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added Best First search in search.ipynb #708

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Merged
merged 3 commits into from
Feb 6, 2018
Merged

added Best First search in search.ipynb #708

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6dd92e0
added Best First search
nvinayvarma189 Feb 2, 2018
26ab258
fixed minor conflicts
nvinayvarma189 Feb 2, 2018
ff7e3c7
minor changes
nvinayvarma189 Feb 5, 2018
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127 changes: 125 additions & 2 deletions search.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@
"* Breadth-First Search\n",
"* Uniform Cost Search\n",
"* A\\* Search\n",
"* Best First Search\n",
"* Genetic Algorithm"
]
},
Expand Down Expand Up @@ -445,7 +446,7 @@
"2. Depth First Tree Search - Implemented\n",
"3. Depth First Graph Search - Implemented\n",
"4. Breadth First Search - Implemented\n",
"5. Best First Graph Search\n",
"5. Best First Graph Search - Implemented\n",
"6. Uniform Cost Search - Implemented\n",
"7. Depth Limited Search\n",
"8. Iterative Deepening Search\n",
Expand Down Expand Up @@ -1188,7 +1189,7 @@
],
"source": [
"all_node_colors = []\n",
"romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
"romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
"display_visual(user_input = False, algorithm = astar_search, problem = romania_problem)"
]
},
Expand Down Expand Up @@ -1251,6 +1252,128 @@
"display_visual(user_input = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BEST FIRST SEARCH\n",
"Let's change all the node_colors to starting position and define a different problem statement."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def best_first_graph_search(problem, f):\n",
" \"\"\"Search the nodes with the lowest f scores first.\n",
" You specify the function f(node) that you want to minimize; for example,\n",
" if f is a heuristic estimate to the goal, then we have greedy best\n",
" first search; if f is node.depth then we have breadth-first search.\n",
" There is a subtlety: the line \"f = memoize(f, 'f')\" means that the f\n",
" values will be cached on the nodes as they are computed. So after doing\n",
" a best first search you can examine the f values of the path returned.\"\"\"\n",
" \n",
" # we use these two variables at the time of visualisations\n",
" iterations = 0\n",
" all_node_colors = []\n",
" node_colors = dict(initial_node_colors)\n",
" \n",
" f = memoize(f, 'f')\n",
" node = Node(problem.initial)\n",
" \n",
" node_colors[node.state] = \"red\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" \n",
" if problem.goal_test(node.state):\n",
" node_colors[node.state] = \"green\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" return(iterations, all_node_colors, node)\n",
" \n",
" frontier = PriorityQueue(min, f)\n",
" frontier.append(node)\n",
" \n",
" node_colors[node.state] = \"orange\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" \n",
" explored = set()\n",
" while frontier:\n",
" node = frontier.pop()\n",
" \n",
" node_colors[node.state] = \"red\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" \n",
" if problem.goal_test(node.state):\n",
" node_colors[node.state] = \"green\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" return(iterations, all_node_colors, node)\n",
" \n",
" explored.add(node.state)\n",
" for child in node.expand(problem):\n",
" if child.state not in explored and child not in frontier:\n",
" frontier.append(child)\n",
" node_colors[child.state] = \"orange\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" elif child in frontier:\n",
" incumbent = frontier[child]\n",
" if f(child) < f(incumbent):\n",
" del frontier[incumbent]\n",
" frontier.append(child)\n",
" node_colors[child.state] = \"orange\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
"\n",
" node_colors[node.state] = \"gray\"\n",
" iterations += 1\n",
" all_node_colors.append(dict(node_colors))\n",
" return None\n",
"\n",
"def best_first_search(problem, h=None):\n",
" \"\"\"Best-first graph search is an informative searching algorithm with f(n) = h(n).\n",
" You need to specify the h function when you call best_first_search, or\n",
" else in your Problem subclass.\"\"\"\n",
" h = memoize(h or problem.h, 'h')\n",
" iterations, all_node_colors, node = best_first_graph_search(problem, lambda n: h(n))\n",
" return(iterations, all_node_colors, node)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5ae2d521b74743afa988c462a851c269"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "559c20b044a4469db7f0ab8c3fae1022"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_node_colors = []\n",
"romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n",
"display_visual(user_input = False, algorithm = best_first_search, problem = romania_problem)"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down