csp.ipynb

{
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   "source": [
    "# Constraint Satisfaction Problems (CSPs)\n",
    "\n",
    "This IPy notebook acts as supporting material for topics covered in **Chapter 6 Constraint Satisfaction Problems** of the book* Artificial Intelligence: A Modern Approach*. We make use of the implementations in **csp.py** module. Even though this notebook includes a brief summary of the main topics familiarity with the material present in the book is expected. We will look at some visualizations and solve some of the CSP problems described in the book. Let us import everything from the csp module to get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from csp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review\n",
    "\n",
    "CSPs are a special kind of search problems. Here we don't treat the space as a black box but the state has a particular form and we use that to our advantage to tweak our algorithms to be more suited to the problems. A CSP State is defined by a set of variables which can take values from corresponding domains. These variables can take only certain values in their domains to satisfy the constraints. A set of assignments which satisfies all constraints passes the goal test. Let us start by exploring the CSP class which we will use to model our CSPs. You can keep the popup open and read the main page to get a better idea of the code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%psource CSP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __ _ _init_ _ __ method parameters specify the CSP. Variable can be passed as a list of strings or integers. Domains are passed as dict where key specify the variables and value specify the domains. The variables are passed as an empty list. Variables are extracted from the keys of the domain dictionary. Neighbor is a dict of variables that essentially describes the constraint graph. Here each variable key has a list its value which are the variables that are constraint along with it. The constraint parameter should be a function **f(A, a, B, b**) that **returns true** if neighbors A, B **satisfy the constraint** when they have values **A=a, B=b**. We have additional parameters like nassings which is incremented each time an assignment is made when calling the assign method. You can read more about the methods and parameters in the class doc string. We will talk more about them as we encounter their use. Let us jump to an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Coloring\n",
    "\n",
    "We use the graph coloring problem as our running example for demonstrating the different algorithms in the **csp module**. The idea of map coloring problem is that the adjacent nodes (those connected by edges) should not have the same color throughout the graph. The graph can be colored using a fixed number of colors. Here each node is a variable and the values are the colors that can be assigned to them. Given that the domain will be the same for all our nodes we use a custom dict defined by the **UniversalDict** class. The **UniversalDict** Class takes in a parameter which it returns as value for all the keys of the dict. It is very similar to **defaultdict** in Python except that it does not support item assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R', 'G', 'B']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = UniversalDict(['R','G','B'])\n",
    "s[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our CSP we also need to define a constraint function **f(A, a, B, b)**. In this what we need is that the neighbors must not have the same color. This is defined in the function **different_values_constraint** of the module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource different_values_constraint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CSP class takes neighbors in the form of a Dict. The module specifies a simple helper function named **parse_neighbors** which allows to take input in the form of strings and return a Dict of the form compatible with the **CSP Class**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%pdoc parse_neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **MapColoringCSP** function creates and returns a CSP with the above constraint function and states. The variables our the keys of the neighbors dict and the constraint is the one specified by the **different_values_constratint** function. **australia**, **usa** and **france** are three CSPs that have been created using **MapColoringCSP**. **australia** corresponds to ** Figure 6.1 ** in the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource MapColoringCSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<csp.CSP at 0x7f79ec25ca90>,\n",
       " <csp.CSP at 0x7f79ec262a90>,\n",
       " <csp.CSP at 0x7f79ec262b38>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "australia, usa, france"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions\n",
    "\n",
    "We will now implement few helper functions that will help us visualize the Coloring Problem. We will make some modifications to the existing Classes and Functions for additional book keeping. To begin with we modify the **assign** and **unassign** methods in the **CSP** to add a copy of the assignment to the **assingment_history**. We call this new class **InstruCSP**. This would allow us to see how the assignment evolves over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "class InstruCSP(CSP):\n",
    "    \n",
    "    def __init__(self, variables, domains, neighbors, constraints):\n",
    "        super().__init__(variables, domains, neighbors, constraints)\n",
    "        self.assingment_history = []\n",
    "        \n",
    "    def assign(self, var, val, assignment):\n",
    "        super().assign(var,val, assignment)\n",
    "        self.assingment_history.append(copy.deepcopy(assignment))\n",
    "    \n",
    "    def unassign(self, var, assignment):\n",
    "        super().unassign(var,assignment)\n",
    "        self.assingment_history.append(copy.deepcopy(assignment)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define **make_instru** which takes an instance of **CSP** and returns a **InstruCSP** instance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_instru(csp):\n",
    "    return InstruCSP(csp.variables, csp.domains, csp.neighbors,\n",
    "               csp.constraints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now use a graph defined as a dictonary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their corresponding values are the nodes are they are connected to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neighbors = {\n",
    "    0: [6, 11, 15, 18, 4, 11, 6, 15, 18, 4], \n",
    "    1: [12, 12, 14, 14], \n",
    "    2: [17, 6, 11, 6, 11, 10, 17, 14, 10, 14], \n",
    "    3: [20, 8, 19, 12, 20, 19, 8, 12], \n",
    "    4: [11, 0, 18, 5, 18, 5, 11, 0], \n",
    "    5: [4, 4], \n",
    "    6: [8, 15, 0, 11, 2, 14, 8, 11, 15, 2, 0, 14], \n",
    "    7: [13, 16, 13, 16], \n",
    "    8: [19, 15, 6, 14, 12, 3, 6, 15, 19, 12, 3, 14], \n",
    "    9: [20, 15, 19, 16, 15, 19, 20, 16], \n",
    "    10: [17, 11, 2, 11, 17, 2], \n",
    "    11: [6, 0, 4, 10, 2, 6, 2, 0, 10, 4], \n",
    "    12: [8, 3, 8, 14, 1, 3, 1, 14], \n",
    "    13: [7, 15, 18, 15, 16, 7, 18, 16], \n",
    "    14: [8, 6, 2, 12, 1, 8, 6, 2, 1, 12], \n",
    "    15: [8, 6, 16, 13, 18, 0, 6, 8, 19, 9, 0, 19, 13, 18, 9, 16], \n",
    "    16: [7, 15, 13, 9, 7, 13, 15, 9], \n",
    "    17: [10, 2, 2, 10], \n",
    "    18: [15, 0, 13, 4, 0, 15, 13, 4], \n",
    "    19: [20, 8, 15, 9, 15, 8, 3, 20, 3, 9], \n",
    "    20: [3, 19, 9, 19, 3, 9]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to create an InstruCSP instance for our problem. We are doing this for an instance of **MapColoringProblem** class which inherits from the **CSP** Class. This means that our **make_instru** function will work perfectly for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coloring_problem = MapColoringCSP('RGBY', neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coloring_problem1 = make_instru(coloring_problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtracking Search\n",
    "\n",
    "For solving a CSP the main issue with Naive search algorithms is that they can continue expanding obviously wrong paths. In backtracking search, we check constraints as we go. Backtracking is just the above idea combined with the fact that we are dealing with one variable at a time. Backtracking Search is implemented in the repository as the function **backtracking_search**. This is the same as **Figure 6.5** in the book. The function takes as input a CSP and few other optional parameters which can be used to further speed it up. The function returns the correct assignment if it satisfies the goal. We will discuss these later. Let us solve our **coloring_problem1** with **backtracking_search**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = backtracking_search(coloring_problem1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'R',\n",
       " 1: 'R',\n",
       " 2: 'R',\n",
       " 3: 'R',\n",
       " 4: 'G',\n",
       " 5: 'R',\n",
       " 6: 'G',\n",
       " 7: 'R',\n",
       " 8: 'B',\n",
       " 9: 'R',\n",
       " 10: 'G',\n",
       " 11: 'B',\n",
       " 12: 'G',\n",
       " 13: 'G',\n",
       " 14: 'Y',\n",
       " 15: 'Y',\n",
       " 16: 'B',\n",
       " 17: 'B',\n",
       " 18: 'B',\n",
       " 19: 'G',\n",
       " 20: 'B'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result # A dictonary of assingments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also check the number of assingments made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coloring_problem1.nassigns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us check the total number of assingments and unassingments which is the lentgh ofour assingment history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(coloring_problem1.assingment_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "\n",
    "Next, we define some functions to create the visualisation from the assingment_history of **coloring_problem1**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as the graph that needs to be colored or as a constraint graph for this problem. If interested you can read a dead simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ipython widgets we will be using require the plots in the form of a step function such that there is a graph corresponding to each value. We define the **make_update_step_function** which return such a function. It takes in as inputs the neighbors/graph along with an instance of the **InstruCSP**. This will be more clear with the example below. If this sounds confusing do not worry this is not the part of the core material and our only goal is to help you visualize how the process works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_update_step_function(graph, instru_csp):\n",
    "    \n",
    "    def draw_graph(graph):\n",
    "        # create networkx graph\n",
    "        G=nx.Graph(graph)\n",
    "        # draw graph\n",
    "        pos = nx.spring_layout(G,k=0.15)\n",
    "        return (G, pos)\n",
    "    \n",
    "    G, pos = draw_graph(graph)\n",
    "    \n",
    "    def update_step(iteration):\n",
    "        # here iteration is the index of the assingment_history we want to visualize.\n",
    "        current = instru_csp.assingment_history[iteration]\n",
    "        #  We convert the particular assingment to a default dict so that the color for nodes which \n",
    "        # have not been assigned defaults to black.\n",
    "        current = defaultdict(lambda: 'Black', current)\n",
    "\n",
    "        # Now we use colors in the list and default to black otherwise.\n",
    "        colors = [current[node] for node in G.node.keys()]\n",
    "        # Finally drawing the nodes.\n",
    "        nx.draw(G, pos, node_color=colors, node_size=500)\n",
    "\n",
    "        labels = {label:label for label in G.node}\n",
    "        # Labels shifted by offset so as to not overlap nodes.\n",
    "        label_pos = {key:[value[0], value[1]+0.03] for key, value in pos.items()}\n",
    "        nx.draw_networkx_labels(G, label_pos, labels, font_size=20)\n",
    "\n",
    "        # show graph\n",
    "        plt.show()\n",
    "\n",
    "    return update_step  # <-- this is a function\n",
    "\n",
    "def make_visualize(slider):\n",
    "    ''' Takes an input a slider and returns \n",
    "        callback function for timer and animation\n",
    "    '''\n",
    "    \n",
    "    def visualize_callback(Visualize, time_step):\n",
    "        if Visualize is True:\n",
    "            for i in range(slider.min, slider.max + 1):\n",
    "                slider.value = i\n",
    "                time.sleep(time_step)\n",
    "    \n",
    "    return visualize_callback\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let us plot our problem. We first use the function above to obtain a step function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_func = make_update_step_function(neighbors, coloring_problem1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we set the canvas size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (18.0, 18.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MmJF11123rGtDU/nHP/6Rrl27ZubMmU364J8pU6bkoIMOyqRJk9KlS5cmu24l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B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\n7dy5c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79cc161e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assingment_history)-1, step=1, value=0)\n",
    "w=widgets.interactive(step_func,iteration=iteration_slider)\n",
    "display(w)\n",
    "\n",
    "visualize_callback = make_visualize(iteration_slider)\n",
    "\n",
    "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n",
    "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n",
    "\n",
    "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n",
    "display(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NQueens Visualization\n",
    "\n",
    "Just like the Graph Coloring Problem we will start with defining a few helper functions to help us visualize the assignments as they evolve over time. The **make_plot_board_step_function** behaves similar to the **make_update_step_function** introduced earlier. It initializes a chess board in the form of a 2D grid with alternating 0s and 1s. This is used by **plot_board_step** function which draws the board using matplotlib and adds queens to it. This function also calls the **label_queen_conflicts** which modifies the grid placing 3 in positions in a position where there is a conflict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label_queen_conflicts(assingment,grid):\n",
    "    ''' Mark grid with queens that are under conflict. '''\n",
    "    for col, row in assingment.items(): # check each queen for conflict\n",
    "        row_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n",
    "                         if temp_row == row and temp_col != col}\n",
    "        up_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n",
    "                        if temp_row+temp_col == row+col and temp_col != col}\n",
    "        down_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n",
    "                          if temp_row-temp_col == row-col and temp_col != col}\n",
    "        \n",
    "        # Now marking the grid.\n",
    "        for col, row in row_conflicts.items():\n",
    "                grid[col][row] = 3\n",
    "        for col, row in up_conflicts.items():\n",
    "                grid[col][row] = 3\n",
    "        for col, row in down_conflicts.items():\n",
    "                grid[col][row] = 3\n",
    "\n",
    "    return grid\n",
    "\n",
    "def make_plot_board_step_function(instru_csp):\n",
    "    '''ipywidgets interactive function supports\n",
    "       single parameter as input. This function\n",
    "       creates and return such a function by taking\n",
    "       in input other parameters.\n",
    "    '''\n",
    "    n = len(instru_csp.variables)\n",
    "    \n",
    "    \n",
    "    def plot_board_step(iteration):\n",
    "        ''' Add Queens to the Board.'''\n",
    "        data = instru_csp.assingment_history[iteration]\n",
    "        \n",
    "        grid = [[(col+row+1)%2 for col in range(n)] for row in range(n)]\n",
    "        grid = label_queen_conflicts(data, grid) # Update grid with conflict labels.\n",
    "        \n",
    "        # color map of fixed colors\n",
    "        cmap = matplotlib.colors.ListedColormap(['white','lightsteelblue','red'])\n",
    "        bounds=[0,1,2,3] # 0 for white 1 for black 2 onwards for conflict labels (red).\n",
    "        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
    "        \n",
    "        fig = plt.imshow(grid, interpolation='nearest', cmap = cmap,norm=norm)\n",
    "\n",
    "        plt.axis('off')\n",
    "        fig.axes.get_xaxis().set_visible(False)\n",
    "        fig.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "        # Place the Queens Unicode Symbol\n",
    "        for col, row in data.items():\n",
    "            fig.axes.text(row, col, u\"\\u265B\", va='center', ha='center', family='Dejavu Sans', fontsize=32)\n",
    "        plt.show()\n",
    "    \n",
    "    return plot_board_step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us visualize a solution obtained via backtracking. We use of the previosuly defined **make_instru** function for keeping a history of steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "twelve_queens_csp = NQueensCSP(12)\n",
    "backtracking_instru_queen = make_instru(twelve_queens_csp)\n",
    "result = backtracking_search(backtracking_instru_queen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "backtrack_queen_step = make_plot_board_step_function(backtracking_instru_queen) # Step Function for Widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIiIOKDBFREQcUGCKiIg4oMAUERFx4CsfXLBtV+tQ1eFY6ayB+ebsgaa1ciYW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\nRBxQYIqIiDigwBQREXFAgSkiIuLA/wGx9HtR0bJVGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79cdf15f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (8.0, 8.0)\n",
    "matplotlib.rcParams['font.family'].append(u'Dejavu Sans')\n",
    "\n",
    "iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assingment_history)-1, step=0, value=0)\n",
    "w=widgets.interactive(backtrack_queen_step,iteration=iteration_slider)\n",
    "display(w)\n",
    "\n",
    "visualize_callback = make_visualize(iteration_slider)\n",
    "\n",
    "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n",
    "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n",
    "\n",
    "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n",
    "display(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us finally repeat the above steps for **min_conflicts** solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conflicts_instru_queen = make_instru(twelve_queens_csp)\n",
    "result = min_conflicts(conflicts_instru_queen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conflicts_step = make_plot_board_step_function(conflicts_instru_queen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization has same features as the above. But here it also highlights the conflicts by labeling the conflicted queens with a red background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L+v2mH0mS8mFAdJjZTT2SJH1WA6LDvPfee+nq6or0k0gkSKfTxS5ZklRiBkSH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\ny5cv57nnniMejzNnzhzKy8t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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79ce05f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assingment_history)-1, step=0, value=0)\n",
    "w=widgets.interactive(conflicts_step,iteration=iteration_slider)\n",
    "display(w)\n",
    "\n",
    "visualize_callback = make_visualize(iteration_slider)\n",
    "\n",
    "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n",
    "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n",
    "\n",
    "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n",
    "display(a)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "version": 3
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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