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Changed plotting function for NQueensCSP (aimacode#847)
* Updated README.md * Added function to plot NQueensProblem * Added queen image * Changed plotting function for NQueensCSP * Replaced f'{}' with .format() notation * Added Pillow to travis.yml
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‎.travis.yml‎

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@@ -14,6 +14,7 @@ install:
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- pip install matplotlib
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- pip install networkx
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- pip install ipywidgets
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- pip install Pillow
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script:
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- py.test

‎README.md‎

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@@ -90,7 +90,7 @@ Here is a table of algorithms, the figure, name of the algorithm in the book and
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| 6 | CSP | `CSP` | [`csp.py`][csp] | Done | Included |
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| 6.3 | AC-3 | `AC3` | [`csp.py`][csp] | Done | |
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| 6.5 | Backtracking-Search | `backtracking_search` | [`csp.py`][csp] | Done | Included |
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| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`][csp] | Done | |
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| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`][csp] | Done | Included |
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| 6.11 | Tree-CSP-Solver | `tree_csp_solver` | [`csp.py`][csp] | Done | Included |
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| 7 | KB | `KB` | [`logic.py`][logic] | Done | Included |
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| 7.1 | KB-Agent | `KB_Agent` | [`logic.py`][logic] | Done | |

‎csp.ipynb‎

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‎images/queen_s.png‎

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‎notebook.py‎

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@@ -9,6 +9,7 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image
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import os, struct
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import array
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slider = widgets.IntSlider(min=0, max=1, step=1, value=0)
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slider_visual = widgets.interactive(slider_callback, iteration=slider)
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display(slider_visual)
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display(slider_visual)
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# Function to plot NQueensCSP in csp.py and NQueensProblem in search.py
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def plot_NQueens(solution):
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n = len(solution)
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board = np.array([2 * int((i + j) % 2) for j in range(n) for i in range(n)]).reshape((n, n))
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im = Image.open('images/queen_s.png')
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height = im.size[1]
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im = np.array(im).astype(np.float) / 255
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fig = plt.figure(figsize=(7, 7))
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ax = fig.add_subplot(111)
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ax.set_title('{} Queens'.format(n))
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plt.imshow(board, cmap='binary', interpolation='nearest')
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# NQueensCSP gives a solution as a dictionary
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if isinstance(solution, dict):
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for (k, v) in solution.items():
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newax = fig.add_axes([0.064 + (k * 0.112), 0.062 + ((7 - v) * 0.112), 0.1, 0.1], zorder=1)
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newax.imshow(im)
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newax.axis('off')
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# NQueensProblem gives a solution as a list
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elif isinstance(solution, list):
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for (k, v) in enumerate(solution):
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newax = fig.add_axes([0.064 + (k * 0.112), 0.062 + ((7 - v) * 0.112), 0.1, 0.1], zorder=1)
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newax.imshow(im)
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newax.axis('off')
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fig.tight_layout()
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plt.show()

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