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forecasting:meeting_minutes_august_31_2016 [2016/09/08 03:31] jobatake created |
forecasting:meeting_minutes_august_31_2016 [2021/09/19 21:59] |
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- | Went over Masaki’s use of 3D plots for zenith-angle normalized plots, etc. | ||
- | He used his own parsing for the date | ||
- | Use of np.meshgrid() and Axes3D() | ||
- | New explanation of basics of linear regression/machine learning. | ||
- | W is the slope | ||
- | b is the intercept | ||
- | X [n x m] | ||
- | 2D matrix of inputs (row = sample; column = data) | ||
- | W [n x 1] | ||
- | 1D column of weights | ||
- | D [m x 1] | ||
- | 1d column of stuff | ||
- | Y = XTW + b*1 (where 1 is a 1 column vector) | ||
- | E = D - Y | ||
- | Error | ||
- | J(W, b) | ||
- | = (1/2m) * ETE | ||
- | = (1/2m) * (|D|2 - DTY - YTD + YTY) | ||
- | = (1/2m) * (|D|2) - (1/m) * DT * (XTW + b*1) + (1/2m)(XWT + b*1T)(XTW + b*1) | ||
- | dJ/dW = 0 and dJ/db = 0 → W = C-1Q | ||
- | dJ/db | ||
- | = (-1/m)DT1 + (1/2m)1T(XTW + l1) + ( | ||
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- | Build functions | ||
- | Explanation of math | ||
- | Handout with math and functions and documentation. | ||
- | Functional specification | ||
- | Focus on “legacy” and the base for continuation. | ||
- | Go by “black box” before understanding. | ||
- | PDFs or slides for presentation | ||
- | 1. We want reference for code and the supporting concepts. | ||
- | 2. We want to focus on learning specific algorithms. | ||
- | 3. We want to have material to have a sense of continuation and legacy in a project and also to support understanding and learning. | ||
- | Setup for continuity and focus on code. | ||
- | |||
- | Main Future Points | ||
- | Git tutorial? | ||
- | Code + Concept Documentation | ||
- | We want to learn Python in-code documentation | ||
- | Meeting Minutes | ||
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- | Code + Concept Documentation Skeleton/Template: | ||
- | Title | ||
- | Function Title | ||
- | parameters/types | ||
- | What it does | ||
- | Explanation | ||
- | Primary Overview | ||
- | Math explanation | ||
- | Notes/Remarks | ||
- | (optional) | ||
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