65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import matplotlib.pyplot as plt
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def plot_learning(fitness_list):
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"""
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Plot the fitness through iterations.
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"""
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plt.plot([i for i in range(len(fitness_list))], fitness_list)
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plt.ylabel("Fitness")
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plt.xlabel("Iteration")
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plt.show()
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def plotTSP(paths, points, num_iters=1):
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"""
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path: List of lists with the different orders in which the nodes are visited
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points: coordinates for the different nodes
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num_iters: number of paths that are in the path list
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"""
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# Unpack the primary TSP path and transform it into a list of ordered
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# coordinates
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x = []; y = []
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for i in paths[0]:
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x.append(points[i][0])
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y.append(points[i][1])
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plt.plot(x, y, 'co')
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# Set a scale for the arrow heads (there should be a reasonable default for this, WTF?)
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# a_scale = float(max(x))/float(100)
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# Draw the older paths, if provided
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if num_iters > 1:
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for i in range(1, num_iters):
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# Transform the old paths into a list of coordinates
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xi = []; yi = [];
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for j in paths[i]:
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xi.append(points[j][0])
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yi.append(points[j][1])
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plt.arrow(xi[-1], yi[-1], (xi[0] - xi[-1]), (yi[0] - yi[-1]),
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color = 'r', length_includes_head = True, ls = 'dashed',
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width = 0.001/float(num_iters))
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for i in range(0, len(x) - 1):
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plt.arrow(xi[i], yi[i], (xi[i+1] - xi[i]), (yi[i+1] - yi[i]),
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color = 'r', length_includes_head = True,
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ls = 'dashed', width = 0.001/float(num_iters))
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# Draw the primary path for the TSP problem
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plt.arrow(x[-1], y[-1], (x[0] - x[-1]), (y[0] - y[-1]),
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color ='g', length_includes_head=True)
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for i in range(0,len(x)-1):
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plt.arrow(x[i], y[i], (x[i+1] - x[i]), (y[i+1] - y[i]),
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color = 'g', length_includes_head = True)
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#Set axis too slitghtly larger than the set of x and y
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# plt.xlim(min(x)*1.1, max(x)*1.1)
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# plt.ylim(min(y)*1.1, max(y)*1.1)
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plt.show()
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