Added Simulated Annealing to search notebook (#866)

* A few helper functions

* Added Simulated Annealing

* Updated README.md

README.md

@@ -80,7 +80,7 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
 | 3.24   | A\*-Search                        | `astar_search`                | [`search.py`][search]           | Done | Included |
 | 3.26   | Recursive-Best-First-Search       | `recursive_best_first_search` | [`search.py`][search]           | Done |          |
 | 4.2    | Hill-Climbing                     | `hill_climbing`               | [`search.py`][search]           | Done | Included |
-| 4.5    | Simulated-Annealing               | `simulated_annealing`         | [`search.py`][search]           | Done |          |
+| 4.5    | Simulated-Annealing               | `simulated_annealing`         | [`search.py`][search]           | Done | Included |
 | 4.8    | Genetic-Algorithm                 | `genetic_algorithm`           | [`search.py`][search]           | Done | Included |
 | 4.11   | And-Or-Graph-Search               | `and_or_graph_search`         | [`search.py`][search]           | Done |          |
 | 4.21   | Online-DFS-Agent                  | `online_dfs_agent`            | [`search.py`][search]           |      |          |

notebook.py

@@ -1070,3 +1070,19 @@ def plot_NQueens(solution):
             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