![]() IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The following license only applies to code cells of the notebook.Ĭopyright 2018 Christian Herta, Klaus Strohmenger Is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. It does however not apply to any referenced external media (e.g., images).Įxercise: Multivariate Linear Regression with PyTorch A linear separator is a a vector-threshold pair, (w, k) that satisfies the two relations given above. ![]() The following license applies to the complete notebook, including code cells. It is simply the plane/line that separates the two sets of data (I must say, that formal definitions make simple concepts appear difficult) But let’s take a formal definition. Plot_decision_bounday (X, mean, std, model, ax ) ) return axĪx = plot_data_scatter (X_tensor, y_tensor ) # Here we pass our trained model into the function jet, antialiased = True, shade = True, alpha = 0.5, linewidth = 0. float32 ) # IMPORTANT: As we trained our model with scaled X, we need to scale new data # (here the grid IS new data) also with the same mean and std we already calculated Def plot_decision_bounday (X, mean, std, model, ax ) :
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