Mercurial > public > think_complexity
comparison ch5ex6-2.py @ 35:10db8c3a6b83
Chapter 5.5, exercise 6, #2: plot P(k) vs. k for a WS graph.
| author | Brian Neal <bgneal@gmail.com> |
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| date | Wed, 09 Jan 2013 20:51:16 -0600 |
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| 34:66a5e7f7c10f | 35:10db8c3a6b83 |
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| 1 """Chapter 5.5, exercise 6 in Allen Downey's Think Complexity book. | |
| 2 | |
| 3 2. Use the WS model to generate the largest graph you can in a reasonable amount | |
| 4 of time. Plot P(k) versus k and see if you can characterize the tail | |
| 5 behavior. | |
| 6 | |
| 7 """ | |
| 8 import sys | |
| 9 | |
| 10 from matplotlib import pyplot | |
| 11 | |
| 12 from Graph import Vertex | |
| 13 from SmallWorldGraph import SmallWorldGraph | |
| 14 | |
| 15 | |
| 16 | |
| 17 def main(script, n, k, p): | |
| 18 | |
| 19 # create a SmallWorldGraph with n vertices, k regular edges between | |
| 20 # vertices, and rewiring probability p. | |
| 21 | |
| 22 n, k, p = int(n), int(k), float(p) | |
| 23 vs = [Vertex() for i in xrange(n)] | |
| 24 | |
| 25 g = SmallWorldGraph(vs, k, p) | |
| 26 | |
| 27 # retrieve probabilities | |
| 28 p = g.get_p() | |
| 29 | |
| 30 # plot P(k) versus k on a log-log scale | |
| 31 | |
| 32 vals = p.items() | |
| 33 vals.sort(key=lambda t: t[0]) | |
| 34 x, y = zip(*vals) | |
| 35 | |
| 36 assert abs(sum(y) - 1.0) < 1e-6 | |
| 37 | |
| 38 pyplot.clf() | |
| 39 pyplot.xscale('log') | |
| 40 pyplot.yscale('log') | |
| 41 pyplot.title('P(k) versus k') | |
| 42 pyplot.xlabel('k') | |
| 43 pyplot.ylabel('P(k)') | |
| 44 pyplot.plot(x, y, label='P(k) vs. k', color='green', linewidth=3) | |
| 45 pyplot.legend(loc='upper right') | |
| 46 pyplot.show() | |
| 47 | |
| 48 | |
| 49 if __name__ == '__main__': | |
| 50 main(*sys.argv) |