From 961feba3c54d6de42dac6a5fbc0bf162953d22aa Mon Sep 17 00:00:00 2001 From: Aabir Abubaker Kar Date: Tue, 6 Mar 2018 11:02:57 -0500 Subject: [PATCH 1/2] Rewrote parts of search.ipynb --- search-4e.ipynb | 3 +- search.ipynb | 314 +++++++++++++++++++++++++++--------------------- 2 files changed, 181 insertions(+), 136 deletions(-) diff --git a/search-4e.ipynb b/search-4e.ipynb index c2d0dae61..1912a7fa8 100644 --- a/search-4e.ipynb +++ b/search-4e.ipynb @@ -1929,6 +1929,7 @@ "execution_count": 52, "metadata": { "button": false, + "collapsed": true, "new_sheet": false, "run_control": { "read_only": false @@ -3822,7 +3823,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.1" }, "widgets": { "state": {}, diff --git a/search.ipynb b/search.ipynb index edcdf592f..c4cf543cc 100644 --- a/search.ipynb +++ b/search.ipynb @@ -13,8 +13,9 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 1, "metadata": { + "collapsed": true, "scrolled": true }, "outputs": [], @@ -53,22 +54,24 @@ "source": [ "## OVERVIEW\n", "\n", - "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular, navigation problem/route finding problem. First, we will start the problem solving by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n", + "Here, we learn about a specific kind of problem solving - building goal-based agents that can plan ahead to solve problems. In particular, we examine navigation problem/route finding problem. We must begin by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms.\n", + "\n", + "Search algorithms can be classified into two types:\n", "\n", "* **Uninformed search algorithms**: Search algorithms which explore the search space without having any information about the problem other than its definition.\n", - "* Examples:\n", - " 1. Breadth First Search\n", - " 2. Depth First Search\n", - " 3. Depth Limited Search\n", - " 4. Iterative Deepening Search\n", + " * Examples:\n", + " 1. Breadth First Search\n", + " 2. Depth First Search\n", + " 3. Depth Limited Search\n", + " 4. Iterative Deepening Search\n", "\n", "\n", "* **Informed search algorithms**: These type of algorithms leverage any information (heuristics, path cost) on the problem to search through the search space to find the solution efficiently.\n", - "* Examples:\n", - " 1. Best First Search\n", - " 2. Uniform Cost Search\n", - " 3. A\\* Search\n", - " 4. Recursive Best First Search\n", + " * Examples:\n", + " 1. Best First Search\n", + " 2. Uniform Cost Search\n", + " 3. A\\* Search\n", + " 4. Recursive Best First Search\n", "\n", "*Don't miss the visualisations of these algorithms solving the route-finding problem defined on Romania map at the end of this notebook.*" ] @@ -84,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -247,7 +250,7 @@ "source": [ "The `Problem` class has six methods.\n", "\n", - "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of the class. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", + "* `__init__(self, initial, goal)` : This is what is called a `constructor`. It is the first method called when you create an instance of the class as `Problem(initial, goal)`. The variable `initial` specifies the initial state $s_0$ of the search problem. It represents the beginning state. From here, our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", "\n", "\n", "* `actions(self, state)` : This method returns all the possible actions agent can execute in the given state `state`.\n", @@ -256,7 +259,7 @@ "* `result(self, state, action)` : This returns the resulting state if action `action` is taken in the state `state`. This `Problem` class only deals with deterministic outcomes. So we know for sure what every action in a state would result to.\n", "\n", "\n", - "* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else, of course, `False` is returned.\n", + "* `goal_test(self, state)` : Return a boolean for a given state - `True` if it is a goal state, else `False`.\n", "\n", "\n", "* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n", @@ -276,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -449,13 +452,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `Node` class has nine methods.\n", + "The `Node` class has nine methods. The first is the `__init__` method.\n", "\n", "* `__init__(self, state, parent, action, path_cost)` : This method creates a node. `parent` represents the node that this is a successor of and `action` is the action required to get from the parent node to this node. `path_cost` is the cost to reach current node from parent node.\n", "\n", - "* `__repr__(self)` : This returns the state of this node.\n", - "\n", - "* `__lt__(self, node)` : Given a `node`, this method returns `True` if the state of current node is less than the state of the `node`. Otherwise it returns `False`.\n", + "The next 4 methods are specific `Node`-related functions.\n", "\n", "* `expand(self, problem)` : This method lists all the neighbouring(reachable in one step) nodes of current node. \n", "\n", @@ -465,6 +466,12 @@ "\n", "* `path(self)` : This returns a list of all the nodes that lies in the path from the root to this node.\n", "\n", + "The remaining 4 methods override standards Python functionality for representing an object as a string, the less-than ($<$) operator, the equal-to ($=$) operator, and the \n", + "\n", + "* `__repr__(self)` : This returns the state of this node.\n", + "\n", + "* `__lt__(self, node)` : Given a `node`, this method returns `True` if the state of current node is less than the state of the `node`. Otherwise it returns `False`.\n", + "\n", "* `__eq__(self, other)` : This method returns `True` if the state of current node is equal to the other node. Else it returns `False`.\n", "\n", "* `__hash__(self)` : This returns the hash of the state of current node." @@ -479,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -629,13 +636,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now it's time to define our problem. We will define it by passing `initial`, `goal`, `graph` to `GraphProblem`. So, our problem is to find the goal state starting from the given initial state on the provided graph. Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values." + "Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values." ] }, { "cell_type": "code", - "execution_count": 138, - "metadata": {}, + "execution_count": 5, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "romania_map = UndirectedGraph(dict(\n", @@ -674,13 +683,17 @@ "And `romania_map.locations` contains the positions of each of the nodes. We will use the straight line distance (which is different from the one provided in `romania_map`) between two cities in algorithms like A\\*-search and Recursive Best First Search.\n", "\n", "**Define a problem:**\n", - "Hmm... say we want to start exploring from **Arad** and try to find **Bucharest** in our romania_map. So, this is how we do it." + "Now it's time to define our problem. We will define it by passing `initial`, `goal`, `graph` to `GraphProblem`. So, our problem is to find the goal state starting from the given initial state on the provided graph. \n", + "\n", + "Say we want to start exploring from **Arad** and try to find **Bucharest** in our romania_map. So, this is how we do it." ] }, { "cell_type": "code", - "execution_count": 139, - "metadata": {}, + "execution_count": 6, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" @@ -704,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -729,8 +742,10 @@ }, { "cell_type": "code", - "execution_count": 141, - "metadata": {}, + "execution_count": 8, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -753,8 +768,10 @@ }, { "cell_type": "code", - "execution_count": 142, - "metadata": {}, + "execution_count": 9, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# initialise a graph\n", @@ -804,8 +821,10 @@ }, { "cell_type": "code", - "execution_count": 143, - "metadata": {}, + "execution_count": 10, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def show_map(node_colors):\n", @@ -845,16 +864,16 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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MZSdwG5988omuXbumoKAga0d5JERGRsrX11d9+vSRv7+/teMAAAAAKMiMdEn3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\nEhMTNWnSJEnSzJkz9frrr2vnzp0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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -883,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1031,7 +1050,7 @@ "source": [ "The SimpleProblemSolvingAgentProgram class has six methods: \n", "\n", - "* `__init__(self, intial_state=None)`: This is the `contructor` of the class and is the first method to be called when the class is instantiated. It takes in a keyword argument, `initial_state` which is initially `None`. The argument `intial_state` represents the state from which the agent starts.\n", + "* `__init__(self, intial_state=None)`: This is the `contructor` of the class and is the first method to be called when the class is instantiated. It takes in a keyword argument, `initial_state` which is initially `None`. The argument `initial_state` represents the state from which the agent starts.\n", "\n", "* `__call__(self, percept)`: This method updates the `state` of the agent based on its `percept` using the `update_state` method. It then formulates a `goal` with the help of `formulate_goal` method and a `problem` using the `formulate_problem` method and returns a sequence of actions to solve it (using the `search` method).\n", "\n", @@ -1055,8 +1074,10 @@ }, { "cell_type": "code", - "execution_count": 146, - "metadata": {}, + "execution_count": 13, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "class vacuumAgent(SimpleProblemSolvingAgentProgram):\n", @@ -1096,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1110,20 +1131,20 @@ } ], "source": [ - " state1 = [(0, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Dirty\"]]]\n", - " state2 = [(1, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Dirty\"]]]\n", - " state3 = [(0, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Dirty\"]]]\n", - " state4 = [(1, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Dirty\"]]]\n", - " state5 = [(0, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Clean\"]]]\n", - " state6 = [(1, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Clean\"]]]\n", - " state7 = [(0, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Clean\"]]]\n", - " state8 = [(1, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Clean\"]]]\n", + "state1 = [(0, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Dirty\"]]]\n", + "state2 = [(1, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Dirty\"]]]\n", + "state3 = [(0, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Dirty\"]]]\n", + "state4 = [(1, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Dirty\"]]]\n", + "state5 = [(0, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Clean\"]]]\n", + "state6 = [(1, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Clean\"]]]\n", + "state7 = [(0, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Clean\"]]]\n", + "state8 = [(1, 0), [(0, 0), \"Clean\"], [(1, 0), [\"Clean\"]]]\n", "\n", - " a = vacuumAgent(state1)\n", + "a = vacuumAgent(state1)\n", "\n", - " print(a(state6)) \n", - " print(a(state1))\n", - " print(a(state3))" + "print(a(state6)) \n", + "print(a(state1))\n", + "print(a(state3))" ] }, { @@ -1156,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -1278,7 +1299,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -1346,14 +1367,34 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d55324f7343a4c71a9a2d4da6d037037" - } + "model_id": "4c1f644bb8914a0bb67be8058eae9a50", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type interactive.

\n", + "

\n", + " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another notebook frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-interact',))" + ] }, "metadata": {}, "output_type": "display_data" @@ -1361,8 +1402,28 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b07a3813dd724c51a9b37f646cf2be25" - } + "model_id": "46c4be3b14264343b57d75639ae588b9", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type interactive.

\n", + "

\n", + " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another notebook frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "interactive(children=(ToggleButton(value=False, description='Visualize'), Output()), _dom_classes=('widget-interact',))" + ] }, "metadata": {}, "output_type": "display_data" @@ -1384,7 +1445,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -1400,14 +1461,34 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "523b10cf84e54798a044ee714b864b52" - } + "model_id": "001acd676935473392f015b61161c273", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type interactive.

\n", + "

\n", + " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another notebook frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-interact',))" + ] }, "metadata": {}, "output_type": "display_data" @@ -1415,8 +1496,28 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aecea953f6a448c192ac8e173cf46e35" - } + "model_id": "45f358dec42e42f09590cebb0b447830", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type interactive.

\n", + "

\n", + " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another notebook frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "interactive(children=(ToggleButton(value=False, description='Visualize'), Output()), _dom_classes=('widget-interact',))" + ] }, "metadata": {}, "output_type": "display_data" @@ -1441,7 +1542,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1505,28 +1606,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "735a3dea191a42b6bd97fdfd337ea3e7" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ef445770d70a4b7c9d1544b98a55ca4d" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -1543,7 +1625,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1609,28 +1691,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "61149ffbc02846af97170f8975d4f11d" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "90b1f8f77fdb4207a3570fbe88a0bdf6" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -1648,7 +1711,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1735,7 +1798,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1750,28 +1813,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "46b8200b4a8f47e7b18145234a8469da" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ca9b2d01bbd5458bb037585c719d73fc" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -3915,7 +3959,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.1" } }, "nbformat": 4, From 999d25391cf8d313f85528ab95ce80be1f745764 Mon Sep 17 00:00:00 2001 From: Aabir Abubaker Kar Date: Tue, 6 Mar 2018 17:28:49 -0500 Subject: [PATCH 2/2] Fixed typo and cleared cell output --- search.ipynb | 1863 ++++---------------------------------------------- 1 file changed, 115 insertions(+), 1748 deletions(-) diff --git a/search.ipynb b/search.ipynb index c4cf543cc..7329ccc09 100644 --- a/search.ipynb +++ b/search.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": true, "scrolled": true @@ -87,159 +87,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\n", - "
class Problem(object):\n",
-       "\n",
-       "    """The abstract class for a formal problem. You should subclass\n",
-       "    this and implement the methods actions and result, and possibly\n",
-       "    __init__, goal_test, and path_cost. Then you will create instances\n",
-       "    of your subclass and solve them with the various search functions."""\n",
-       "\n",
-       "    def __init__(self, initial, goal=None):\n",
-       "        """The constructor specifies the initial state, and possibly a goal\n",
-       "        state, if there is a unique goal. Your subclass's constructor can add\n",
-       "        other arguments."""\n",
-       "        self.initial = initial\n",
-       "        self.goal = goal\n",
-       "\n",
-       "    def actions(self, state):\n",
-       "        """Return the actions that can be executed in the given\n",
-       "        state. The result would typically be a list, but if there are\n",
-       "        many actions, consider yielding them one at a time in an\n",
-       "        iterator, rather than building them all at once."""\n",
-       "        raise NotImplementedError\n",
-       "\n",
-       "    def result(self, state, action):\n",
-       "        """Return the state that results from executing the given\n",
-       "        action in the given state. The action must be one of\n",
-       "        self.actions(state)."""\n",
-       "        raise NotImplementedError\n",
-       "\n",
-       "    def goal_test(self, state):\n",
-       "        """Return True if the state is a goal. The default method compares the\n",
-       "        state to self.goal or checks for state in self.goal if it is a\n",
-       "        list, as specified in the constructor. Override this method if\n",
-       "        checking against a single self.goal is not enough."""\n",
-       "        if isinstance(self.goal, list):\n",
-       "            return is_in(state, self.goal)\n",
-       "        else:\n",
-       "            return state == self.goal\n",
-       "\n",
-       "    def path_cost(self, c, state1, action, state2):\n",
-       "        """Return the cost of a solution path that arrives at state2 from\n",
-       "        state1 via action, assuming cost c to get up to state1. If the problem\n",
-       "        is such that the path doesn't matter, this function will only look at\n",
-       "        state2.  If the path does matter, it will consider c and maybe state1\n",
-       "        and action. The default method costs 1 for every step in the path."""\n",
-       "        return c + 1\n",
-       "\n",
-       "    def value(self, state):\n",
-       "        """For optimization problems, each state has a value.  Hill-climbing\n",
-       "        and related algorithms try to maximize this value."""\n",
-       "        raise NotImplementedError\n",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(Problem)" ] @@ -279,171 +129,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\n", - "
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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(Node)" ] @@ -466,7 +154,7 @@ "\n", "* `path(self)` : This returns a list of all the nodes that lies in the path from the root to this node.\n", "\n", - "The remaining 4 methods override standards Python functionality for representing an object as a string, the less-than ($<$) operator, the equal-to ($=$) operator, and the \n", + "The remaining 4 methods override standards Python functionality for representing an object as a string, the less-than ($<$) operator, the equal-to ($=$) operator, and the `hash` function.\n", "\n", "* `__repr__(self)` : This returns the state of this node.\n", "\n", @@ -486,148 +174,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(GraphProblem)" ] @@ -641,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": true }, @@ -690,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": true }, @@ -717,17 +266,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Arad': (91, 492), 'Bucharest': (400, 327), 'Craiova': (253, 288), 'Drobeta': (165, 299), 'Eforie': (562, 293), 'Fagaras': (305, 449), 'Giurgiu': (375, 270), 'Hirsova': (534, 350), 'Iasi': (473, 506), 'Lugoj': (165, 379), 'Mehadia': (168, 339), 'Neamt': (406, 537), 'Oradea': (131, 571), 'Pitesti': (320, 368), 'Rimnicu': (233, 410), 'Sibiu': (207, 457), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Vaslui': (509, 444), 'Zerind': (108, 531)}\n" - ] - } - ], + "outputs": [], "source": [ "romania_locations = romania_map.locations\n", "print(romania_locations)" @@ -742,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": true }, @@ -768,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": true }, @@ -821,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": true }, @@ -864,22 +405,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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MZSdwG5988omuXbumoKAga0d5JERGRsrX11d9+vSRv7+/teMAAAAAKMiMdEn3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\nEhMTNWnSJEnSzJkz9frrr2vnzp0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "show_map(node_colors)" ] @@ -902,144 +432,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(SimpleProblemSolvingAgentProgram)" ] @@ -1074,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1117,19 +512,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Left\n", - "Suck\n", - "Right\n" - ] - } - ], + "outputs": [], "source": [ "state1 = [(0, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Dirty\"]]]\n", "state2 = [(1, 0), [(0, 0), \"Dirty\"], [(1, 0), [\"Dirty\"]]]\n", @@ -1177,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1299,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1367,68 +752,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4c1f644bb8914a0bb67be8058eae9a50", - "version_major": 2, - "version_minor": 0 - }, - "text/html": [ - "

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Failed to display Jupyter Widget of type interactive.

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\n", - "

\n", - " If you're reading this message in another notebook frontend (for example, a static\n", - " rendering on GitHub or NBViewer),\n", - " it may mean that your frontend doesn't currently support widgets.\n", - "

\n" - ], - "text/plain": [ - "interactive(children=(ToggleButton(value=False, description='Visualize'), Output()), _dom_classes=('widget-interact',))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Oradea', romania_map)\n", @@ -1607,7 +874,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "all_node_colors = []\n", @@ -1692,7 +961,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "all_node_colors = []\n", @@ -1814,7 +1085,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "all_node_colors = []\n", @@ -1832,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1849,28 +1122,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e3ddd0260d7d4a8aa62d610976b9568a" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dae485b1f4224c34a88de42d252da76c" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -1888,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "collapsed": true }, @@ -1905,28 +1159,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "15a78d815f0c4ea589cdd5ad40bc8794" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "10450687dd574be2a380e9e40403fa83" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -1935,57 +1170,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9019790cf8324d73966373bb3f5373a8" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b8a3195598da472d996e4e8b81595cb7" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "aabe167a0d6440f0a020df8a85a9206c" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "25d146d187004f4f9db6a7dccdbc7e93" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "68d532810a9e46309415fd353c474a4d" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "# display_visual(user_input = True, algorithm = breadth_first_tree_search)\n", @@ -2026,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": true }, @@ -2079,57 +1268,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "Number of explored nodes by the following heuristic are: 145\n", - "[2, 4, 3, 1, 5, 6, 7, 8, 0]\n", - "[2, 4, 3, 1, 5, 6, 7, 0, 8]\n", - "[2, 4, 3, 1, 0, 6, 7, 5, 8]\n", - "[2, 0, 3, 1, 4, 6, 7, 5, 8]\n", - "[0, 2, 3, 1, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 0, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 0, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 0, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 8, 0]\n", - "Number of explored nodes by the following heuristic are: 153\n", - "[2, 4, 3, 1, 5, 6, 7, 8, 0]\n", - "[2, 4, 3, 1, 5, 6, 7, 0, 8]\n", - "[2, 4, 3, 1, 0, 6, 7, 5, 8]\n", - "[2, 0, 3, 1, 4, 6, 7, 5, 8]\n", - "[0, 2, 3, 1, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 0, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 0, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 0, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 8, 0]\n", - "Number of explored nodes by the following heuristic are: 145\n", - "[2, 4, 3, 1, 5, 6, 7, 8, 0]\n", - "[2, 4, 3, 1, 5, 6, 7, 0, 8]\n", - "[2, 4, 3, 1, 0, 6, 7, 5, 8]\n", - "[2, 0, 3, 1, 4, 6, 7, 5, 8]\n", - "[0, 2, 3, 1, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 0, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 0, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 0, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 8, 0]\n", - "Number of explored nodes by the following heuristic are: 169\n", - "[2, 4, 3, 1, 5, 6, 7, 8, 0]\n", - "[2, 4, 3, 1, 5, 6, 7, 0, 8]\n", - "[2, 4, 3, 1, 0, 6, 7, 5, 8]\n", - "[2, 0, 3, 1, 4, 6, 7, 5, 8]\n", - "[0, 2, 3, 1, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 0, 4, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 0, 6, 7, 5, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 0, 8]\n", - "[1, 2, 3, 4, 5, 6, 7, 8, 0]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Solving the puzzle \n", "puzzle = EightPuzzle()\n", @@ -2161,124 +1302,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(hill_climbing)" ] @@ -2296,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "collapsed": true }, @@ -2348,17 +1374,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Arad', 'Bucharest', 'Craiova', 'Drobeta', 'Eforie', 'Fagaras', 'Giurgiu', 'Hirsova', 'Iasi', 'Lugoj', 'Mehadia', 'Neamt', 'Oradea', 'Pitesti', 'Rimnicu', 'Sibiu', 'Timisoara', 'Urziceni', 'Vaslui', 'Zerind']\n" - ] - } - ], + "outputs": [], "source": [ "distances = {}\n", "all_cities = []\n", @@ -2380,7 +1398,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "collapsed": true }, @@ -2407,7 +1425,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "collapsed": true }, @@ -2456,7 +1474,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "collapsed": true }, @@ -2475,39 +1493,9 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Fagaras',\n", - " 'Neamt',\n", - " 'Iasi',\n", - " 'Vaslui',\n", - " 'Hirsova',\n", - " 'Eforie',\n", - " 'Urziceni',\n", - " 'Bucharest',\n", - " 'Giurgiu',\n", - " 'Pitesti',\n", - " 'Craiova',\n", - " 'Drobeta',\n", - " 'Mehadia',\n", - " 'Lugoj',\n", - " 'Timisoara',\n", - " 'Arad',\n", - " 'Zerind',\n", - " 'Oradea',\n", - " 'Sibiu',\n", - " 'Rimnicu']" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "hill_climbing(tsp)" ] @@ -2631,122 +1619,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

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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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(genetic_algorithm)" ] @@ -2783,114 +1658,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\n", - "
def recombine(x, y):\n",
-       "    n = len(x)\n",
-       "    c = random.randrange(0, n)\n",
-       "    return x[:c] + y[c:]\n",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(recombine)" ] @@ -2906,121 +1676,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(mutate)" ] @@ -3036,122 +1694,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(init_population)" ] @@ -3203,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3223,7 +1768,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3249,7 +1794,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3267,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3285,7 +1830,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3310,7 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3328,7 +1873,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3339,7 +1884,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3358,7 +1903,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3380,7 +1925,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3398,7 +1943,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3416,17 +1961,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['j', 'F', 'm', 'F', 'N', 'i', 'c', 'v', 'm', 'j', 'V', 'o', 'd', 'r', 't', 'V', 'H']\n" - ] - } - ], + "outputs": [], "source": [ "print(current_best)" ] @@ -3440,17 +1977,9 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jFmFNicvmjVodrtVH\n" - ] - } - ], + "outputs": [], "source": [ "current_best_string = ''.join(current_best)\n", "print(current_best_string)" @@ -3469,7 +1998,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3493,7 +2022,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3524,122 +2053,9 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "

\n", - "\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",
-       "
\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "psource(genetic_algorithm)" ] @@ -3653,17 +2069,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current best: Genetic Algorithm\t\tGeneration: 472\t\tFitness: 17\r" - ] - } - ], + "outputs": [], "source": [ "population = init_population(max_population, gene_pool, len(target))\n", "solution, generations = genetic_algorithm_stepwise(population, fitness_fn, gene_pool, f_thres, ngen, mutation_rate)" @@ -3706,7 +2114,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3731,17 +2139,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[['R', 'G', 'G', 'R'], ['R', 'G', 'R', 'R'], ['G', 'R', 'G', 'R'], ['R', 'G', 'R', 'G'], ['G', 'R', 'R', 'G'], ['G', 'R', 'G', 'R'], ['G', 'R', 'R', 'R'], ['R', 'G', 'G', 'G']]\n" - ] - } - ], + "outputs": [], "source": [ "population = init_population(8, ['R', 'G'], 4)\n", "print(population)" @@ -3758,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3777,17 +2177,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['R', 'G', 'R', 'G']\n" - ] - } - ], + "outputs": [], "source": [ "solution = genetic_algorithm(population, fitness, gene_pool=['R', 'G'])\n", "print(solution)" @@ -3802,17 +2194,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n" - ] - } - ], + "outputs": [], "source": [ "print(fitness(solution))" ] @@ -3847,17 +2231,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0, 2, 7, 1, 7, 3, 2, 4], [2, 7, 5, 4, 4, 5, 2, 0], [7, 1, 6, 0, 1, 3, 0, 2], [0, 3, 6, 1, 3, 0, 5, 4], [0, 4, 6, 4, 7, 4, 1, 6]]\n" - ] - } - ], + "outputs": [], "source": [ "population = init_population(100, range(8), 8)\n", "print(population[:5])" @@ -3878,7 +2254,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3910,18 +2286,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[5, 0, 6, 3, 7, 4, 1, 3]\n", - "26\n" - ] - } - ], + "outputs": [], "source": [ "solution = genetic_algorithm(population, fitness, f_thres=25, gene_pool=range(8))\n", "print(solution)\n",