From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
---
 sourcecodes/bnt-master/KPMtools/plotgauss2d.m | 130 ++++++++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 sourcecodes/bnt-master/KPMtools/plotgauss2d.m

(limited to 'sourcecodes/bnt-master/KPMtools/plotgauss2d.m')

diff --git a/sourcecodes/bnt-master/KPMtools/plotgauss2d.m b/sourcecodes/bnt-master/KPMtools/plotgauss2d.m
new file mode 100644
index 00000000..a507b258
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMtools/plotgauss2d.m
@@ -0,0 +1,130 @@
+function h=plotgauss2d(mu, Sigma)
+% PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs
+% h=plotgauss2(mu, Sigma)
+%
+
+h = plotcov2(mu, Sigma);
+return;
+
+%%%%%%%%%%%%%%%%%%%%%%%%
+
+% PLOTCOV2 - Plots a covariance ellipse with major and minor axes
+%            for a bivariate Gaussian distribution.
+%
+% Usage:
+%   h = 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' - the number of points to be used to plot the
+%               ellipse; default is 100.
+%
+% This function also accepts options for PLOT.
+%
+% Outputs:
+%   h     - a vector of figure handles to the ellipse boundary and
+%           its major and minor axes
+%
+% See also: PLOTCOV3
+
+% Copyright (C) 2002 Mark A. Paskin
+
+function h = plotcov2(mu, Sigma, varargin)
+
+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
+
+[p, ...
+ n, ...
+ plot_opts] = process_options(varargin, 'conf', 0.9, ...
+					'num-pts', 100);
+h = [];
+holding = ishold;
+if (Sigma == zeros(2, 2))
+  z = mu;
+else
+  % Compute the Mahalanobis radius of the ellipsoid that encloses
+  % the desired probability mass.
+  k = conf2mahal(p, 2);
+  % The major and minor 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.
+  if (issparse(Sigma))
+    [V, D] = eigs(Sigma);
+  else
+    [V, D] = eig(Sigma);
+  end
+  % Compute the points on the surface of the ellipse.
+  t = linspace(0, 2*pi, n);
+  u = [cos(t); sin(t)];
+  w = (k * V * sqrt(D)) * u;
+  z = repmat(mu, [1 n]) + w;
+  % Plot the major and minor axes.
+  L = k * 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{:});
+  hold on;
+  h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ...
+	       [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})];
+end
+
+h = [h; plot(z(1, :), z(2, :), plot_opts{:})];
+if (~holding) hold off; end
+
+%%%%%%%%%%%%
+
+% CONF2MAHAL - Translates a confidence interval to a Mahalanobis
+%              distance.  Consider a multivariate Gaussian
+%              distribution of the form
+%
+%   p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C)))
+%
+%              where MD(x, m, P) is the Mahalanobis distance from x
+%              to m under P:
+%
+%                 MD(x, m, P) = (x - m) * P * (x - m)'
+%
+%              A particular Mahalanobis distance k identifies an
+%              ellipsoid centered at the mean of the distribution.
+%              The confidence interval associated with this ellipsoid
+%              is the probability mass enclosed by it.  Similarly,
+%              a particular confidence interval uniquely determines
+%              an ellipsoid with a fixed Mahalanobis distance.
+%
+%              If X is an d dimensional Gaussian-distributed vector,
+%              then the Mahalanobis distance of X is distributed
+%              according to the Chi-squared distribution with d
+%              degrees of freedom.  Thus, the Mahalanobis distance is
+%              determined by evaluating the inverse cumulative
+%              distribution function of the chi squared distribution
+%              up to the confidence value.
+%
+% Usage:
+% 
+%   m = conf2mahal(c, d);
+%
+% Inputs:
+%
+%   c    - the confidence interval
+%   d    - the number of dimensions of the Gaussian distribution
+%
+% Outputs:
+%
+%   m    - the Mahalanobis radius of the ellipsoid enclosing the
+%          fraction c of the distribution's probability mass
+%
+% See also: MAHAL2CONF
+
+% Copyright (C) 2002 Mark A. Paskin
+
+function m = conf2mahal(c, d)
+
+m = chi2inv(c, d); % matlab stats toolbox
-- 
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