Generate 3D spiral data with labels

Nc = 10;

Ns = 100;

h = 15;

r = 3;

[data, label, run] = generateSpiralDataWithLabels(Nc,Ns,h,r);

function [data, label, run] = generateSpiralDataWithLabels(Nc,Ns,h,r,option)

% Generate 3D, Nc classes data, each with Ns observation

if nargin < 5

    option.display = 1;

end

if nargin < 1

    Nc = 5;

    Ns = 200;

    h = 10;

    r = 2;

    option.display = 1;

end

% Come up with the mu and sigma for each Gaussian

degree_sepa = linspace(0,360,Nc+1);

rad_sepa = degree_sepa(1:end-1)'*pi/180; % make it radian

h_sepa = linspace(0,h,Nc)';

Mu = [r*cos(rad_sepa) r*sin(rad_sepa) h_sepa];

% If you want to input each Gaussian model's parameter manually, just do it

% here.

Sigma = zeros(3,3,Nc);

n = zeros(Nc,1);

for i = 1:Nc

    Sigma(:,:,i) = eye(3);

    n(i) = Ns;

end

% Generate the Gaussian distributed data

% The way the data is added is NOT efficient yet, but it's OK

label = [];

data = [];

run = [];

for i = 1:Nc

    tmp = mvnrnd(Mu(i,:), Sigma(:,:,i), n(i));

    data = [data; tmp];

    label = [label; i*ones(n(i),1)];

    run = [run; (1:n(i))'];

end

if option.display == 1

    % figure; scatter3(Mu(:,1), Mu(:,2), Mu(:,3), 30, h_sepa/h);

    % Plot the data set

    colorList = generateColorList(Nc);

    colorPlot = colorList(label,:);

    figure; scatter3(data(:,1), data(:,2), data(:,3), 30, colorPlot,'filled');

end