Improved programming chapter

regression/code/errorGradient.m

@@ -1,5 +1,4 @@
 load('lin_regression.mat')
-
 ms = -1:0.5:5;
 ns = -10:1:10;
 
@@ -27,4 +26,4 @@ ylabel('Intercept b')
 zlabel('Mean squared error')
 set(gcf, 'paperunits', 'centimeters', 'papersize', [15, 10.5], ... 
     'paperposition', [0., 0., 15, 10.5])
-saveas(gcf, 'error_gradient', 'pdf')
+saveas(gcf, 'error_gradient', 'pdf')

regression/code/lsqGradient.m

@@ -7,11 +7,8 @@ function gradient = lsqGradient(parameter, x, y)
 %            y, vector of the corresponding measured output values
 %
 % Returns: the gradient as a vector with two elements
-
   h = 1e-6;   % stepsize for derivatives
-
   partial_m = (lsqError([parameter(1)+h, parameter(2)], x, y) - lsqError(parameter, x, y))/ h;
   partial_n = (lsqError([parameter(1), parameter(2)+h], x, y) - lsqError(parameter, x, y))/ h;
-
   gradient = [partial_m, partial_n];
 end