Gist c432d79c438c772e0332

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Gist c432d79c438c772e0332

✓ Published0🌍 Public

Last edited Oct 1, 2014

Created on Oct 1, 2014

This visualization demonstrates a regression analysis workflow from a Julia notebook, showing how a random feature map (phi) transforms 2D samples into a 250-dimensional space for least-squares fitting. The code computes the optimal weight vector w using normal equations with ridge regularization, evaluating the objective function across different lambda values. It uses Julia’s linear algebra operations (`transpose`, `\` for matrix division, `eye`) and `randn` for Gaussian sampling, with SVG rendering and animation to display the fitted model’s error surface and weight trajectories.

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