class Node:\n",
- "\n",
- " """A node in a search tree. Contains a pointer to the parent (the node\n",
- " that this is a successor of) and to the actual state for this node. Note\n",
- " that if a state is arrived at by two paths, then there are two nodes with\n",
- " the same state. Also includes the action that got us to this state, and\n",
- " the total path_cost (also known as g) to reach the node. Other functions\n",
- " may add an f and h value; see best_first_graph_search and astar_search for\n",
- " an explanation of how the f and h values are handled. You will not need to\n",
- " subclass this class."""\n",
- "\n",
- " def __init__(self, state, parent=None, action=None, path_cost=0):\n",
- " """Create a search tree Node, derived from a parent by an action."""\n",
- " self.state = state\n",
- " self.parent = parent\n",
- " self.action = action\n",
- " self.path_cost = path_cost\n",
- " self.depth = 0\n",
- " if parent:\n",
- " self.depth = parent.depth + 1\n",
- "\n",
- " def __repr__(self):\n",
- " return "<Node {}>".format(self.state)\n",
- "\n",
- " def __lt__(self, node):\n",
- " return self.state < node.state\n",
- "\n",
- " def expand(self, problem):\n",
- " """List the nodes reachable in one step from this node."""\n",
- " return [self.child_node(problem, action)\n",
- " for action in problem.actions(self.state)]\n",
- "\n",
- " def child_node(self, problem, action):\n",
- " """[Figure 3.10]"""\n",
- " next = problem.result(self.state, action)\n",
- " return Node(next, self, action,\n",
- " problem.path_cost(self.path_cost, self.state,\n",
- " action, next))\n",
- "\n",
- " def solution(self):\n",
- " """Return the sequence of actions to go from the root to this node."""\n",
- " return [node.action for node in self.path()[1:]]\n",
- "\n",
- " def path(self):\n",
- " """Return a list of nodes forming the path from the root to this node."""\n",
- " node, path_back = self, []\n",
- " while node:\n",
- " path_back.append(node)\n",
- " node = node.parent\n",
- " return list(reversed(path_back))\n",
- "\n",
- " # We want for a queue of nodes in breadth_first_search or\n",
- " # astar_search to have no duplicated states, so we treat nodes\n",
- " # with the same state as equal. [Problem: this may not be what you\n",
- " # want in other contexts.]\n",
- "\n",
- " def __eq__(self, other):\n",
- " return isinstance(other, Node) and self.state == other.state\n",
- "\n",
- " def __hash__(self):\n",
- " return hash(self.state)\n",
- "class GraphProblem(Problem):\n",
- "\n",
- " """The problem of searching a graph from one node to another."""\n",
- "\n",
- " def __init__(self, initial, goal, graph):\n",
- " Problem.__init__(self, initial, goal)\n",
- " self.graph = graph\n",
- "\n",
- " def actions(self, A):\n",
- " """The actions at a graph node are just its neighbors."""\n",
- " return list(self.graph.get(A).keys())\n",
- "\n",
- " def result(self, state, action):\n",
- " """The result of going to a neighbor is just that neighbor."""\n",
- " return action\n",
- "\n",
- " def path_cost(self, cost_so_far, A, action, B):\n",
- " return cost_so_far + (self.graph.get(A, B) or infinity)\n",
- "\n",
- " def find_min_edge(self):\n",
- " """Find minimum value of edges."""\n",
- " m = infinity\n",
- " for d in self.graph.dict.values():\n",
- " local_min = min(d.values())\n",
- " m = min(m, local_min)\n",
- "\n",
- " return m\n",
- "\n",
- " def h(self, node):\n",
- " """h function is straight-line distance from a node's state to goal."""\n",
- " locs = getattr(self.graph, 'locations', None)\n",
- " if locs:\n",
- " if type(node) is str:\n",
- " return int(distance(locs[node], locs[self.goal]))\n",
- "\n",
- " return int(distance(locs[node.state], locs[self.goal]))\n",
- " else:\n",
- " return infinity\n",
- "
class SimpleProblemSolvingAgentProgram:\n",
- "\n",
- " """Abstract framework for a problem-solving agent. [Figure 3.1]"""\n",
- "\n",
- " def __init__(self, initial_state=None):\n",
- " """State is an abstract representation of the state\n",
- " of the world, and seq is the list of actions required\n",
- " to get to a particular state from the initial state(root)."""\n",
- " self.state = initial_state\n",
- " self.seq = []\n",
- "\n",
- " def __call__(self, percept):\n",
- " """[Figure 3.1] Formulate a goal and problem, then\n",
- " search for a sequence of actions to solve it."""\n",
- " self.state = self.update_state(self.state, percept)\n",
- " if not self.seq:\n",
- " goal = self.formulate_goal(self.state)\n",
- " problem = self.formulate_problem(self.state, goal)\n",
- " self.seq = self.search(problem)\n",
- " if not self.seq:\n",
- " return None\n",
- " return self.seq.pop(0)\n",
- "\n",
- " def update_state(self, percept):\n",
- " raise NotImplementedError\n",
- "\n",
- " def formulate_goal(self, state):\n",
- " raise NotImplementedError\n",
- "\n",
- " def formulate_problem(self, state, goal):\n",
- " raise NotImplementedError\n",
- "\n",
- " def search(self, problem):\n",
- " raise NotImplementedError\n",
- "
def hill_climbing(problem):\n",
- " """From the initial node, keep choosing the neighbor with highest value,\n",
- " stopping when no neighbor is better. [Figure 4.2]"""\n",
- " current = Node(problem.initial)\n",
- " while True:\n",
- " neighbors = current.expand(problem)\n",
- " if not neighbors:\n",
- " break\n",
- " neighbor = argmax_random_tie(neighbors,\n",
- " key=lambda node: problem.value(node.state))\n",
- " if problem.value(neighbor.state) <= problem.value(current.state):\n",
- " break\n",
- " current = neighbor\n",
- " return current.state\n",
- "
def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1):\n",
- " """[Figure 4.8]"""\n",
- " for i in range(ngen):\n",
- " population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, pmut)\n",
- " for i in range(len(population))]\n",
- "\n",
- " fittest_individual = fitness_threshold(fitness_fn, f_thres, population)\n",
- " if fittest_individual:\n",
- " return fittest_individual\n",
- "\n",
- "\n",
- " return argmax(population, key=fitness_fn)\n",
- "
def recombine(x, y):\n",
- " n = len(x)\n",
- " c = random.randrange(0, n)\n",
- " return x[:c] + y[c:]\n",
- "
def mutate(x, gene_pool, pmut):\n",
- " if random.uniform(0, 1) >= pmut:\n",
- " return x\n",
- "\n",
- " n = len(x)\n",
- " g = len(gene_pool)\n",
- " c = random.randrange(0, n)\n",
- " r = random.randrange(0, g)\n",
- "\n",
- " new_gene = gene_pool[r]\n",
- " return x[:c] + [new_gene] + x[c+1:]\n",
- "
def init_population(pop_number, gene_pool, state_length):\n",
- " """Initializes population for genetic algorithm\n",
- " pop_number : Number of individuals in population\n",
- " gene_pool : List of possible values for individuals\n",
- " state_length: The length of each individual"""\n",
- " g = len(gene_pool)\n",
- " population = []\n",
- " for i in range(pop_number):\n",
- " new_individual = [gene_pool[random.randrange(0, g)] for j in range(state_length)]\n",
- " population.append(new_individual)\n",
- "\n",
- " return population\n",
- "
def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1):\n",
- " """[Figure 4.8]"""\n",
- " for i in range(ngen):\n",
- " population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, pmut)\n",
- " for i in range(len(population))]\n",
- "\n",
- " fittest_individual = fitness_threshold(fitness_fn, f_thres, population)\n",
- " if fittest_individual:\n",
- " return fittest_individual\n",
- "\n",
- "\n",
- " return argmax(population, key=fitness_fn)\n",
- "