|
|
@@ -330,22 +330,25 @@ def main():
|
|
|
good_sol_counts[i] = tempgood / 10.0
|
|
|
|
|
|
figure, axes = plt.subplots(3, 1, True)
|
|
|
- figure.suptitle("Hill Climbing Aggegated")
|
|
|
+ figure.suptitle("Hill Climbing Aggegated\n")
|
|
|
|
|
|
axes[0].plot(range(14, 17), solution_steps, color = 'b')
|
|
|
axes[0].set_xlabel("Problem Instances w/ x cities")
|
|
|
axes[0].set_ylabel("Steps Used", color = 'b')
|
|
|
axes[0].title.set_text('Avg Steps of Algorithm')
|
|
|
+ axes[0].xticks([14, 15, 16])
|
|
|
|
|
|
axes[1].plot(range(14, 17), solution_scores, color = 'b')
|
|
|
axes[1].set_xlabel("Problem Instances w/ x cities")
|
|
|
axes[1].set_ylabel("Solution Quality", color = 'b')
|
|
|
- axes[1].title.set_text('Avg Quality of Solutions')
|
|
|
+ axes[1].title.set_text('Avg Relative Quality of Solutions')
|
|
|
+ axes[1].xticks([14, 15, 16])
|
|
|
|
|
|
axes[2].plot(range(14, 17), good_sol_counts, color = 'b')
|
|
|
axes[2].set_xlabel("Problem Instances w/ x cities")
|
|
|
axes[2].set_ylabel("% of runs <= NEOS", color = 'b')
|
|
|
axes[2].title.set_text('Avg % of Algorithms Equal or Better than NEOS')
|
|
|
+ axes[2].xticks([14, 15, 16])
|
|
|
|
|
|
plt.tight_layout()
|
|
|
pdf.savefig()
|
