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@@ -315,6 +315,41 @@ def main():
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plt.tight_layout()
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pdf.savefig()
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plt.close()
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+
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+ # now we can aggregate and replace the old values
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+ for i in range(0, 3):
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+ tempsteps = 0
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+ tempqual = 0
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+ tempgood = 0
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+ for j in range(0, 10):
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+ tempsteps += solution_steps[i][j]
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+ tempqual += solution_scores[i][j]
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+ tempgood += good_sol_counts[i][j]
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+ solution_steps[i] = tempsteps / 10.0
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+ solution_scores[i] = tempqual / 10.0
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+ good_sol_counts[i] = tempgood / 10.0
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+
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+ figure, axes = plt.subplots(3, 1, True)
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+ figure.suptitle("Hill Climbing Aggegated")
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+
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+ axes[0].plot(range(14, 17), solution_steps, color = 'b')
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+ axes[0].set_xlabel("Problem Instances w/ x cities")
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+ axes[0].set_ylabel("Steps Used", color = 'b')
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+ axes[0].title.set_text('Avg Steps of Algorithm')
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+
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+ axes[1].plot(range(14, 17), solution_scores, color = 'b')
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+ axes[1].set_xlabel("Problem Instances w/ x cities")
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+ axes[1].set_ylabel("Solution Quality", color = 'b')
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+ axes[1].title.set_text('Avg Quality of Solutions')
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+
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+ axes[2].plot(range(14, 17), good_sol_counts, color = 'b')
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+ axes[2].set_xlabel("Problem Instances w/ x cities")
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+ axes[2].set_ylabel("% of runs <= NEOS", color = 'b')
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+ axes[2].title.set_text('Avg % of Algorithms Equal or Better than NEOS')
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+
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+ plt.tight_layout()
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+ pdf.savefig()
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+ plt.close()
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#plt.savefig("hill_climbing.pdf")
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