diff --git a/README.md b/README.md
index 5791f59e7..ca68ad5ee 100644
--- a/README.md
+++ b/README.md
@@ -46,11 +46,11 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
| 3.14 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`][search] | Done |
| 3.17 | Depth-Limited-Search | `depth_limited_search` | [`search.py`][search] | Done |
| 3.18 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`][search] | Done |
-| 3.22 | Best-First-Search | `best_first_graph_search` | [`search.py`][search] | |
+| 3.22 | Best-First-Search | `best_first_graph_search` | [`search.py`][search] | Done |
| 3.24 | A\*-Search | `astar_search` | [`search.py`][search] | Done |
| 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`][search] | Done |
-| 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`][search] | |
-| 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`][search] | |
+| 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`][search] | Done |
+| 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`][search] | Done |
| 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`][search] | Done |
| 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`][search] | Done |
| 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`][search] | |
@@ -60,7 +60,7 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
| 6 | CSP | `CSP` | [`csp.py`][csp] | Done |
| 6.3 | AC-3 | `AC3` | [`csp.py`][csp] | Done |
| 6.5 | Backtracking-Search | `backtracking_search` | [`csp.py`][csp] | Done |
-| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`][csp] | |
+| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`][csp] | Done |
| 6.11 | Tree-CSP-Solver | `tree_csp_solver` | [`csp.py`][csp] | Done |
| 7 | KB | `KB` | [`logic.py`][logic] | Done |
| 7.1 | KB-Agent | `KB_Agent` | [`logic.py`][logic] | Done |
@@ -71,7 +71,7 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
| 7.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`][logic] | Done |
| 7.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`][logic] | Done |
| 7.18 | WalkSAT | `WalkSAT` | [`logic.py`][logic] | Done |
-| 7.20 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`][logic]\* | |
+| 7.20 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | | |
| 7.22 | SATPlan | `SAT_plan` | [`logic.py`][logic] | Done |
| 9 | Subst | `subst` | [`logic.py`][logic] | Done |
| 9.1 | Unify | `unify` | [`logic.py`][logic] | Done |
@@ -102,7 +102,7 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
| 16.9 | Information-Gathering-Agent | | |
| 17.4 | Value-Iteration | `value_iteration` | [`mdp.py`][mdp] | Done |
| 17.7 | Policy-Iteration | `policy_iteration` | [`mdp.py`][mdp] | Done |
-| 17.7 | POMDP-Value-Iteration | | | |
+| 17.9 | POMDP-Value-Iteration | | | |
| 18.5 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`][learning] | Done |
| 18.8 | Cross-Validation | `cross_validation` | [`learning.py`][learning] | |
| 18.11 | Decision-List-Learning | `DecisionListLearner` | [`learning.py`][learning]\* | |
@@ -110,13 +110,13 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
| 18.34 | AdaBoost | `AdaBoost` | [`learning.py`][learning] | |
| 19.2 | Current-Best-Learning | `current_best_learning` | [`knowledge.py`](knowledge.py) | Done |
| 19.3 | Version-Space-Learning | `version_space_learning` | [`knowledge.py`](knowledge.py) | Done |
-| 19.8 | Minimal-Consistent-Det | | |
+| 19.8 | Minimal-Consistent-Det | `minimal_consistent_det` | [`knowledge.py`](knowledge.py) | Done |
| 19.12 | FOIL | | |
| 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`][rl] | Done |
| 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`][rl] | Done |
| 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`][rl] | Done |
| 22.1 | HITS | `HITS` | [`nlp.py`][nlp] | Done |
-| 23 | Chart-Parse | `Chart` | [`nlp.py`][nlp] | |
+| 23 | Chart-Parse | `Chart` | [`nlp.py`][nlp] | Done |
| 23.5 | CYK-Parse | `CYK_parse` | [`nlp.py`][nlp] | Done |
| 25.9 | Monte-Carlo-Localization| `monte_carlo_localization` | [`probability.py`][probability] | Done |
diff --git a/knowledge.py b/knowledge.py
index a5d165e3e..6330923bd 100644
--- a/knowledge.py
+++ b/knowledge.py
@@ -207,6 +207,7 @@ def build_h_combinations(hypotheses):
def minimal_consistent_det(E, A):
+ """Returns a minimal set of attributes which give consistent determination"""
n = len(A)
for i in range(n + 1):
@@ -216,6 +217,7 @@ def minimal_consistent_det(E, A):
def consistent_det(A, E):
+ """Checks if the attributes(A) is consistent with the examples(E)"""
H = {}
for e in E:
diff --git a/search.py b/search.py
index 31d3f0940..68b77a5a8 100644
--- a/search.py
+++ b/search.py
@@ -509,6 +509,47 @@ def and_search(states, problem, path):
return or_search(problem.initial, problem, [])
+class PeakFindingProblem(Problem):
+ """Problem of finding the highest peak in a limited grid"""
+
+ def __init__(self, initial, grid):
+ """The grid is a 2 dimensional array/list whose state is specified by tuple of indices"""
+ Problem.__init__(self, initial)
+ self.grid = grid
+ self.n = len(grid)
+ assert self.n > 0
+ self.m = len(grid[0])
+ assert self.m > 0
+
+ def actions(self, state):
+ """Allows movement in only 4 directions"""
+ # TODO: Add flag to allow diagonal motion
+ allowed_actions = []
+ if state[0] > 0:
+ allowed_actions.append('N')
+ if state[0] < self.n - 1:
+ allowed_actions.append('S')
+ if state[1] > 0:
+ allowed_actions.append('W')
+ if state[1] < self.m - 1:
+ allowed_actions.append('E')
+ return allowed_actions
+
+ def result(self, state, action):
+ """Moves in the direction specified by action"""
+ x, y = state
+ x = x + (1 if action == 'S' else (-1 if action == 'N' else 0))
+ y = y + (1 if action == 'E' else (-1 if action == 'W' else 0))
+ return (x, y)
+
+ def value(self, state):
+ """Value of a state is the value it is the index to"""
+ x, y = state
+ assert 0 <= x < self.n
+ assert 0 <= y < self.m
+ return self.grid[x][y]
+
+
class OnlineDFSAgent:
"""[Figure 4.21] The abstract class for an OnlineDFSAgent. Override
diff --git a/tests/test_csp.py b/tests/test_csp.py
index 78afac673..5a10f5ce5 100644
--- a/tests/test_csp.py
+++ b/tests/test_csp.py
@@ -330,6 +330,15 @@ def test_backtracking_search():
order_domain_values=lcv, inference=mac)
+def test_min_conflicts():
+ random.seed("aima-python")
+ assert min_conflicts(australia)
+ assert min_conflicts(usa)
+ assert min_conflicts(france)
+ australia_impossible = MapColoringCSP(list('RG'), 'SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: ')
+ assert min_conflicts(australia_impossible, 1000) is None
+
+
def test_universal_dict():
d = UniversalDict(42)
assert d['life'] == 42
diff --git a/tests/test_search.py b/tests/test_search.py
index af892f6f1..f22ca6f89 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -6,7 +6,6 @@
vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], vacumm_world)
LRTA_problem = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space)
-
def test_find_min_edge():
assert romania_problem.find_min_edge() == 70
@@ -20,6 +19,22 @@ def test_breadth_first_search():
assert breadth_first_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest']
+def test_best_first_graph_search():
+ # uniform_cost_search and astar_search test it indirectly
+ assert best_first_graph_search(
+ romania_problem,
+ lambda node: node.state).solution() == ['Sibiu', 'Fagaras', 'Bucharest']
+ assert best_first_graph_search(
+ romania_problem,
+ lambda node: node.state[::-1]).solution() == ['Timisoara',
+ 'Lugoj',
+ 'Mehadia',
+ 'Drobeta',
+ 'Craiova',
+ 'Pitesti',
+ 'Bucharest']
+
+
def test_uniform_cost_search():
assert uniform_cost_search(
romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']
@@ -56,6 +71,33 @@ def test_recursive_best_first_search():
romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']
+def test_hill_climbing():
+ prob = PeakFindingProblem((0, 0), [[0, 5, 10, 20],
+ [-3, 7, 11, 5]])
+ assert hill_climbing(prob) == (0, 3)
+ prob = PeakFindingProblem((0, 0), [[0, 5, 10, 8],
+ [-3, 7, 9, 999],
+ [1, 2, 5, 11]])
+ assert hill_climbing(prob) == (0, 2)
+ prob = PeakFindingProblem((2, 0), [[0, 5, 10, 8],
+ [-3, 7, 9, 999],
+ [1, 2, 5, 11]])
+ assert hill_climbing(prob) == (1, 3)
+
+
+def test_simulated_annealing():
+ random.seed("aima-python")
+ prob = PeakFindingProblem((0, 0), [[0, 5, 10, 20],
+ [-3, 7, 11, 5]])
+ sols = {prob.value(simulated_annealing(prob)) for i in range(100)}
+ assert max(sols) == 20
+ prob = PeakFindingProblem((0, 0), [[0, 5, 10, 8],
+ [-3, 7, 9, 999],
+ [1, 2, 5, 11]])
+ sols = {prob.value(simulated_annealing(prob)) for i in range(100)}
+ assert max(sols) == 999
+
+
def test_BoggleFinder():
board = list('SARTELNID')
"""
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