move regression code from statistics to regression folder

statistics/code/lsq_gradient_sigmoid.m

@@ -1,9 +0,0 @@
-function gradient = lsq_gradient_sigmoid(parameter, x, y)
-h = 1e-6;
-
-gradient = zeros(size(parameter));
-for i = 1:length(parameter)
-    parameter_h = parameter;
-    parameter_h(i) = parameter_h(i) + h;
-    gradient(i) = (lsq_sigmoid_error(parameter_h, x, y) - lsq_sigmoid_error(parameter, x, y)) / h;
-end

statistics/code/lsq_sigmoid_error.m

@@ -1,8 +0,0 @@
-function error = lsq_sigmoid_error(parameter, x, y)
-% p(1) the amplitude
-% p(2) the slope
-% p(3) the x-shift
-% p(4) the y-shift
-
-y_est = parameter(1)./(1+ exp(-parameter(2) .* (x - parameter(3)))) + parameter(4);
-error = mean((y_est - y).^2);

statistics/code/sigmoidal_gradient_descent.m

@@ -1,44 +0,0 @@
-
-
-%% fit the sigmoid
-
-clear 
-close all
-
-load('iv_curve.mat')
-
-figure()
-plot(voltage, current, 'o')
-xlabel('voltate [mV]')
-ylabel('current [pA]')
-
-% amplitude, slope, x-shift, y-shift
-%parameter = [10 0.25 -50, 2.5];
-parameter = [20 0.5 -50, 2.5];
-
-eps = 0.1;
-% do the descent
-gradient = [];
-steps = 0;
-error = [];
-
-while isempty(gradient) || norm(gradient) > 0.01
-    steps = steps + 1; 
-    gradient = lsq_gradient_sigmoid(parameter, voltage, current);
-    error(steps) = lsq_sigmoid_error(parameter, voltage, current);
-    parameter = parameter - eps .* gradient; 
-end
-plot(1:steps, error)
-
-disp('gradient descent done!')
-disp(strcat('final position: ', num2str(parameter)))
-disp(strcat('final error: ', num2str(error(end))))
-
-%% use fminsearch
-parameter = [10 0.5 -50, 2.5];
-
-objective_function = @(p)lsq_sigmoid_error(p, voltage, current);
-param = fminunc(objective_function, parameter);
-disp(param)
-param1 = fminsearch(objective_function, parameter);
-disp(param1)