[regression] smaller steps for derivative

regression/code/gradientDescent.m

@@ -30,7 +30,7 @@ end
 
 function gradmse = meanSquaredGradient(x, y, func, p)
   gradmse = zeros(size(p, 1), size(p, 2));
-  h = 1e-5;               % stepsize for derivatives
+  h = 1e-7;               % stepsize for derivatives
   mse = meanSquaredError(x, y, func, p);
   for i = 1:length(p)     % for each coordinate ...
     pi = p;

regression/code/meanSquaredGradientCubic.m

@@ -7,7 +7,7 @@ function dmsedc = meanSquaredGradientCubic(x, y, c)
 %
 % Returns: the derivative of the mean squared error at c.
 
-  h = 1e-5;   % stepsize for derivatives
+  h = 1e-7;   % stepsize for derivatives
   mse = meanSquaredErrorCubic(x, y, c);
   mseh = meanSquaredErrorCubic(x, y, c+h);
   dmsedc = (mseh - mse)/h;

regression/code/plotcubicgradient.m

@@ -1,3 +1,5 @@
+meansquarederrorline;               % generate data
+
 cs = 2.0:0.1:8.0;
 mseg = zeros(length(cs));
 for i = 1:length(cs)

regression/code/plotgradientdescentcubic.m

@@ -1,8 +1,8 @@
 meansquarederrorline;               % generate data
 
 c0 = 2.0;
-eps = 0.0001;
-thresh = 0.1;
+eps = 0.00001;
+thresh = 1.0;
 [cest, cs, mses] = gradientDescentCubic(x, y, c0, eps, thresh);
 
 subplot(2, 2, 1);                   % top left panel

regression/code/plotgradientdescentpower.m

@@ -2,7 +2,7 @@ meansquarederrorline;               % generate data
 
 p0 = [2.0, 1.0];
 eps = 0.00001;
-thresh = 50.0;
+thresh = 1.0;
 [pest, ps, mses] = gradientDescent(x, y, @powerLaw, p0, eps, thresh);
 pest
 
@@ -28,5 +28,3 @@ plot(x, y, 'o');                    % plot original data
 xlabel('Size [m]');
 ylabel('Weight [kg]');
 legend('fit', 'data', 'location', 'northwest');
-pause
-