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coursera-ml-007-exercises/ex5/plotFit.m

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2014-11-05 11:20:50 +01:00
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