Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex5-008/mlclass-ex5/plotFit.m

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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