Fix the rosenbrock function #98
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Resolve the issue of no matching shape between the input and the output of the function.
| j = [] | ||
| for e in x: | ||
| r = np.sum(100*(e[1:] - e[:-1]**2.0)**2 + (1-e[:-1])**2.0, axis=0) | ||
| j.append(r) | ||
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Awesome, thanks for this @mamadyonline
Hmmm, as an added challenge, do you think you can do this without for-loops? (i.e., just using vectorization techniques). This is so that we can reduce overhead complexity when given for-loops. Imagine if we run this in 10k particles, the changes might be more pronounced. I'm thinking of something like this:
sum(100.0* np.square(x[1:] - x[:-1]**2.0) + (1 - x[:-1])**2.0)Would you mind checking if this is correct?
Vectorized version to reduce computation overhead for higher dimensions.
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Awesome! @mamadyonline I will merge this now |
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Resolve the issue of no matching shape between
the input and the output of the function.