 0) + + +def test_PropKB(): + kb = PropKB() + assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 + kb.tell(A & E) + assert kb.ask(A) == kb.ask(E) == {} + kb.tell(E |'==>'| C) + assert kb.ask(C) == {} + kb.retract(E) + assert kb.ask(E) is False + assert kb.ask(C) is False + + +def test_wumpus_kb(): + # Statement: There is no pit in [1,1]. + assert wumpus_kb.ask(~P11) == {} + + # Statement: There is no pit in [1,2]. + assert wumpus_kb.ask(~P12) == {} + + # Statement: There is a pit in [2,2]. + assert wumpus_kb.ask(P22) is False + + # Statement: There is a pit in [3,1]. + assert wumpus_kb.ask(P31) is False + + # Statement: Neither [1,2] nor [2,1] contains a pit. + assert wumpus_kb.ask(~P12 & ~P21) == {} + + # Statement: There is a pit in either [2,2] or [3,1]. + assert wumpus_kb.ask(P22 | P31) == {} + + +def test_is_definite_clause(): + assert is_definite_clause(expr('A & B & C & D ==> E')) + assert is_definite_clause(expr('Farmer(Mac)')) + assert not is_definite_clause(expr('~Farmer(Mac)')) + assert is_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) + assert not is_definite_clause(expr('(Farmer(f) & ~Rabbit(r)) ==> Hates(f, r)')) + assert not is_definite_clause(expr('(Farmer(f) | Rabbit(r)) ==> Hates(f, r)')) + + +def test_parse_definite_clause(): + assert parse_definite_clause(expr('A & B & C & D ==> E')) == ([A, B, C, D], E) + assert parse_definite_clause(expr('Farmer(Mac)')) == ([], expr('Farmer(Mac)')) + assert parse_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) == ([expr('Farmer(f)'), expr('Rabbit(r)')], expr('Hates(f, r)')) + + +def test_pl_true(): + assert pl_true(P, {}) is None + assert pl_true(P, {P: False}) is False + assert pl_true(P | Q, {P: True}) is True + assert pl_true((A | B) & (C | D), {A: False, B: True, D: True}) is True + assert pl_true((A & B) & (C | D), {A: False, B: True, D: True}) is False + assert pl_true((A & B) | (A & C), {A: False, B: True, C: True}) is False + assert pl_true((A | B) & (C | D), {A: True, D: False}) is None + assert pl_true(P | P, {}) is None + + +def test_tt_true(): + assert tt_true(P | ~P) + assert tt_true('~~P <=> P') + assert not tt_true((P | ~Q) & (~P | Q)) + assert not tt_true(P & ~P) + assert not tt_true(P & Q) + assert tt_true((P | ~Q) | (~P | Q)) + assert tt_true('(A & B) ==> (A | B)') + assert tt_true('((A & B) & C) <=> (A & (B & C))') + assert tt_true('((A | B) | C) <=> (A | (B | C))') + assert tt_true('(A ==> B) <=> (~B ==> ~A)') + assert tt_true('(A ==> B) <=> (~A | B)') + assert tt_true('(A <=> B) <=> ((A ==> B) & (B ==> A))') + assert tt_true('~(A & B) <=> (~A | ~B)') + assert tt_true('~(A | B) <=> (~A & ~B)') + assert tt_true('(A & (B | C)) <=> ((A & B) | (A & C))') + assert tt_true('(A | (B & C)) <=> ((A | B) & (A | C))') + + +def test_dpll(): + assert (dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) + & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) + == {B: False, C: True, A: True, F: False, D: True, E: False}) + assert dpll_satisfiable(A & B & ~C & D) == {C: False, A: True, D: True, B: True} + assert dpll_satisfiable((A | (B & C)) |'<=>'| ((A | B) & (A | C))) == {C: True, A: True} or {C: True, B: True} + assert dpll_satisfiable(A |'<=>'| B) == {A: True, B: True} + assert dpll_satisfiable(A & ~B) == {A: True, B: False} + assert dpll_satisfiable(P & ~P) is False + + +def test_find_pure_symbol(): + assert find_pure_symbol([A, B, C], [A|~B,~B|~C,C|A]) == (A, True) + assert find_pure_symbol([A, B, C], [~A|~B,~B|~C,C|A]) == (B, False) + assert find_pure_symbol([A, B, C], [~A|B,~B|~C,C|A]) == (None, None) + + +def test_unit_clause_assign(): + assert unit_clause_assign(A|B|C, {A:True}) == (None, None) + assert unit_clause_assign(B|C, {A:True}) == (None, None) + assert unit_clause_assign(B|~A, {A:True}) == (B, True) + + +def test_find_unit_clause(): + assert find_unit_clause([A|B|C, B|~C, ~A|~B], {A:True}) == (B, False) + + +def test_unify(): + assert unify(x, x, {}) == {} + assert unify(x, 3, {}) == {x: 3} + assert unify(x & 4 & y, 6 & y & 4, {}) == {x: 6, y: 4} + assert unify(expr('A(x)'), expr('A(B)')) == {x: B} + assert unify(expr('American(x) & Weapon(B)'), expr('American(A) & Weapon(y)')) == {x: A, y: B} + + +def test_pl_fc_entails(): + assert pl_fc_entails(horn_clauses_KB, expr('Q')) + assert pl_fc_entails(definite_clauses_KB, expr('G')) + assert pl_fc_entails(definite_clauses_KB, expr('H')) + assert not pl_fc_entails(definite_clauses_KB, expr('I')) + assert not pl_fc_entails(definite_clauses_KB, expr('J')) + assert not pl_fc_entails(horn_clauses_KB, expr('SomethingSilly')) + + +def test_tt_entails(): + assert tt_entails(P & Q, Q) + assert not tt_entails(P | Q, Q) + assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) + assert not tt_entails(P |'<=>'| Q, Q) + assert tt_entails((P |'==>'| Q) & P, Q) + assert not tt_entails((P |'<=>'| Q) & ~P, Q) + + +def test_prop_symbols(): + assert prop_symbols(expr('x & y & z | A')) == {A} + assert prop_symbols(expr('(x & B(z)) ==> Farmer(y) | A')) == {A, expr('Farmer(y)'), expr('B(z)')} + + +def test_constant_symbols(): + assert constant_symbols(expr('x & y & z | A')) == {A} + assert constant_symbols(expr('(x & B(z)) & Father(John) ==> Farmer(y) | A')) == {A, expr('John')} + + +def test_predicate_symbols(): + assert predicate_symbols(expr('x & y & z | A')) == set() + assert predicate_symbols(expr('(x & B(z)) & Father(John) ==> Farmer(y) | A')) == { + ('B', 1), + ('Father', 1), + ('Farmer', 1)} + assert predicate_symbols(expr('(x & B(x, y, z)) & F(G(x, y), x) ==> P(Q(R(x, y)), x, y, z)')) == { + ('B', 3), + ('F', 2), + ('G', 2), + ('P', 4), + ('Q', 1), + ('R', 2)} + + +def test_eliminate_implications(): + assert repr(eliminate_implications('A ==> (~B <== C)')) == '((~B | ~C) | ~A)' + assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' + assert repr(eliminate_implications(A & B | C & ~D)) == '((A & B) | (C & ~D))' + + +def test_dissociate(): + assert dissociate('&', [A & B]) == [A, B] + assert dissociate('|', [A, B, C & D, P | Q]) == [A, B, C & D, P, Q] + assert dissociate('&', [A, B, C & D, P | Q]) == [A, B, C, D, P | Q] + + +def test_associate(): + assert (repr(associate('&', [(A & B), (B | C), (B & C)])) + == '(A & B & (B | C) & B & C)') + assert (repr(associate('|', [A | (B | (C | (A & B)))])) + == '(A | B | C | (A & B))') + + +def test_move_not_inwards(): + assert repr(move_not_inwards(~(A | B))) == '(~A & ~B)' + assert repr(move_not_inwards(~(A & B))) == '(~A | ~B)' + assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' + + +def test_distribute_and_over_or(): + def test_entailment(s, has_and = False): + result = distribute_and_over_or(s) + if has_and: + assert result.op == '&' + assert tt_entails(s, result) + assert tt_entails(result, s) + test_entailment((A & B) | C, True) + test_entailment((A | B) & C, True) + test_entailment((A | B) | C, False) + test_entailment((A & B) | (C | D), True) + + +def test_to_cnf(): + assert (repr(to_cnf(wumpus_world_inference & ~expr('~P12'))) == + "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") + assert repr(to_cnf((P & Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' + assert repr(to_cnf('A <=> B')) == '((A | ~B) & (B | ~A))' + assert repr(to_cnf("B <=> (P1 | P2)")) == '((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B))' + assert repr(to_cnf('A <=> (B & C)')) == '((A | ~B | ~C) & (B | ~A) & (C | ~A))' + assert repr(to_cnf("a | (b & c) | d")) == '((b | a | d) & (c | a | d))' + assert repr(to_cnf("A & (B | (D & E))")) == '(A & (D | B) & (E | B))' + assert repr(to_cnf("A | (B | (C | (D & E)))")) == '((D | A | B | C) & (E | A | B | C))' + assert repr(to_cnf('(A <=> ~B) ==> (C | ~D)')) == '((B | ~A | C | ~D) & (A | ~A | C | ~D) & (B | ~B | C | ~D) & (A | ~B | C | ~D))' + + +def test_pl_resolution(): + assert pl_resolution(wumpus_kb, ~P11) + assert pl_resolution(wumpus_kb, ~B11) + assert not pl_resolution(wumpus_kb, P22) + assert pl_resolution(horn_clauses_KB, A) + assert pl_resolution(horn_clauses_KB, B) + assert not pl_resolution(horn_clauses_KB, P) + assert not pl_resolution(definite_clauses_KB, P) + + +def test_standardize_variables(): + e = expr('F(a, b, c) & G(c, A, 23)') + assert len(variables(standardize_variables(e))) == 3 + # assert variables(e).intersection(variables(standardize_variables(e))) == {} + assert is_variable(standardize_variables(expr('x'))) + + +def test_fol_bc_ask(): + def test_ask(query, kb=None): + q = expr(query) + test_variables = variables(q) + answers = fol_bc_ask(kb or test_kb, q) + return sorted( + [dict((x, v) for x, v in list(a.items()) if x in test_variables) + for a in answers], key=repr) + assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' + assert repr(test_ask('Human(x)')) == '[{x: Mac}, {x: MrsMac}]' + assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' + assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' + + +def test_fol_fc_ask(): + def test_ask(query, kb=None): + q = expr(query) + test_variables = variables(q) + answers = fol_fc_ask(kb or test_kb, q) + return sorted( + [dict((x, v) for x, v in list(a.items()) if x in test_variables) + for a in answers], key=repr) + assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' + assert repr(test_ask('Enemy(x, America)', crime_kb)) == '[{x: Nono}]' + assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' + assert repr(test_ask('Human(x)')) == '[{x: Mac}, {x: MrsMac}]' + assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' + + +def test_d(): + assert d(x * x - x, x) == 2 * x - 1 + + +def test_WalkSAT(): + def check_SAT(clauses, single_solution={}): + # Make sure the solution is correct if it is returned by WalkSat + # Sometimes WalkSat may run out of flips before finding a solution + soln = WalkSAT(clauses) + if soln: + assert all(pl_true(x, soln) for x in clauses) + if single_solution: # Cross check the solution if only one exists + assert all(pl_true(x, single_solution) for x in clauses) + assert soln == single_solution + # Test WalkSat for problems with solution + check_SAT([A & B, A & C]) + check_SAT([A | B, P & Q, P & B]) + check_SAT([A & B, C | D, ~(D | P)], {A: True, B: True, C: True, D: False, P: False}) + check_SAT([A, B, ~C, D], {C: False, A: True, B: True, D: True}) + # Test WalkSat for problems without solution + assert WalkSAT([A & ~A], 0.5, 100) is None + assert WalkSAT([A & B, C | D, ~(D | B)], 0.5, 100) is None + assert WalkSAT([A | B, ~A, ~(B | C), C | D, P | Q], 0.5, 100) is None + assert WalkSAT([A | B, B & C, C | D, D & A, P, ~P], 0.5, 100) is None + + +def test_SAT_plan(): + transition = {'A': {'Left': 'A', 'Right': 'B'}, + 'B': {'Left': 'A', 'Right': 'C'}, + 'C': {'Left': 'B', 'Right': 'C'}} + assert SAT_plan('A', transition, 'C', 2) is None + assert SAT_plan('A', transition, 'B', 3) == ['Right'] + assert SAT_plan('C', transition, 'A', 3) == ['Left', 'Left'] + + transition = {(0, 0): {'Right': (0, 1), 'Down': (1, 0)}, + (0, 1): {'Left': (1, 0), 'Down': (1, 1)}, + (1, 0): {'Right': (1, 0), 'Up': (1, 0), 'Left': (1, 0), 'Down': (1, 0)}, + (1, 1): {'Left': (1, 0), 'Up': (0, 1)}} + assert SAT_plan((0, 0), transition, (1, 1), 4) == ['Right', 'Down'] + + +if __name__ == '__main__': + pytest.main() From 5aeaf615d2e3d485cde72b4ad1f4050aee01d5ff Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Thu, 11 Jun 2020 08:10:44 +0800 Subject: [PATCH 25/31] games.py Gomoku (#1080) * update games.py connect 4 display method original code displays board sideways. Fixed display method to print board bottom down * update games.py add Gomoku game Trivially addition of Gomoku, thanks to flexible implementation of TicTacToe class --- games.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/games.py b/games.py index 97bceb198..94a21f6ee 100644 --- a/games.py +++ b/games.py @@ -424,7 +424,13 @@ def __init__(self, h=7, v=6, k=4): def actions(self, state): return [(x, y) for (x, y) in state.moves - if y == 1 or (x, y - 1) in state.board] + if x == self.h or (x + 1 , y ) in state.board] + +class Gomoku(TicTacToe): + """Also known as Five in a row.""" + + def __init__(self, h=15, v=16, k=5): + TicTacToe.__init__(self, h, v, k) class Backgammon(StochasticGame): From ca301ea363674ec719b58f23e794998de4f623c9 Mon Sep 17 00:00:00 2001 From: Gabriel Silveira Date: Wed, 10 Jun 2020 21:11:20 -0300 Subject: [PATCH 26/31] Imported utils4e to resolve some dependency bugs (#1186) --- search.py | 1 + 1 file changed, 1 insertion(+) diff --git a/search.py b/search.py index 89f872079..7e23bfffa 100644 --- a/search.py +++ b/search.py @@ -10,6 +10,7 @@ from collections import deque from utils import * +from utils4e import * class Problem: From a4d938954f90266301db664e3dc5ca3f4f8fb5b3 Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Mon, 22 Jun 2020 23:16:34 +0200 Subject: [PATCH 27/31] fixed svm for not posdef kernel matrix, updated .travis.yml with Python 3.8 and added svr with r2 and accuracy metrics (#1185) --- .travis.yml | 18 +- csp.py | 9 +- deep_learning4e.py | 64 ++++-- learning.py | 182 ++++++++++----- learning4e.py | 412 +++++++++++++++++----------------- notebook.py | 2 +- notebook4e.py | 2 +- perception4e.py | 6 +- requirements.txt | 6 +- search.py | 4 +- tests/test_deep_learning4e.py | 26 +-- tests/test_learning.py | 8 +- tests/test_learning4e.py | 52 ++--- tests/test_search.py | 2 +- utils.py | 11 +- utils4e.py | 16 +- 16 files changed, 441 insertions(+), 379 deletions(-) diff --git a/.travis.yml b/.travis.yml index 12cebb35b..e465e8e4c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,27 +4,13 @@ python: - 3.5 - 3.6 - 3.7 + - 3.8 before_install: - git submodule update --remote install: - - pip install flake8 - - pip install ipython - - pip install ipythonblocks - - pip install ipywidgets - - pip install keras - - pip install matplotlib - - pip install networkx - - pip install numpy - - pip install opencv-python - - pip install Pillow - - pip install pytest-cov - - pip install qpsolvers - - pip install quadprog - - pip install six - - pip install sortedcontainers - - pip install tensorflow + - pip install --upgrade -r requirements.txt script: - py.test --cov=./ diff --git a/csp.py b/csp.py index 9cfdafdef..46ae07dd5 100644 --- a/csp.py +++ b/csp.py @@ -758,8 +758,9 @@ class Sudoku(CSP): . . 2 | 6 . 9 | 5 . . 8 . . | 2 . 3 | . . 9 . . 5 | . 1 . | 3 . . - >>> AC3(e); e.display(e.infer_assignment()) - (True, 6925) + >>> AC3(e) # doctest: +ELLIPSIS + (True, ...) + >>> e.display(e.infer_assignment()) 4 8 3 | 9 2 1 | 6 5 7 9 6 7 | 3 4 5 | 8 2 1 2 5 1 | 8 7 6 | 4 9 3 @@ -1265,7 +1266,7 @@ def display(self, assignment=None): else: var = "p" + str(j) + str(i) if assignment is not None: - if isinstance(assignment[var], set) and len(assignment[var]) is 1: + if isinstance(assignment[var], set) and len(assignment[var]) == 1: puzzle += "[" + str(first(assignment[var])).upper() + "] " elif isinstance(assignment[var], str): puzzle += "[" + str(assignment[var]).upper() + "] " @@ -1393,7 +1394,7 @@ def display(self, assignment=None): var2 = "0" + var2 var = "X" + var1 + var2 if assignment is not None: - if isinstance(assignment[var], set) and len(assignment[var]) is 1: + if isinstance(assignment[var], set) and len(assignment[var]) == 1: puzzle += "[" + str(first(assignment[var])) + "]\t" elif isinstance(assignment[var], int): puzzle += "[" + str(assignment[var]) + "]\t" diff --git a/deep_learning4e.py b/deep_learning4e.py index 0e2aec242..9f5b0a8f7 100644 --- a/deep_learning4e.py +++ b/deep_learning4e.py @@ -8,7 +8,7 @@ from keras.layers import Embedding, SimpleRNN, Dense from keras.preprocessing import sequence -from utils4e import (softmax1D, conv1D, gaussian_kernel, element_wise_product, vector_add, random_weights, +from utils4e import (conv1D, gaussian_kernel, element_wise_product, vector_add, random_weights, scalar_vector_product, map_vector, mean_squared_error_loss) @@ -46,6 +46,9 @@ def function(self, x): def derivative(self, x): return NotImplementedError + def __call__(self, x): + return self.function(x) + class Sigmoid(Activation): @@ -56,7 +59,7 @@ def derivative(self, value): return value * (1 - value) -class Relu(Activation): +class ReLU(Activation): def function(self, x): return max(0, x) @@ -65,13 +68,28 @@ def derivative(self, value): return 1 if value > 0 else 0 -class Elu(Activation): +class ELU(Activation): + + def __init__(self, alpha=0.01): + self.alpha = alpha - def function(self, x, alpha=0.01): - return x if x > 0 else alpha * (np.exp(x) - 1) + def function(self, x): + return x if x > 0 else self.alpha * (np.exp(x) - 1) - def derivative(self, value, alpha=0.01): - return 1 if value > 0 else alpha * np.exp(value) + def derivative(self, value): + return 1 if value > 0 else self.alpha * np.exp(value) + + +class LeakyReLU(Activation): + + def __init__(self, alpha=0.01): + self.alpha = alpha + + def function(self, x): + return max(x, self.alpha * x) + + def derivative(self, value): + return 1 if value > 0 else self.alpha class Tanh(Activation): @@ -83,13 +101,31 @@ def derivative(self, value): return 1 - (value ** 2) -class LeakyRelu(Activation): +class SoftMax(Activation): + + def function(self, x): + return np.exp(x) / np.sum(np.exp(x)) + + def derivative(self, x): + return np.ones_like(x) + + +class SoftPlus(Activation): - def function(self, x, alpha=0.01): - return x if x > 0 else alpha * x + def function(self, x): + return np.log(1. + np.exp(x)) + + def derivative(self, x): + return 1. / (1. + np.exp(-x)) - def derivative(self, value, alpha=0.01): - return 1 if value > 0 else alpha + +class Linear(Activation): + + def function(self, x): + return x + + def derivative(self, x): + return np.ones_like(x) class InputLayer(Layer): @@ -112,9 +148,9 @@ class OutputLayer(Layer): def __init__(self, size=3): super().__init__(size) - def forward(self, inputs): + def forward(self, inputs, activation=SoftMax): assert len(self.nodes) == len(inputs) - res = softmax1D(inputs) + res = activation().function(inputs) for node, val in zip(self.nodes, res): node.value = val return res diff --git a/learning.py b/learning.py index e83467c43..71b6b15e7 100644 --- a/learning.py +++ b/learning.py @@ -527,17 +527,17 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): # pass over all examples for example in examples: x = [1] + example - y = dot_product(w, x) + y = np.dot(w, x) t = example[idx_t] err.append(t - y) # update weights for i in range(len(w)): - w[i] = w[i] + learning_rate * (dot_product(err, X_col[i]) / num_examples) + w[i] = w[i] + learning_rate * (np.dot(err, X_col[i]) / num_examples) def predict(example): x = [1] + example - return dot_product(w, x) + return np.dot(w, x) return predict @@ -569,7 +569,7 @@ def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): # pass over all examples for example in examples: x = [1] + example - y = sigmoid(dot_product(w, x)) + y = sigmoid(np.dot(w, x)) h.append(sigmoid_derivative(y)) t = example[idx_t] err.append(t - y) @@ -577,11 +577,11 @@ def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): # update weights for i in range(len(w)): buffer = [x * y for x, y in zip(err, h)] - w[i] = w[i] + learning_rate * (dot_product(buffer, X_col[i]) / num_examples) + w[i] = w[i] + learning_rate * (np.dot(buffer, X_col[i]) / num_examples) def predict(example): x = [1] + example - return sigmoid(dot_product(w, x)) + return sigmoid(np.dot(w, x)) return predict @@ -807,16 +807,16 @@ def find_max_node(nodes): return nodes.index(max(nodes, key=lambda node: node.value)) -class BinarySVM: - def __init__(self, kernel=linear_kernel, C=1.0): +class SVC: + + def __init__(self, kernel=linear_kernel, C=1.0, verbose=False): self.kernel = kernel self.C = C # hyper-parameter - self.eps = 1e-6 - self.n_sv = -1 - self.sv_x, self.sv_y, = np.zeros(0), np.zeros(0) + self.sv_idx, self.sv, self.sv_y = np.zeros(0), np.zeros(0), np.zeros(0) self.alphas = np.zeros(0) self.w = None self.b = 0.0 # intercept + self.verbose = verbose def fit(self, X, y): """ @@ -825,57 +825,123 @@ def fit(self, X, y): :param y: array of size [n_samples] holding the class labels """ # In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations) - self.QP(X, y) - sv_indices = list(filter(lambda i: self.alphas[i] > self.eps, range(len(y)))) - self.sv_x, self.sv_y, self.alphas = X[sv_indices], y[sv_indices], self.alphas[sv_indices] - self.n_sv = len(sv_indices) + self.solve_qp(X, y) + sv = self.alphas > 1e-5 + self.sv_idx = np.arange(len(self.alphas))[sv] + self.sv, self.sv_y, self.alphas = X[sv], y[sv], self.alphas[sv] + if self.kernel == linear_kernel: - self.w = np.dot(self.alphas * self.sv_y, self.sv_x) - # calculate b: average over all support vectors - sv_boundary = self.alphas < self.C - self.eps - self.b = np.mean(self.sv_y[sv_boundary] - np.dot(self.alphas * self.sv_y, - self.kernel(self.sv_x, self.sv_x[sv_boundary]))) + self.w = np.dot(self.alphas * self.sv_y, self.sv) + + for n in range(len(self.alphas)): + self.b += self.sv_y[n] + self.b -= np.sum(self.alphas * self.sv_y * self.K[self.sv_idx[n], sv]) + self.b /= len(self.alphas) + return self - def QP(self, X, y): + def solve_qp(self, X, y): """ Solves a quadratic programming problem. In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations). :param X: array of size [n_samples, n_features] holding the training samples :param y: array of size [n_samples] holding the class labels """ - # m = len(y) # m = n_samples - K = self.kernel(X) # gram matrix - P = K * np.outer(y, y) + self.K = self.kernel(X) # gram matrix + P = self.K * np.outer(y, y) q = -np.ones(m) - G = np.vstack((-np.identity(m), np.identity(m))) - h = np.hstack((np.zeros(m), np.ones(m) * self.C)) - A = y.reshape((1, -1)) - b = np.zeros(1) - # make sure P is positive definite - P += np.eye(P.shape[0]).__mul__(1e-3) - self.alphas = solve_qp(P, q, G, h, A, b, sym_proj=True) - - def predict_score(self, x): + lb = np.zeros(m) # lower bounds + ub = np.ones(m) * self.C # upper bounds + A = y.astype(np.float64) # equality matrix + b = np.zeros(1) # equality vector + self.alphas = solve_qp(P, q, A=A, b=b, lb=lb, ub=ub, solver='cvxopt', + sym_proj=True, verbose=self.verbose) + + def predict_score(self, X): """ Predicts the score for a given example. """ if self.w is None: - return np.dot(self.alphas * self.sv_y, self.kernel(self.sv_x, x)) + self.b - return np.dot(x, self.w) + self.b + return np.dot(self.alphas * self.sv_y, self.kernel(self.sv, X)) + self.b + return np.dot(X, self.w) + self.b - def predict(self, x): + def predict(self, X): """ Predicts the class of a given example. """ - return np.sign(self.predict_score(x)) + return np.sign(self.predict_score(X)) + +class SVR: -class MultiSVM: - def __init__(self, kernel=linear_kernel, decision_function='ovr', C=1.0): + def __init__(self, kernel=linear_kernel, C=1.0, epsilon=0.1, verbose=False): self.kernel = kernel - self.decision_function = decision_function self.C = C # hyper-parameter + self.epsilon = epsilon # epsilon insensitive loss value + self.sv_idx, self.sv = np.zeros(0), np.zeros(0) + self.alphas_p, self.alphas_n = np.zeros(0), np.zeros(0) + self.w = None + self.b = 0.0 # intercept + self.verbose = verbose + + def fit(self, X, y): + """ + Trains the model by solving a quadratic programming problem. + :param X: array of size [n_samples, n_features] holding the training samples + :param y: array of size [n_samples] holding the class labels + """ + # In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations) + self.solve_qp(X, y) + + sv = np.logical_or(self.alphas_p > 1e-5, self.alphas_n > 1e-5) + self.sv_idx = np.arange(len(self.alphas_p))[sv] + self.sv, sv_y = X[sv], y[sv] + self.alphas_p, self.alphas_n = self.alphas_p[sv], self.alphas_n[sv] + + if self.kernel == linear_kernel: + self.w = np.dot(self.alphas_p - self.alphas_n, self.sv) + + for n in range(len(self.alphas_p)): + self.b += sv_y[n] + self.b -= np.sum((self.alphas_p - self.alphas_n) * self.K[self.sv_idx[n], sv]) + self.b -= self.epsilon + self.b /= len(self.alphas_p) + + return self + + def solve_qp(self, X, y): + """ + Solves a quadratic programming problem. In QP formulation (dual): + m variables, 2m+1 constraints (1 equation, 2m inequations). + :param X: array of size [n_samples, n_features] holding the training samples + :param y: array of size [n_samples] holding the class labels + """ + # + m = len(y) # m = n_samples + self.K = self.kernel(X) # gram matrix + P = np.vstack((np.hstack((self.K, -self.K)), # alphas_p, alphas_n + np.hstack((-self.K, self.K)))) # alphas_n, alphas_p + q = np.hstack((-y, y)) + self.epsilon + lb = np.zeros(2 * m) # lower bounds + ub = np.ones(2 * m) * self.C # upper bounds + A = np.hstack((np.ones(m), -np.ones(m))) # equality matrix + b = np.zeros(1) # equality vector + alphas = solve_qp(P, q, A=A, b=b, lb=lb, ub=ub, solver='cvxopt', + sym_proj=True, verbose=self.verbose) + self.alphas_p = alphas[:m] + self.alphas_n = alphas[m:] + + def predict(self, X): + if self.kernel != linear_kernel: + return np.dot(self.alphas_p - self.alphas_n, self.kernel(self.sv, X)) + self.b + return np.dot(X, self.w) + self.b + + +class MultiClassLearner: + + def __init__(self, clf, decision_function='ovr'): + self.clf = clf + self.decision_function = decision_function self.n_class, self.classifiers = 0, [] def fit(self, X, y): @@ -893,34 +959,33 @@ def fit(self, X, y): y1 = np.array(y) y1[y1 != label] = -1.0 y1[y1 == label] = 1.0 - clf = BinarySVM(self.kernel, self.C) - clf.fit(X, y1) - self.classifiers.append(copy.deepcopy(clf)) + self.clf.fit(X, y1) + self.classifiers.append(copy.deepcopy(self.clf)) elif self.decision_function == 'ovo': # use one-vs-one method n_labels = len(labels) for i in range(n_labels): for j in range(i + 1, n_labels): neg_id, pos_id = y == labels[i], y == labels[j] - x1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]] + X1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]] y1[y1 == labels[i]] = -1.0 y1[y1 == labels[j]] = 1.0 - clf = BinarySVM(self.kernel, self.C) - clf.fit(x1, y1) - self.classifiers.append(copy.deepcopy(clf)) + self.clf.fit(X1, y1) + self.classifiers.append(copy.deepcopy(self.clf)) else: return ValueError("Decision function must be either 'ovr' or 'ovo'.") + return self - def predict(self, x): + def predict(self, X): """ Predicts the class of a given example according to the training method. """ - n_samples = len(x) + n_samples = len(X) if self.decision_function == 'ovr': # one-vs-rest method assert len(self.classifiers) == self.n_class score = np.zeros((n_samples, self.n_class)) for i in range(self.n_class): clf = self.classifiers[i] - score[:, i] = clf.predict_score(x) + score[:, i] = clf.predict_score(X) return np.argmax(score, axis=1) elif self.decision_function == 'ovo': # use one-vs-one method assert len(self.classifiers) == self.n_class * (self.n_class - 1) / 2 @@ -928,7 +993,7 @@ def predict(self, x): clf_id = 0 for i in range(self.n_class): for j in range(i + 1, self.n_class): - res = self.classifiers[clf_id].predict(x) + res = self.classifiers[clf_id].predict(X) vote[res < 0, i] += 1.0 # negative sample: class i vote[res > 0, j] += 1.0 # positive sample: class j clf_id += 1 @@ -1055,9 +1120,20 @@ def weighted_replicate(seq, weights, n): weighted_sample_with_replacement(n - sum(wholes), seq, fractions)) -def flatten(seqs): - return sum(seqs, []) +# metrics + +def accuracy_score(y_pred, y_true): + assert y_pred.shape == y_true.shape + return np.mean(np.equal(y_pred, y_true)) + + +def r2_score(y_pred, y_true): + assert y_pred.shape == y_true.shape + return 1. - (np.sum(np.square(y_pred - y_true)) / # sum of square of residuals + np.sum(np.square(y_true - np.mean(y_true)))) # total sum of squares + +# datasets orings = DataSet(name='orings', target='Distressed', attr_names='Rings Distressed Temp Pressure Flightnum') diff --git a/learning4e.py b/learning4e.py index 4ef022e83..12c0defa5 100644 --- a/learning4e.py +++ b/learning4e.py @@ -5,7 +5,6 @@ from statistics import stdev from qpsolvers import solve_qp -from scipy.optimize import minimize from deep_learning4e import Sigmoid from probabilistic_learning import NaiveBayesLearner @@ -505,177 +504,82 @@ def predict(self, example): return mode(e[self.dataset.target] for (d, e) in best) -class LossFunction: - def __init__(self, X, y): - self.X = X - self.y = y.flatten() +class SVC: - @staticmethod - def predict(X, theta): - return NotImplementedError - - def function(self, theta): - return NotImplementedError - - def jacobian(self, theta): - return NotImplementedError - - -class MeanSquaredError(LossFunction): - def __init__(self, X, y): - super().__init__(X, y) - self.x_star = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y) # or np.linalg.lstsq(X, y)[0] - - @staticmethod - def predict(X, theta): - return np.dot(X, theta) - - def function(self, theta): - return (1 / 2 * self.X.shape[0]) * np.sum(np.square(self.predict(self.X, theta) - self.y)) - - def jacobian(self, theta): - return (1 / self.X.shape[0]) * np.dot(self.X.T, self.predict(self.X, theta) - self.y) - - -class CrossEntropy(LossFunction): - def __init__(self, X, y): - super().__init__(X, y) - - @staticmethod - def predict(X, theta): - return Sigmoid().function(np.dot(X, theta)) - - def function(self, theta): - pred = self.predict(self.X, theta) - return -(1 / self.X.shape[0]) * np.sum(self.y * np.log(pred) + (1 - self.y) * np.log(1 - pred)) - - def jacobian(self, theta): - return (1 / self.X.shape[0]) * np.dot(self.X.T, self.predict(self.X, theta) - self.y) - - -class LinearRegressionLearner: - """ - [Section 18.6.4] - Linear Regressor - """ - - def __init__(self, l_rate=0.01, epochs=1000, optimizer='bfgs'): - self.l_rate = l_rate - self.epochs = epochs - self.optimizer = optimizer + def __init__(self, kernel=linear_kernel, C=1.0, verbose=False): + self.kernel = kernel + self.C = C # hyper-parameter + self.sv_idx, self.sv, self.sv_y = np.zeros(0), np.zeros(0), np.zeros(0) + self.alphas = np.zeros(0) + self.w = None + self.b = 0.0 # intercept + self.verbose = verbose def fit(self, X, y): - loss = MeanSquaredError(X, y) - self.w = minimize(fun=loss.function, x0=np.zeros((X.shape[1], 1)), method=self.optimizer, jac=loss.jacobian).x - return self - - def predict(self, example): - return np.dot(example, self.w) - - -class BinaryLogisticRegressionLearner: - """ - [Section 18.6.5] - Logistic Regression Classifier - """ + """ + Trains the model by solving a quadratic programming problem. + :param X: array of size [n_samples, n_features] holding the training samples + :param y: array of size [n_samples] holding the class labels + """ + # In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations) + self.solve_qp(X, y) + sv = self.alphas > 1e-5 + self.sv_idx = np.arange(len(self.alphas))[sv] + self.sv, self.sv_y, self.alphas = X[sv], y[sv], self.alphas[sv] - def __init__(self, l_rate=0.01, epochs=1000, optimizer='bfgs'): - self.l_rate = l_rate - self.epochs = epochs - self.optimizer = optimizer + if self.kernel == linear_kernel: + self.w = np.dot(self.alphas * self.sv_y, self.sv) - def fit(self, X, y): - self.labels = np.unique(y) - y = np.where(y == self.labels[0], 0, 1) - loss = CrossEntropy(X, y) - self.w = minimize(fun=loss.function, x0=np.zeros((X.shape[1], 1)), method=self.optimizer, jac=loss.jacobian).x + for n in range(len(self.alphas)): + self.b += self.sv_y[n] + self.b -= np.sum(self.alphas * self.sv_y * self.K[self.sv_idx[n], sv]) + self.b /= len(self.alphas) return self - def predict_score(self, x): - return CrossEntropy.predict(x, self.w) - - def predict(self, x): - return np.where(self.predict_score(x) >= 0.5, self.labels[1], self.labels[0]).astype(int) - - -class MultiLogisticRegressionLearner: - def __init__(self, l_rate=0.01, epochs=1000, optimizer='bfgs', decision_function='ovr'): - self.l_rate = l_rate - self.epochs = epochs - self.optimizer = optimizer - self.decision_function = decision_function - self.n_class, self.classifiers = 0, [] - - def fit(self, X, y): + def solve_qp(self, X, y): """ - Trains n_class or n_class * (n_class - 1) / 2 classifiers - according to the training method, ovr or ovo respectively. + Solves a quadratic programming problem. In QP formulation (dual): + m variables, 2m+1 constraints (1 equation, 2m inequations). :param X: array of size [n_samples, n_features] holding the training samples :param y: array of size [n_samples] holding the class labels - :return: array of classifiers """ - labels = np.unique(y) - self.n_class = len(labels) - if self.decision_function == 'ovr': # one-vs-rest method - for label in labels: - y1 = np.array(y) - y1[y1 != label] = -1.0 - y1[y1 == label] = 1.0 - clf = BinaryLogisticRegressionLearner(self.l_rate, self.epochs, self.optimizer) - clf.fit(X, y1) - self.classifiers.append(copy.deepcopy(clf)) - elif self.decision_function == 'ovo': # use one-vs-one method - n_labels = len(labels) - for i in range(n_labels): - for j in range(i + 1, n_labels): - neg_id, pos_id = y == labels[i], y == labels[j] - x1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]] - y1[y1 == labels[i]] = -1.0 - y1[y1 == labels[j]] = 1.0 - clf = BinaryLogisticRegressionLearner(self.l_rate, self.epochs, self.optimizer) - clf.fit(x1, y1) - self.classifiers.append(copy.deepcopy(clf)) - else: - return ValueError("Decision function must be either 'ovr' or 'ovo'.") - return self + m = len(y) # m = n_samples + self.K = self.kernel(X) # gram matrix + P = self.K * np.outer(y, y) + q = -np.ones(m) + lb = np.zeros(m) # lower bounds + ub = np.ones(m) * self.C # upper bounds + A = y.astype(np.float64) # equality matrix + b = np.zeros(1) # equality vector + self.alphas = solve_qp(P, q, A=A, b=b, lb=lb, ub=ub, solver='cvxopt', + sym_proj=True, verbose=self.verbose) + + def predict_score(self, X): + """ + Predicts the score for a given example. + """ + if self.w is None: + return np.dot(self.alphas * self.sv_y, self.kernel(self.sv, X)) + self.b + return np.dot(X, self.w) + self.b - def predict(self, x): + def predict(self, X): """ - Predicts the class of a given example according to the training method. + Predicts the class of a given example. """ - n_samples = len(x) - if self.decision_function == 'ovr': # one-vs-rest method - assert len(self.classifiers) == self.n_class - score = np.zeros((n_samples, self.n_class)) - for i in range(self.n_class): - clf = self.classifiers[i] - score[:, i] = clf.predict_score(x) - return np.argmax(score, axis=1) - elif self.decision_function == 'ovo': # use one-vs-one method - assert len(self.classifiers) == self.n_class * (self.n_class - 1) / 2 - vote = np.zeros((n_samples, self.n_class)) - clf_id = 0 - for i in range(self.n_class): - for j in range(i + 1, self.n_class): - res = self.classifiers[clf_id].predict(x) - vote[res < 0, i] += 1.0 # negative sample: class i - vote[res > 0, j] += 1.0 # positive sample: class j - clf_id += 1 - return np.argmax(vote, axis=1) - else: - return ValueError("Decision function must be either 'ovr' or 'ovo'.") + return np.sign(self.predict_score(X)) + +class SVR: -class BinarySVM: - def __init__(self, kernel=linear_kernel, C=1.0): + def __init__(self, kernel=linear_kernel, C=1.0, epsilon=0.1, verbose=False): self.kernel = kernel self.C = C # hyper-parameter - self.eps = 1e-6 - self.n_sv = -1 - self.sv_x, self.sv_y, = np.zeros(0), np.zeros(0) - self.alphas = np.zeros(0) + self.epsilon = epsilon # epsilon insensitive loss value + self.sv_idx, self.sv = np.zeros(0), np.zeros(0) + self.alphas_p, self.alphas_n = np.zeros(0), np.zeros(0) self.w = None self.b = 0.0 # intercept + self.verbose = verbose def fit(self, X, y): """ @@ -684,58 +588,56 @@ def fit(self, X, y): :param y: array of size [n_samples] holding the class labels """ # In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations) - self.QP(X, y) - sv_indices = list(filter(lambda i: self.alphas[i] > self.eps, range(len(y)))) - self.sv_x, self.sv_y, self.alphas = X[sv_indices], y[sv_indices], self.alphas[sv_indices] - self.n_sv = len(sv_indices) + self.solve_qp(X, y) + + sv = np.logical_or(self.alphas_p > 1e-5, self.alphas_n > 1e-5) + self.sv_idx = np.arange(len(self.alphas_p))[sv] + self.sv, sv_y = X[sv], y[sv] + self.alphas_p, self.alphas_n = self.alphas_p[sv], self.alphas_n[sv] + if self.kernel == linear_kernel: - self.w = np.dot(self.alphas * self.sv_y, self.sv_x) - # calculate b: average over all support vectors - sv_boundary = self.alphas < self.C - self.eps - self.b = np.mean(self.sv_y[sv_boundary] - np.dot(self.alphas * self.sv_y, - self.kernel(self.sv_x, self.sv_x[sv_boundary]))) + self.w = np.dot(self.alphas_p - self.alphas_n, self.sv) + + for n in range(len(self.alphas_p)): + self.b += sv_y[n] + self.b -= np.sum((self.alphas_p - self.alphas_n) * self.K[self.sv_idx[n], sv]) + self.b -= self.epsilon + self.b /= len(self.alphas_p) + return self - def QP(self, X, y): + def solve_qp(self, X, y): """ Solves a quadratic programming problem. In QP formulation (dual): m variables, 2m+1 constraints (1 equation, 2m inequations). :param X: array of size [n_samples, n_features] holding the training samples :param y: array of size [n_samples] holding the class labels """ - # m = len(y) # m = n_samples - K = self.kernel(X) # gram matrix - P = K * np.outer(y, y) - q = -np.ones(m) - G = np.vstack((-np.identity(m), np.identity(m))) - h = np.hstack((np.zeros(m), np.ones(m) * self.C)) - A = y.reshape((1, -1)) - b = np.zeros(1) - # make sure P is positive definite - P += np.eye(P.shape[0]).__mul__(1e-3) - self.alphas = solve_qp(P, q, G, h, A, b, sym_proj=True) - - def predict_score(self, x): - """ - Predicts the score for a given example. - """ - if self.w is None: - return np.dot(self.alphas * self.sv_y, self.kernel(self.sv_x, x)) + self.b - return np.dot(x, self.w) + self.b - - def predict(self, x): - """ - Predicts the class of a given example. - """ - return np.sign(self.predict_score(x)) - - -class MultiSVM: - def __init__(self, kernel=linear_kernel, decision_function='ovr', C=1.0): - self.kernel = kernel + self.K = self.kernel(X) # gram matrix + P = np.vstack((np.hstack((self.K, -self.K)), # alphas_p, alphas_n + np.hstack((-self.K, self.K)))) # alphas_n, alphas_p + q = np.hstack((-y, y)) + self.epsilon + lb = np.zeros(2 * m) # lower bounds + ub = np.ones(2 * m) * self.C # upper bounds + A = np.hstack((np.ones(m), -np.ones(m))) # equality matrix + b = np.zeros(1) # equality vector + alphas = solve_qp(P, q, A=A, b=b, lb=lb, ub=ub, solver='cvxopt', + sym_proj=True, verbose=self.verbose) + self.alphas_p = alphas[:m] + self.alphas_n = alphas[m:] + + def predict(self, X): + if self.kernel != linear_kernel: + return np.dot(self.alphas_p - self.alphas_n, self.kernel(self.sv, X)) + self.b + return np.dot(X, self.w) + self.b + + +class MultiClassLearner: + + def __init__(self, clf, decision_function='ovr'): + self.clf = clf self.decision_function = decision_function - self.C = C # hyper-parameter self.n_class, self.classifiers = 0, [] def fit(self, X, y): @@ -753,35 +655,33 @@ def fit(self, X, y): y1 = np.array(y) y1[y1 != label] = -1.0 y1[y1 == label] = 1.0 - clf = BinarySVM(self.kernel, self.C) - clf.fit(X, y1) - self.classifiers.append(copy.deepcopy(clf)) + self.clf.fit(X, y1) + self.classifiers.append(copy.deepcopy(self.clf)) elif self.decision_function == 'ovo': # use one-vs-one method n_labels = len(labels) for i in range(n_labels): for j in range(i + 1, n_labels): neg_id, pos_id = y == labels[i], y == labels[j] - x1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]] + X1, y1 = np.r_[X[neg_id], X[pos_id]], np.r_[y[neg_id], y[pos_id]] y1[y1 == labels[i]] = -1.0 y1[y1 == labels[j]] = 1.0 - clf = BinarySVM(self.kernel, self.C) - clf.fit(x1, y1) - self.classifiers.append(copy.deepcopy(clf)) + self.clf.fit(X1, y1) + self.classifiers.append(copy.deepcopy(self.clf)) else: return ValueError("Decision function must be either 'ovr' or 'ovo'.") return self - def predict(self, x): + def predict(self, X): """ Predicts the class of a given example according to the training method. """ - n_samples = len(x) + n_samples = len(X) if self.decision_function == 'ovr': # one-vs-rest method assert len(self.classifiers) == self.n_class score = np.zeros((n_samples, self.n_class)) for i in range(self.n_class): clf = self.classifiers[i] - score[:, i] = clf.predict_score(x) + score[:, i] = clf.predict_score(X) return np.argmax(score, axis=1) elif self.decision_function == 'ovo': # use one-vs-one method assert len(self.classifiers) == self.n_class * (self.n_class - 1) / 2 @@ -789,7 +689,7 @@ def predict(self, x): clf_id = 0 for i in range(self.n_class): for j in range(i + 1, self.n_class): - res = self.classifiers[clf_id].predict(x) + res = self.classifiers[clf_id].predict(X) vote[res < 0, i] += 1.0 # negative sample: class i vote[res > 0, j] += 1.0 # positive sample: class j clf_id += 1 @@ -798,6 +698,91 @@ def predict(self, x): return ValueError("Decision function must be either 'ovr' or 'ovo'.") +def LinearLearner(dataset, learning_rate=0.01, epochs=100): + """ + [Section 18.6.3] + Linear classifier with hard threshold. + """ + idx_i = dataset.inputs + idx_t = dataset.target + examples = dataset.examples + num_examples = len(examples) + + # X transpose + X_col = [dataset.values[i] for i in idx_i] # vertical columns of X + + # add dummy + ones = [1 for _ in range(len(examples))] + X_col = [ones] + X_col + + # initialize random weights + num_weights = len(idx_i) + 1 + w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights) + + for epoch in range(epochs): + err = [] + # pass over all examples + for example in examples: + x = [1] + example + y = np.dot(w, x) + t = example[idx_t] + err.append(t - y) + + # update weights + for i in range(len(w)): + w[i] = w[i] + learning_rate * (np.dot(err, X_col[i]) / num_examples) + + def predict(example): + x = [1] + example + return np.dot(w, x) + + return predict + + +def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100): + """ + [Section 18.6.4] + Linear classifier with logistic regression. + """ + idx_i = dataset.inputs + idx_t = dataset.target + examples = dataset.examples + num_examples = len(examples) + + # X transpose + X_col = [dataset.values[i] for i in idx_i] # vertical columns of X + + # add dummy + ones = [1 for _ in range(len(examples))] + X_col = [ones] + X_col + + # initialize random weights + num_weights = len(idx_i) + 1 + w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights) + + for epoch in range(epochs): + err = [] + h = [] + # pass over all examples + for example in examples: + x = [1] + example + y = Sigmoid()(np.dot(w, x)) + h.append(Sigmoid().derivative(y)) + t = example[idx_t] + err.append(t - y) + + # update weights + for i in range(len(w)): + buffer = [x * y for x, y in zip(err, h)] + w[i] = w[i] + learning_rate * (np.dot(buffer, X_col[i]) / num_examples) + + def predict(example): + x = [1] + example + return Sigmoid()(np.dot(w, x)) + + return predict + + class EnsembleLearner: """Given a list of learning algorithms, have them vote.""" @@ -890,8 +875,8 @@ def WeightedLearner(unweighted_learner): def train(dataset, weights): dataset = replicated_dataset(dataset, weights) n_samples, n_features = len(dataset.examples), dataset.target - X, y = np.array([x[:n_features] for x in dataset.examples]), \ - np.array([x[n_features] for x in dataset.examples]) + X, y = (np.array([x[:n_features] for x in dataset.examples]), + np.array([x[n_features] for x in dataset.examples])) return unweighted_learner.fit(X, y) return train @@ -921,9 +906,20 @@ def weighted_replicate(seq, weights, n): weighted_sample_with_replacement(n - sum(wholes), seq, fractions)) -def flatten(seqs): - return sum(seqs, []) +# metrics + +def accuracy_score(y_pred, y_true): + assert y_pred.shape == y_true.shape + return np.mean(np.equal(y_pred, y_true)) + + +def r2_score(y_pred, y_true): + assert y_pred.shape == y_true.shape + return 1. - (np.sum(np.square(y_pred - y_true)) / # sum of square of residuals + np.sum(np.square(y_true - np.mean(y_true)))) # total sum of squares + +# datasets orings = DataSet(name='orings', target='Distressed', attr_names='Rings Distressed Temp Pressure Flightnum') diff --git a/notebook.py b/notebook.py index 507aec330..5847a905b 100644 --- a/notebook.py +++ b/notebook.py @@ -784,7 +784,7 @@ def __init__(self, varname, kb, query, width=800, height=600, cid=None): self.l = 1 / 20 self.b = 3 * self.l bc_out = list(self.fol_bc_ask()) - if len(bc_out) is 0: + if len(bc_out) == 0: self.valid = False else: self.valid = True diff --git a/notebook4e.py b/notebook4e.py index fa19b12d2..4d61c226b 100644 --- a/notebook4e.py +++ b/notebook4e.py @@ -820,7 +820,7 @@ def __init__(self, varname, kb, query, width=800, height=600, cid=None): self.l = 1 / 20 self.b = 3 * self.l bc_out = list(self.fol_bc_ask()) - if len(bc_out) is 0: + if len(bc_out) == 0: self.valid = False else: self.valid = True diff --git a/perception4e.py b/perception4e.py index 2cb4b3891..d88c17419 100644 --- a/perception4e.py +++ b/perception4e.py @@ -311,9 +311,9 @@ def load_MINST(train_size, val_size, test_size): test_x /= 255 y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) - return (x_train[:train_size], y_train[:train_size]), \ - (x_train[train_size:train_size + val_size], y_train[train_size:train_size + val_size]), \ - (x_test[:test_size], y_test[:test_size]) + return ((x_train[:train_size], y_train[:train_size]), + (x_train[train_size:train_size + val_size], y_train[train_size:train_size + val_size]), + (x_test[:test_size], y_test[:test_size])) def simple_convnet(size=3, num_classes=10): diff --git a/requirements.txt b/requirements.txt index 5d0d607dd..dd6b1be8a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ -Image +cvxopt +image ipython ipythonblocks ipywidgets @@ -10,9 +11,8 @@ numpy opencv-python pandas pillow -pytest +pytest-cov qpsolvers -quadprog scipy sortedcontainers tensorflow \ No newline at end of file diff --git a/search.py b/search.py index 7e23bfffa..71c1d1304 100644 --- a/search.py +++ b/search.py @@ -1251,7 +1251,7 @@ def __init__(self, N): def actions(self, state): """In the leftmost empty column, try all non-conflicting rows.""" - if state[-1] is not -1: + if state[-1] != -1: return [] # All columns filled; no successors else: col = state.index(-1) @@ -1279,7 +1279,7 @@ def conflict(self, row1, col1, row2, col2): def goal_test(self, state): """Check if all columns filled, no conflicts.""" - if state[-1] is -1: + if state[-1] == -1: return False return not any(self.conflicted(state, state[col], col) for col in range(len(state))) diff --git a/tests/test_deep_learning4e.py b/tests/test_deep_learning4e.py index ca1f061f0..34676b02b 100644 --- a/tests/test_deep_learning4e.py +++ b/tests/test_deep_learning4e.py @@ -22,14 +22,14 @@ def test_neural_net(): classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) n_samples, n_features = len(iris.examples), iris.target - - X, y = np.array([x[:n_features] for x in iris.examples]), \ - np.array([x[n_features] for x in iris.examples]) - + + X, y = (np.array([x[:n_features] for x in iris.examples]), + np.array([x[n_features] for x in iris.examples])) + nnl_gd = NeuralNetworkLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) assert grade_learner(nnl_gd, iris_tests) > 0.7 assert err_ratio(nnl_gd, iris) < 0.15 - + nnl_adam = NeuralNetworkLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam).fit(X, y) assert grade_learner(nnl_adam, iris_tests) > 0.7 assert err_ratio(nnl_adam, iris) < 0.15 @@ -40,14 +40,14 @@ def test_perceptron(): classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) n_samples, n_features = len(iris.examples), iris.target - - X, y = np.array([x[:n_features] for x in iris.examples]), \ - np.array([x[n_features] for x in iris.examples]) - + + X, y = (np.array([x[:n_features] for x in iris.examples]), + np.array([x[n_features] for x in iris.examples])) + pl_gd = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y) assert grade_learner(pl_gd, iris_tests) == 1 assert err_ratio(pl_gd, iris) < 0.2 - + pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam).fit(X, y) assert grade_learner(pl_adam, iris_tests) == 1 assert err_ratio(pl_adam, iris) < 0.2 @@ -55,11 +55,11 @@ def test_perceptron(): def test_rnn(): data = imdb.load_data(num_words=5000) - + train, val, test = keras_dataset_loader(data) train = (train[0][:1000], train[1][:1000]) val = (val[0][:200], val[1][:200]) - + rnn = SimpleRNNLearner(train, val) score = rnn.evaluate(test[0][:200], test[1][:200], verbose=False) assert score[1] >= 0.2 @@ -70,7 +70,7 @@ def test_autoencoder(): classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) inputs = np.asarray(iris.examples) - + al = AutoencoderLearner(inputs, 100) print(inputs[0]) print(al.predict(inputs[:1])) diff --git a/tests/test_learning.py b/tests/test_learning.py index 57d603b86..63a7fd9aa 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -56,14 +56,14 @@ def test_decision_tree_learner(): assert dtl([7.5, 4, 6, 2]) == 'virginica' -def test_svm(): +def test_svc(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) - svm = MultiSVM() n_samples, n_features = len(iris.examples), iris.target - X, y = np.array([x[:n_features] for x in iris.examples]), np.array([x[n_features] for x in iris.examples]) - svm.fit(X, y) + X, y = (np.array([x[:n_features] for x in iris.examples]), + np.array([x[n_features] for x in iris.examples])) + svm = MultiClassLearner(SVC()).fit(X, y) assert svm.predict([[5.0, 3.1, 0.9, 0.1]]) == 0 assert svm.predict([[5.1, 3.5, 1.0, 0.0]]) == 0 assert svm.predict([[4.9, 3.3, 1.1, 0.1]]) == 0 diff --git a/tests/test_learning4e.py b/tests/test_learning4e.py index f0fc50493..b345efad7 100644 --- a/tests/test_learning4e.py +++ b/tests/test_learning4e.py @@ -57,49 +57,23 @@ def test_decision_tree_learner(): assert dtl.predict([7.5, 4, 6, 2]) == 'virginica' -def test_linear_learner(): +def test_svc(): iris = DataSet(name='iris') classes = ['setosa', 'versicolor', 'virginica'] iris.classes_to_numbers(classes) n_samples, n_features = len(iris.examples), iris.target - X, y = np.array([x[:n_features] for x in iris.examples]), \ - np.array([x[n_features] for x in iris.examples]) - ll = LinearRegressionLearner().fit(X, y) - assert np.allclose(ll.w, MeanSquaredError(X, y).x_star) - - -iris_tests = [([[5.0, 3.1, 0.9, 0.1]], 0), - ([[5.1, 3.5, 1.0, 0.0]], 0), - ([[4.9, 3.3, 1.1, 0.1]], 0), - ([[6.0, 3.0, 4.0, 1.1]], 1), - ([[6.1, 2.2, 3.5, 1.0]], 1), - ([[5.9, 2.5, 3.3, 1.1]], 1), - ([[7.5, 4.1, 6.2, 2.3]], 2), - ([[7.3, 4.0, 6.1, 2.4]], 2), - ([[7.0, 3.3, 6.1, 2.5]], 2)] - - -def test_logistic_learner(): - iris = DataSet(name='iris') - classes = ['setosa', 'versicolor', 'virginica'] - iris.classes_to_numbers(classes) - n_samples, n_features = len(iris.examples), iris.target - X, y = np.array([x[:n_features] for x in iris.examples]), \ - np.array([x[n_features] for x in iris.examples]) - ll = MultiLogisticRegressionLearner().fit(X, y) - assert grade_learner(ll, iris_tests) == 1 - assert np.allclose(err_ratio(ll, iris), 0.04) - - -def test_svm(): - iris = DataSet(name='iris') - classes = ['setosa', 'versicolor', 'virginica'] - iris.classes_to_numbers(classes) - n_samples, n_features = len(iris.examples), iris.target - X, y = np.array([x[:n_features] for x in iris.examples]), np.array([x[n_features] for x in iris.examples]) - svm = MultiSVM().fit(X, y) - assert grade_learner(svm, iris_tests) == 1 - assert np.isclose(err_ratio(svm, iris), 0.04) + X, y = (np.array([x[:n_features] for x in iris.examples]), + np.array([x[n_features] for x in iris.examples])) + svm = MultiClassLearner(SVC()).fit(X, y) + assert svm.predict([[5.0, 3.1, 0.9, 0.1]]) == 0 + assert svm.predict([[5.1, 3.5, 1.0, 0.0]]) == 0 + assert svm.predict([[4.9, 3.3, 1.1, 0.1]]) == 0 + assert svm.predict([[6.0, 3.0, 4.0, 1.1]]) == 1 + assert svm.predict([[6.1, 2.2, 3.5, 1.0]]) == 1 + assert svm.predict([[5.9, 2.5, 3.3, 1.1]]) == 1 + assert svm.predict([[7.5, 4.1, 6.2, 2.3]]) == 2 + assert svm.predict([[7.3, 4.0, 6.1, 2.4]]) == 2 + assert svm.predict([[7.0, 3.3, 6.1, 2.5]]) == 2 def test_information_content(): diff --git a/tests/test_search.py b/tests/test_search.py index 075a57312..d93e9a306 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -226,7 +226,7 @@ def test_and_or_graph_search(): def run_plan(state, problem, plan): if problem.goal_test(state): return True - if len(plan) is not 2: + if len(plan) != 2: return False predicate = lambda x: run_plan(x, problem, plan[1][x]) return all(predicate(r) for r in problem.result(state, plan[0])) diff --git a/utils.py b/utils.py index fd683d34a..3158e3793 100644 --- a/utils.py +++ b/utils.py @@ -92,12 +92,11 @@ def power_set(iterable): def extend(s, var, val): """Copy dict s and extend it by setting var to val; return copy.""" - try: # Python 3.5 and later - return eval('{**s, var: val}') - except SyntaxError: # Python 3.4 - s2 = s.copy() - s2[var] = val - return s2 + return {**s, var: val} + + +def flatten(seqs): + return sum(seqs, []) # ______________________________________________________________________________ diff --git a/utils4e.py b/utils4e.py index 178e887b4..65cb9026f 100644 --- a/utils4e.py +++ b/utils4e.py @@ -157,12 +157,11 @@ def power_set(iterable): def extend(s, var, val): """Copy dict s and extend it by setting var to val; return copy.""" - try: # Python 3.5 and later - return eval('{**s, var: val}') - except SyntaxError: # Python 3.4 - s2 = s.copy() - s2[var] = val - return s2 + return {**s, var: val} + + +def flatten(seqs): + return sum(seqs, []) # ______________________________________________________________________________ @@ -359,11 +358,6 @@ def random_weights(min_value, max_value, num_weights): return [random.uniform(min_value, max_value) for _ in range(num_weights)] -def softmax1D(x): - """Return the softmax vector of input vector x.""" - return np.exp(x) / np.sum(np.exp(x)) - - def conv1D(x, k): """1D convolution. x: input vector; K: kernel vector.""" return np.convolve(x, k, mode='same') From 6baf56e323a078a3200fda30b0bfc55161c1fab5 Mon Sep 17 00:00:00 2001 From: Abhinav Talari <49162896+AbhinavTalari@users.noreply.github.com> Date: Tue, 23 Jun 2020 02:48:58 +0530 Subject: [PATCH 28/31] Added a MinMax Player (#1184) * MinMax Player Added a MiniMax PLayer * Changed OP --- games.ipynb | 20 +++++++++++++++----- games.py | 4 ++++ 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/games.ipynb b/games.ipynb index 51a2015b4..edf955be8 100644 --- a/games.ipynb +++ b/games.ipynb @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -135,11 +135,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": true }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "text": "\u001b[1;32mclass\u001b[0m \u001b[0mTicTacToe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;34m\"\"\"Play TicTacToe on an h x v board, with Max (first player) playing 'X'.\n A state has the player to move, a cached utility, a list of moves in\n the form of a list of (x, y) positions, and a board, in the form of\n a dict of {(x, y): Player} entries, where Player is 'X' or 'O'.\"\"\"\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mactions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;34m\"\"\"Legal moves are any square not yet taken.\"\"\"\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoves\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmove\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoves\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mstate\u001b[0m \u001b[1;31m# Illegal move has no effect\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mboard\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboard\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mboard\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmove\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_move\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mmoves\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoves\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mmoves\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmove\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mreturn\u001b[0m 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\u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mh\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mv\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboard\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcompute_utility\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboard\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplayer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;34m\"\"\"If 'X' wins with this move, return 1; if 'O' wins return -1; else return 0.\"\"\"\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_in_row\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboard\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplayer\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;32mor\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_in_row\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboard\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplayer\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'X'\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mk_in_row\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboard\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelta_x_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;34m\"\"\"Return true if there is a line through move on board for player.\"\"\"\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdelta_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelta_y\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdelta_x_y\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;31m# n is number of moves in row\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mboard\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mplayer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdelta_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdelta_y\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmove\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mboard\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mplayer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdelta_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdelta_y\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;31m# Because we counted move itself twice\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "metadata": {}, + "execution_count": 4 + } + ], "source": [ "%psource TicTacToe" ] @@ -849,6 +856,9 @@ "## alphabeta_player\n", "The `alphabeta_player`, on the other hand, calls the `alphabeta_search` function, which returns the best move in the current game state. Thus, the `alphabeta_player` always plays the best move given a game state, assuming that the game tree is small enough to search entirely.\n", "\n", + "## minimax_player\n", + "The `minimax_player`, on the other hand calls the `minimax_search` function which returns the best move in the current game state.\n", + "\n", "## play_game\n", "The `play_game` function will be the one that will actually be used to play the game. You pass as arguments to it an instance of the game you want to play and the players you want in this game. Use it to play AI vs AI, AI vs human, or even human vs human matches!" ] @@ -1651,9 +1661,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.8.2-final" } }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/games.py b/games.py index 94a21f6ee..d22b2e640 100644 --- a/games.py +++ b/games.py @@ -202,6 +202,10 @@ def alpha_beta_player(game, state): return alpha_beta_search(state, game) +def minmax_player(game,state): + return minmax_decision(state,game) + + def expect_minmax_player(game, state): return expect_minmax(state, game) From 9ea91c1d3a644fdb007e8dd0870202dcd9d078b6 Mon Sep 17 00:00:00 2001 From: Donato Meoli Date: Tue, 23 Jun 2020 13:33:26 +0200 Subject: [PATCH 29/31] fixed tests (#1191) * Revert "reformulated the map coloring problem" This reverts commit 20ab0e5afa238a0556e68f173b07ad32d0779d3b. * Revert "fixed typo errors and removed unnecessary brackets" This reverts commit f743146c43b28e0525b0f0b332faebc78c15946f. * Revert "added map coloring SAT problems" This reverts commit 9e0fa550e85081cf5b92fb6a3418384ab5a9fdfd. * Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py" This reverts commit b3cd24c511a82275f5b43c9f176396e6ba05f67e. * Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference" This reverts commit 6986247481a05f1e558b93b2bf3cdae395f9c4ee. * Revert "added the mentioned AC4 algorithm for constraint propagation" This reverts commit 03551fbf2aa3980b915d4b6fefcbc70f24547b03. * added map coloring SAT problem * fixed build error * Revert "added map coloring SAT problem" This reverts commit 93af259e4811ddd775429f8a334111b9dd9e268c. * Revert "fixed build error" This reverts commit 6641c2c861728f3d43d3931ef201c6f7093cbc96. * 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 9d37ae0def15b9e058862cb465da13d2eb926968. * Revert "added tests for monkey & bananas planning problem" This reverts commit 24041e9a1a0ab936f7a2608e3662c8efec559382. * Revert "simplified condition in search.py" This reverts commit 6d229ce9bde5033802aca29ad3047f37ee6d870d. * Revert "added monkey & bananas planning problem" This reverts commit c74933a8905de7bb569bcaed7230930780560874. * 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 c4139e50e3a75a036607f4627717d70ad0919554. * 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 #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 658309d32a3baa0a6b8aac247c0d4ae39cf39ea4. * Revert "fixed typos" This reverts commit 08ad6603ce7b6a6442a28bc0a07c46fa25af3452. * 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 * fixed numpy imports * fixed superclass call * removed utils import from 4e py file * removed unnecessary norm function in utils and fixed Activation definition * removed unnecessary clip function * removed unnecessary import and functions from utils * added tests and fxed some functions * fixed doc * fixed typos in gui folder * removed unnecessary Keras classes and updated pytest.ini * fixed some details * readded Keras classes * fixed import * fixed some parameters * removed unnecessary superclass * fixed neural net * added LinearLearner, LogisticLearner with tests and fixed NeuralNetLearner and PerceptronLearner * removed random_weights and substituted with np.random.uniform * fixed imports * Revert "fixed imports" This reverts commit aaf9c7b4501386bdb00cf61caadd66f06d1513a8. * Revert "removed random_weights and substituted with np.random.uniform" This reverts commit 70d662b5a7e47830add2b4d42f69f624d6915b15. * revert * fixed typo * fixed .ini and DecisionTreeLearner * fixed tests * removed main and fixed AutoencoderLearner * revert NeuralNetLearner and PerceptronLearner definition * fixed all tests and removed Learner class * fixed tests * fixed tests * fixed tests * fixed some function definition * fixed verbose definition * fixed tests * fixed tests * fixed tests * updated .travis.yml * fixed .travis.yml * fixed .travis.yml * fixed all tests * fixed requirements.txt * fixed .travis.yml * update .travis.yml * rollback .travis.yml * rollback tests * fixed output layer with softmax as activation function * updated yml * updated requirements.txt * fixed svc * fixed syntax warns * fixed syntax warns * removed 3.8 * added python 3.8 support * fixed doctests * fixed spaces and doctest * added SVR with r2 and accuracy metrics * fixed imports * fixed tests * removed not allowed imports * fixed * fixed keras * fixed * updated requirements.txt --- gui/grid_mdp.py | 2 +- logic4e.py | 149 +++++++++++++++++++----------------- notebook.py | 20 ++--- notebook4e.py | 20 ++--- perception4e.py | 2 - search.py | 1 - tests/test_logic4e.py | 60 +++++++++------ tests/test_nlp4e.py | 4 +- tests/test_probability4e.py | 16 ++-- tests/test_search.py | 76 +++++++++--------- 10 files changed, 184 insertions(+), 166 deletions(-) diff --git a/gui/grid_mdp.py b/gui/grid_mdp.py index cb04c54b9..e60b49247 100644 --- a/gui/grid_mdp.py +++ b/gui/grid_mdp.py @@ -636,7 +636,7 @@ def animate_graph(self, i): self.grid_to_show[k[1]][k[0]] = v if (self.delta < self.epsilon * (1 - self.gamma) / self.gamma) or ( - self.iterations > 60) and self.terminated == False: + self.iterations > 60) and self.terminated is False: self.terminated = True display(self.grid_to_show, self._height, self._width) diff --git a/logic4e.py b/logic4e.py index f05634436..75608ad74 100644 --- a/logic4e.py +++ b/logic4e.py @@ -30,17 +30,14 @@ unify Do unification of two FOL sentences diff, simp Symbolic differentiation and simplification """ +import itertools +import random +from collections import defaultdict -from utils import ( - removeall, unique, first, argmax, probability, - isnumber, issequence, Expr, expr, subexpressions -) from agents import Agent, Glitter, Bump, Stench, Breeze, Scream from search import astar_search, PlanRoute +from utils4e import remove_all, unique, first, probability, isnumber, issequence, Expr, expr, subexpressions -import itertools -import random -from collections import defaultdict # ______________________________________________________________________________ # Chapter 7 Logical Agents @@ -48,7 +45,6 @@ class KB: - """ A knowledge base to which you can tell and ask sentences. To create a KB, subclass this class and implement tell, ask_generator, and retract. @@ -132,6 +128,7 @@ def make_action_sentence(action, t): return program + # _____________________________________________________________________________ # 7.2 The Wumpus World @@ -143,19 +140,19 @@ def facing_east(time): return Expr('FacingEast', time) -def facing_west (time): +def facing_west(time): return Expr('FacingWest', time) -def facing_north (time): +def facing_north(time): return Expr('FacingNorth', time) -def facing_south (time): +def facing_south(time): return Expr('FacingSouth', time) -def wumpus (x, y): +def wumpus(x, y): return Expr('W', x, y) @@ -219,12 +216,13 @@ def ok_to_move(x, y, time): return Expr('OK', x, y, time) -def location(x, y, time = None): +def location(x, y, time=None): if time is None: return Expr('L', x, y) else: return Expr('L', x, y, time) + # Symbols @@ -235,15 +233,17 @@ def implies(lhs, rhs): def equiv(lhs, rhs): return Expr('<=>', lhs, rhs) + # Helper Function def new_disjunction(sentences): t = sentences[0] - for i in range(1,len(sentences)): + for i in range(1, len(sentences)): t |= sentences[i] return t + # ______________________________________________________________________________ # 7.4 Propositional Logic @@ -441,6 +441,7 @@ def pl_true(exp, model={}): else: raise ValueError("illegal operator in logic expression" + str(exp)) + # ______________________________________________________________________________ # 7.5 Propositional Theorem Proving @@ -489,6 +490,7 @@ def move_not_inwards(s): if s.op == '~': def NOT(b): return move_not_inwards(~b) + a = s.args[0] if a.op == '~': return move_not_inwards(a.args[0]) # ~~A ==> A @@ -566,6 +568,7 @@ def collect(subargs): collect(arg.args) else: result.append(arg) + collect(args) return result @@ -589,6 +592,7 @@ def disjuncts(s): """ return dissociate('|', [s]) + # ______________________________________________________________________________ @@ -603,7 +607,7 @@ def pl_resolution(KB, alpha): while True: n = len(clauses) pairs = [(clauses[i], clauses[j]) - for i in range(n) for j in range(i+1, n)] + for i in range(n) for j in range(i + 1, n)] for (ci, cj) in pairs: resolvents = pl_resolve(ci, cj) if False in resolvents: @@ -622,11 +626,12 @@ def pl_resolve(ci, cj): for di in disjuncts(ci): for dj in disjuncts(cj): if di == ~dj or ~di == dj: - dnew = unique(removeall(di, disjuncts(ci)) + - removeall(dj, disjuncts(cj))) + dnew = unique(remove_all(di, disjuncts(ci)) + + remove_all(dj, disjuncts(cj))) clauses.append(associate('|', dnew)) return clauses + # ______________________________________________________________________________ # 7.5.4 Forward and backward chaining @@ -683,7 +688,6 @@ def pl_fc_entails(KB, q): """ wumpus_world_inference = expr("(B11 <=> (P12 | P21)) & ~B11") - """ [Figure 7.16] Propositional Logic Forward Chaining example """ @@ -695,9 +699,11 @@ def pl_fc_entails(KB, q): Definite clauses KB example """ definite_clauses_KB = PropDefiniteKB() -for clause in ['(B & F)==>E', '(A & E & F)==>G', '(B & C)==>F', '(A & B)==>D', '(E & F)==>H', '(H & I)==>J', 'A', 'B', 'C']: +for clause in ['(B & F)==>E', '(A & E & F)==>G', '(B & C)==>F', '(A & B)==>D', '(E & F)==>H', '(H & I)==>J', 'A', 'B', + 'C']: definite_clauses_KB.tell(expr(clause)) + # ______________________________________________________________________________ # 7.6 Effective Propositional Model Checking # DPLL-Satisfiable [Figure 7.17] @@ -730,10 +736,10 @@ def dpll(clauses, symbols, model): return model P, value = find_pure_symbol(symbols, unknown_clauses) if P: - return dpll(clauses, removeall(P, symbols), extend(model, P, value)) + return dpll(clauses, remove_all(P, symbols), extend(model, P, value)) P, value = find_unit_clause(clauses, model) if P: - return dpll(clauses, removeall(P, symbols), extend(model, P, value)) + return dpll(clauses, remove_all(P, symbols), extend(model, P, value)) if not symbols: raise TypeError("Argument should be of the type Expr.") P, symbols = symbols[0], symbols[1:] @@ -791,7 +797,7 @@ def unit_clause_assign(clause, model): if model[sym] == positive: return None, None # clause already True elif P: - return None, None # more than 1 unbound variable + return None, None # more than 1 unbound variable else: P, value = sym, positive return P, value @@ -810,6 +816,7 @@ def inspect_literal(literal): else: return literal, True + # ______________________________________________________________________________ # 7.6.2 Local search algorithms # Walk-SAT [Figure 7.18] @@ -842,11 +849,13 @@ def sat_count(sym): count = len([clause for clause in clauses if pl_true(clause, model)]) model[sym] = not model[sym] return count - sym = argmax(prop_symbols(clause), key=sat_count) + + sym = max(prop_symbols(clause), key=sat_count) model[sym] = not model[sym] # If no solution is found within the flip limit, we return failure return None + # ______________________________________________________________________________ # 7.7 Agents Based on Propositional Logic # 7.7.1 The current state of the world @@ -857,31 +866,31 @@ class WumpusKB(PropKB): Create a Knowledge Base that contains the atemporal "Wumpus physics" and temporal rules with time zero. """ - def __init__(self,dimrow): + def __init__(self, dimrow): super().__init__() self.dimrow = dimrow - self.tell( ~wumpus(1, 1) ) - self.tell( ~pit(1, 1) ) + self.tell(~wumpus(1, 1)) + self.tell(~pit(1, 1)) - for y in range(1, dimrow+1): - for x in range(1, dimrow+1): + for y in range(1, dimrow + 1): + for x in range(1, dimrow + 1): pits_in = list() wumpus_in = list() - if x > 1: # West room exists + if x > 1: # West room exists pits_in.append(pit(x - 1, y)) wumpus_in.append(wumpus(x - 1, y)) - if y < dimrow: # North room exists + if y < dimrow: # North room exists pits_in.append(pit(x, y + 1)) wumpus_in.append(wumpus(x, y + 1)) - if x < dimrow: # East room exists + if x < dimrow: # East room exists pits_in.append(pit(x + 1, y)) wumpus_in.append(wumpus(x + 1, y)) - if y > 1: # South room exists + if y > 1: # South room exists pits_in.append(pit(x, y - 1)) wumpus_in.append(wumpus(x, y - 1)) @@ -890,23 +899,23 @@ def __init__(self,dimrow): # Rule that describes existence of at least one Wumpus wumpus_at_least = list() - for x in range(1, dimrow+1): + for x in range(1, dimrow + 1): for y in range(1, dimrow + 1): wumpus_at_least.append(wumpus(x, y)) self.tell(new_disjunction(wumpus_at_least)) # Rule that describes existence of at most one Wumpus - for i in range(1, dimrow+1): - for j in range(1, dimrow+1): - for u in range(1, dimrow+1): - for v in range(1, dimrow+1): - if i!=u or j!=v: + for i in range(1, dimrow + 1): + for j in range(1, dimrow + 1): + for u in range(1, dimrow + 1): + for v in range(1, dimrow + 1): + if i != u or j != v: self.tell(~wumpus(i, j) | ~wumpus(u, v)) # Temporal rules at time zero self.tell(location(1, 1, 0)) - for i in range(1, dimrow+1): + for i in range(1, dimrow + 1): for j in range(1, dimrow + 1): self.tell(implies(location(i, j, 0), equiv(percept_breeze(0), breeze(i, j)))) self.tell(implies(location(i, j, 0), equiv(percept_stench(0), stench(i, j)))) @@ -970,8 +979,8 @@ def add_temporal_sentences(self, time): t = time - 1 # current location rules - for i in range(1, self.dimrow+1): - for j in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): + for j in range(1, self.dimrow + 1): self.tell(implies(location(i, j, time), equiv(percept_breeze(time), breeze(i, j)))) self.tell(implies(location(i, j, time), equiv(percept_stench(time), stench(i, j)))) @@ -1043,7 +1052,7 @@ def ask_if_true(self, query): # ______________________________________________________________________________ -class WumpusPosition(): +class WumpusPosition: def __init__(self, x, y, orientation): self.X = x self.Y = y @@ -1063,12 +1072,13 @@ def set_orientation(self, orientation): self.orientation = orientation def __eq__(self, other): - if other.get_location() == self.get_location() and \ - other.get_orientation()==self.get_orientation(): + if (other.get_location() == self.get_location() and + other.get_orientation() == self.get_orientation()): return True else: return False + # ______________________________________________________________________________ # 7.7.2 A hybrid agent @@ -1076,7 +1086,7 @@ def __eq__(self, other): class HybridWumpusAgent(Agent): """An agent for the wumpus world that does logical inference. [Figure 7.20]""" - def __init__(self,dimentions): + def __init__(self, dimentions): self.dimrow = dimentions self.kb = WumpusKB(self.dimrow) self.t = 0 @@ -1090,8 +1100,8 @@ def execute(self, percept): temp = list() - for i in range(1, self.dimrow+1): - for j in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): + for j in range(1, self.dimrow + 1): if self.kb.ask_if_true(location(i, j, self.t)): temp.append(i) temp.append(j) @@ -1106,8 +1116,8 @@ def execute(self, percept): self.current_position = WumpusPosition(temp[0], temp[1], 'RIGHT') safe_points = list() - for i in range(1, self.dimrow+1): - for j in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): + for j in range(1, self.dimrow + 1): if self.kb.ask_if_true(ok_to_move(i, j, self.t)): safe_points.append([i, j]) @@ -1115,14 +1125,14 @@ def execute(self, percept): goals = list() goals.append([1, 1]) self.plan.append('Grab') - actions = self.plan_route(self.current_position,goals,safe_points) + actions = self.plan_route(self.current_position, goals, safe_points) self.plan.extend(actions) self.plan.append('Climb') if len(self.plan) == 0: unvisited = list() - for i in range(1, self.dimrow+1): - for j in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): + for j in range(1, self.dimrow + 1): for k in range(self.t): if self.kb.ask_if_true(location(i, j, k)): unvisited.append([i, j]) @@ -1132,13 +1142,13 @@ def execute(self, percept): if u not in unvisited_and_safe and s == u: unvisited_and_safe.append(u) - temp = self.plan_route(self.current_position,unvisited_and_safe,safe_points) + temp = self.plan_route(self.current_position, unvisited_and_safe, safe_points) self.plan.extend(temp) if len(self.plan) == 0 and self.kb.ask_if_true(have_arrow(self.t)): possible_wumpus = list() - for i in range(1, self.dimrow+1): - for j in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): + for j in range(1, self.dimrow + 1): if not self.kb.ask_if_true(wumpus(i, j)): possible_wumpus.append([i, j]) @@ -1147,8 +1157,8 @@ def execute(self, percept): if len(self.plan) == 0: not_unsafe = list() - for i in range(1, self.dimrow+1): - for j in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): + for j in range(1, self.dimrow + 1): if not self.kb.ask_if_true(ok_to_move(i, j, self.t)): not_unsafe.append([i, j]) temp = self.plan_route(self.current_position, not_unsafe, safe_points) @@ -1178,7 +1188,7 @@ def plan_shot(self, current, goals, allowed): for loc in goals: x = loc[0] y = loc[1] - for i in range(1, self.dimrow+1): + for i in range(1, self.dimrow + 1): if i < x: shooting_positions.add(WumpusPosition(i, y, 'EAST')) if i > x: @@ -1190,7 +1200,7 @@ def plan_shot(self, current, goals, allowed): # Can't have a shooting position from any of the rooms the Wumpus could reside orientations = ['EAST', 'WEST', 'NORTH', 'SOUTH'] - for loc in goals: + for loc in goals: for orientation in orientations: shooting_positions.remove(WumpusPosition(loc[0], loc[1], orientation)) @@ -1220,7 +1230,7 @@ def translate_to_SAT(init, transition, goal, time): # Symbol claiming state s at time t state_counter = itertools.count() for s in states: - for t in range(time+1): + for t in range(time + 1): state_sym[s, t] = Expr("State_{}".format(next(state_counter))) # Add initial state axiom @@ -1240,11 +1250,11 @@ def translate_to_SAT(init, transition, goal, time): "Transition_{}".format(next(transition_counter))) # Change the state from s to s_ - clauses.append(action_sym[s, action, t] |'==>'| state_sym[s, t]) - clauses.append(action_sym[s, action, t] |'==>'| state_sym[s_, t + 1]) + clauses.append(action_sym[s, action, t] | '==>' | state_sym[s, t]) + clauses.append(action_sym[s, action, t] | '==>' | state_sym[s_, t + 1]) # Allow only one state at any time - for t in range(time+1): + for t in range(time + 1): # must be a state at any time clauses.append(associate('|', [state_sym[s, t] for s in states])) @@ -1287,6 +1297,7 @@ def extract_solution(model): return extract_solution(model) return None + # ______________________________________________________________________________ # Chapter 9 Inference in First Order Logic # 9.2 Unification and First Order Inference @@ -1505,6 +1516,7 @@ def fol_bc_and(KB, goals, theta): for theta2 in fol_bc_and(KB, rest, theta1): yield theta2 + # ______________________________________________________________________________ # A simple KB that defines the relevant conditions of the Wumpus World as in Fig 7.4. # See Sec. 7.4.3 @@ -1512,8 +1524,8 @@ def fol_bc_and(KB, goals, theta): P11, P12, P21, P22, P31, B11, B21 = expr('P11, P12, P21, P22, P31, B11, B21') wumpus_kb.tell(~P11) -wumpus_kb.tell(B11 | '<=>' | ((P12 | P21))) -wumpus_kb.tell(B21 | '<=>' | ((P11 | P22 | P31))) +wumpus_kb.tell(B11 | '<=>' | (P12 | P21)) +wumpus_kb.tell(B21 | '<=>' | (P11 | P22 | P31)) wumpus_kb.tell(~B11) wumpus_kb.tell(B21) @@ -1529,8 +1541,7 @@ def fol_bc_and(KB, goals, theta): # Note that this order of conjuncts # would result in infinite recursion: # '(Human(h) & Mother(m, h)) ==> Human(m)' - '(Mother(m, h) & Human(h)) ==> Human(m)' - ])) + '(Mother(m, h) & Human(h)) ==> Human(m)'])) crime_kb = FolKB( map(expr, ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', @@ -1540,8 +1551,8 @@ def fol_bc_and(KB, goals, theta): 'Missile(x) ==> Weapon(x)', 'Enemy(x, America) ==> Hostile(x)', 'American(West)', - 'Enemy(Nono, America)' - ])) + 'Enemy(Nono, America)'])) + # ______________________________________________________________________________ diff --git a/notebook.py b/notebook.py index 5847a905b..7f0306335 100644 --- a/notebook.py +++ b/notebook.py @@ -238,8 +238,8 @@ def make_visualize(slider): """Takes an input a sliderand returns callback function for timer and animation.""" - def visualize_callback(Visualize, time_step): - if Visualize is True: + def visualize_callback(visualize, time_step): + if visualize is True: for i in range(slider.min, slider.max + 1): slider.value = i time.sleep(float(time_step)) @@ -957,7 +957,7 @@ def final_path_colors(initial_node_colors, problem, solution): def display_visual(graph_data, user_input, algorithm=None, problem=None): initial_node_colors = graph_data['node_colors'] - if user_input == False: + if user_input is False: def slider_callback(iteration): # don't show graph for the first time running the cell calling this function try: @@ -965,8 +965,8 @@ def slider_callback(iteration): except: pass - def visualize_callback(Visualize): - if Visualize is True: + def visualize_callback(visualize): + if visualize is True: button.value = False global all_node_colors @@ -986,10 +986,10 @@ def visualize_callback(Visualize): display(slider_visual) button = widgets.ToggleButton(value=False) - button_visual = widgets.interactive(visualize_callback, Visualize=button) + button_visual = widgets.interactive(visualize_callback, visualize=button) display(button_visual) - if user_input == True: + if user_input is True: node_colors = dict(initial_node_colors) if isinstance(algorithm, dict): assert set(algorithm.keys()).issubset({"Breadth First Tree Search", @@ -1019,8 +1019,8 @@ def slider_callback(iteration): except: pass - def visualize_callback(Visualize): - if Visualize is True: + def visualize_callback(visualize): + if visualize is True: button.value = False problem = GraphProblem(start_dropdown.value, end_dropdown.value, romania_map) @@ -1047,7 +1047,7 @@ def visualize_callback(Visualize): display(end_dropdown) button = widgets.ToggleButton(value=False) - button_visual = widgets.interactive(visualize_callback, Visualize=button) + button_visual = widgets.interactive(visualize_callback, visualize=button) display(button_visual) slider = widgets.IntSlider(min=0, max=1, step=1, value=0) diff --git a/notebook4e.py b/notebook4e.py index 4d61c226b..5b03081c6 100644 --- a/notebook4e.py +++ b/notebook4e.py @@ -274,8 +274,8 @@ def make_visualize(slider): """Takes an input a sliderand returns callback function for timer and animation.""" - def visualize_callback(Visualize, time_step): - if Visualize is True: + def visualize_callback(visualize, time_step): + if visualize is True: for i in range(slider.min, slider.max + 1): slider.value = i time.sleep(float(time_step)) @@ -993,7 +993,7 @@ def final_path_colors(initial_node_colors, problem, solution): def display_visual(graph_data, user_input, algorithm=None, problem=None): initial_node_colors = graph_data['node_colors'] - if user_input == False: + if user_input is False: def slider_callback(iteration): # don't show graph for the first time running the cell calling this function try: @@ -1001,8 +1001,8 @@ def slider_callback(iteration): except: pass - def visualize_callback(Visualize): - if Visualize is True: + def visualize_callback(visualize): + if visualize is True: button.value = False global all_node_colors @@ -1022,10 +1022,10 @@ def visualize_callback(Visualize): display(slider_visual) button = widgets.ToggleButton(value=False) - button_visual = widgets.interactive(visualize_callback, Visualize=button) + button_visual = widgets.interactive(visualize_callback, visualize=button) display(button_visual) - if user_input == True: + if user_input is True: node_colors = dict(initial_node_colors) if isinstance(algorithm, dict): assert set(algorithm.keys()).issubset({"Breadth First Tree Search", @@ -1055,8 +1055,8 @@ def slider_callback(iteration): except: pass - def visualize_callback(Visualize): - if Visualize is True: + def visualize_callback(visualize): + if visualize is True: button.value = False problem = GraphProblem(start_dropdown.value, end_dropdown.value, romania_map) @@ -1083,7 +1083,7 @@ def visualize_callback(Visualize): display(end_dropdown) button = widgets.ToggleButton(value=False) - button_visual = widgets.interactive(visualize_callback, Visualize=button) + button_visual = widgets.interactive(visualize_callback, visualize=button) display(button_visual) slider = widgets.IntSlider(min=0, max=1, step=1, value=0) diff --git a/perception4e.py b/perception4e.py index d88c17419..edd556607 100644 --- a/perception4e.py +++ b/perception4e.py @@ -337,9 +337,7 @@ def simple_convnet(size=3, num_classes=10): model.add(Activation('softmax')) # compile model - opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6) model.compile(loss='categorical_crossentropy', - optimizer=opt, metrics=['accuracy']) print(model.summary()) return model diff --git a/search.py b/search.py index 71c1d1304..5012c1a18 100644 --- a/search.py +++ b/search.py @@ -10,7 +10,6 @@ from collections import deque from utils import * -from utils4e import * class Problem: diff --git a/tests/test_logic4e.py b/tests/test_logic4e.py index f8ed203d6..5a7399281 100644 --- a/tests/test_logic4e.py +++ b/tests/test_logic4e.py @@ -1,10 +1,17 @@ import pytest + from logic4e import * -from utils4e import expr_handle_infix_ops, count, Symbol +from utils4e import expr_handle_infix_ops, count definite_clauses_KB = PropDefiniteKB() -for clause in ['(B & F)==>E', '(A & E & F)==>G', '(B & C)==>F', '(A & B)==>D', '(E & F)==>H', '(H & I)==>J', 'A', 'B', 'C']: - definite_clauses_KB.tell(expr(clause)) +for clause in ['(B & F)==>E', + '(A & E & F)==>G', + '(B & C)==>F', + '(A & B)==>D', + '(E & F)==>H', + '(H & I)==>J', + 'A', 'B', 'C']: + definite_clauses_KB.tell(expr(clause)) def test_is_symbol(): @@ -38,8 +45,7 @@ def test_variables(): def test_expr(): assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' - assert (expr_handle_infix_ops('P & Q ==> R & ~S') - == "P & Q |'==>'| R & ~S") + assert (expr_handle_infix_ops('P & Q ==> R & ~S') == "P & Q |'==>'| R & ~S") def test_extend(): @@ -47,7 +53,7 @@ def test_extend(): def test_subst(): - assert subst({x: 42, y:0}, F(x) + y) == (F(42) + 0) + assert subst({x: 42, y: 0}, F(x) + y) == (F(42) + 0) def test_PropKB(): @@ -55,7 +61,7 @@ def test_PropKB(): assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 kb.tell(A & E) assert kb.ask(A) == kb.ask(E) == {} - kb.tell(E |'==>'| C) + kb.tell(E | '==>' | C) assert kb.ask(C) == {} kb.retract(E) assert kb.ask(E) is False @@ -94,7 +100,8 @@ def test_is_definite_clause(): def test_parse_definite_clause(): assert parse_definite_clause(expr('A & B & C & D ==> E')) == ([A, B, C, D], E) assert parse_definite_clause(expr('Farmer(Mac)')) == ([], expr('Farmer(Mac)')) - assert parse_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) == ([expr('Farmer(f)'), expr('Rabbit(r)')], expr('Hates(f, r)')) + assert parse_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) == ( + [expr('Farmer(f)'), expr('Rabbit(r)')], expr('Hates(f, r)')) def test_pl_true(): @@ -131,28 +138,28 @@ def test_dpll(): assert (dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) == {B: False, C: True, A: True, F: False, D: True, E: False}) - assert dpll_satisfiable(A & B & ~C & D) == {C: False, A: True, D: True, B: True} - assert dpll_satisfiable((A | (B & C)) |'<=>'| ((A | B) & (A | C))) == {C: True, A: True} or {C: True, B: True} - assert dpll_satisfiable(A |'<=>'| B) == {A: True, B: True} + assert dpll_satisfiable(A & B & ~C & D) == {C: False, A: True, D: True, B: True} + assert dpll_satisfiable((A | (B & C)) | '<=>' | ((A | B) & (A | C))) == {C: True, A: True} or {C: True, B: True} + assert dpll_satisfiable(A | '<=>' | B) == {A: True, B: True} assert dpll_satisfiable(A & ~B) == {A: True, B: False} assert dpll_satisfiable(P & ~P) is False def test_find_pure_symbol(): - assert find_pure_symbol([A, B, C], [A|~B,~B|~C,C|A]) == (A, True) - assert find_pure_symbol([A, B, C], [~A|~B,~B|~C,C|A]) == (B, False) - assert find_pure_symbol([A, B, C], [~A|B,~B|~C,C|A]) == (None, None) + assert find_pure_symbol([A, B, C], [A | ~B, ~B | ~C, C | A]) == (A, True) + assert find_pure_symbol([A, B, C], [~A | ~B, ~B | ~C, C | A]) == (B, False) + assert find_pure_symbol([A, B, C], [~A | B, ~B | ~C, C | A]) == (None, None) def test_unit_clause_assign(): - assert unit_clause_assign(A|B|C, {A:True}) == (None, None) - assert unit_clause_assign(B|C, {A:True}) == (None, None) - assert unit_clause_assign(B|~A, {A:True}) == (B, True) + assert unit_clause_assign(A | B | C, {A: True}) == (None, None) + assert unit_clause_assign(B | C, {A: True}) == (None, None) + assert unit_clause_assign(B | ~A, {A: True}) == (B, True) def test_find_unit_clause(): - assert find_unit_clause([A|B|C, B|~C, ~A|~B], {A:True}) == (B, False) - + assert find_unit_clause([A | B | C, B | ~C, ~A | ~B], {A: True}) == (B, False) + def test_unify(): assert unify(x, x, {}) == {} @@ -175,9 +182,9 @@ def test_tt_entails(): assert tt_entails(P & Q, Q) assert not tt_entails(P | Q, Q) assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) - assert not tt_entails(P |'<=>'| Q, Q) - assert tt_entails((P |'==>'| Q) & P, Q) - assert not tt_entails((P |'<=>'| Q) & ~P, Q) + assert not tt_entails(P | '<=>' | Q, Q) + assert tt_entails((P | '==>' | Q) & P, Q) + assert not tt_entails((P | '<=>' | Q) & ~P, Q) def test_prop_symbols(): @@ -231,12 +238,13 @@ def test_move_not_inwards(): def test_distribute_and_over_or(): - def test_entailment(s, has_and = False): + def test_entailment(s, has_and=False): result = distribute_and_over_or(s) if has_and: assert result.op == '&' assert tt_entails(s, result) assert tt_entails(result, s) + test_entailment((A & B) | C, True) test_entailment((A | B) & C, True) test_entailment((A | B) | C, False) @@ -253,7 +261,8 @@ def test_to_cnf(): assert repr(to_cnf("a | (b & c) | d")) == '((b | a | d) & (c | a | d))' assert repr(to_cnf("A & (B | (D & E))")) == '(A & (D | B) & (E | B))' assert repr(to_cnf("A | (B | (C | (D & E)))")) == '((D | A | B | C) & (E | A | B | C))' - assert repr(to_cnf('(A <=> ~B) ==> (C | ~D)')) == '((B | ~A | C | ~D) & (A | ~A | C | ~D) & (B | ~B | C | ~D) & (A | ~B | C | ~D))' + assert repr(to_cnf( + '(A <=> ~B) ==> (C | ~D)')) == '((B | ~A | C | ~D) & (A | ~A | C | ~D) & (B | ~B | C | ~D) & (A | ~B | C | ~D))' def test_pl_resolution(): @@ -281,6 +290,7 @@ def test_ask(query, kb=None): return sorted( [dict((x, v) for x, v in list(a.items()) if x in test_variables) for a in answers], key=repr) + assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' assert repr(test_ask('Human(x)')) == '[{x: Mac}, {x: MrsMac}]' assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' @@ -295,6 +305,7 @@ def test_ask(query, kb=None): return sorted( [dict((x, v) for x, v in list(a.items()) if x in test_variables) for a in answers], key=repr) + assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' assert repr(test_ask('Enemy(x, America)', crime_kb)) == '[{x: Nono}]' assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' @@ -316,6 +327,7 @@ def check_SAT(clauses, single_solution={}): if single_solution: # Cross check the solution if only one exists assert all(pl_true(x, single_solution) for x in clauses) assert soln == single_solution + # Test WalkSat for problems with solution check_SAT([A & B, A & C]) check_SAT([A | B, P & Q, P & B]) diff --git a/tests/test_nlp4e.py b/tests/test_nlp4e.py index 4117d2a4b..2d16a3196 100644 --- a/tests/test_nlp4e.py +++ b/tests/test_nlp4e.py @@ -131,8 +131,8 @@ def test_text_parsing(): assert astar_search_parsing(words, grammer) == 'S' assert beam_search_parsing(words, grammer) == 'S' words = ["the", "is", "wupus", "dead"] - assert astar_search_parsing(words, grammer) == False - assert beam_search_parsing(words, grammer) == False + assert astar_search_parsing(words, grammer) is False + assert beam_search_parsing(words, grammer) is False if __name__ == '__main__': diff --git a/tests/test_probability4e.py b/tests/test_probability4e.py index 975f4d8bf..d07954e0a 100644 --- a/tests/test_probability4e.py +++ b/tests/test_probability4e.py @@ -201,10 +201,10 @@ def test_elimination_ask(): def test_prior_sample(): random.seed(42) all_obs = [prior_sample(burglary) for x in range(1000)] - john_calls_true = [observation for observation in all_obs if observation['JohnCalls'] == True] - mary_calls_true = [observation for observation in all_obs if observation['MaryCalls'] == True] - burglary_and_john = [observation for observation in john_calls_true if observation['Burglary'] == True] - burglary_and_mary = [observation for observation in mary_calls_true if observation['Burglary'] == True] + john_calls_true = [observation for observation in all_obs if observation['JohnCalls'] is True] + mary_calls_true = [observation for observation in all_obs if observation['MaryCalls'] is True] + burglary_and_john = [observation for observation in john_calls_true if observation['Burglary'] is True] + burglary_and_mary = [observation for observation in mary_calls_true if observation['Burglary'] is True] assert len(john_calls_true) / 1000 == 46 / 1000 assert len(mary_calls_true) / 1000 == 13 / 1000 assert len(burglary_and_john) / len(john_calls_true) == 1 / 46 @@ -214,10 +214,10 @@ def test_prior_sample(): def test_prior_sample2(): random.seed(128) all_obs = [prior_sample(sprinkler) for x in range(1000)] - rain_true = [observation for observation in all_obs if observation['Rain'] == True] - sprinkler_true = [observation for observation in all_obs if observation['Sprinkler'] == True] - rain_and_cloudy = [observation for observation in rain_true if observation['Cloudy'] == True] - sprinkler_and_cloudy = [observation for observation in sprinkler_true if observation['Cloudy'] == True] + rain_true = [observation for observation in all_obs if observation['Rain'] is True] + sprinkler_true = [observation for observation in all_obs if observation['Sprinkler'] is True] + rain_and_cloudy = [observation for observation in rain_true if observation['Cloudy'] is True] + sprinkler_and_cloudy = [observation for observation in sprinkler_true if observation['Cloudy'] is True] assert len(rain_true) / 1000 == 0.476 assert len(sprinkler_true) / 1000 == 0.291 assert len(rain_and_cloudy) / len(rain_true) == 376 / 476 diff --git a/tests/test_search.py b/tests/test_search.py index d93e9a306..9be3e4a47 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -8,7 +8,7 @@ LRTA_problem = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space) eight_puzzle = EightPuzzle((1, 2, 3, 4, 5, 7, 8, 6, 0)) eight_puzzle2 = EightPuzzle((1, 0, 6, 8, 7, 5, 4, 2), (0, 1, 2, 3, 4, 5, 6, 7, 8)) -nqueens = NQueensProblem(8) +n_queens = NQueensProblem(8) def test_find_min_edge(): @@ -18,7 +18,7 @@ def test_find_min_edge(): def test_breadth_first_tree_search(): assert breadth_first_tree_search( romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] - assert breadth_first_graph_search(nqueens).solution() == [0, 4, 7, 5, 2, 6, 1, 3] + assert breadth_first_graph_search(n_queens).solution() == [0, 4, 7, 5, 2, 6, 1, 3] def test_breadth_first_graph_search(): @@ -44,11 +44,11 @@ def test_best_first_graph_search(): def test_uniform_cost_search(): assert uniform_cost_search( romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] - assert uniform_cost_search(nqueens).solution() == [0, 4, 7, 5, 2, 6, 1, 3] + assert uniform_cost_search(n_queens).solution() == [0, 4, 7, 5, 2, 6, 1, 3] def test_depth_first_tree_search(): - assert depth_first_tree_search(nqueens).solution() == [7, 3, 0, 2, 5, 1, 6, 4] + assert depth_first_tree_search(n_queens).solution() == [7, 3, 0, 2, 5, 1, 6, 4] def test_depth_first_graph_search(): @@ -80,7 +80,7 @@ def test_astar_search(): assert astar_search(eight_puzzle).solution() == ['LEFT', 'LEFT', 'UP', 'RIGHT', 'RIGHT', 'DOWN', 'LEFT', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'RIGHT'] assert astar_search(EightPuzzle((1, 2, 3, 4, 5, 6, 0, 7, 8))).solution() == ['RIGHT', 'RIGHT'] - assert astar_search(nqueens).solution() == [7, 1, 3, 0, 6, 4, 2, 5] + assert astar_search(n_queens).solution() == [7, 1, 3, 0, 6, 4, 2, 5] def test_find_blank_square(): @@ -115,42 +115,42 @@ def test_result(): def test_goal_test(): - assert eight_puzzle.goal_test((0, 1, 2, 3, 4, 5, 6, 7, 8)) == False - assert eight_puzzle.goal_test((6, 3, 5, 1, 8, 4, 2, 0, 7)) == False - assert eight_puzzle.goal_test((3, 4, 1, 7, 6, 0, 2, 8, 5)) == False - assert eight_puzzle.goal_test((1, 2, 3, 4, 5, 6, 7, 8, 0)) == True - assert eight_puzzle2.goal_test((4, 8, 1, 6, 0, 2, 3, 5, 7)) == False - assert eight_puzzle2.goal_test((3, 4, 1, 7, 6, 0, 2, 8, 5)) == False - assert eight_puzzle2.goal_test((1, 2, 3, 4, 5, 6, 7, 8, 0)) == False - assert eight_puzzle2.goal_test((0, 1, 2, 3, 4, 5, 6, 7, 8)) == True - assert nqueens.goal_test((7, 3, 0, 2, 5, 1, 6, 4)) == True - assert nqueens.goal_test((0, 4, 7, 5, 2, 6, 1, 3)) == True - assert nqueens.goal_test((7, 1, 3, 0, 6, 4, 2, 5)) == True - assert nqueens.goal_test((0, 1, 2, 3, 4, 5, 6, 7)) == False + assert not eight_puzzle.goal_test((0, 1, 2, 3, 4, 5, 6, 7, 8)) + assert not eight_puzzle.goal_test((6, 3, 5, 1, 8, 4, 2, 0, 7)) + assert not eight_puzzle.goal_test((3, 4, 1, 7, 6, 0, 2, 8, 5)) + assert eight_puzzle.goal_test((1, 2, 3, 4, 5, 6, 7, 8, 0)) + assert not eight_puzzle2.goal_test((4, 8, 1, 6, 0, 2, 3, 5, 7)) + assert not eight_puzzle2.goal_test((3, 4, 1, 7, 6, 0, 2, 8, 5)) + assert not eight_puzzle2.goal_test((1, 2, 3, 4, 5, 6, 7, 8, 0)) + assert eight_puzzle2.goal_test((0, 1, 2, 3, 4, 5, 6, 7, 8)) + assert n_queens.goal_test((7, 3, 0, 2, 5, 1, 6, 4)) + assert n_queens.goal_test((0, 4, 7, 5, 2, 6, 1, 3)) + assert n_queens.goal_test((7, 1, 3, 0, 6, 4, 2, 5)) + assert not n_queens.goal_test((0, 1, 2, 3, 4, 5, 6, 7)) def test_check_solvability(): - assert eight_puzzle.check_solvability((0, 1, 2, 3, 4, 5, 6, 7, 8)) == True - assert eight_puzzle.check_solvability((6, 3, 5, 1, 8, 4, 2, 0, 7)) == True - assert eight_puzzle.check_solvability((3, 4, 1, 7, 6, 0, 2, 8, 5)) == True - assert eight_puzzle.check_solvability((1, 8, 4, 7, 2, 6, 3, 0, 5)) == True - assert eight_puzzle.check_solvability((4, 8, 1, 6, 0, 2, 3, 5, 7)) == True - assert eight_puzzle.check_solvability((1, 0, 6, 8, 7, 5, 4, 2, 3)) == True - assert eight_puzzle.check_solvability((1, 2, 3, 4, 5, 6, 7, 8, 0)) == True - assert eight_puzzle.check_solvability((1, 2, 3, 4, 5, 6, 8, 7, 0)) == False - assert eight_puzzle.check_solvability((1, 0, 3, 2, 4, 5, 6, 7, 8)) == False - assert eight_puzzle.check_solvability((7, 0, 2, 8, 5, 3, 6, 4, 1)) == False + assert eight_puzzle.check_solvability((0, 1, 2, 3, 4, 5, 6, 7, 8)) + assert eight_puzzle.check_solvability((6, 3, 5, 1, 8, 4, 2, 0, 7)) + assert eight_puzzle.check_solvability((3, 4, 1, 7, 6, 0, 2, 8, 5)) + assert eight_puzzle.check_solvability((1, 8, 4, 7, 2, 6, 3, 0, 5)) + assert eight_puzzle.check_solvability((4, 8, 1, 6, 0, 2, 3, 5, 7)) + assert eight_puzzle.check_solvability((1, 0, 6, 8, 7, 5, 4, 2, 3)) + assert eight_puzzle.check_solvability((1, 2, 3, 4, 5, 6, 7, 8, 0)) + assert not eight_puzzle.check_solvability((1, 2, 3, 4, 5, 6, 8, 7, 0)) + assert not eight_puzzle.check_solvability((1, 0, 3, 2, 4, 5, 6, 7, 8)) + assert not eight_puzzle.check_solvability((7, 0, 2, 8, 5, 3, 6, 4, 1)) def test_conflict(): - assert not nqueens.conflict(7, 0, 1, 1) - assert not nqueens.conflict(0, 3, 6, 4) - assert not nqueens.conflict(2, 6, 5, 7) - assert not nqueens.conflict(2, 4, 1, 6) - assert nqueens.conflict(0, 0, 1, 1) - assert nqueens.conflict(4, 3, 4, 4) - assert nqueens.conflict(6, 5, 5, 6) - assert nqueens.conflict(0, 6, 1, 7) + assert not n_queens.conflict(7, 0, 1, 1) + assert not n_queens.conflict(0, 3, 6, 4) + assert not n_queens.conflict(2, 6, 5, 7) + assert not n_queens.conflict(2, 4, 1, 6) + assert n_queens.conflict(0, 0, 1, 1) + assert n_queens.conflict(4, 3, 4, 4) + assert n_queens.conflict(6, 5, 5, 6) + assert n_queens.conflict(0, 6, 1, 7) def test_recursive_best_first_search(): @@ -179,8 +179,7 @@ def manhattan(node): assert recursive_best_first_search( EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0)), h=manhattan).solution() == [ - 'LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'UP', 'DOWN', 'RIGHT' - ] + 'LEFT', 'UP', 'UP', 'LEFT', 'DOWN', 'RIGHT', 'DOWN', 'UP', 'DOWN', 'RIGHT'] def test_hill_climbing(): @@ -198,10 +197,9 @@ def test_hill_climbing(): def test_simulated_annealing(): - random.seed("aima-python") prob = PeakFindingProblem((0, 0), [[0, 5, 10, 20], [-3, 7, 11, 5]], directions4) - sols = {prob.value(simulated_annealing(prob)) for i in range(100)} + sols = {prob.value(simulated_annealing(prob)) for _ in range(100)} assert max(sols) == 20 prob = PeakFindingProblem((0, 0), [[0, 5, 10, 8], [-3, 7, 9, 999], From 668a2fb0bcd28b4963648c1425f904baa3826a8f Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 14 Sep 2020 15:53:37 -0700 Subject: [PATCH 30/31] Update README.md --- README.md | 35 +++++++++++++++++++++++------------ 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index a94d6fd21..17f1d6085 100644 --- a/README.md +++ b/README.md @@ -1,30 +1,41 @@ -
-

-
+ # `aima-python` [![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/aimacode/aima-python) Python code for the book *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu).* You can use this in conjunction with a course on AI, or for study on your own. We're looking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. +# Updates for 4th Edition + +The 4th edition of the book as out now in 2020, and thus we are updating the code. All code here will reflect the 4th edition. Changes include: + +- Move from Python 3.5 to 3.7. +- More emphasis on Jupyter (Ipython) notebooks. +- More projects using external packages (tensorflow, etc.). -## Structure of the Project -When complete, this project will have Python implementations for all the pseudocode algorithms in the book, as well as tests and examples of use. For each major topic, such as `nlp` (natural language processing), we provide the following files: +# Structure of the Project -- `nlp.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. -- `tests/test_nlp.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/), but also usable on their own. -- `nlp.ipynb`: A Jupyter (IPython) notebook that explains and gives examples of how to use the code. -- `nlp_apps.ipynb`: A Jupyter notebook that gives example applications of the code. +When complete, this project will have Python implementations for all the pseudocode algorithms in the book, as well as tests and examples of use. For each major topic, such as `search`, we provide the following files: +- `search.ipynb` and `search.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. The `.py` file is generated automatically from the `.ipynb` file; the idea is that it is easier to read the documentation in the `.ipynb` file. +- `search_XX.ipynb`: Notebooks that show how to use the code, broken out into various topics (the `XX`). +- `tests/test_search.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/), but also usable on their own. -## Python 3.5 and up +# Python 3.7 and up -This code requires Python 3.5 or later, and does not run in Python 2. You can [install Python](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +The code for the 3rd edition was in Python 3.5; the current 4th edition code is in Python 3.7. It should also run in later versions, but does not run in Python 2. You can [install Python](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). You can run the code in an IDE, or from the command line with `python -i filename.py` where the `-i` option puts you in an interactive loop where you can run Python functions. All notebooks are available in a [binder environment](http://mybinder.org/repo/aimacode/aima-python). Alternatively, visit [jupyter.org](http://jupyter.org/) for instructions on setting up your own Jupyter notebook environment. -There is a sibling [aima-docker](https://github.com/rajatjain1997/aima-docker) project that shows you how to use docker containers to run more complex problems in more complex software environments. +Features from Python 3.6 and 3.7 that we will be using for this version of the code: +- [f-strings](https://docs.python.org/3.6/whatsnew/3.6.html#whatsnew36-pep498): all string formatting should be done with `f'var = {var}'`, not with `'var = {}'.format(var)` nor `'var = %s' % var`. +- [`typing` module](https://docs.python.org/3.7/library/typing.html): declare functions with type hints: `def successors(state) -> List[State]:`; that is, give type declarations, but omit them when it is obvious. I don't need to say `state: State`, but in another context it would make sense to say `s: State`. +- Underscores in numerics: write a million as `1_000_000` not as `1000000`. +- [`dataclasses` module](https://docs.python.org/3.7/library/dataclasses.html#module-dataclasses): replace `namedtuple` with `dataclass`. + + +[//]: # (There is a sibling [aima-docker]https://github.com/rajatjain1997/aima-docker project that shows you how to use docker containers to run more complex problems in more complex software environments.) ## Installation Guide From 61d695b37c6895902081da1f37baf645b0d2658a Mon Sep 17 00:00:00 2001 From: Marce Penide Date: Sun, 5 Dec 2021 02:44:47 +0100 Subject: [PATCH 31/31] Fixed bug in treatment of repeated nodes in frontier in best_first_graph_search_for_vis method (#1242) --- search.ipynb | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/search.ipynb b/search.ipynb index 72300557e..caf231dcc 100644 --- a/search.ipynb +++ b/search.ipynb @@ -808,7 +808,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "" ] @@ -1520,8 +1520,8 @@ " all_node_colors.append(dict(node_colors))\n", " elif child in frontier:\n", " incumbent = frontier[child]\n", - " if f(child) < f(incumbent):\n", - " del frontier[incumbent]\n", + " if f(child) < incumbent:\n", + " del frontier[child]\n", " frontier.append(child)\n", " node_colors[child.state] = \"orange\"\n", " iterations += 1\n", @@ -3344,7 +3344,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "" ] @@ -3534,7 +3534,7 @@ "outputs": [ { "data": { - "image/png": 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6Jd7zrw7773+vnq+v8k8AAGgKAnaLmbZo4PWujZWBNmyY+8NXe88TLgtOU51X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\nK5392bUDr8POUbvu6MPfrls61SeNmSe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", "text/plain": [ "" ] @@ -5377,7 +5377,7 @@ "outputs": [ { "data": { - "image/png": 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dUF8960XABgBkR5Mzrl8Pmav2ymve85HjwWnC9kXSRP0J2ACAXJk/K3jf5PnB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\n/P+uMuaa/akHvAAAAABJRU5ErkJggg==\n", 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", "text/plain": [ "" ] @@ -5433,7 +5433,7 @@ "outputs": [ { "data": { - "image/png": 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dUF8960XABgBkR5Mzrl8Pmav2ymve85HjwWnC9kXSRP0J2ACAXJk/K3jf5PnB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\n/P+uMuaa/akHvAAAAABJRU5ErkJggg==\n", 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", "text/plain": [ "" ] @@ -5626,7 +5626,7 @@ "outputs": [ { "data": { - "image/png": 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e856PHg9OE7YvCmaMA2hnBGy01MI5wfumLgzeF0VY73vRJc3lDQBpI2AjESd3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\nELABAMgAAjYAABnw/wPRIOc/pYUmbAAAAABJRU5ErkJggg==\n", 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", "text/plain": [ "" ] @@ -6531,6 +6531,15 @@ "pygments_lexer": "ipython3", "version": "3.7.6" }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + }, "widgets": { "state": { "1516e2501ddd4a2e8e3250bffc0164db": { @@ -6563,17 +6572,8 @@ } }, "version": "1.2.0" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } } }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +}