notebook.py

                                                       "Recursive Best First Search"})

            algo_dropdown = widgets.Dropdown(description="Search algorithm: ",
                                             options=sorted(list(algorithm.keys())),
                                             value="Breadth First Tree Search")
            display(algo_dropdown)
        elif algorithm is None:
            print("No algorithm to run.")
            return 0
        
        def slider_callback(iteration):
            # don't show graph for the first time running the cell calling this function
            try:
                show_map(graph_data, node_colors=all_node_colors[iteration])
            except:
                pass
            
        def visualize_callback(Visualize):
            if Visualize is True:
                button.value = False
                
                problem = GraphProblem(start_dropdown.value, end_dropdown.value, romania_map)
                global all_node_colors
                
                user_algorithm = algorithm[algo_dropdown.value]
                
                iterations, all_node_colors, node = user_algorithm(problem)
                solution = node.solution()
                all_node_colors.append(final_path_colors(all_node_colors[0], problem, solution))

                slider.max = len(all_node_colors) - 1
                
                for i in range(slider.max + 1):
                    slider.value = i
                    #time.sleep(.5)
                         
        start_dropdown = widgets.Dropdown(description="Start city: ",
                                          options=sorted(list(node_colors.keys())), value="Arad")
        display(start_dropdown)

        end_dropdown = widgets.Dropdown(description="Goal city: ",
                                        options=sorted(list(node_colors.keys())), value="Fagaras")
        display(end_dropdown)
        
        button = widgets.ToggleButton(value=False)
        button_visual = widgets.interactive(visualize_callback, Visualize=button)
        display(button_visual)
        
        slider = widgets.IntSlider(min=0, max=1, step=1, value=0)
        slider_visual = widgets.interactive(slider_callback, iteration=slider)
        display(slider_visual)


# Function to plot NQueensCSP in csp.py and NQueensProblem in search.py
def plot_NQueens(solution):
    n = len(solution)
    board = np.array([2 * int((i + j) % 2) for j in range(n) for i in range(n)]).reshape((n, n))        
    im = Image.open('images/queen_s.png')
    height = im.size[1]
    im = np.array(im).astype(np.float) / 255
    fig = plt.figure(figsize=(7, 7))
    ax = fig.add_subplot(111)
    ax.set_title('{} Queens'.format(n))
    plt.imshow(board, cmap='binary', interpolation='nearest')
    # NQueensCSP gives a solution as a dictionary
    if isinstance(solution, dict):
        for (k, v) in solution.items():
            newax = fig.add_axes([0.064 + (k * 0.112), 0.062 + ((7 - v) * 0.112), 0.1, 0.1], zorder=1)
            newax.imshow(im)
            newax.axis('off')
    # NQueensProblem gives a solution as a list
    elif isinstance(solution, list):
        for (k, v) in enumerate(solution):
            newax = fig.add_axes([0.064 + (k * 0.112), 0.062 + ((7 - v) * 0.112), 0.1, 0.1], zorder=1)
            newax.imshow(im)
            newax.axis('off')
    fig.tight_layout()
    plt.show()

# Function to plot a heatmap, given a grid
def heatmap(grid, cmap='binary', interpolation='nearest'):
    fig = plt.figure(figsize=(7, 7))
    ax = fig.add_subplot(111)
    ax.set_title('Heatmap')
    plt.imshow(grid, cmap=cmap, interpolation=interpolation)
    fig.tight_layout()
    plt.show()

# Generates a gaussian kernel
def gaussian_kernel(l=5, sig=1.0):
    ax = np.arange(-l // 2 + 1., l // 2 + 1.)
    xx, yy = np.meshgrid(ax, ax)
    kernel = np.exp(-(xx**2 + yy**2) / (2. * sig**2))
    return kernel

# Plots utility function for a POMDP
def plot_pomdp_utility(utility):
    save = utility['0'][0]
    delete = utility['1'][0]
    ask_save = utility['2'][0]
    ask_delete = utility['2'][-1]
    left = (save[0] - ask_save[0]) / (save[0] - ask_save[0] + ask_save[1] - save[1])
    right = (delete[0] - ask_delete[0]) / (delete[0] - ask_delete[0] + ask_delete[1] - delete[1])

    colors = ['g', 'b', 'k']
    for action in utility:
        for value in utility[action]:
            plt.plot(value, color=colors[int(action)])
    plt.vlines([left, right], -20, 10, linestyles='dashed', colors='c')
    plt.ylim(-20, 13)
    plt.xlim(0, 1)
    plt.text(left/2 - 0.05, 10, 'Save')
    plt.text((right + left)/2 - 0.02, 10, 'Ask')
    plt.text((right + 1)/2 - 0.07, 10, 'Delete')
    plt.show()