diego898

clear all;
clc;
%% Parameters
cvx_expert true;
a = 3;
J = 5;
c1 = 1;
c2 = 2;
y = 2;

diego898

function output = parseBrainAmpFile(filename)
%parseBrainAmpFile   Read in and parse a BrainAmp File
%
%   parseBrainAmpFile(filename) will read in the specified file
%   which was exported from the brainamp
%
%   output is struct containing two members:
%       names - a matrix where each row is the name of the data row
%       values - a [#names * #samples] matrix containing the data

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function [approx, error] = taylor_approx(func, point, order, range_offset)
%taylor_approx   Construct, return and plot the taylor series approx.
%
%   taylor_approx(func, point, order, range_offset) will construct the
%   taylor series approx of the string in func to the specified order
%   evaluated at some range offset centered around point.
%
%   approx is the symbolic approximation
%
%   error is the error values in the range

diego898

function color_average(image,diameter,type,thresh)
%% image is the name of the file 'image.format'
%% diameter is the cell size
%% type: either 'circle' or 'square'
%% thresh: from 0 to 1. Size of the gap between cells

[colormat,~,~] = imread(image);
[x,y,z] = size(colormat);

rows = floor(x/diameter);

diego898

Windows Registry Editor Version 5.00

[HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Keyboard Layout]
"Scancode Map"=hex:00,00,00,00,00,00,00,00,03,00,00,00,3a,00,46,00,01,00,3a,00,00,00,00,00

diego898

%% A imple Least Squares problem
% https://en.wikipedia.org/wiki/Least_squares

% We first consider the most basic convex optimization problem, least-squares 
% (also known as linear regression). In a least-squares problem, we seek 
% x \in R^n that minimizes norm(Ax?b)

% Setup parameters
n = 100;
A = randn(2*n,n);

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license: gpl-3.0

diego898

license: gpl-3.0