| 89 | 89 | This algorithm uses the gradient calculated from three points to attempt to hill climb towards the access point. Again it was not very successful because it has no mechanism to account for noisy data. The second generation of this algorithm, ''bigtri.py'', attempted to account for noise by surveying nine points in groups of three, averaging each group, and using the averages to compute the gradient. This method was much more successful than the original. The final iteration of this algorithm is ''lsbigtri.py'', which takes the averaging a step farther and computes a least-squared-regression plane using all nine points. To introduce the least squared process we began using the packages scipy and numpy. The functions which make use of the scipy libraries are located in ''datafit.py''. |
