Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/KPMtools/plotcov2New.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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% PLOTCOV2 - Plots a covariance ellipsoid with axes for a bivariate % Gaussian distribution. % % Usage: % [h, s] = plotcov2(mu, Sigma[, OPTIONS]); % % Inputs: % mu - a 2 x 1 vector giving the mean of the distribution. % Sigma - a 2 x 2 symmetric positive semi-definite matrix giving % the covariance of the distribution (or the zero matrix). % % Options: % 'conf' - a scalar between 0 and 1 giving the confidence % interval (i.e., the fraction of probability mass to % be enclosed by the ellipse); default is 0.9. % 'num-pts' - if the value supplied is n, then (n + 1)^2 points % to be used to plot the ellipse; default is 20. % 'label' - if non-empty, a string that will label the % ellipsoid (default: []) % 'plot-axes' - a 0/1 flag indicating if the ellipsoid's axes % should be plotted (default: 1) % 'plot-opts' - a cell vector of arguments to be handed to PLOT3 % to contol the appearance of the axes, e.g., % {'Color', 'g', 'LineWidth', 1}; the default is {} % 'fill-color' - a color specifier; is this is not [], the % covariance ellipse is filled with this color % (default: []) % % Outputs: % h - a vector of handles on the axis lines % % See also: PLOTCOV3 % Copyright (C) 2002 Mark A. Paskin % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation; either version 2 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, but % WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU % General Public License for more details. % % You should have received a copy of the GNU General Public License % along with this program; if not, write to the Free Software % Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 % USA. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [h, s] = plotcov2New(mu, Sigma, varargin) h = []; s = []; if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end Sigma = checkpsd(Sigma); [p, ... n, ... label, ... plot_axes, ... plot_opts, ... fill_color] = process_options(varargin, 'conf', 0.9, ... 'num-pts', 20, ... 'label', [], ... 'plot-axes', 1, ... 'plot-opts', {}, ... 'fill-color', []); holding = ishold; % Compute the Mahalanobis radius of the ellipsoid that encloses % the desired probability mass. k = conf2mahal(p, 2); % Scale the covariance matrix so the confidence region has unit % Mahalanobis distance. Sigma = Sigma * k; % The axes of the covariance ellipse are given by the eigenvectors of % the covariance matrix. Their lengths (for the ellipse with unit % Mahalanobis radius) are given by the square roots of the % corresponding eigenvalues. [V, D] = eig(full(Sigma)); V = real(V); D = real(D); D = abs(D); % Compute the points on the boundary of the ellipsoid. t = linspace(0, 2*pi, n); u = [cos(t(:))'; sin(t(:))']; w = (V * sqrt(D)) * u; z = repmat(mu(:), [1 n]) + w; h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; if (~isempty(fill_color)) s = patch(z(1, :), z(2, :), fill_color); end % Plot the axes. if (plot_axes) hold on; L = sqrt(diag(D)); h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; end if (~isempty(label)) th = text(mu(1), mu(2), label); set(th, 'FontSize', 18); set(th, 'FontName', 'Times'); set(th, 'FontWeight', 'bold'); set(th, 'FontAngle', 'italic'); set(th, 'HorizontalAlignment', 'center'); end if (~holding & plot_axes) hold off; end