generate_data_set.m

最新的模式识别分类工具箱,希望对朋友们有用!

function [features, targets] = generate_data_set(m0, m1, s0, s1, w0, w1, p0, region)

% Generate a new data set given it's Gaussian parameters
% Inputs:
%	m0, m1	- Means of the gaussians for both classes
%	s0, s1 	- Standard deviations of the gaussians for both classes
%	w0, w1 	- Weights of the gaussians for both classes
%	p0			- Probability of class 0
%	region	- Decision region vector: [-x x -y y number_of_points]
% 
% Outputs:
%	features - New features
%	targets  - New targets 

n0 = size(s0,1);
n1 = size(s1,1);

%Get the number of points from the user
N = str2num(char(inputdlg('Enter the number of points in data set:','Generate Data set')));

set(gcf,'pointer','watch');

%First, select the indices for class0 and the indices for class 1
N0 = round(p0*N);
N1 = N - N0;

indices  = randperm(N);
indices0 = indices(1:N0);
indices1 = indices(N0+1:N);

targets 				= zeros(1,N);
targets(indices1) = 1;
features 			= zeros(2,N);

%Now, make the points for class 0
%First, divide N0 to n0 pieces according to w0
w0 = round(N0 * w0);
if (sum(w0) ~= N0),
   w0(1) = w0(1) - (sum(w0) - N0);
end

cw0 = [0 ; cumsum(w0)];

for i = 1:n0,
   %Make w0(i) data points according to the distribution
   if (length(size(s0))>2),
      sigma = squeeze(s0(i,:,:));
   else
      sigma = s0;
   end
   A		= sigma;
   dist 	= A'*randn(2,w0(i));
   dist  = dist + m0(i,:)' * ones(1,w0(i));
   
   %Place them in one of the remaining places of indices0
	features(:,indices0(1+cw0(i):cw0(i+1))) = dist;
end


%Do the same for class 1
%First, divide N1 to n1 pieces according to w1
w1 = round(N1 * w1);
if (sum(w1) ~= N1),
   w1(1) = w1(1) - (sum(w1) - N1);
end
cw1 = [0 ; cumsum(w1)];

for i = 1:n1,
   %Make w1(i) data points according to the distribution
   if (length(size(s1))>2),
      sigma = squeeze(s1(i,:,:));
   else
      sigma = s1;
   end
   A		= sigma;
   dist 	= A'*randn(2,w1(i));
   dist  = dist + m1(i,:)' * ones(1,w1(i));
   
   %Place them in one of the remaining places of indices1
	features(:,indices1(1+cw1(i):cw1(i+1))) = dist;
end

N 		= region(5);
param = max(max(abs(features)));
plot_scatter(features, targets) 
axis([-param,param,-param,param])

set(gcf,'pointer','arrow');