From 03ecff6b6d271f5c46de5cdef6aa58502106d123 Mon Sep 17 00:00:00 2001 From: RAHUL KUMAR <55033230+pctablet505@users.noreply.github.com> Date: Sat, 23 Jan 2021 18:57:30 +0530 Subject: [PATCH] Update search4e.ipynb Added RBFS implementation corrected some errors --- search4e.ipynb | 1793 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 1189 insertions(+), 604 deletions(-) diff --git a/search4e.ipynb b/search4e.ipynb index 7c636f2e7..160c870ee 100644 --- a/search4e.ipynb +++ b/search4e.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -29,6 +29,7 @@ "import sys\n", "from collections import defaultdict, deque, Counter\n", "from itertools import combinations\n", + "import copy\n", "\n", "\n", "class Problem(object):\n", @@ -67,7 +68,7 @@ " \n", " \n", "def expand(problem, node):\n", - " \"Expand a node, generating the children nodes.\"\n", + " \"\"\"Expand a node, generating the children nodes.\"\"\"\n", " s = node.state\n", " for action in problem.actions(s):\n", " s1 = problem.result(s, action)\n", @@ -76,14 +77,14 @@ " \n", "\n", "def path_actions(node):\n", - " \"The sequence of actions to get to this node.\"\n", + " \"\"\"The sequence of actions to get to this node.\"\"\"\n", " if node.parent is None:\n", " return [] \n", " return path_actions(node.parent) + [node.action]\n", "\n", "\n", "def path_states(node):\n", - " \"The sequence of states to get to this node.\"\n", + " \"\"\"The sequence of states to get to this node.\"\"\"\n", " if node in (cutoff, failure, None): \n", " return []\n", " return path_states(node.parent) + [node.state]" @@ -100,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -142,12 +143,12 @@ }, { "cell_type": "code", - "execution_count": 356, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def best_first_search(problem, f):\n", - " \"Search nodes with minimum f(node) value first.\"\n", + " \"\"\"Search nodes with minimum f(node) value first.\"\"\"\n", " node = Node(problem.initial)\n", " frontier = PriorityQueue([node], key=f)\n", " reached = {problem.initial: node}\n", @@ -164,7 +165,7 @@ "\n", "\n", "def best_first_tree_search(problem, f):\n", - " \"A version of best_first_search without the `reached` table.\"\n", + " \"\"\"A version of best_first_search without the `reached` table.\"\"\"\n", " frontier = PriorityQueue([Node(problem.initial)], key=f)\n", " while frontier:\n", " node = frontier.pop()\n", @@ -214,12 +215,12 @@ "\n", "\n", "def depth_first_bfs(problem):\n", - " \"Search deepest nodes in the search tree first; using best-first.\"\n", + " \"\"\"Search deepest nodes in the search tree first; using best-first.\"\"\"\n", " return best_first_search(problem, f=lambda n: -len(n))\n", "\n", "\n", "def is_cycle(node, k=30):\n", - " \"Does this node form a cycle of length k or less?\"\n", + " \"\"\"Does this node form a cycle of length k or less?\"\"\"\n", " def find_cycle(ancestor, k):\n", " return (ancestor is not None and k > 0 and\n", " (ancestor.state == node.state or find_cycle(ancestor.parent, k - 1)))\n", @@ -238,12 +239,12 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "def breadth_first_search(problem):\n", - " \"Search shallowest nodes in the search tree first.\"\n", + " \"\"\"Search shallowest nodes in the search tree first.\"\"\"\n", " node = Node(problem.initial)\n", " if problem.is_goal(problem.initial):\n", " return node\n", @@ -262,7 +263,7 @@ "\n", "\n", "def iterative_deepening_search(problem):\n", - " \"Do depth-limited search with increasing depth limits.\"\n", + " \"\"\"Do depth-limited search with increasing depth limits.\"\"\"\n", " for limit in range(1, sys.maxsize):\n", " result = depth_limited_search(problem, limit)\n", " if result != cutoff:\n", @@ -270,7 +271,7 @@ " \n", " \n", "def depth_limited_search(problem, limit=10):\n", - " \"Search deepest nodes in the search tree first.\"\n", + " \"\"\"Search deepest nodes in the search tree first.\"\"\"\n", " frontier = LIFOQueue([Node(problem.initial)])\n", " result = failure\n", " while frontier:\n", @@ -286,6 +287,7 @@ "\n", "\n", "def depth_first_recursive_search(problem, node=None):\n", + " \"\"\"Recursive version of depth first search.\"\"\"\n", " if node is None: \n", " node = Node(problem.initial)\n", " if problem.is_goal(node.state):\n", @@ -297,27 +299,71 @@ " result = depth_first_recursive_search(problem, child)\n", " if result:\n", " return result\n", - " return failure" + " return failure\n", + "\n", + "\n", + "def recursive_best_first_search(problem, h=None):\n", + " \"\"\"Recursive version of best_first_search,\n", + " Returns a solution or failure\"\"\"\n", + " h = h or problem.h\n", + "\n", + " def RBFS(problem, node, f_limit):\n", + " \"\"\"Returns a solution or failure, and a new f-cost limit\"\"\"\n", + " if problem.is_goal(node.state):\n", + " return node,0\n", + " successors = list(expand(problem,node))\n", + " if successors == []:\n", + " return failure, math.inf\n", + " for s in successors:\n", + " # update f with value from previous search\n", + " s.f = max(s.path_cost+h(s), node.f)\n", + " while True:\n", + " successors.sort(key=lambda x: x.f)\n", + " best = successors[0]\n", + " if best.f > f_limit:\n", + " return failure, best.f\n", + " if len(successors) > 1:\n", + " alternative = successors[1].f\n", + " else:\n", + " alternative = math.inf\n", + " result, best.f = RBFS(problem, best, min(f_limit, alternative))\n", + " if result != failure:\n", + " return result, best.f\n", + "\n", + " node = Node(problem.initial)\n", + " node.f = h(node)\n", + " solution, fvalue = RBFS(problem, node, math.inf)\n", + "\n", + " return solution" ] }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['N', 'I', 'V', 'U', 'B', 'F', 'S', 'O', 'Z', 'A', 'T', 'L']" + "['Neamt',\n", + " 'Iasi',\n", + " 'Vaslui',\n", + " 'Urziceni',\n", + " 'Bucharest',\n", + " 'Pitesti',\n", + " 'Craiova',\n", + " 'Drobeta',\n", + " 'Mehadia',\n", + " 'Lugoj']" ] }, - "execution_count": 236, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "path_states(depth_first_recursive_search(r2))" + "path_states(recursive_best_first_search(r2))" ] }, { @@ -329,24 +375,48 @@ }, { "cell_type": "code", - "execution_count": 412, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "def bidirectional_best_first_search(problem_f, f_f, problem_b, f_b, terminated):\n", + " \"\"\"\n", + " Searches in 2 directions, from initial to goal and from goal to initial.\n", + " it has forward and backward problems and separate evaluation function\n", + " for each problem.\n", + "\n", + " Parameters\n", + " ----------\n", + " problem_f :forward Problem\n", + " f_f :heuristic for forward problem.\n", + " problem_b : backward Problem\n", + " f_b : heuristic for backward problem.\n", + " terminated : if search is over or not\n", + "\n", + " Returns\n", + " -------\n", + " solution node, or failure\n", + "\n", + " \"\"\"\n", " node_f = Node(problem_f.initial)\n", - " node_b = Node(problem_f.goal)\n", - " frontier_f, reached_f = PriorityQueue([node_f], key=f_f), {node_f.state: node_f}\n", - " frontier_b, reached_b = PriorityQueue([node_b], key=f_b), {node_b.state: node_b}\n", + " node_b = Node(problem_f.initial)\n", + " frontier_f = PriorityQueue([node_f], key=f_f)\n", + " frontier_b = PriorityQueue([node_b], key=f_b)\n", + " reached_f = {node_f.state: node_f}\n", + " reached_b = {node_b.state: node_b}\n", " solution = failure\n", + "\n", " while frontier_f and frontier_b and not terminated(solution, frontier_f, frontier_b):\n", " def S1(node, f):\n", " return str(int(f(node))) + ' ' + str(path_states(node))\n", + "\n", " print('Bi:', S1(frontier_f.top(), f_f), S1(frontier_b.top(), f_b))\n", " if f_f(frontier_f.top()) < f_b(frontier_b.top()):\n", - " solution = proceed('f', problem_f, frontier_f, reached_f, reached_b, solution)\n", + " solution = proceed('f', problem_f, frontier_f,\n", + " reached_f, reached_b, solution)\n", " else:\n", - " solution = proceed('b', problem_b, frontier_b, reached_b, reached_f, solution)\n", + " solution = proceed('b', problem_b, frontier_b,\n", + " reached_b, reached_f, solution)\n", " return solution\n", "\n", "def inverse_problem(problem):\n", @@ -360,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 413, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -368,17 +438,21 @@ " def terminated(solution, frontier_f, frontier_b):\n", " n_f, n_b = frontier_f.top(), frontier_b.top()\n", " return g(n_f) + g(n_b) > g(solution)\n", + "\n", " return bidirectional_best_first_search(problem_f, g, inverse_problem(problem_f), g, terminated)\n", "\n", + "\n", "def bidirectional_astar_search(problem_f):\n", " def terminated(solution, frontier_f, frontier_b):\n", - " nf, nb = frontier_f.top(), frontier_b.top()\n", - " return g(nf) + g(nb) > g(solution)\n", - " problem_f = inverse_problem(problem_f)\n", - " return bidirectional_best_first_search(problem_f, lambda n: g(n) + problem_f.h(n),\n", - " problem_b, lambda n: g(n) + problem_b.h(n), \n", - " terminated)\n", - " \n", + " n_f, n_b = frontier_f.top(), frontier_b.top()\n", + " return g(n_f) + g(n_b) > g(solution)\n", + "\n", + " problem_b = inverse_problem(problem_f)\n", + " return bidirectional_best_first_search(\n", + " problem_f, lambda n: g(n) + problem_f.h(n),\n", + " problem_b, lambda n: g(n) + problem_b.h(n),\n", + " terminated)\n", + "\n", "\n", "def proceed(direction, problem, frontier, reached, reached2, solution):\n", " node = frontier.pop()\n", @@ -388,56 +462,95 @@ " if s not in reached or child.path_cost < reached[s].path_cost:\n", " frontier.add(child)\n", " reached[s] = child\n", - " if s in reached2: # Frontiers collide; solution found\n", - " solution2 = (join_nodes(child, reached2[s]) if direction == 'f' else\n", - " join_nodes(reached2[s], child))\n", - " #print('solution', path_states(solution2), solution2.path_cost, \n", - " # path_states(child), path_states(reached2[s]))\n", + " if s in reached2: # Frontiers collide; solution found\n", + " solution2 = (join_nodes(child, reached2[s]) if direction == 'f'\n", + " else join_nodes(reached2[s], child))\n", " if solution2.path_cost < solution.path_cost:\n", " solution = solution2\n", " return solution\n", "\n", + "\n", "S = path_states\n", "\n", - "#A-S-R + B-P-R => A-S-R-P + B-P\n", + "\n", + "# A-S-R + B-P-R => A-S-R-P + B-P\n", + "\n", + "\n", "def join_nodes(nf, nb):\n", - " \"\"\"Join the reverse of the backward node nb to the forward node nf.\"\"\"\n", - " #print('join', S(nf), S(nb))\n", + " \"\"\"Join the reverse of the backward node `nb` to the forward node `nf`.\"\"\"\n", + " # print('join', S(nf), S(nb))\n", " join = nf\n", " while nb.parent is not None:\n", " cost = join.path_cost + nb.path_cost - nb.parent.path_cost\n", " join = Node(nb.parent.state, join, nb.action, cost)\n", " nb = nb.parent\n", - " #print(' now join', S(join), 'with nb', S(nb), 'parent', S(nb.parent))\n", - " return join\n", - " \n", - " " + " # print(' now join', S(join), 'with nb', S(nb), 'parent', S(nb.parent))\n", + "\n", + " return join" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(['Arad', 'Sibiu', 'Rimnicu Vilcea', 'Pitesti', 'Bucharest'],\n", + " ['Neamt',\n", + " 'Iasi',\n", + " 'Vaslui',\n", + " 'Urziceni',\n", + " 'Bucharest',\n", + " 'Pitesti',\n", + " 'Craiova',\n", + " 'Drobeta',\n", + " 'Mehadia',\n", + " 'Lugoj'])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#A , B = uniform_cost_search(r1), uniform_cost_search(r2)\n", - "#path_states(A), path_states(B)" + "A , B = uniform_cost_search(r1), uniform_cost_search(r2)\n", + "path_states(A), path_states(B)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#path_states(append_nodes(A, B))" - ] - }, - { - "cell_type": "markdown", + "execution_count": 65, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Arad',\n", + " 'Sibiu',\n", + " 'Rimnicu Vilcea',\n", + " 'Pitesti',\n", + " 'Bucharest',\n", + " 'Mehadia',\n", + " 'Drobeta',\n", + " 'Craiova',\n", + " 'Pitesti',\n", + " 'Bucharest',\n", + " 'Urziceni',\n", + " 'Vaslui',\n", + " 'Iasi',\n", + " 'Neamt']" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# TODO: RBFS" + "path_states(join_nodes(A, B))" ] }, { @@ -462,14 +575,14 @@ }, { "cell_type": "code", - "execution_count": 398, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "class RouteProblem(Problem):\n", " \"\"\"A problem to find a route between locations on a `Map`.\n", " Create a problem with RouteProblem(start, goal, map=Map(...)}).\n", - " States are the vertexes in the Map graph; actions are destination states.\"\"\"\n", + " States are the vertices in the Map graph; actions are destination states.\"\"\"\n", " \n", " def actions(self, state): \n", " \"\"\"The places neighboring `state`.\"\"\"\n", @@ -496,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -518,7 +631,7 @@ "\n", " \n", "def multimap(pairs) -> dict:\n", - " \"Given (key, val) pairs, make a dict of {key: [val,...]}.\"\n", + " \"\"\"Given (key, val) pairs, make a dict of {key: [val,...]}.\"\"\"\n", " result = defaultdict(list)\n", " for key, val in pairs:\n", " result[key].append(val)\n", @@ -527,43 +640,77 @@ }, { "cell_type": "code", - "execution_count": 400, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "# Some specific RouteProblems\n", - "\n", - "romania = Map(\n", - " {('O', 'Z'): 71, ('O', 'S'): 151, ('A', 'Z'): 75, ('A', 'S'): 140, ('A', 'T'): 118, \n", - " ('L', 'T'): 111, ('L', 'M'): 70, ('D', 'M'): 75, ('C', 'D'): 120, ('C', 'R'): 146, \n", - " ('C', 'P'): 138, ('R', 'S'): 80, ('F', 'S'): 99, ('B', 'F'): 211, ('B', 'P'): 101, \n", - " ('B', 'G'): 90, ('B', 'U'): 85, ('H', 'U'): 98, ('E', 'H'): 86, ('U', 'V'): 142, \n", - " ('I', 'V'): 92, ('I', 'N'): 87, ('P', 'R'): 97},\n", - " {'A': ( 76, 497), 'B': (400, 327), 'C': (246, 285), 'D': (160, 296), 'E': (558, 294), \n", - " 'F': (285, 460), 'G': (368, 257), 'H': (548, 355), 'I': (488, 535), 'L': (162, 379),\n", - " 'M': (160, 343), 'N': (407, 561), 'O': (117, 580), 'P': (311, 372), 'R': (227, 412),\n", - " 'S': (187, 463), 'T': ( 83, 414), 'U': (471, 363), 'V': (535, 473), 'Z': (92, 539)})\n", - "\n", - "\n", - "r0 = RouteProblem('A', 'A', map=romania)\n", - "r1 = RouteProblem('A', 'B', map=romania)\n", - "r2 = RouteProblem('N', 'L', map=romania)\n", - "r3 = RouteProblem('E', 'T', map=romania)\n", - "r4 = RouteProblem('O', 'M', map=romania)" + "# Some specific RoutProblems\n", + "\n", + "romania_links = {('Oradea', 'Zerind'): 71,\n", + " ('Oradea', 'Sibiu'): 151,\n", + " ('Arad', 'Zerind'): 75,\n", + " ('Arad', 'Sibiu'): 140,\n", + " ('Arad', 'Timisoara'): 118,\n", + " ('Lugoj', 'Timisoara'): 111,\n", + " ('Lugoj', 'Mehadia'): 70,\n", + " ('Drobeta', 'Mehadia'): 75,\n", + " ('Craiova', 'Drobeta'): 120,\n", + " ('Craiova', 'Rimnicu Vilcea'): 146,\n", + " ('Craiova', 'Pitesti'): 138,\n", + " ('Rimnicu Vilcea', 'Sibiu'): 80,\n", + " ('Fagaras', 'Sibiu'): 99,\n", + " ('Bucharest', 'Fagaras'): 211,\n", + " ('Bucharest', 'Pitesti'): 101,\n", + " ('Bucharest', 'Giurgiu'): 90,\n", + " ('Bucharest', 'Urziceni'): 85,\n", + " ('Hirsova', 'Urziceni'): 98,\n", + " ('Eforie', 'Hirsova'): 86,\n", + " ('Urziceni', 'Vaslui'): 142,\n", + " ('Iasi', 'Vaslui'): 92,\n", + " ('Iasi', 'Neamt'): 87,\n", + " ('Pitesti', 'Rimnicu Vilcea'): 97}\n", + "\n", + "romania_locations = {'Arad': (76, 497),\n", + " 'Bucharest': (400, 327),\n", + " 'Craiova': (246, 285),\n", + " 'Drobeta': (160, 296),\n", + " 'Eforie': (558, 294),\n", + " 'Fagaras': (285, 460),\n", + " 'Giurgiu': (368, 257),\n", + " 'Hirsova': (548, 355),\n", + " 'Iasi': (488, 535),\n", + " 'Lugoj': (162, 379),\n", + " 'Mehadia': (160, 343),\n", + " 'Neamt': (407, 561),\n", + " 'Oradea': (117, 580),\n", + " 'Pitesti': (311, 372),\n", + " 'Rimnicu Vilcea': (227, 412),\n", + " 'Sibiu': (187, 463),\n", + " 'Timisoara': (83, 414),\n", + " 'Urziceni': (471, 363),\n", + " 'Vaslui': (535, 473),\n", + " 'Zerind': (92, 539)}\n", + "romania = Map(romania_links, romania_locations)\n", + "\n", + "r0 = RouteProblem('Arad', 'Arad', map=romania)\n", + "r1 = RouteProblem('Arad', 'Bucharest', map=romania)\n", + "r2 = RouteProblem('Neamt', 'Lugoj', map=romania)\n", + "r3 = RouteProblem('Eforie', 'Timisoara', map=romania)\n", + "r4 = RouteProblem('Oradea', 'Mehadia', map=romania)" ] }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['A', 'S', 'R', 'P', 'B']" + "['Arad', 'Sibiu', 'Rimnicu Vilcea', 'Pitesti', 'Bucharest']" ] }, - "execution_count": 232, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -574,16 +721,16 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['A', 'S', 'F', 'B']" + "['Arad', 'Sibiu', 'Fagaras', 'Bucharest']" ] }, - "execution_count": 233, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -603,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -611,7 +758,7 @@ " \"\"\"Finding a path on a 2D grid with obstacles. Obstacles are (x, y) cells.\"\"\"\n", "\n", " def __init__(self, initial=(15, 30), goal=(130, 30), obstacles=(), **kwds):\n", - " Problem.__init__(self, initial=initial, goal=goal, \n", + " super().__init__(initial=initial, goal=goal, \n", " obstacles=set(obstacles) - {initial, goal}, **kwds)\n", "\n", " directions = [(-1, -1), (0, -1), (1, -1),\n", @@ -644,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -696,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 397, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +862,7 @@ " self.initial, self.goal = initial, goal\n", " \n", " def actions(self, state):\n", - " \"\"\"The indexes of the squares that the blank can move to.\"\"\"\n", + " \"\"\"The indices of the squares that the blank can move to.\"\"\"\n", " moves = ((1, 3), (0, 2, 4), (1, 5),\n", " (0, 4, 6), (1, 3, 5, 7), (2, 4, 8),\n", " (3, 7), (4, 6, 8), (7, 5))\n", @@ -740,7 +887,7 @@ " return sum(abs(X[s] - X[g]) + abs(Y[s] - Y[g])\n", " for (s, g) in zip(node.state, self.goal) if s != 0)\n", " \n", - " def h(self, node): return h2(self, node)\n", + " def h(self, node): return self.h2(node)\n", " \n", " \n", "def hamming_distance(A, B):\n", @@ -785,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -800,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -857,14 +1004,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "class PourProblem(Problem):\n", " \"\"\"Problem about pouring water between jugs to achieve some water level.\n", " Each state is a tuples of water levels. In the initialization, also provide a tuple of \n", - " jug sizes, e.g. PourProblem(initial=(0, 0), goal=4, sizes=(5, 3)), \n", + " jug sizes, e.g. `PourProblem(initial=(0, 0), goal=4, sizes=(5, 3))`, \n", " which means two jugs of sizes 5 and 3, initially both empty, with the goal\n", " of getting a level of 4 in either jug.\"\"\"\n", " \n", @@ -904,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -918,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -939,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -949,7 +1096,7 @@ " [(1, 1, 1), (1, 16, 1), (2, 15, 1), (0, 15, 1), (2, 13, 1)])" ] }, - "execution_count": 28, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -976,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -1000,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -1013,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -1027,7 +1174,7 @@ " (1, 2, 3, 4, 5, 6)]" ] }, - "execution_count": 31, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -1050,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -1072,7 +1219,7 @@ " \"\"\"An action (i, j) means swap the pieces at positions i and j.\"\"\"\n", " i, j = action\n", " result = list(state)\n", - " result[i], result[j] = state[j], state[i]\n", + " result[i], result[j] = result[j], result[i]\n", " return ''.join(result)\n", " \n", " def h(self, node): return hamming_distance(node.state, self.goal)" @@ -1080,7 +1227,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -1089,7 +1236,7 @@ "{(1, 2), (3, 2)}" ] }, - "execution_count": 33, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -1100,7 +1247,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -1124,7 +1271,7 @@ " 'RRR.LLL']" ] }, - "execution_count": 34, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -1146,40 +1293,42 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "class CountCalls:\n", - " \"\"\"Delegate all attribute gets to the object, and count them in ._counts\"\"\"\n", + " \"\"\"Delegate all attribute gets to the object, and count them in `._count`.\"\"\"\n", + "\n", " def __init__(self, obj):\n", " self._object = obj\n", " self._counts = Counter()\n", - " \n", + "\n", " def __getattr__(self, attr):\n", - " \"Delegate to the original object, after incrementing a counter.\"\n", + " \"\"\"Delegate to the original object, after incrementing a counter.\"\"\"\n", " self._counts[attr] += 1\n", " return getattr(self._object, attr)\n", "\n", - " \n", + "\n", "def report(searchers, problems, verbose=True):\n", - " \"\"\"Show summary statistics for each searcher (and on each problem unless verbose is false).\"\"\"\n", " for searcher in searchers:\n", " print(searcher.__name__ + ':')\n", " total_counts = Counter()\n", " for p in problems:\n", - " prob = CountCalls(p)\n", - " soln = searcher(prob)\n", - " counts = prob._counts; \n", + " prob = CountCalls(p)\n", + " soln = searcher(prob)\n", + " counts = prob._counts\n", " counts.update(actions=len(soln), cost=soln.path_cost)\n", " total_counts += counts\n", - " if verbose: report_counts(counts, str(p)[:40])\n", - " report_counts(total_counts, 'TOTAL\\n')\n", - " \n", + " if verbose:\n", + " report_counts(counts, str(p)[:40])\n", + " report_counts(total_counts, 'TOTAL\\n')\n", + "\n", + "\n", "def report_counts(counts, name):\n", - " \"\"\"Print one line of the counts report.\"\"\"\n", - " print('{:9,d} nodes |{:9,d} goal |{:5.0f} cost |{:8,d} actions | {}'.format(\n", - " counts['result'], counts['is_goal'], counts['cost'], counts['actions'], name))" + " \"\"\"Print one line of counts report.\"\"\"\n", + " print('{:9,d} nodes|{:9,d} goal |{:5.0f} cost |{:8,d} actions | {}'.format(\n", + " counts['result'], counts['is_goal'], counts['cost'], counts['actions'], name))" ] }, { @@ -1191,7 +1340,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -1199,12 +1348,20 @@ "output_type": "stream", "text": [ "uniform_cost_search:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 52 nodes | 14 goal | 6 cost | 19 actions | PourProblem((0, 0), 4)\n", - " 8,122 nodes | 931 goal | 42 cost | 968 actions | TOTAL\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 4,447 nodes| 498 goal | 13 cost | 509 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 4,571 nodes| 528 goal | 27 cost | 552 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 8,070 nodes| 917 goal | 36 cost | 949 actions | TOTAL\n", + "\n", + " 52 nodes| 14 goal | 6 cost | 19 actions | PourProblem((0, 0), 4)\n", + " 8,122 nodes| 931 goal | 42 cost | 968 actions | TOTAL\n", "\n" ] } @@ -1215,7 +1372,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -1223,36 +1380,84 @@ "output_type": "stream", "text": [ "uniform_cost_search:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,696 nodes | 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 3,590 nodes | 719 goal | 7 cost | 725 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", - " 30,204 nodes | 5,035 goal | 8 cost | 5,042 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", - " 22,068 nodes | 3,679 goal | 6 cost | 3,684 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", - " 81,467 nodes | 12,321 goal | 174 cost | 12,435 actions | TOTAL\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,696 nodes| 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,644 nodes| 299 goal | 14 cost | 316 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 6,143 nodes| 688 goal | 23 cost | 713 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 10,215 nodes| 1,142 goal | 44 cost | 1,176 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 10,339 nodes| 1,172 goal | 58 cost | 1,219 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", + " 10,463 nodes| 1,202 goal | 93 cost | 1,264 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,962 nodes| 1,591 goal | 102 cost | 1,661 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 18,034 nodes| 2,045 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 21,533 nodes| 2,434 goal | 132 cost | 2,521 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 25,605 nodes| 2,888 goal | 153 cost | 2,984 actions | TOTAL\n", + "\n", + " 3,590 nodes| 719 goal | 7 cost | 725 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", + " 29,195 nodes| 3,607 goal | 160 cost | 3,709 actions | TOTAL\n", + "\n", + " 30,204 nodes| 5,035 goal | 8 cost | 5,042 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", + " 59,399 nodes| 8,642 goal | 168 cost | 8,751 actions | TOTAL\n", + "\n", + " 22,068 nodes| 3,679 goal | 6 cost | 3,684 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", + " 81,467 nodes| 12,321 goal | 174 cost | 12,435 actions | TOTAL\n", "\n", "breadth_first_search:\n", - " 596 nodes | 597 goal | 4 cost | 73 actions | PourProblem((1, 1, 1), 13)\n", - " 596 nodes | 597 goal | 15 cost | 73 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 2,618 nodes | 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", - " 2,618 nodes | 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 120 nodes | 121 goal | 14 cost | 42 actions | PourProblem((0, 0), 8)\n", - " 120 nodes | 121 goal | 36 cost | 42 actions | GreenPourProblem((0, 0), 8)\n", - " 2,618 nodes | 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", - " 2,618 nodes | 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 2,618 nodes | 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", - " 2,618 nodes | 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 2,951 nodes | 2,952 goal | 7 cost | 598 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", - " 25,945 nodes | 25,946 goal | 8 cost | 4,333 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", - " 5,975 nodes | 5,976 goal | 6 cost | 1,002 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", - " 52,011 nodes | 52,024 goal | 213 cost | 7,975 actions | TOTAL\n", + " 596 nodes| 597 goal | 4 cost | 73 actions | PourProblem((1, 1, 1), 13)\n", + " 596 nodes| 597 goal | 4 cost | 73 actions | TOTAL\n", + "\n", + " 596 nodes| 597 goal | 15 cost | 73 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 1,192 nodes| 1,194 goal | 19 cost | 146 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", + " 3,810 nodes| 3,813 goal | 28 cost | 448 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 6,428 nodes| 6,432 goal | 60 cost | 750 actions | TOTAL\n", + "\n", + " 120 nodes| 121 goal | 14 cost | 42 actions | PourProblem((0, 0), 8)\n", + " 6,548 nodes| 6,553 goal | 74 cost | 792 actions | TOTAL\n", + "\n", + " 120 nodes| 121 goal | 36 cost | 42 actions | GreenPourProblem((0, 0), 8)\n", + " 6,668 nodes| 6,674 goal | 110 cost | 834 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", + " 9,286 nodes| 9,293 goal | 119 cost | 1,136 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 11,904 nodes| 11,912 goal | 151 cost | 1,438 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", + " 14,522 nodes| 14,531 goal | 160 cost | 1,740 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 17,140 nodes| 17,150 goal | 192 cost | 2,042 actions | TOTAL\n", + "\n", + " 2,951 nodes| 2,952 goal | 7 cost | 598 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", + " 20,091 nodes| 20,102 goal | 199 cost | 2,640 actions | TOTAL\n", + "\n", + " 25,945 nodes| 25,946 goal | 8 cost | 4,333 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", + " 46,036 nodes| 46,048 goal | 207 cost | 6,973 actions | TOTAL\n", + "\n", + " 5,975 nodes| 5,976 goal | 6 cost | 1,002 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", + " 52,011 nodes| 52,024 goal | 213 cost | 7,975 actions | TOTAL\n", "\n" ] } @@ -1273,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 89, "metadata": { "scrolled": false }, @@ -1283,28 +1488,52 @@ "output_type": "stream", "text": [ "breadth_first_search:\n", - " 81 nodes | 82 goal | 5 cost | 35 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 160,948 nodes | 160,949 goal | 22 cost | 59,960 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 218,263 nodes | 218,264 goal | 23 cost | 81,829 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 418,771 nodes | 418,772 goal | 26 cost | 156,533 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 448,667 nodes | 448,668 goal | 27 cost | 167,799 actions | EightPuzzle((8, 6, 7, 2, 5, 4, 3, 0, 1),\n", - "1,246,730 nodes |1,246,735 goal | 103 cost | 466,156 actions | TOTAL\n", + " 81 nodes| 82 goal | 5 cost | 35 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 81 nodes| 82 goal | 5 cost | 35 actions | TOTAL\n", + "\n", + " 160,948 nodes| 160,949 goal | 22 cost | 59,960 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 161,029 nodes| 161,031 goal | 27 cost | 59,995 actions | TOTAL\n", + "\n", + " 218,263 nodes| 218,264 goal | 23 cost | 81,829 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 379,292 nodes| 379,295 goal | 50 cost | 141,824 actions | TOTAL\n", + "\n", + " 418,771 nodes| 418,772 goal | 26 cost | 156,533 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 798,063 nodes| 798,067 goal | 76 cost | 298,357 actions | TOTAL\n", + "\n", + " 448,667 nodes| 448,668 goal | 27 cost | 167,799 actions | EightPuzzle((8, 6, 7, 2, 5, 4, 3, 0, 1),\n", + "1,246,730 nodes|1,246,735 goal | 103 cost | 466,156 actions | TOTAL\n", "\n", "astar_misplaced_tiles:\n", - " 17 nodes | 7 goal | 5 cost | 11 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 23,407 nodes | 8,726 goal | 22 cost | 8,747 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 38,632 nodes | 14,433 goal | 23 cost | 14,455 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 124,324 nodes | 46,553 goal | 26 cost | 46,578 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 156,111 nodes | 58,475 goal | 27 cost | 58,501 actions | EightPuzzle((8, 6, 7, 2, 5, 4, 3, 0, 1),\n", - " 342,491 nodes | 128,194 goal | 103 cost | 128,292 actions | TOTAL\n", + " 17 nodes| 7 goal | 5 cost | 11 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 17 nodes| 7 goal | 5 cost | 11 actions | TOTAL\n", + "\n", + " 23,407 nodes| 8,726 goal | 22 cost | 8,747 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 23,424 nodes| 8,733 goal | 27 cost | 8,758 actions | TOTAL\n", + "\n", + " 38,632 nodes| 14,433 goal | 23 cost | 14,455 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 62,056 nodes| 23,166 goal | 50 cost | 23,213 actions | TOTAL\n", + "\n", + " 124,324 nodes| 46,553 goal | 26 cost | 46,578 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 186,380 nodes| 69,719 goal | 76 cost | 69,791 actions | TOTAL\n", + "\n", + " 156,111 nodes| 58,475 goal | 27 cost | 58,501 actions | EightPuzzle((8, 6, 7, 2, 5, 4, 3, 0, 1),\n", + " 342,491 nodes| 128,194 goal | 103 cost | 128,292 actions | TOTAL\n", "\n", "astar_search:\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 3,614 nodes | 1,349 goal | 22 cost | 1,370 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 5,373 nodes | 2,010 goal | 23 cost | 2,032 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 10,832 nodes | 4,086 goal | 26 cost | 4,111 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 11,669 nodes | 4,417 goal | 27 cost | 4,443 actions | EightPuzzle((8, 6, 7, 2, 5, 4, 3, 0, 1),\n", - " 31,503 nodes | 11,868 goal | 103 cost | 11,966 actions | TOTAL\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | TOTAL\n", + "\n", + " 3,614 nodes| 1,349 goal | 22 cost | 1,370 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 3,629 nodes| 1,355 goal | 27 cost | 1,380 actions | TOTAL\n", + "\n", + " 5,373 nodes| 2,010 goal | 23 cost | 2,032 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 9,002 nodes| 3,365 goal | 50 cost | 3,412 actions | TOTAL\n", + "\n", + " 10,832 nodes| 4,086 goal | 26 cost | 4,111 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 19,834 nodes| 7,451 goal | 76 cost | 7,523 actions | TOTAL\n", + "\n", + " 11,669 nodes| 4,417 goal | 27 cost | 4,443 actions | EightPuzzle((8, 6, 7, 2, 5, 4, 3, 0, 1),\n", + " 31,503 nodes| 11,868 goal | 103 cost | 11,966 actions | TOTAL\n", "\n" ] } @@ -1328,7 +1557,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -1336,18 +1565,30 @@ "output_type": "stream", "text": [ "astar_search:\n", - " 1,285 nodes | 258 goal | 7 cost | 264 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", - " 3,804 nodes | 635 goal | 8 cost | 642 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", - " 294 nodes | 50 goal | 6 cost | 55 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", - " 2,256 nodes | 283 goal | 9 cost | 291 actions | PancakeProblem((1, 3, 5, 7, 9, 2, 4, 6, \n", - " 7,639 nodes | 1,226 goal | 30 cost | 1,252 actions | TOTAL\n", + " 1,285 nodes| 258 goal | 7 cost | 264 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", + " 1,285 nodes| 258 goal | 7 cost | 264 actions | TOTAL\n", + "\n", + " 3,804 nodes| 635 goal | 8 cost | 642 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", + " 5,089 nodes| 893 goal | 15 cost | 906 actions | TOTAL\n", + "\n", + " 294 nodes| 50 goal | 6 cost | 55 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", + " 5,383 nodes| 943 goal | 21 cost | 961 actions | TOTAL\n", + "\n", + " 2,256 nodes| 283 goal | 9 cost | 291 actions | PancakeProblem((1, 3, 5, 7, 9, 2, 4, 6, \n", + " 7,639 nodes| 1,226 goal | 30 cost | 1,252 actions | TOTAL\n", "\n", "uniform_cost_search:\n", - " 3,590 nodes | 719 goal | 7 cost | 725 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", - " 30,204 nodes | 5,035 goal | 8 cost | 5,042 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", - " 22,068 nodes | 3,679 goal | 6 cost | 3,684 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", - "2,271,792 nodes | 283,975 goal | 9 cost | 283,983 actions | PancakeProblem((1, 3, 5, 7, 9, 2, 4, 6, \n", - "2,327,654 nodes | 293,408 goal | 30 cost | 293,434 actions | TOTAL\n", + " 3,590 nodes| 719 goal | 7 cost | 725 actions | PancakeProblem((4, 6, 2, 5, 1, 3), (1, 2\n", + " 3,590 nodes| 719 goal | 7 cost | 725 actions | TOTAL\n", + "\n", + " 30,204 nodes| 5,035 goal | 8 cost | 5,042 actions | PancakeProblem((1, 3, 7, 5, 2, 6, 4), (1\n", + " 33,794 nodes| 5,754 goal | 15 cost | 5,767 actions | TOTAL\n", + "\n", + " 22,068 nodes| 3,679 goal | 6 cost | 3,684 actions | PancakeProblem((1, 7, 2, 6, 3, 5, 4), (1\n", + " 55,862 nodes| 9,433 goal | 21 cost | 9,451 actions | TOTAL\n", + "\n", + "2,271,792 nodes| 283,975 goal | 9 cost | 283,983 actions | PancakeProblem((1, 3, 5, 7, 9, 2, 4, 6, \n", + "2,327,654 nodes| 293,408 goal | 30 cost | 293,434 actions | TOTAL\n", "\n" ] } @@ -1369,7 +1610,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -1377,26 +1618,54 @@ "output_type": "stream", "text": [ "astar_search:\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 3,614 nodes | 1,349 goal | 22 cost | 1,370 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 5,373 nodes | 2,010 goal | 23 cost | 2,032 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 10,832 nodes | 4,086 goal | 26 cost | 4,111 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 15 nodes | 6 goal | 418 cost | 9 actions | RouteProblem('A', 'B')\n", - " 34 nodes | 15 goal | 910 cost | 23 actions | RouteProblem('N', 'L')\n", - " 33 nodes | 14 goal | 805 cost | 21 actions | RouteProblem('E', 'T')\n", - " 20 nodes | 9 goal | 445 cost | 13 actions | RouteProblem('O', 'M')\n", - " 19,936 nodes | 7,495 goal | 2654 cost | 7,589 actions | TOTAL\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | TOTAL\n", + "\n", + " 3,614 nodes| 1,349 goal | 22 cost | 1,370 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 3,629 nodes| 1,355 goal | 27 cost | 1,380 actions | TOTAL\n", + "\n", + " 5,373 nodes| 2,010 goal | 23 cost | 2,032 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 9,002 nodes| 3,365 goal | 50 cost | 3,412 actions | TOTAL\n", + "\n", + " 10,832 nodes| 4,086 goal | 26 cost | 4,111 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 19,834 nodes| 7,451 goal | 76 cost | 7,523 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 418 cost | 9 actions | RouteProblem('Arad', 'Bucharest')\n", + " 19,849 nodes| 7,457 goal | 494 cost | 7,532 actions | TOTAL\n", + "\n", + " 34 nodes| 15 goal | 910 cost | 23 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 19,883 nodes| 7,472 goal | 1404 cost | 7,555 actions | TOTAL\n", + "\n", + " 33 nodes| 14 goal | 805 cost | 21 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 19,916 nodes| 7,486 goal | 2209 cost | 7,576 actions | TOTAL\n", + "\n", + " 20 nodes| 9 goal | 445 cost | 13 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 19,936 nodes| 7,495 goal | 2654 cost | 7,589 actions | TOTAL\n", "\n", "astar_tree_search:\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 5,384 nodes | 2,000 goal | 22 cost | 2,021 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 9,116 nodes | 3,404 goal | 23 cost | 3,426 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 19,084 nodes | 7,185 goal | 26 cost | 7,210 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 15 nodes | 6 goal | 418 cost | 9 actions | RouteProblem('A', 'B')\n", - " 47 nodes | 19 goal | 910 cost | 27 actions | RouteProblem('N', 'L')\n", - " 46 nodes | 18 goal | 805 cost | 25 actions | RouteProblem('E', 'T')\n", - " 24 nodes | 10 goal | 445 cost | 14 actions | RouteProblem('O', 'M')\n", - " 33,731 nodes | 12,648 goal | 2654 cost | 12,742 actions | TOTAL\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | TOTAL\n", + "\n", + " 5,384 nodes| 2,000 goal | 22 cost | 2,021 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 5,399 nodes| 2,006 goal | 27 cost | 2,031 actions | TOTAL\n", + "\n", + " 9,116 nodes| 3,404 goal | 23 cost | 3,426 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 14,515 nodes| 5,410 goal | 50 cost | 5,457 actions | TOTAL\n", + "\n", + " 19,084 nodes| 7,185 goal | 26 cost | 7,210 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 33,599 nodes| 12,595 goal | 76 cost | 12,667 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 418 cost | 9 actions | RouteProblem('Arad', 'Bucharest')\n", + " 33,614 nodes| 12,601 goal | 494 cost | 12,676 actions | TOTAL\n", + "\n", + " 47 nodes| 19 goal | 910 cost | 27 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 33,661 nodes| 12,620 goal | 1404 cost | 12,703 actions | TOTAL\n", + "\n", + " 46 nodes| 18 goal | 805 cost | 25 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 33,707 nodes| 12,638 goal | 2209 cost | 12,728 actions | TOTAL\n", + "\n", + " 24 nodes| 10 goal | 445 cost | 14 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 33,731 nodes| 12,648 goal | 2654 cost | 12,742 actions | TOTAL\n", "\n" ] } @@ -1426,7 +1695,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 94, "metadata": { "scrolled": false }, @@ -1436,99 +1705,255 @@ "output_type": "stream", "text": [ "greedy_bfs:\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 9 nodes | 4 goal | 450 cost | 6 actions | RouteProblem('A', 'B')\n", - " 29 nodes | 12 goal | 910 cost | 20 actions | RouteProblem('N', 'L')\n", - " 19 nodes | 8 goal | 837 cost | 14 actions | RouteProblem('E', 'T')\n", - " 14 nodes | 6 goal | 572 cost | 10 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 909 nodes | 138 goal | 136 cost | 258 actions | GridProblem((15, 30), (130, 30))\n", - " 974 nodes | 147 goal | 152 cost | 277 actions | GridProblem((15, 30), (130, 30))\n", - " 5,146 nodes | 4,984 goal | 99 cost | 5,082 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", - " 1,569 nodes | 568 goal | 58 cost | 625 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 1,424 nodes | 257 goal | 164 cost | 406 actions | GridProblem((15, 30), (130, 30))\n", - " 1,899 nodes | 342 goal | 153 cost | 470 actions | GridProblem((15, 30), (130, 30))\n", - " 18,239 nodes | 2,439 goal | 134 cost | 2,564 actions | GridProblem((15, 30), (130, 30))\n", - " 18,329 nodes | 2,460 goal | 152 cost | 2,594 actions | GridProblem((15, 30), (130, 30))\n", - " 287 nodes | 109 goal | 33 cost | 141 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 1,128 nodes | 408 goal | 46 cost | 453 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 49,990 nodes | 11,889 goal | 3901 cost | 12,930 actions | TOTAL\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | TOTAL\n", + "\n", + " 9 nodes| 4 goal | 450 cost | 6 actions | RouteProblem('Arad', 'Bucharest')\n", + " 9 nodes| 5 goal | 450 cost | 6 actions | TOTAL\n", + "\n", + " 28 nodes| 12 goal | 1207 cost | 22 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 37 nodes| 17 goal | 1657 cost | 28 actions | TOTAL\n", + "\n", + " 19 nodes| 8 goal | 837 cost | 14 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 56 nodes| 25 goal | 2494 cost | 42 actions | TOTAL\n", + "\n", + " 14 nodes| 6 goal | 572 cost | 10 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 70 nodes| 31 goal | 3066 cost | 52 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 85 nodes| 37 goal | 3071 cost | 62 actions | TOTAL\n", + "\n", + " 909 nodes| 138 goal | 136 cost | 258 actions | GridProblem((15, 30), (130, 30))\n", + " 994 nodes| 175 goal | 3207 cost | 320 actions | TOTAL\n", + "\n", + " 974 nodes| 147 goal | 152 cost | 277 actions | GridProblem((15, 30), (130, 30))\n", + " 1,968 nodes| 322 goal | 3359 cost | 597 actions | TOTAL\n", + "\n", + " 5,151 nodes| 4,989 goal | 99 cost | 5,087 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", + " 7,119 nodes| 5,311 goal | 3458 cost | 5,684 actions | TOTAL\n", + "\n", + " 1,569 nodes| 568 goal | 58 cost | 625 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 8,688 nodes| 5,879 goal | 3516 cost | 6,309 actions | TOTAL\n", + "\n", + " 1,425 nodes| 257 goal | 164 cost | 406 actions | GridProblem((15, 30), (130, 30))\n", + " 10,113 nodes| 6,136 goal | 3680 cost | 6,715 actions | TOTAL\n", + "\n", + " 1,899 nodes| 342 goal | 153 cost | 470 actions | GridProblem((15, 30), (130, 30))\n", + " 12,012 nodes| 6,478 goal | 3833 cost | 7,185 actions | TOTAL\n", + "\n", + " 18,239 nodes| 2,439 goal | 134 cost | 2,564 actions | GridProblem((15, 30), (130, 30))\n", + " 30,251 nodes| 8,917 goal | 3967 cost | 9,749 actions | TOTAL\n", + "\n", + " 18,339 nodes| 2,462 goal | 152 cost | 2,596 actions | GridProblem((15, 30), (130, 30))\n", + " 48,590 nodes| 11,379 goal | 4119 cost | 12,345 actions | TOTAL\n", + "\n", + " 287 nodes| 109 goal | 33 cost | 141 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 48,877 nodes| 11,488 goal | 4152 cost | 12,486 actions | TOTAL\n", + "\n", + " 1,128 nodes| 408 goal | 46 cost | 453 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 50,005 nodes| 11,896 goal | 4198 cost | 12,939 actions | TOTAL\n", "\n", "extra_weighted_astar_search:\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 9 nodes | 4 goal | 450 cost | 6 actions | RouteProblem('A', 'B')\n", - " 29 nodes | 12 goal | 910 cost | 20 actions | RouteProblem('N', 'L')\n", - " 23 nodes | 9 goal | 805 cost | 16 actions | RouteProblem('E', 'T')\n", - " 18 nodes | 8 goal | 445 cost | 12 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 1,575 nodes | 239 goal | 136 cost | 357 actions | GridProblem((15, 30), (130, 30))\n", - " 1,384 nodes | 231 goal | 133 cost | 349 actions | GridProblem((15, 30), (130, 30))\n", - " 10,990 nodes | 10,660 goal | 99 cost | 10,758 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", - " 1,720 nodes | 633 goal | 24 cost | 656 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 9,282 nodes | 1,412 goal | 163 cost | 1,551 actions | GridProblem((15, 30), (130, 30))\n", - " 1,354 nodes | 228 goal | 134 cost | 345 actions | GridProblem((15, 30), (130, 30))\n", - " 16,024 nodes | 2,098 goal | 129 cost | 2,214 actions | GridProblem((15, 30), (130, 30))\n", - " 16,950 nodes | 2,237 goal | 140 cost | 2,359 actions | GridProblem((15, 30), (130, 30))\n", - " 1,908 nodes | 709 goal | 25 cost | 733 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 1,312 nodes | 489 goal | 30 cost | 518 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 62,593 nodes | 18,976 goal | 3628 cost | 19,904 actions | TOTAL\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | TOTAL\n", + "\n", + " 9 nodes| 4 goal | 450 cost | 6 actions | RouteProblem('Arad', 'Bucharest')\n", + " 9 nodes| 5 goal | 450 cost | 6 actions | TOTAL\n", + "\n", + " 31 nodes| 13 goal | 910 cost | 21 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 40 nodes| 18 goal | 1360 cost | 27 actions | TOTAL\n", + "\n", + " 23 nodes| 9 goal | 805 cost | 16 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 63 nodes| 27 goal | 2165 cost | 43 actions | TOTAL\n", + "\n", + " 18 nodes| 8 goal | 445 cost | 12 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 81 nodes| 35 goal | 2610 cost | 55 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 96 nodes| 41 goal | 2615 cost | 65 actions | TOTAL\n", + "\n", + " 1,575 nodes| 239 goal | 136 cost | 357 actions | GridProblem((15, 30), (130, 30))\n", + " 1,671 nodes| 280 goal | 2751 cost | 422 actions | TOTAL\n", + "\n", + " 1,384 nodes| 231 goal | 133 cost | 349 actions | GridProblem((15, 30), (130, 30))\n", + " 3,055 nodes| 511 goal | 2884 cost | 771 actions | TOTAL\n", + "\n", + " 10,990 nodes| 10,660 goal | 99 cost | 10,758 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", + " 14,045 nodes| 11,171 goal | 2983 cost | 11,529 actions | TOTAL\n", + "\n", + " 1,720 nodes| 633 goal | 24 cost | 656 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 15,765 nodes| 11,804 goal | 3007 cost | 12,185 actions | TOTAL\n", + "\n", + " 9,282 nodes| 1,412 goal | 163 cost | 1,551 actions | GridProblem((15, 30), (130, 30))\n", + " 25,047 nodes| 13,216 goal | 3169 cost | 13,736 actions | TOTAL\n", + "\n", + " 1,354 nodes| 228 goal | 134 cost | 345 actions | GridProblem((15, 30), (130, 30))\n", + " 26,401 nodes| 13,444 goal | 3304 cost | 14,081 actions | TOTAL\n", + "\n", + " 16,024 nodes| 2,098 goal | 129 cost | 2,214 actions | GridProblem((15, 30), (130, 30))\n", + " 42,425 nodes| 15,542 goal | 3432 cost | 16,295 actions | TOTAL\n", + "\n", + " 16,945 nodes| 2,236 goal | 140 cost | 2,358 actions | GridProblem((15, 30), (130, 30))\n", + " 59,370 nodes| 17,778 goal | 3573 cost | 18,653 actions | TOTAL\n", + "\n", + " 1,908 nodes| 709 goal | 25 cost | 733 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 61,278 nodes| 18,487 goal | 3598 cost | 19,386 actions | TOTAL\n", + "\n", + " 1,312 nodes| 489 goal | 30 cost | 518 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 62,590 nodes| 18,976 goal | 3628 cost | 19,904 actions | TOTAL\n", "\n", "weighted_astar_search:\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 9 nodes | 4 goal | 450 cost | 6 actions | RouteProblem('A', 'B')\n", - " 32 nodes | 14 goal | 910 cost | 22 actions | RouteProblem('N', 'L')\n", - " 29 nodes | 12 goal | 805 cost | 19 actions | RouteProblem('E', 'T')\n", - " 18 nodes | 8 goal | 445 cost | 12 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 1,631 nodes | 236 goal | 128 cost | 350 actions | GridProblem((15, 30), (130, 30))\n", - " 1,706 nodes | 275 goal | 131 cost | 389 actions | GridProblem((15, 30), (130, 30))\n", - " 10,990 nodes | 10,660 goal | 99 cost | 10,758 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", - " 2,082 nodes | 771 goal | 22 cost | 792 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 8,385 nodes | 1,266 goal | 154 cost | 1,396 actions | GridProblem((15, 30), (130, 30))\n", - " 1,400 nodes | 229 goal | 133 cost | 344 actions | GridProblem((15, 30), (130, 30))\n", - " 12,122 nodes | 1,572 goal | 124 cost | 1,686 actions | GridProblem((15, 30), (130, 30))\n", - " 24,129 nodes | 3,141 goal | 127 cost | 3,255 actions | GridProblem((15, 30), (130, 30))\n", - " 3,960 nodes | 1,475 goal | 25 cost | 1,499 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 1,992 nodes | 748 goal | 26 cost | 773 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 68,500 nodes | 20,418 goal | 3585 cost | 21,311 actions | TOTAL\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | TOTAL\n", + "\n", + " 9 nodes| 4 goal | 450 cost | 6 actions | RouteProblem('Arad', 'Bucharest')\n", + " 9 nodes| 5 goal | 450 cost | 6 actions | TOTAL\n", + "\n", + " 32 nodes| 14 goal | 910 cost | 22 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 41 nodes| 19 goal | 1360 cost | 28 actions | TOTAL\n", + "\n", + " 28 nodes| 11 goal | 805 cost | 18 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 69 nodes| 30 goal | 2165 cost | 46 actions | TOTAL\n", + "\n", + " 18 nodes| 8 goal | 445 cost | 12 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 87 nodes| 38 goal | 2610 cost | 58 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 102 nodes| 44 goal | 2615 cost | 68 actions | TOTAL\n", + "\n", + " 1,631 nodes| 236 goal | 128 cost | 350 actions | GridProblem((15, 30), (130, 30))\n", + " 1,733 nodes| 280 goal | 2743 cost | 418 actions | TOTAL\n", + "\n", + " 1,706 nodes| 275 goal | 131 cost | 389 actions | GridProblem((15, 30), (130, 30))\n", + " 3,439 nodes| 555 goal | 2874 cost | 807 actions | TOTAL\n", + "\n", + " 10,990 nodes| 10,660 goal | 99 cost | 10,758 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", + " 14,429 nodes| 11,215 goal | 2973 cost | 11,565 actions | TOTAL\n", + "\n", + " 2,082 nodes| 771 goal | 22 cost | 792 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 16,511 nodes| 11,986 goal | 2995 cost | 12,357 actions | TOTAL\n", + "\n", + " 8,385 nodes| 1,266 goal | 154 cost | 1,396 actions | GridProblem((15, 30), (130, 30))\n", + " 24,896 nodes| 13,252 goal | 3149 cost | 13,753 actions | TOTAL\n", + "\n", + " 1,400 nodes| 229 goal | 133 cost | 344 actions | GridProblem((15, 30), (130, 30))\n", + " 26,296 nodes| 13,481 goal | 3282 cost | 14,097 actions | TOTAL\n", + "\n", + " 12,130 nodes| 1,573 goal | 124 cost | 1,687 actions | GridProblem((15, 30), (130, 30))\n", + " 38,426 nodes| 15,054 goal | 3406 cost | 15,784 actions | TOTAL\n", + "\n", + " 24,137 nodes| 3,142 goal | 127 cost | 3,256 actions | GridProblem((15, 30), (130, 30))\n", + " 62,563 nodes| 18,196 goal | 3534 cost | 19,040 actions | TOTAL\n", + "\n", + " 3,960 nodes| 1,475 goal | 25 cost | 1,499 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 66,523 nodes| 19,671 goal | 3559 cost | 20,539 actions | TOTAL\n", + "\n", + " 1,992 nodes| 748 goal | 26 cost | 773 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 68,515 nodes| 20,419 goal | 3585 cost | 21,312 actions | TOTAL\n", "\n", "astar_search:\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 15 nodes | 6 goal | 418 cost | 9 actions | RouteProblem('A', 'B')\n", - " 34 nodes | 15 goal | 910 cost | 23 actions | RouteProblem('N', 'L')\n", - " 33 nodes | 14 goal | 805 cost | 21 actions | RouteProblem('E', 'T')\n", - " 20 nodes | 9 goal | 445 cost | 13 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 26,711 nodes | 3,620 goal | 127 cost | 3,734 actions | GridProblem((15, 30), (130, 30))\n", - " 12,932 nodes | 1,822 goal | 124 cost | 1,936 actions | GridProblem((15, 30), (130, 30))\n", - " 10,991 nodes | 10,661 goal | 99 cost | 10,759 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", - " 3,614 nodes | 1,349 goal | 22 cost | 1,370 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 62,509 nodes | 8,729 goal | 154 cost | 8,859 actions | GridProblem((15, 30), (130, 30))\n", - " 15,190 nodes | 2,276 goal | 133 cost | 2,391 actions | GridProblem((15, 30), (130, 30))\n", - " 25,303 nodes | 3,196 goal | 124 cost | 3,310 actions | GridProblem((15, 30), (130, 30))\n", - " 32,572 nodes | 4,149 goal | 127 cost | 4,263 actions | GridProblem((15, 30), (130, 30))\n", - " 5,373 nodes | 2,010 goal | 23 cost | 2,032 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 10,832 nodes | 4,086 goal | 26 cost | 4,111 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - " 206,144 nodes | 41,949 goal | 3543 cost | 42,841 actions | TOTAL\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 418 cost | 9 actions | RouteProblem('Arad', 'Bucharest')\n", + " 15 nodes| 7 goal | 418 cost | 9 actions | TOTAL\n", + "\n", + " 34 nodes| 15 goal | 910 cost | 23 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 49 nodes| 22 goal | 1328 cost | 32 actions | TOTAL\n", + "\n", + " 33 nodes| 14 goal | 805 cost | 21 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 82 nodes| 36 goal | 2133 cost | 53 actions | TOTAL\n", + "\n", + " 20 nodes| 9 goal | 445 cost | 13 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 102 nodes| 45 goal | 2578 cost | 66 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 117 nodes| 51 goal | 2583 cost | 76 actions | TOTAL\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 26,711 nodes| 3,620 goal | 127 cost | 3,734 actions | GridProblem((15, 30), (130, 30))\n", + " 26,828 nodes| 3,671 goal | 2710 cost | 3,810 actions | TOTAL\n", + "\n", + " 12,932 nodes| 1,822 goal | 124 cost | 1,936 actions | GridProblem((15, 30), (130, 30))\n", + " 39,760 nodes| 5,493 goal | 2835 cost | 5,746 actions | TOTAL\n", + "\n", + " 10,991 nodes| 10,661 goal | 99 cost | 10,759 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", + " 50,751 nodes| 16,154 goal | 2934 cost | 16,505 actions | TOTAL\n", + "\n", + " 3,614 nodes| 1,349 goal | 22 cost | 1,370 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 54,365 nodes| 17,503 goal | 2956 cost | 17,875 actions | TOTAL\n", + "\n", + " 62,509 nodes| 8,729 goal | 154 cost | 8,859 actions | GridProblem((15, 30), (130, 30))\n", + " 116,874 nodes| 26,232 goal | 3110 cost | 26,734 actions | TOTAL\n", + "\n", + " 15,190 nodes| 2,276 goal | 133 cost | 2,391 actions | GridProblem((15, 30), (130, 30))\n", + " 132,064 nodes| 28,508 goal | 3243 cost | 29,125 actions | TOTAL\n", + "\n", + " 25,303 nodes| 3,196 goal | 124 cost | 3,310 actions | GridProblem((15, 30), (130, 30))\n", + " 157,367 nodes| 31,704 goal | 3367 cost | 32,435 actions | TOTAL\n", + "\n", + " 32,572 nodes| 4,149 goal | 127 cost | 4,263 actions | GridProblem((15, 30), (130, 30))\n", + " 189,939 nodes| 35,853 goal | 3494 cost | 36,698 actions | TOTAL\n", + "\n", + " 5,373 nodes| 2,010 goal | 23 cost | 2,032 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + " 195,312 nodes| 37,863 goal | 3517 cost | 38,730 actions | TOTAL\n", + "\n", + " 10,832 nodes| 4,086 goal | 26 cost | 4,111 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + " 206,144 nodes| 41,949 goal | 3543 cost | 42,841 actions | TOTAL\n", "\n", "uniform_cost_search:\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 30 nodes | 13 goal | 418 cost | 16 actions | RouteProblem('A', 'B')\n", - " 42 nodes | 19 goal | 910 cost | 27 actions | RouteProblem('N', 'L')\n", - " 44 nodes | 20 goal | 805 cost | 27 actions | RouteProblem('E', 'T')\n", - " 30 nodes | 12 goal | 445 cost | 16 actions | RouteProblem('O', 'M')\n", - " 124 nodes | 46 goal | 5 cost | 50 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 355,452 nodes | 44,984 goal | 127 cost | 45,098 actions | GridProblem((15, 30), (130, 30))\n", - " 326,962 nodes | 41,650 goal | 124 cost | 41,764 actions | GridProblem((15, 30), (130, 30))\n", - " 10,992 nodes | 10,662 goal | 99 cost | 10,760 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", - " 214,952 nodes | 79,187 goal | 22 cost | 79,208 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", - " 558,084 nodes | 70,738 goal | 154 cost | 70,868 actions | GridProblem((15, 30), (130, 30))\n", - " 370,370 nodes | 47,243 goal | 133 cost | 47,358 actions | GridProblem((15, 30), (130, 30))\n", - " 349,062 nodes | 43,693 goal | 124 cost | 43,807 actions | GridProblem((15, 30), (130, 30))\n", - " 366,996 nodes | 45,970 goal | 127 cost | 46,084 actions | GridProblem((15, 30), (130, 30))\n", - " 300,925 nodes | 112,082 goal | 23 cost | 112,104 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", - " 457,766 nodes | 171,571 goal | 26 cost | 171,596 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", - "3,311,831 nodes | 667,891 goal | 3543 cost | 668,783 actions | TOTAL\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | TOTAL\n", + "\n", + " 30 nodes| 13 goal | 418 cost | 16 actions | RouteProblem('Arad', 'Bucharest')\n", + " 30 nodes| 14 goal | 418 cost | 16 actions | TOTAL\n", + "\n", + " 42 nodes| 19 goal | 910 cost | 27 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 72 nodes| 33 goal | 1328 cost | 43 actions | TOTAL\n", + "\n", + " 44 nodes| 20 goal | 805 cost | 27 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 116 nodes| 53 goal | 2133 cost | 70 actions | TOTAL\n", + "\n", + " 30 nodes| 12 goal | 445 cost | 16 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 146 nodes| 65 goal | 2578 cost | 86 actions | TOTAL\n", + "\n", + " 124 nodes| 46 goal | 5 cost | 50 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 270 nodes| 111 goal | 2583 cost | 136 actions | TOTAL\n", + "\n", + " 355,460 nodes| 44,985 goal | 127 cost | 45,099 actions | GridProblem((15, 30), (130, 30))\n", + " 355,730 nodes| 45,096 goal | 2710 cost | 45,235 actions | TOTAL\n", + "\n", + " 326,954 nodes| 41,649 goal | 124 cost | 41,763 actions | GridProblem((15, 30), (130, 30))\n", + " 682,684 nodes| 86,745 goal | 2835 cost | 86,998 actions | TOTAL\n", + "\n", + " 10,992 nodes| 10,662 goal | 99 cost | 10,760 actions | JumpingPuzzle('LLLLLLLLL.RRRRRRRRR', 'RR\n", + " 693,676 nodes| 97,407 goal | 2934 cost | 97,758 actions | TOTAL\n", + "\n", + " 214,952 nodes| 79,187 goal | 22 cost | 79,208 actions | EightPuzzle((1, 2, 3, 4, 5, 6, 7, 8, 0),\n", + " 908,628 nodes| 176,594 goal | 2956 cost | 176,966 actions | TOTAL\n", + "\n", + " 558,084 nodes| 70,738 goal | 154 cost | 70,868 actions | GridProblem((15, 30), (130, 30))\n", + "1,466,712 nodes| 247,332 goal | 3110 cost | 247,834 actions | TOTAL\n", + "\n", + " 370,370 nodes| 47,243 goal | 133 cost | 47,358 actions | GridProblem((15, 30), (130, 30))\n", + "1,837,082 nodes| 294,575 goal | 3243 cost | 295,192 actions | TOTAL\n", + "\n", + " 349,078 nodes| 43,695 goal | 124 cost | 43,809 actions | GridProblem((15, 30), (130, 30))\n", + "2,186,160 nodes| 338,270 goal | 3367 cost | 339,001 actions | TOTAL\n", + "\n", + " 366,988 nodes| 45,969 goal | 127 cost | 46,083 actions | GridProblem((15, 30), (130, 30))\n", + "2,553,148 nodes| 384,239 goal | 3494 cost | 385,084 actions | TOTAL\n", + "\n", + " 300,925 nodes| 112,082 goal | 23 cost | 112,104 actions | EightPuzzle((4, 0, 2, 5, 1, 3, 7, 8, 6),\n", + "2,854,073 nodes| 496,321 goal | 3517 cost | 497,188 actions | TOTAL\n", + "\n", + " 457,766 nodes| 171,571 goal | 26 cost | 171,596 actions | EightPuzzle((7, 2, 4, 5, 0, 6, 8, 3, 1),\n", + "3,311,839 nodes| 667,892 goal | 3543 cost | 668,784 actions | TOTAL\n", "\n" ] } @@ -1553,7 +1978,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 95, "metadata": { "scrolled": false }, @@ -1563,163 +1988,403 @@ "output_type": "stream", "text": [ "astar_search:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,696 nodes | 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 15 nodes | 6 goal | 418 cost | 9 actions | RouteProblem('A', 'B')\n", - " 34 nodes | 15 goal | 910 cost | 23 actions | RouteProblem('N', 'L')\n", - " 33 nodes | 14 goal | 805 cost | 21 actions | RouteProblem('E', 'T')\n", - " 20 nodes | 9 goal | 445 cost | 13 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 18,151 nodes | 2,096 goal | 2706 cost | 2,200 actions | TOTAL\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,696 nodes| 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,644 nodes| 299 goal | 14 cost | 316 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 6,143 nodes| 688 goal | 23 cost | 713 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 10,215 nodes| 1,142 goal | 44 cost | 1,176 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 10,339 nodes| 1,172 goal | 58 cost | 1,219 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", + " 10,463 nodes| 1,202 goal | 93 cost | 1,264 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,962 nodes| 1,591 goal | 102 cost | 1,661 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 18,034 nodes| 2,045 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 18,034 nodes| 2,046 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 418 cost | 9 actions | RouteProblem('Arad', 'Bucharest')\n", + " 18,049 nodes| 2,052 goal | 541 cost | 2,133 actions | TOTAL\n", + "\n", + " 34 nodes| 15 goal | 910 cost | 23 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 18,083 nodes| 2,067 goal | 1451 cost | 2,156 actions | TOTAL\n", + "\n", + " 33 nodes| 14 goal | 805 cost | 21 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 18,116 nodes| 2,081 goal | 2256 cost | 2,177 actions | TOTAL\n", + "\n", + " 20 nodes| 9 goal | 445 cost | 13 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 18,136 nodes| 2,090 goal | 2701 cost | 2,190 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 18,151 nodes| 2,096 goal | 2706 cost | 2,200 actions | TOTAL\n", "\n", "uniform_cost_search:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,696 nodes | 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 30 nodes | 13 goal | 418 cost | 16 actions | RouteProblem('A', 'B')\n", - " 42 nodes | 19 goal | 910 cost | 27 actions | RouteProblem('N', 'L')\n", - " 44 nodes | 20 goal | 805 cost | 27 actions | RouteProblem('E', 'T')\n", - " 30 nodes | 12 goal | 445 cost | 16 actions | RouteProblem('O', 'M')\n", - " 124 nodes | 46 goal | 5 cost | 50 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 18,304 nodes | 2,156 goal | 2706 cost | 2,260 actions | TOTAL\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,696 nodes| 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,644 nodes| 299 goal | 14 cost | 316 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 6,143 nodes| 688 goal | 23 cost | 713 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 10,215 nodes| 1,142 goal | 44 cost | 1,176 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 10,339 nodes| 1,172 goal | 58 cost | 1,219 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", + " 10,463 nodes| 1,202 goal | 93 cost | 1,264 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,962 nodes| 1,591 goal | 102 cost | 1,661 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 18,034 nodes| 2,045 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 18,034 nodes| 2,046 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 30 nodes| 13 goal | 418 cost | 16 actions | RouteProblem('Arad', 'Bucharest')\n", + " 18,064 nodes| 2,059 goal | 541 cost | 2,140 actions | TOTAL\n", + "\n", + " 42 nodes| 19 goal | 910 cost | 27 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 18,106 nodes| 2,078 goal | 1451 cost | 2,167 actions | TOTAL\n", + "\n", + " 44 nodes| 20 goal | 805 cost | 27 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 18,150 nodes| 2,098 goal | 2256 cost | 2,194 actions | TOTAL\n", + "\n", + " 30 nodes| 12 goal | 445 cost | 16 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 18,180 nodes| 2,110 goal | 2701 cost | 2,210 actions | TOTAL\n", + "\n", + " 124 nodes| 46 goal | 5 cost | 50 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 18,304 nodes| 2,156 goal | 2706 cost | 2,260 actions | TOTAL\n", "\n", "breadth_first_search:\n", - " 596 nodes | 597 goal | 4 cost | 73 actions | PourProblem((1, 1, 1), 13)\n", - " 596 nodes | 597 goal | 15 cost | 73 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 2,618 nodes | 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", - " 2,618 nodes | 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 120 nodes | 121 goal | 14 cost | 42 actions | PourProblem((0, 0), 8)\n", - " 120 nodes | 121 goal | 36 cost | 42 actions | GreenPourProblem((0, 0), 8)\n", - " 2,618 nodes | 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", - " 2,618 nodes | 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 18 nodes | 19 goal | 450 cost | 10 actions | RouteProblem('A', 'B')\n", - " 42 nodes | 43 goal | 1085 cost | 27 actions | RouteProblem('N', 'L')\n", - " 36 nodes | 37 goal | 837 cost | 22 actions | RouteProblem('E', 'T')\n", - " 30 nodes | 31 goal | 445 cost | 16 actions | RouteProblem('O', 'M')\n", - " 81 nodes | 82 goal | 5 cost | 35 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 12,111 nodes | 12,125 goal | 2973 cost | 1,548 actions | TOTAL\n", + " 596 nodes| 597 goal | 4 cost | 73 actions | PourProblem((1, 1, 1), 13)\n", + " 596 nodes| 597 goal | 4 cost | 73 actions | TOTAL\n", + "\n", + " 596 nodes| 597 goal | 15 cost | 73 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 1,192 nodes| 1,194 goal | 19 cost | 146 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", + " 3,810 nodes| 3,813 goal | 28 cost | 448 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 6,428 nodes| 6,432 goal | 60 cost | 750 actions | TOTAL\n", + "\n", + " 120 nodes| 121 goal | 14 cost | 42 actions | PourProblem((0, 0), 8)\n", + " 6,548 nodes| 6,553 goal | 74 cost | 792 actions | TOTAL\n", + "\n", + " 120 nodes| 121 goal | 36 cost | 42 actions | GreenPourProblem((0, 0), 8)\n", + " 6,668 nodes| 6,674 goal | 110 cost | 834 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 9 cost | 302 actions | PourProblem((0, 0, 0), 21)\n", + " 9,286 nodes| 9,293 goal | 119 cost | 1,136 actions | TOTAL\n", + "\n", + " 2,618 nodes| 2,619 goal | 32 cost | 302 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 11,904 nodes| 11,912 goal | 151 cost | 1,438 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 11,904 nodes| 11,913 goal | 151 cost | 1,438 actions | TOTAL\n", + "\n", + " 18 nodes| 19 goal | 450 cost | 10 actions | RouteProblem('Arad', 'Bucharest')\n", + " 11,922 nodes| 11,932 goal | 601 cost | 1,448 actions | TOTAL\n", + "\n", + " 42 nodes| 43 goal | 1085 cost | 27 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 11,964 nodes| 11,975 goal | 1686 cost | 1,475 actions | TOTAL\n", + "\n", + " 36 nodes| 37 goal | 837 cost | 22 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 12,000 nodes| 12,012 goal | 2523 cost | 1,497 actions | TOTAL\n", + "\n", + " 30 nodes| 31 goal | 445 cost | 16 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 12,030 nodes| 12,043 goal | 2968 cost | 1,513 actions | TOTAL\n", + "\n", + " 81 nodes| 82 goal | 5 cost | 35 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 12,111 nodes| 12,125 goal | 2973 cost | 1,548 actions | TOTAL\n", "\n", "breadth_first_bfs:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,062 nodes | 124 goal | 15 cost | 127 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 3,757 nodes | 420 goal | 24 cost | 428 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 36 cost | 43 actions | GreenPourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 3,757 nodes | 420 goal | 24 cost | 428 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 28 nodes | 12 goal | 450 cost | 14 actions | RouteProblem('A', 'B')\n", - " 55 nodes | 24 goal | 910 cost | 32 actions | RouteProblem('N', 'L')\n", - " 51 nodes | 22 goal | 837 cost | 28 actions | RouteProblem('E', 'T')\n", - " 40 nodes | 16 goal | 445 cost | 20 actions | RouteProblem('O', 'M')\n", - " 124 nodes | 46 goal | 5 cost | 50 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 17,068 nodes | 2,032 goal | 2782 cost | 2,119 actions | TOTAL\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,062 nodes| 124 goal | 15 cost | 127 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,010 nodes| 233 goal | 19 cost | 239 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 5,509 nodes| 622 goal | 28 cost | 636 actions | TOTAL\n", + "\n", + " 3,757 nodes| 420 goal | 24 cost | 428 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 9,266 nodes| 1,042 goal | 52 cost | 1,064 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 9,390 nodes| 1,072 goal | 66 cost | 1,107 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 36 cost | 43 actions | GreenPourProblem((0, 0), 8)\n", + " 9,514 nodes| 1,102 goal | 102 cost | 1,150 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,013 nodes| 1,491 goal | 111 cost | 1,547 actions | TOTAL\n", + "\n", + " 3,757 nodes| 420 goal | 24 cost | 428 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 16,770 nodes| 1,911 goal | 135 cost | 1,975 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 16,770 nodes| 1,912 goal | 135 cost | 1,975 actions | TOTAL\n", + "\n", + " 28 nodes| 12 goal | 450 cost | 14 actions | RouteProblem('Arad', 'Bucharest')\n", + " 16,798 nodes| 1,924 goal | 585 cost | 1,989 actions | TOTAL\n", + "\n", + " 55 nodes| 24 goal | 910 cost | 32 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 16,853 nodes| 1,948 goal | 1495 cost | 2,021 actions | TOTAL\n", + "\n", + " 51 nodes| 22 goal | 837 cost | 28 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 16,904 nodes| 1,970 goal | 2332 cost | 2,049 actions | TOTAL\n", + "\n", + " 40 nodes| 16 goal | 445 cost | 20 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 16,944 nodes| 1,986 goal | 2777 cost | 2,069 actions | TOTAL\n", + "\n", + " 124 nodes| 46 goal | 5 cost | 50 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 17,068 nodes| 2,032 goal | 2782 cost | 2,119 actions | TOTAL\n", "\n", "iterative_deepening_search:\n", - " 6,133 nodes | 6,118 goal | 4 cost | 822 actions | PourProblem((1, 1, 1), 13)\n", - " 6,133 nodes | 6,118 goal | 15 cost | 822 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 288,706 nodes | 288,675 goal | 9 cost | 36,962 actions | PourProblem((0, 0, 0), 21)\n", - " 288,706 nodes | 288,675 goal | 62 cost | 36,962 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 3,840 nodes | 3,824 goal | 14 cost | 949 actions | PourProblem((0, 0), 8)\n", - " 3,840 nodes | 3,824 goal | 36 cost | 949 actions | GreenPourProblem((0, 0), 8)\n", - " 288,706 nodes | 288,675 goal | 9 cost | 36,962 actions | PourProblem((0, 0, 0), 21)\n", - " 288,706 nodes | 288,675 goal | 62 cost | 36,962 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 27 nodes | 25 goal | 450 cost | 13 actions | RouteProblem('A', 'B')\n", - " 167 nodes | 173 goal | 910 cost | 82 actions | RouteProblem('N', 'L')\n", - " 117 nodes | 120 goal | 837 cost | 56 actions | RouteProblem('E', 'T')\n", - " 108 nodes | 109 goal | 572 cost | 44 actions | RouteProblem('O', 'M')\n", - " 116 nodes | 118 goal | 5 cost | 47 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - "1,175,305 nodes |1,175,130 goal | 2985 cost | 151,632 actions | TOTAL\n", + " 6,133 nodes| 6,118 goal | 4 cost | 822 actions | PourProblem((1, 1, 1), 13)\n", + " 6,133 nodes| 6,118 goal | 4 cost | 822 actions | TOTAL\n", + "\n", + " 6,133 nodes| 6,118 goal | 15 cost | 822 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 12,266 nodes| 12,236 goal | 19 cost | 1,644 actions | TOTAL\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 288,706 nodes| 288,675 goal | 9 cost | 36,962 actions | PourProblem((0, 0, 0), 21)\n", + " 300,972 nodes| 300,911 goal | 28 cost | 38,606 actions | TOTAL\n", + "\n", + " 288,706 nodes| 288,675 goal | 62 cost | 36,962 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 589,678 nodes| 589,586 goal | 90 cost | 75,568 actions | TOTAL\n", + "\n", + " 3,840 nodes| 3,824 goal | 14 cost | 949 actions | PourProblem((0, 0), 8)\n", + " 593,518 nodes| 593,410 goal | 104 cost | 76,517 actions | TOTAL\n", + "\n", + " 3,840 nodes| 3,824 goal | 36 cost | 949 actions | GreenPourProblem((0, 0), 8)\n", + " 597,358 nodes| 597,234 goal | 140 cost | 77,466 actions | TOTAL\n", + "\n", + " 288,706 nodes| 288,675 goal | 9 cost | 36,962 actions | PourProblem((0, 0, 0), 21)\n", + " 886,064 nodes| 885,909 goal | 149 cost | 114,428 actions | TOTAL\n", + "\n", + " 288,706 nodes| 288,675 goal | 62 cost | 36,962 actions | GreenPourProblem((0, 0, 0), 21)\n", + "1,174,770 nodes|1,174,584 goal | 211 cost | 151,390 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + "1,174,770 nodes|1,174,585 goal | 211 cost | 151,390 actions | TOTAL\n", + "\n", + " 27 nodes| 25 goal | 450 cost | 13 actions | RouteProblem('Arad', 'Bucharest')\n", + "1,174,797 nodes|1,174,610 goal | 661 cost | 151,403 actions | TOTAL\n", + "\n", + " 167 nodes| 173 goal | 910 cost | 82 actions | RouteProblem('Neamt', 'Lugoj')\n", + "1,174,964 nodes|1,174,783 goal | 1571 cost | 151,485 actions | TOTAL\n", + "\n", + " 117 nodes| 120 goal | 837 cost | 56 actions | RouteProblem('Eforie', 'Timisoara')\n", + "1,175,081 nodes|1,174,903 goal | 2408 cost | 151,541 actions | TOTAL\n", + "\n", + " 108 nodes| 109 goal | 572 cost | 44 actions | RouteProblem('Oradea', 'Mehadia')\n", + "1,175,189 nodes|1,175,012 goal | 2980 cost | 151,585 actions | TOTAL\n", + "\n", + " 116 nodes| 118 goal | 5 cost | 47 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + "1,175,305 nodes|1,175,130 goal | 2985 cost | 151,632 actions | TOTAL\n", "\n", "depth_limited_search:\n", - " 4,433 nodes | 4,374 goal | 10 cost | 627 actions | PourProblem((1, 1, 1), 13)\n", - " 4,433 nodes | 4,374 goal | 30 cost | 627 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 37,149 nodes | 37,106 goal | 10 cost | 4,753 actions | PourProblem((0, 0, 0), 21)\n", - " 37,149 nodes | 37,106 goal | 54 cost | 4,753 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 452 nodes | 453 goal | inf cost | 110 actions | PourProblem((0, 0), 8)\n", - " 452 nodes | 453 goal | inf cost | 110 actions | GreenPourProblem((0, 0), 8)\n", - " 37,149 nodes | 37,106 goal | 10 cost | 4,753 actions | PourProblem((0, 0, 0), 21)\n", - " 37,149 nodes | 37,106 goal | 54 cost | 4,753 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 17 nodes | 8 goal | 733 cost | 14 actions | RouteProblem('A', 'B')\n", - " 40 nodes | 38 goal | 910 cost | 26 actions | RouteProblem('N', 'L')\n", - " 29 nodes | 23 goal | 992 cost | 20 actions | RouteProblem('E', 'T')\n", - " 35 nodes | 29 goal | 895 cost | 22 actions | RouteProblem('O', 'M')\n", - " 351 nodes | 349 goal | 5 cost | 138 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 158,838 nodes | 158,526 goal | inf cost | 20,706 actions | TOTAL\n", + " 4,433 nodes| 4,374 goal | 10 cost | 627 actions | PourProblem((1, 1, 1), 13)\n", + " 4,433 nodes| 4,374 goal | 10 cost | 627 actions | TOTAL\n", + "\n", + " 4,433 nodes| 4,374 goal | 30 cost | 627 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 8,866 nodes| 8,748 goal | 40 cost | 1,254 actions | TOTAL\n", + "\n", + " 37,149 nodes| 37,106 goal | 10 cost | 4,753 actions | PourProblem((0, 0, 0), 21)\n", + " 46,015 nodes| 45,854 goal | 50 cost | 6,007 actions | TOTAL\n", + "\n", + " 37,149 nodes| 37,106 goal | 54 cost | 4,753 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 83,164 nodes| 82,960 goal | 104 cost | 10,760 actions | TOTAL\n", + "\n", + " 452 nodes| 453 goal | inf cost | 110 actions | PourProblem((0, 0), 8)\n", + " 83,616 nodes| 83,413 goal | inf cost | 10,870 actions | TOTAL\n", + "\n", + " 452 nodes| 453 goal | inf cost | 110 actions | GreenPourProblem((0, 0), 8)\n", + " 84,068 nodes| 83,866 goal | inf cost | 10,980 actions | TOTAL\n", + "\n", + " 37,149 nodes| 37,106 goal | 10 cost | 4,753 actions | PourProblem((0, 0, 0), 21)\n", + " 121,217 nodes| 120,972 goal | inf cost | 15,733 actions | TOTAL\n", + "\n", + " 37,149 nodes| 37,106 goal | 54 cost | 4,753 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 158,366 nodes| 158,078 goal | inf cost | 20,486 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 158,366 nodes| 158,079 goal | inf cost | 20,486 actions | TOTAL\n", + "\n", + " 17 nodes| 8 goal | 733 cost | 14 actions | RouteProblem('Arad', 'Bucharest')\n", + " 158,383 nodes| 158,087 goal | inf cost | 20,500 actions | TOTAL\n", + "\n", + " 40 nodes| 38 goal | 910 cost | 26 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 158,423 nodes| 158,125 goal | inf cost | 20,526 actions | TOTAL\n", + "\n", + " 29 nodes| 23 goal | 992 cost | 20 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 158,452 nodes| 158,148 goal | inf cost | 20,546 actions | TOTAL\n", + "\n", + " 35 nodes| 29 goal | 895 cost | 22 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 158,487 nodes| 158,177 goal | inf cost | 20,568 actions | TOTAL\n", + "\n", + " 351 nodes| 349 goal | 5 cost | 138 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 158,838 nodes| 158,526 goal | inf cost | 20,706 actions | TOTAL\n", "\n", "greedy_bfs:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,696 nodes | 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n" + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,696 nodes| 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,644 nodes| 299 goal | 14 cost | 316 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 6,143 nodes| 688 goal | 23 cost | 713 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 10,215 nodes| 1,142 goal | 44 cost | 1,176 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 10,339 nodes| 1,172 goal | 58 cost | 1,219 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", + " 10,463 nodes| 1,202 goal | 93 cost | 1,264 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,962 nodes| 1,591 goal | 102 cost | 1,661 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 18,034 nodes| 2,045 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 18,034 nodes| 2,046 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 9 nodes| 4 goal | 450 cost | 6 actions | RouteProblem('Arad', 'Bucharest')\n", + " 18,043 nodes| 2,050 goal | 573 cost | 2,130 actions | TOTAL\n", + "\n", + " 28 nodes| 12 goal | 1207 cost | 22 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 18,071 nodes| 2,062 goal | 1780 cost | 2,152 actions | TOTAL\n", + "\n", + " 19 nodes| 8 goal | 837 cost | 14 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 18,090 nodes| 2,070 goal | 2617 cost | 2,166 actions | TOTAL\n", + "\n", + " 14 nodes| 6 goal | 572 cost | 10 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 18,104 nodes| 2,076 goal | 3189 cost | 2,176 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 18,119 nodes| 2,082 goal | 3194 cost | 2,186 actions | TOTAL\n", + "\n", + "weighted_astar_search:\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,696 nodes| 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,644 nodes| 299 goal | 14 cost | 316 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 6,143 nodes| 688 goal | 23 cost | 713 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 10,215 nodes| 1,142 goal | 44 cost | 1,176 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 10,339 nodes| 1,172 goal | 58 cost | 1,219 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", + " 10,463 nodes| 1,202 goal | 93 cost | 1,264 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,962 nodes| 1,591 goal | 102 cost | 1,661 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 18,034 nodes| 2,045 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 18,034 nodes| 2,046 goal | 123 cost | 2,124 actions | TOTAL\n", + "\n", + " 9 nodes| 4 goal | 450 cost | 6 actions | RouteProblem('Arad', 'Bucharest')\n", + " 18,043 nodes| 2,050 goal | 573 cost | 2,130 actions | TOTAL\n", + "\n", + " 32 nodes| 14 goal | 910 cost | 22 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 18,075 nodes| 2,064 goal | 1483 cost | 2,152 actions | TOTAL\n", + "\n", + " 28 nodes| 11 goal | 805 cost | 18 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 18,103 nodes| 2,075 goal | 2288 cost | 2,170 actions | TOTAL\n", + "\n", + " 18 nodes| 8 goal | 445 cost | 12 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 18,121 nodes| 2,083 goal | 2733 cost | 2,182 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 18,136 nodes| 2,089 goal | 2738 cost | 2,192 actions | TOTAL\n", + "\n", + "extra_weighted_astar_search:\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", + " 948 nodes| 109 goal | 4 cost | 112 actions | TOTAL\n", + "\n", + " 1,696 nodes| 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", + " 2,644 nodes| 299 goal | 14 cost | 316 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 6,143 nodes| 688 goal | 23 cost | 713 actions | TOTAL\n", + "\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 10,215 nodes| 1,142 goal | 44 cost | 1,176 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", + " 10,339 nodes| 1,172 goal | 58 cost | 1,219 actions | TOTAL\n", + "\n", + " 124 nodes| 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", + " 10,463 nodes| 1,202 goal | 93 cost | 1,264 actions | TOTAL\n", + "\n", + " 3,499 nodes| 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", + " 13,962 nodes| 1,591 goal | 102 cost | 1,661 actions | TOTAL\n", + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 9 nodes | 4 goal | 450 cost | 6 actions | RouteProblem('A', 'B')\n", - " 29 nodes | 12 goal | 910 cost | 20 actions | RouteProblem('N', 'L')\n", - " 19 nodes | 8 goal | 837 cost | 14 actions | RouteProblem('E', 'T')\n", - " 14 nodes | 6 goal | 572 cost | 10 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 18,120 nodes | 2,082 goal | 2897 cost | 2,184 actions | TOTAL\n", + " 4,072 nodes| 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", + " 18,034 nodes| 2,045 goal | 123 cost | 2,124 actions | TOTAL\n", "\n", - "weighted_astar_search:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,696 nodes | 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 9 nodes | 4 goal | 450 cost | 6 actions | RouteProblem('A', 'B')\n", - " 32 nodes | 14 goal | 910 cost | 22 actions | RouteProblem('N', 'L')\n", - " 29 nodes | 12 goal | 805 cost | 19 actions | RouteProblem('E', 'T')\n", - " 18 nodes | 8 goal | 445 cost | 12 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 18,137 nodes | 2,090 goal | 2738 cost | 2,193 actions | TOTAL\n", + " 0 nodes| 1 goal | 0 cost | 0 actions | RouteProblem('Arad', 'Arad')\n", + " 18,034 nodes| 2,046 goal | 123 cost | 2,124 actions | TOTAL\n", "\n", - "extra_weighted_astar_search:\n", - " 948 nodes | 109 goal | 4 cost | 112 actions | PourProblem((1, 1, 1), 13)\n", - " 1,696 nodes | 190 goal | 10 cost | 204 actions | GreenPourProblem((1, 1, 1), 13)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 124 nodes | 30 goal | 14 cost | 43 actions | PourProblem((0, 0), 8)\n", - " 124 nodes | 30 goal | 35 cost | 45 actions | GreenPourProblem((0, 0), 8)\n", - " 3,499 nodes | 389 goal | 9 cost | 397 actions | PourProblem((0, 0, 0), 21)\n", - " 4,072 nodes | 454 goal | 21 cost | 463 actions | GreenPourProblem((0, 0, 0), 21)\n", - " 0 nodes | 1 goal | 0 cost | 0 actions | RouteProblem('A', 'A')\n", - " 9 nodes | 4 goal | 450 cost | 6 actions | RouteProblem('A', 'B')\n", - " 29 nodes | 12 goal | 910 cost | 20 actions | RouteProblem('N', 'L')\n", - " 23 nodes | 9 goal | 805 cost | 16 actions | RouteProblem('E', 'T')\n", - " 18 nodes | 8 goal | 445 cost | 12 actions | RouteProblem('O', 'M')\n", - " 15 nodes | 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", - " 18,128 nodes | 2,085 goal | 2738 cost | 2,188 actions | TOTAL\n", + " 9 nodes| 4 goal | 450 cost | 6 actions | RouteProblem('Arad', 'Bucharest')\n", + " 18,043 nodes| 2,050 goal | 573 cost | 2,130 actions | TOTAL\n", + "\n", + " 31 nodes| 13 goal | 910 cost | 21 actions | RouteProblem('Neamt', 'Lugoj')\n", + " 18,074 nodes| 2,063 goal | 1483 cost | 2,151 actions | TOTAL\n", + "\n", + " 23 nodes| 9 goal | 805 cost | 16 actions | RouteProblem('Eforie', 'Timisoara')\n", + " 18,097 nodes| 2,072 goal | 2288 cost | 2,167 actions | TOTAL\n", + "\n", + " 18 nodes| 8 goal | 445 cost | 12 actions | RouteProblem('Oradea', 'Mehadia')\n", + " 18,115 nodes| 2,080 goal | 2733 cost | 2,179 actions | TOTAL\n", + "\n", + " 15 nodes| 6 goal | 5 cost | 10 actions | EightPuzzle((1, 4, 2, 0, 7, 5, 3, 6, 8),\n", + " 18,130 nodes| 2,086 goal | 2738 cost | 2,189 actions | TOTAL\n", "\n" ] } @@ -1750,7 +2415,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -1792,7 +2457,7 @@ " plot_grid_problem(grid, solution, reached, 'A* search')\n", " for weight in weights:\n", " solution = weighted_astar_search(grid, weight=weight)\n", - " plot_grid_problem(grid, solution, reached, '(b) Weighted ({}) A* search'.format(weight))\n", + " plot_grid_problem(grid, solution, reached, 'Weighted ({}) A* search'.format(weight))\n", " solution = greedy_bfs(grid)\n", " plot_grid_problem(grid, solution, reached, 'Greedy best-first search')\n", " \n", @@ -1801,14 +2466,14 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 103, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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Nmm5NEm6wAyvGXNXjI8V8BGA+K4vQj3MP3Lf8IH4Nevtg2tv25Nb7fKp9fNTevyWcsynB+GIRK4tsTsBNHjWvBkAv1syzylaYKazGc/aSazPrHQ6HoXQf4BIri+Qg4AYA6ufaCrxHsUgOAm4AoH6urcB7FItsbhzDOI7hs8e3r8xtS/H7AMBlrq3AU55ZBJh2F/YRqsH2WhhLtY+PFvZh74oeg9TP/p54vaoTgSE3xSLABB8aSMVYWq+Ffdh7kNGpY9DZNvtjBDziNlSSGoYwDEP49n162uq2FL8PALxvi2sz0B/FIqmtSS6VhgoAeWxxbQY6o1gktTXJpdJQASCPLa7NQGc8s0hS92lpn6Vqm/Ozx+PxVQjhg5cvV3W9KQ/bXOjtPfxP0wrPnzXMPZK59tq89Jrb2fOMsDtWFmnS8MkwDJ8M3xs+GYZQ/4e+c+mCa1IHS25z7fsbLml1DLfa76e2OCeSTy/j8BzjEB6xskhS9w+6fxhC+PzhO5nWtJ1qHz4ZhjCGPwkh/CCE8ONxHMMw1Pt8vZUAgC85J+a3xbW5JofDYfJDwNTq5qXfhb2zskhqmwbc3K8k/kkYn/1OGMIQxme/8+P/9eMwjlVevwCgBltcm6nJMPxqGIZ//uifXy3dJfpgZZHUtgu4+d6PPg8h/EkI4fvh2ZuvhxBCePbm6z/5h5+EEEL4wT/9QdUrjABQyBbXZmoxDL8eQvjrEMIvPWr9f2EY/kUYx/9WqFd0QrFIUlsF3AyfDG8LxV9843fDL//svf/+8/Hn4T//778JISgYAXom3Os6W1ybe5R7fM0M/5ked8Pw6/8Qnv/0g/DqvdsF34QQ7sLzn/7KMPyGgpE13IZK9d7dehrC958Wiu/88s/CT/7hJ2FHt6SWfADfw/+0rtUx3Gq/UxLuVV5t43BOf+aGKtV4jM/36e2tpn/9tFAM4e0H/A/CqxBC+Gu3pLKGlUWSSv0Q/bswm/HZ77y79fSMn48/D3u5JbXVv25DDcwfepb8Ovyk7WH+PG5/+fL45lx/bm8Pz5a+z9TrXRNI0/Gc/ychhF86t/LzLIQwhvBLPwo/+s1/O4T/0FJoEfWwskhqqR+i/80Qwg8uFYoPfj7+PPzV3/9V+Nuf/e0VXQeA5qW+Dl8Mn9uwP6z0Jjx79mn46I+Cfc2VFIuklvoh+v8aQvhxePPs/855868NXwu/9Y9/K/zaN37tiq4DQPNSX4enw+cuB98I0inoWXjz5qPw6R8E+5oruQ2VpFI/RD/+cByHT4bfC8ObEEL4fgjhH517768NXwvf/ZXvdn8L6hKFwyByKBI4sYP9GkLDYR4PKj1Oze9X6pb8OjwRPvfQfjym7c/U66VS6fnhpHNBOF/9sz8L/+r3fz985Wc/O7n68yaE8Cr8ygd/FP7g0xBCuP9odDeO4XnYSWgR61lZpHrjD8cxhPB7IYQ/D7/4xukf+sU3FIqnNXEhXKHU9vW+X0PoYxtr3IYa+9QS4V6k0vxcfP3Nb4a/+uSTcBeeh6cPed4XiuG74Sfh78N7+TbNbzd5WVkkqdQP1n/ZPn4Yvvej3wsf/M/fDb/x5+G9VNRffCOEn34//OD7/1KhCNAxq7Lv2zrM5lzb0/aXL9P2cer1eN+rb30r/M2/+zfht374wzC8+bJk/D9ffC18N/wk/Pfw69HvCLhhCSuLpLbdQ/T/5Ucfhr/80xB++v3wboXxvlAMf/mnCkUA9iZHmI2Am8q9+ta3wn/84z8O/+kP//DdP/8s/I+TheI9+5rZrCyS2sYP0Q8h/OWfvm39zp+9KxRDUCgCsDs5wmwE3DTg9Te/GV5/85vv/v3JradP2dfMplgkqa0fon+7eHhfMP70X4fwd78ZFIoA7FGOMJteA27m+vjjj8IXX3x15k+PoYXPJOeOKZziNlQaNYTwd98LV5yU9xZQ0Pv2ltq+3vdrCH1sY43bUGOfYI9mzcX5hWIILRSKwTmIhawsktTWD9Ev7c/hcGjizL2VHGEQ5yK979+/y/0vZKMNjhM9KRVm01PAzfvv+/b8cKkvIURBo615FmYeUzjFyiKplXyIHgB6VSrMpqeAm5r6kkvv28fGFIukVvIhegDoVakwm54CbmrqSy69bx8bcxsqSeUJuNnW8Xh8FU5/ae2d29r2Z2I8tKrLcWzetqPDOZVFqTCbngJunnyeiMZhj9/AteSYwilWFiF27kOMDzf71Ntx7217Hpi37XBMqMEG47C6x/+E2bCaYpHNDUMYhiF8+/6B6kVtU+0A0KM1180cbVPtKbdljQ3e41kI4TshhGcvXx7D6X/+IoxjGB7/7DiGoWDbc5+hWEuxSA4CbgBgvprCbFoNuEn9Hkt+t6Zj5TMUqygWyUHADQDMV1OYTasBN6nfY8nv1nSsfIZiFQE3bO7ah+hPPXy+1NR3ANbweickDeMoHSTR2v7ivNJjCXow85x4d/8dgJ89bmwt4Objjz96+EL7d99TeB8gczeOYXL7UgTcXAqzWfN6l/pX07ESZsNaVhap2ewT+83N6y37kVPqD+O9f7jPsX29BQRcuz29jyXy6W1OpdbiXIuO6X2heEqJ7Vv7nsYsu2Vlkc3dP1T9YQjh8/u/cM1uO+fly41yteEJK5eQVu1zKsMdERetuW7maIvb3x7Tx23h0Yri0u17+TLt/rrw68/mbPOa/l3b72s+H0FqVhbJwUPYADBfTWEoSwJuUofezJWjL1t8RvH5iOopFsnBQ9gAMF9NYShLAm5Sh97MlaMvW3xG8fmI6g3j2N5q9tQtIofDwffIdGIY5t9qcXPzOrx48emW3aEdQm82UMOteddyXWCJGsZ6a2M2QYDMQ+hNCGHd57ylfbn/fsJFahgjGbiWrtBTrWJlkZrNfqB84kF69qfFcIgWCHhgL0qP9dLvf421592U5+0lr9Xivs7FtZQQgoAbMrj2Ae6HvzLOfWD+lFN/vbn84Prx7Huk+mvQTv4quelfz/ayD0u6FGBROmRj7ryFJS6tpqQei7e3h8UBK6XbnrZPbd/jQLrb28PZn5sbIJMjzAb4kpVFctjiIfoc/YG9qylQw7ylFqXCWWpqm2q/1tzXW7P/nUdgIcUiOWzxEH2O/sDe1RSoYd5Si1LhLDW1TbVfK0c4jvMILOQ2VDZ3f1vHZ9e0PW0fEtzUeOm9j77CEUII6+Zu7jbzllxSj8Ua5s+StgRhNue8u3339vZwNrjuyWeCRX15fIvww3F6fMur8wjErCwCAKTTe2jK7OLs5ub15L9PmRlcd3VfuKj3ccxMVhYpYouH6Ne8z9wH60upOWZZ0Ey/agrUaHHe0qfL4zMOg5oKvalh/lwTcHPGuwCZU9v8sFL4ONQnLAyuuyZYp0c1fy6gL1YWKSXXQ/SCMuB6NQVqmLfUIvX4rGn+rJ1na7Z5LvMeMlIsUkquh+gFZcD1agrUMG+pRerxWdP8WTvP1mzzXOY9ZOQ2VIrIFXDTS1DGiVs97y59FxisdXmuHF+FED44FRCRu62kh/1Quh+JOLdckPq6UsOc+vrXP4qeETx1vb10Db5mm9dc11OE3gHTrCxCm3r5YErbjMO3etoPPW1Li4rs/5lhMpcIRMnHviYbK4sUIeAG6mKuwPvmXJNSzJUW59Q4hiHVNi9977m/+zhE53T/zocOXQqPmQp2EzxDb6wsUoqAG6iLuQLvW3JNSh1w04JS25w6OAiYoFikFAE3UBdzBd635JqUOuCmBaW2OXVwEDDBbagUIeAG6mKuxDoLrmGhOdekuW3zA27m9e3jj+NAmtxSbfNWATd7P39BKlYWAbhW7yELeywUez+mtZu1/wsXisYI7IiVRaoh4AbKuWaujOPbr1i47nfnzsd5IRRTgROcJ4zjvBIBN3PnVAjh7LxYaTIUZsl+KB1w41oPaVhZpCYCbqCcNXMlRxvkVjLgptS8WPK+Am5gBxSL1ETADZSzZq7kaIPcSgbclJoXa8PnBNxAZ9yGSjXWBtzc3h7e+/ebm9dCO2Cma+ZKjjbzkVJKBNycahuGkC1o6drwuVNtqQNuHoX6zLoF17kF0rCySGtmP1hfOikOaF7vQR69b18vcgUtVT0eFl7Tq94WaImVRar35CH16OH/MPFXRgE3MM+WITXrAm5ybP1ph8PbsBH6N3dsLvnZ6wJuTobZTJkVSJO6LeU2X9i+uRbvB9d6mMfKIi3oJTgAarZmruRogy1tEeySI4il5HxM3e81nFtgI4pFWtBLcADUrKYwG/OR3LYIdskRxFJyPpYKuJn7es4tkIDbUKnejIf/p7y7RfX+5+7ub2U9+3qtPPRe8Hvl7tyeN8/xeDwXTHFxH0787iYe35L1MAdOtN3dfw/cZ49/t7WAm9z79krmWUYpg11OtS0Ie1k0Nrede2/nyalzQwiXzxlTHn9/6tNwusem/ttjtZxboEdWFunBkgfZa/+A2AL7cL5z+2rOPqxxP9fYp2u0sB0t9JH0lhz3rUNcsozBm5vXa19CmA1sSLFIk4YhDMMQvj0MYRjH8HwcwxDejufvhAvj+vHvTrVt2eet3gO2Nnf+pG6DLS0Zh6nH8YLx/u4aN45vr309zL0XLz4NL18e3/vngqT7AZimWKRVpcIE1vBgPT0QcEOPagu4WdPH3ude79sHVVEs0qpSYQJreLCeHgi4oUe1Bdys6WPvc6/37YOqCLihSSlDb25uXoeXLz+d9b5rQmV8pxM9SB2UsSYkY+58LBgGRWJbhRPNGZsff/xR9MXwp641p9pubw/h5uZ1ePHiy2vNo9c7+13Bj20ZZnOqrdYAmN63D2rT6sriuYeZPeTMg9lj4enFn4vMszxq3M/X9klYy3I1Hv8aFBtLa68VT39/4euVGA/FxuBE6I15QSu6qVWaXFkUJ84p9w+sfxhC+Pz+6zHeawsz/3qb0+3t4Vm479/jKPE5DoeDB/Q7NnWeezLWx3NtS352y7YeV9XNPzb27tpQat7ef03O1eeWqWvaqfmzpt9Qm55qlVZXFuGUFh9mr71/1ClXGIcgCSijpnm79twyl3MLVEixSE9afJi99v5Rp1xhHIIkoIya5u3ac8tczi1QIcUi3RjHMI5j+OzxbSmn2mpSe/+o05KxPvdnc7QB89Q0b9eeW3JsM7CdJp9ZhCvdhTPhCLe3h4u//DTJLlF/enBuv1a9fVulKi54/60/6NxV9MzE2bnXqKrHdueuHkun0kyXGUNY+V3uc641IeH4Kn2eO2fm+a+mc9hTZ8fhmW2reVtgkmKR3XgIvXlsGOb/ZfKLL74q1OKEhi+A1X2ASqya7VszRqY+VD6ej3N/bu37UNaasXR7u3YlagjjeLlaPDWWporEOa+5QjXngStU2/dz43DiPFLttsAlbkOla8MQhmEI375PTzvblvr11rRBSjnGrHlBbiXHkusKsCeKRXpXU1qbVDdKqClB0bwglZJjyXUF2A3FIr2rKa1Nqhsl1JSgaF6QSsmx5LoC7MYwjsKk2K8lzywucHfq+cjHag0doC89PGuXIQgoN0EXGQxD2OQc29ozi63Pny3PYVs8p+zZZ3pkZZG92yLVcM4HFIUiW5PYWSdzP48t9vPVc+rm5nXy15yp5fNAy32HbkhDZXfuH/r/MITw+cMK4JO28WlbCOHNiveIXu/ly0QbQzbX/FV46q/Mt7eHZ2FijKRo2+I1c7eZK5wy45w95eq5N+dnT43Zh69dyjHv3287zLrGbbMfjmevm1bZoB1WFtmjHCEBQge4JFeoRY73EdBBbmvGTa45lfq9a2pb+rNAoxSL7FGOkAChA1ySK9SippAaAR2ksmbc5JpTqd+7pralPws0ym2o7M797TKfLWkblt8w8+72m/vffQi9+SyEEI7Hxa9HZ64Zh0vbcr3Plm3mCqc8OT8vCrPZek5Njdka5lSKtjk/O7UfWg/egT2xsgjzrH3Q/ukHGQ/uwzy9zZXetqcGS8Js7H8uMUbgESuLEGY91L86COdS6MBUGMApAgKWWxPIkLsvAm7yBnQI46hXqTCblAE3c3+3lba1+6Em5jdMs7IIb819UH9u25r3YDs1HYPUY+7cduR4nxbbptqpS47z7hZzKkd/aps/5hR0ZhjH8fJPQedS/dU1TH/FxuRfuK0sbi931Luvzqi37Wm7lcV6bXnezf2VEfm/OqPkymK+OzXWSDm/p875ziO0SrEICQ1DWDKhHkJvQgjLH/h34Ukv9YXeB4d2bHDsz4Wu3B0Oh+cn2pnhijCb7PNszViaGDdsRLEI09yGCmkteTDeBwLo17n5bd6v03uYjfGRV4tjBLIScAMLXBOEE2aG3rQSBtCy1IEMtQdd5HqfFtuWHCvyqSnMpkTATY/j8NKtt24Bh7pZWYRlPgzbh96wnTXHKvXrrRlLS943x/u02DbVTjk9zak1fezJHrcZuqFYhGU+DyH8dnj/L9xz2+a+HttZc6xSv96asbTkfXO8T4ttU+2U09OcWtPHnuxxm6EbAm5gY0tCb25uXocXLz6d9bNb356zk6CF98JGBNy8VeGx3zwUxrGvTwthNqfMPfYVzrNNzAj16Wau9LQt8MDKImxv9gP0X3zx1eSvuUL3H2LCPrbxGrXtl9r6Qx7CbNrX4nEBHhFwAyulDr055VRAwMz3vrqtx6CFUwTctHHsawu4aXEf1qb2MJstAm5qssVKVy/nG+BLVhZhvbVhCde+x9r3FjrwVurj0moYR+3HPte+Sd0fzqt9/qydU3vUy/kGuKdYhPXWhiVc+x5r31vowFupj0urYRy1H/vaAm5a3Ie1qX3+rJ1Te9TL+Qa4J+AGClgSesNSYwgnci7OhQc9CZw4e1z2FHIy1e892tOxL6XVMJtTWpk/tYzDnuZKT9sCD6wsQhke+t/M6evxmfAgx+E0+4Xceg+zqY19CMwi4AYySR16w3KngoK2DKRpNeBmHA/R+FwXlnRsemwL7VivxTCbXAE3va84mSvQNiuLkI+H/MsrFaiR+vW2COPI1Z8W7XGbU6t9rmwxp3jL/oKGKRYhHw/5l1cqUCP16+UKuNmiPy3a4zanVvtcEXCzHfsLGibgBmY4Ho+LwheucXt72PLlOe3u4ZbgEATcbKWVwI9zWjr2Oc5Va3388UfnniE+KXeYTQv7MIG7w+Hw/PKPba+n82RP2wIPrCzCPJt/cLi5eb3gp5v+7F2TrY/ruRCJvYVL7G17S6q+yFlSKIYyY6f6fZjAHrYRSEDADVTi1Nc6bOlS2EvLbU/bw0R40LaBNGmDYvIF3KRu234/TIXozPmL/tSKgICbrGadl+Zy7ADWsbII+1VTaESuEIpT1vxc7W219SfXNqdWW396lnq/OnYAKygWYb9qCo3IFUJxSoshG1uEcfTStoXa+tOz1PvVsQNYwW2osFOPb9s7Hh/avvzv921397cQfvb4d+9v3aq27Wn7MH0T4rv9cHt7CDc3r0/eElzT9l27H3puexjDW6itPzVYGlIz1+X9ejZ85mRgi2MHsI6VRZhnrwEdvYQgzD5+W3wAhoyynKs2midz+n7unJTyXLWH8/0ethFIwMoizLAmYrz1rw2oyYpglyhgJUyE3qx575raauvPtgE3S47mMrX1Z0qqr0O4tH1h4fw5I2mYTSpbfKWEr1QAWmVlEWjJkmCKmoIySrXV1p9c25xabf3JIcf29b4PAZqnWARaskWwS+r3rqmttv4IuGlHju3rfR8CNM9tqEAzUga7TIXe3N4eTjW/u+3u1O/W3nbhZ+/ub9X97PF/qyG4Zm6bgJu0nsyVc6Eyyd5jqg2AcqwsAlN6DkHoeduW6iXIaI1z48E42WZ82K8ADbCyCAUJNtjOjFCS1aE3PakhpKZkwM1DqEkt/cllTjDShZeYFVKzJJQJgHpYWQR6tcdQkjVqCqkpeexq68/WzvV57rbsbX8B7IpiEejVHkNJ1qgppKbksautP1tbEow09/d73l8Au+I2VGjQ8XjcJHBihbstvptsjWtCSaZCb3bgaYBPU6E3qQJlauvP1p5ux9Iwm9THtGYT593qzn9TKrx+nLXie4qbOiZQMyuL0KbaLvS19edaQje+1MsxZZklx31v8+XcvmltrrTW32vsYRshC8UisGvDEIZhCN8ehjCMY3g+jmEIb8+N3wkhPBvHMLTaNudn5+6bVtqWqK0/W0vQ56dj6Xnt2wzAOopFYO9qCmfZIixkTbBITduyRUBKbf3Z2to+t7jNAKygWAT2rqZwli3CQtYEi9S0LVsEpNTWn62t7XOL2wzACgJu2LXSD/qveHh/V7Y8To+/D+8hlCRD2904HjYPj3na/rAfH/fn9vbw9Fceayb05ppAmdr6s7UnYU7RnLoU8NRDSA0Ay1hZZO88BJ/G1kEXvR2nUtsTve/NzetVv0+zlh7LvYXZ9GwPx7LUNp573z3sczplZRF26nA4CKQo6D4Q5MMQwuf3qzPJ2562P15RfPDixachhBBubw/PHn4uPFpRLNHva9pObdtUn0v2J4dL/Zv63XEMw5IxRlt8pcR27Ft6ZGURoIySATdz+1Oq36kDZZbsm14CboTZALCaYhGgjJIBN3P7U6rfqQNl1ob/tBhwI8wGgNXchgpQQI4AmKftU6ErT8JPplQZejN3287/fhz+sybIaEqOYKvHffn61z8KX3zx1UW/L8wGgBCsLMJeHzpvbbtb6+8ltW/Pkv71EnrTy3ZEFhaKtY9NADKyssiu5XgYfWoVQcjMPK2HBpQKgHnaviAE5vnTtlB56M3agJvSgTQFvQs2uhT+A8D+WFkE2F7JsJfUwTVrfreFgJu9sW8AOEuxCLC9kmEvqYNr1vxuCwE3e2PfAHDWMI7uLoEtuQ2VWqwZi8Ow6FbEh9CbbNbOsxyhM3N9/PHyQJprjWOo9hz0EDpUuh87ctf6Lf9AelYWAbjo5ub1kh/3AX+FXIViqD/MxjjKy/4GIopFgEoMQxiGIXz7PlxkddtU+9L3fvHi0/Dy5fG9f3Juy5JtntOXJb/fkWchhO+EEJ6NYxjGMTzf4T4AYAHFIkA9Wg24WfN6Am7ySX2MAeicYhGgHq0G3Kx5PQE3+aQ+xgB0TrEIUIlxDOM4hs8ef6/dmrap9mvfu9S2rOnf2n3Ti9THGID+faV0B4B96DDZUHJgGEM4+ajbGI7Hv2it+LgLBcZnxuTT2sNsTilyTHZs1hgpfC533oXMFItALr196Otte65wLhOlvayU1B9A536Vx+3t+RW9mr/WIodTx6TkVxH5GqR3Sp77nHchM7ehAjSmRBpq+q3IZ8m+KZnOOufncvQPAB4oFgHa00Iaak2W7JuS6axzfi5H/wAghKBYBGhRC2moNVmyb0qms875uRz9A4AQgmcWgQU6DKlp0n1y5WeX2p62H4/Xv+bU755ye3tY9gvbevPwf4YzN2Ceal/Tdnt7CDc3r8OLF5++a3sUZvMm/o3Y3OO8pg0AplhZBJZQKH6pxXTJpG5uXpfuQtWeppwuTD3d/fg649x+sb/yKbmvHWfIzMoi0J2dJRMW87Bqdnt7eBbePgf3eZi5akbk3T58+M7D+yCa99r2ztcmlOcYwL5YWQRgLcEp6wmkAaA6ikUA1hKcsp5AGgCq4zZUAFZ5HJxyLkBmr+YG/fQQSCMAC6A/VhaBJVoIF2ihjz2z/5frZZ8pFAE6Y2URmE2wAac8CWJ5fqLtbGBLqbat3ydMB/0IswGgCVYWAVhrbjhLTW053+cpYTYANEGxCMBac8NZamrL+T5PCbMBoAluQwVglbnhLDW1bf0+U0E/PYTZALAPVhZhe+fCK3oJtQBie5z3PW8bwC5ZWYSNCYWBfVgT9NOD3s91x+Oxm2MFMJeVRQBII3UQDgAUpVgEgDRSB+EAQFFuQwWgKcfj8VWo7wvg78bx8DysCNwBgNpYWQSgNbUViiHU2ScAWEWxCAAAQESxCAAAQESxCAAAQETADXSs0iCQlO56/243ANJr/Pro2kc2Vhahb61eCOfqfftSu1vYXqsa+1tjn0irl/nDWy1fP1ruO42xsgiwE738JbqX7aAtxh2wR1YWAQAAiCgWAQAAiCgWAQAAiCgWoW+9By/0vn01Ee4B9KTlc1fLfacxwziOpfsAAABAZawsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEFEsAgAAEPn/oy20RR36Ge8AAAAASUVORK5CYII=\n", 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" ] @@ -1841,12 +2506,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (1.4) A* search search: 154.2 path cost, 944 states reached\n" + " Weighted (1.4) A* search search: 154.2 path cost, 944 states reached\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -1860,12 +2525,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (2) A* search search: 162.8 path cost, 782 states reached\n" + " Weighted (2) A* search search: 162.8 path cost, 782 states reached\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1889,14 +2554,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 104, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1929,12 +2594,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (1.4) A* search search: 133.0 path cost, 440 states reached\n" + " Weighted (1.4) A* search search: 133.0 path cost, 440 states reached\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1948,12 +2613,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (2) A* search search: 134.2 path cost, 418 states reached\n" + " Weighted (2) A* search search: 134.2 path cost, 418 states reached\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1986,14 +2651,12 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "scrolled": false - }, + "execution_count": 105, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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" ] @@ -2026,12 +2689,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (1.4) A* search search: 124.1 path cost, 975 states reached\n" + " Weighted (1.4) A* search search: 124.1 path cost, 976 states reached\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2045,12 +2708,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (2) A* search search: 128.6 path cost, 879 states reached\n" + " Weighted (2) A* search search: 128.6 path cost, 879 states reached\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -2081,14 +2744,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 106, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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" ] @@ -2121,12 +2784,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (1.4) A* search search: 127.4 path cost, 1,289 states reached\n" + " Weighted (1.4) A* search search: 127.4 path cost, 1,290 states reached\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -2140,12 +2803,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " (b) Weighted (2) A* search search: 140.4 path cost, 982 states reached\n" + " Weighted (2) A* search search: 140.4 path cost, 980 states reached\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -2159,7 +2822,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Greedy best-first search search: 151.6 path cost, 826 states reached\n" + " Greedy best-first search search: 151.6 path cost, 830 states reached\n" ] } ], @@ -2179,7 +2842,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -2210,7 +2873,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ @@ -2246,7 +2909,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -2255,7 +2918,7 @@ "['suck', {5: ['forward', 'suck'], 7: []}]" ] }, - "execution_count": 52, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2275,7 +2938,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -2291,7 +2954,7 @@ " 8: []}" ] }, - "execution_count": 53, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -2310,7 +2973,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -2336,7 +2999,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 116, "metadata": { "scrolled": false }, @@ -2344,10 +3007,10 @@ { "data": { "text/plain": [ - "2.6724" + "2.6728" ] }, - "execution_count": 78, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -2377,6 +3040,7 @@ " return min(possible_bs, key=lambda b: abs(N - sum(b**i for i in range(1, d+1))))\n", "\n", "def edepth_reduction(d, N, b=2.67):\n", + " pass\n", " \n", " \n", "\n", @@ -2393,7 +3057,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -2428,11 +3092,10 @@ " 26: 621,\n", " 27: 368,\n", " 28: 63,\n", - " 29: 19,\n", - " 30: 0}" + " 29: 19}" ] }, - "execution_count": 72, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -2443,15 +3106,14 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 123, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 24min 7s, sys: 11.6 s, total: 24min 19s\n", - "Wall time: 24min 44s\n" + "Wall time: 7min 56s\n" ] } ], @@ -2461,45 +3123,15 @@ }, { "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2 & 5 & 6 & 6 && 1.79 & 2.00 & 2.00\n", - " 4 & 33 & 12 & 12 && 2.06 & 1.49 & 1.49\n", - " 6 & 128 & 24 & 19 && 2.01 & 1.42 & 1.34\n", - " 8 & 368 & 48 & 31 && 1.91 & 1.40 & 1.30\n", - "10 & 1033 & 116 & 48 && 1.85 & 1.43 & 1.27\n", - "12 & 2672 & 279 & 84 && 1.80 & 1.45 & 1.28\n", - "14 & 6783 & 678 & 174 && 1.77 & 1.47 & 1.31\n", - "16 & 17270 & 1683 & 364 && 1.74 & 1.48 & 1.32\n", - "18 & 41558 & 4102 & 751 && 1.72 & 1.49 & 1.34\n", - "20 & 91493 & 9905 & 1318 && 1.69 & 1.50 & 1.34\n", - "22 & 175921 & 22955 & 2548 && 1.66 & 1.50 & 1.34\n", - "24 & 290082 & 53039 & 5733 && 1.62 & 1.50 & 1.36\n", - "CPU times: user 6min, sys: 3.63 s, total: 6min 4s\n", - "Wall time: 6min 13s\n" - ] - } - ], - "source": [ - "%time report8(table30, 20, range(26, 31, 2))" - ] - }, - { - "cell_type": "code", - "execution_count": 70, + "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "26 & 395355 & 110372 & 10080 && 1.58 & 1.50 & 1.35\n", - "28 & 463234 & 202565 & 22055 && 1.53 & 1.49 & 1.36\n" + "26 & 398123 & 108468 & 10005 && 1.58 & 1.50 & 1.35\n", + "28 & 462049 & 201835 & 22370 && 1.53 & 1.49 & 1.36\n" ] }, { @@ -2507,11 +3139,11 @@ "evalue": "division by zero", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mreport8\u001b[0;34m(table8, M, Ds, searchers)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0msearcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mnodes\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_counts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'result'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mnodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mebf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36mreport8\u001b[1;34m(table8, M, Ds, searchers)\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0msearcher\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mnodes\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mproblem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_counts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'result'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mnodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mebf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mline\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\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero" ] } ], @@ -2521,71 +3153,30 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time report8(table30, 20, range(26, 31, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 133, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 116 116 ['A']\n", - "140 0 140 ['A', 'S']\n", - "0 83 83 ['A']\n", - "118 0 118 ['A', 'T']\n", - "0 45 45 ['A']\n", - "75 0 75 ['A', 'Z']\n", - "0 176 176 ['B']\n", - "101 92 193 ['B', 'P']\n", - "211 0 211 ['B', 'F']\n", - "0 77 77 ['B']\n", - "90 0 90 ['B', 'G']\n", - "0 100 100 ['B']\n", - "101 0 101 ['B', 'P']\n", - "0 80 80 ['B']\n", - "85 0 85 ['B', 'U']\n", - "0 87 87 ['C']\n", - "120 0 120 ['C', 'D']\n", - "0 109 109 ['C']\n", - "138 0 138 ['C', 'P']\n", - "0 128 128 ['C']\n", - "146 0 146 ['C', 'R']\n", - "0 47 47 ['D']\n", - "75 0 75 ['D', 'M']\n", - "0 62 62 ['E']\n", - "86 0 86 ['E', 'H']\n", - "0 98 98 ['F']\n", - "99 0 99 ['F', 'S']\n", - "0 77 77 ['H']\n", - "98 0 98 ['H', 'U']\n", - "0 85 85 ['I']\n", - "87 0 87 ['I', 'N']\n", - "0 78 78 ['I']\n", - "92 0 92 ['I', 'V']\n", - "0 36 36 ['L']\n", - "70 0 70 ['L', 'M']\n", - "0 86 86 ['L']\n", - "111 0 111 ['L', 'T']\n", - "0 136 136 ['O']\n", - "151 0 151 ['O', 'S']\n", - "0 48 48 ['O']\n", - "71 0 71 ['O', 'Z']\n", - "0 93 93 ['P']\n", - "97 0 97 ['P', 'R']\n", - "0 65 65 ['R']\n", - "80 0 80 ['R', 'S']\n", - "0 127 127 ['U']\n", - "142 0 142 ['U', 'V']\n" + "ename": "NameError", + "evalue": "name 'sld' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mL\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mromania\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mratio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mastar_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRouteProblem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mromania\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath_cost\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mnums\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mratio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 1.7, 1.6 # 1.26, 1.2 for adjacent cities\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mL\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mromania\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mratio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mastar_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRouteProblem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mromania\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath_cost\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mL\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mnums\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mratio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 1.7, 1.6 # 1.26, 1.2 for adjacent cities\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36mratio\u001b[1;34m(a, b)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mL\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mromania\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mratio\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mreturn\u001b[0m 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"execute_result" } ], "source": [ @@ -2602,30 +3193,24 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 134, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 300, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'sld' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msld\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'sld' is not defined" + ] } ], "source": [ "sld" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -2644,7 +3229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.8.6" } }, "nbformat": 4,