Mercurial > hg > machine-learning-hw5
view plotFit.m @ 5:eddd33e57f6a default tip
Justify loop in trainLinearReg
author | Jordi Gutiérrez Hermoso <jordigh@octave.org> |
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date | Sun, 27 Nov 2011 15:58:14 -0500 |
parents | 0f14514e907f |
children |
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function plotFit(min_x, max_x, mu, sigma, theta, p) %PLOTFIT Plots a learned polynomial regression fit over an existing figure. %Also works with linear regression. % PLOTFIT(min_x, max_x, mu, sigma, theta, p) plots the learned polynomial % fit with power p and feature normalization (mu, sigma). % Hold on to the current figure hold on; % We plot a range slightly bigger than the min and max values to get % an idea of how the fit will vary outside the range of the data points x = (min_x - 15: 0.05 : max_x + 25)'; % Map the X values X_poly = polyFeatures(x, p); X_poly = bsxfun(@minus, X_poly, mu); X_poly = bsxfun(@rdivide, X_poly, sigma); % Add ones X_poly = [ones(size(x, 1), 1) X_poly]; % Plot plot(x, X_poly * theta, '--', 'LineWidth', 2) % Hold off to the current figure hold off end