The previous section served as a short introduction to NumPy. Recall that, in earlier chapters, we gradually improved the runtime performance of the game. The last recorded timing was the one obtained with optimization pass three. We successfully reduced the total runtime down to nearly 44 seconds from the original time of about 106 seconds. NumPy supports vectorized calculation routines such as element-wise multiplication. It internally uses efficient C loops that help run such operations faster. Let's leverage NumPy capabilities to speed up the Gold Hunt game even further.
It is now time to resume the optimization operation for the Gold Hunt problem. Let's start with optimization pass four. We will focus our attention once again on the function, generate_random_numbers. As a refresher, the cProfiler output of the last optimization run reported the total time as ~ 2.6 seconds and a cumulative time, which includes the time...
