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

7 files changed, 1125 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Entries
new file mode 100644
index 00000000..9e60b950
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Entries
@@ -0,0 +1,5 @@
+/mixexp_data.txt/1.1.1.1/Wed May 29 15:59:54 2002//
+/mixexp_graddesc.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/mixexp_plot.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/sprinkler.bif/1.1.1.1/Wed May 29 15:59:54 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Repository
new file mode 100644
index 00000000..cd252fd6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/static/Misc
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Root b/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_data.txt b/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_data.txt
new file mode 100644
index 00000000..9bff9448
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_data.txt
@@ -0,0 +1,1000 @@
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+  0.48   0.65
+  0.65   1.18
+  0.70   0.73
+ -0.97   0.62
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+  0.76   0.57
+  0.32   0.49
+  0.46   0.62
+  0.95   1.14
+  0.84   0.74
+ -0.78   0.37
+  0.19   0.44
+  0.39   0.44
+  0.16   0.04
+ -0.88   1.11
+ -0.62   0.78
+ -0.20   0.06
+ -0.73   0.87
+  0.49   0.56
+ -0.68   0.67
+  0.31  -0.22
+  0.99   0.74
+  0.50   0.35
+ -0.46   0.75
+ -0.50   0.53
+ -0.02   0.03
+ -0.42   0.23
+  0.91   0.59
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+ -0.57   0.73
+  0.67   0.50
+ -0.52   0.32
+ -0.35   0.15
+ -0.07   0.01
+ -0.02   0.22
+  0.23   0.29
+ -0.10  -0.44
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+  0.43   0.51
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+ -0.19   0.14
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+  0.97   0.99
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+ -0.49   0.18
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+ -0.13  -0.01
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+ -0.64   0.28
+ -0.49   0.44
+ -0.85   0.72
+  0.73   0.74
+  0.43   0.32
+ -0.36   0.44
+  0.30   0.08
+ -0.58   0.28
+ -0.40   0.40
+ -0.69   0.99
+ -0.54   0.30
+ -0.04   0.03
+  0.13   0.33
+  0.10   0.02
+  0.20   0.42
+  0.97   0.93
+ -0.98   1.33
+ -0.60   0.05
+ -0.11  -0.23
+ -0.82   0.75
+ -0.35   0.47
+  0.14   0.11
+  0.87   1.13
+ -0.34   0.66
+ -0.84   1.18
+ -0.82   1.03
+ -0.53   0.61
+  0.60   0.40
+  0.90   1.08
+ -0.56   0.73
+  0.89   0.47
+ -0.72   0.70
+  0.15   0.24
+  0.95   1.05
+  0.63   0.36
+ -0.76   0.64
+  0.38   0.53
+  0.55   0.62
+  0.42   0.35
+ -0.91   0.88
+ -0.93   0.94
+ -0.64   0.18
+ -0.99   1.08
+ -0.71   1.04
+  0.64   0.13
+ -0.48   0.51
+ -0.10  -0.10
+  0.67   0.86
+ -0.83   0.69
+ -0.25   0.18
+ -0.07   0.17
+  0.13   0.11
+  0.77   1.05
+  0.01   0.34
+ -0.12   0.02
+  0.50   0.62
+ -0.07  -0.19
+  0.76   1.08
+ -0.68   0.23
+  0.18   0.01
+ -0.55   1.31
+  0.68   0.83
+ -0.08   0.12
+  0.31   0.53
+  0.35   0.29
+ -0.61   0.51
+ -0.18   0.25
+  0.50   0.58
+ -0.36   0.33
+  0.46  -0.02
+  0.72   0.96
+ -0.56   0.41
+  0.73   0.96
+ -0.14  -0.15
+  0.08   0.08
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+  0.15  -0.25
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+ -0.12   0.11
+  0.12  -0.57
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+ -0.63   0.67
+ -0.44   0.31
+  0.84   0.99
+ -0.74   0.56
+ -0.74   0.51
+  0.25   0.20
+  0.76   0.88
+  0.44   0.49
+  0.32   0.42
+ -0.44   0.87
+  0.33   0.62
+ -0.76   1.23
+  0.74   1.40
+  0.81   0.39
+ -0.40   0.23
+  0.16   0.15
+ -0.54   0.92
+ -0.44   0.64
+  0.85   1.25
+  0.27   0.41
+ -0.94   0.76
+  0.65   0.56
+  0.87   0.82
+ -0.04  -0.10
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+ -0.78   0.77
+ -0.80   0.54
+ -0.04   0.23
+  0.21   0.30
+  0.71   0.64
+  0.51   0.43
+ -0.38   0.33
+ -0.32   0.37
+  0.77   0.95
+ -0.91   0.89
+  0.79   0.70
+ -0.94   0.78
+ -0.05  -0.18
+  0.85   0.98
+ -0.33   0.61
+ -0.51   0.82
+ -0.63   0.47
+ -0.77   0.40
+ -0.56   0.89
+  0.67   0.68
+ -0.87   1.17
+ -0.25   0.43
+  0.17   0.44
+ -0.13   0.20
+ -0.01  -0.14
+  0.87  -0.02
+  0.22   0.05
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+  0.68   0.53
+ -0.69   0.55
+ -0.17   0.28
+ -0.42   0.40
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+ -0.46   0.66
+  0.89   0.73
+ -0.15  -0.02
+  0.30   0.44
+  0.42   0.43
+  0.68   0.87
+ -0.66   0.84
+ -0.18  -0.03
+ -0.86   0.96
+ -0.93   1.08
+ -0.34   0.05
+  0.42   0.40
+ -0.36   0.34
+ -0.44  -0.30
+  0.80   0.66
+ -0.01   0.24
+ -0.40  -0.03
+ -0.47   0.46
+ -0.15   0.07
+ -0.41  -0.54
+  0.25  -0.16
+  0.86   0.92
+  0.35   0.51
+  0.90   1.17
+ -0.82   0.74
+ -0.90   1.10
+  0.88   1.01
+  0.95   0.89
+  0.01   0.23
+  0.54   0.70
+ -0.37   0.33
+ -0.12  -0.71
+ -0.47   0.88
+  0.24   0.47
+ -0.17   0.35
+  0.68   0.56
+ -0.93   0.82
+  0.60   0.86
+  0.49   0.86
+  0.78   0.81
+ -0.01   0.26
+ -0.96   1.34
+ -0.71   0.96
+  0.17   0.37
+ -0.94   0.53
+ -0.42   0.77
+  0.12  -0.05
+  0.84   0.87
+ -0.57   0.48
+ -0.54   0.51
+ -0.98   0.56
+ -0.07  -0.16
+ -0.49   0.47
+ -0.08   0.28
+ -0.59   0.89
+  0.32   0.11
+ -0.59   0.63
+  0.08   0.34
+  0.24   0.21
+ -0.35   0.52
+ -0.18  -0.07
+  0.70   0.56
+ -0.05   0.24
+ -0.13  -0.36
+  0.24   0.14
+  0.54   0.55
+  0.17  -0.32
+ -0.36   0.61
+ -0.64   0.70
+  0.92   1.14
+  0.35   0.29
+ -0.47   0.70
+ -0.96   0.97
+  0.79   0.53
+  0.54   0.32
+ -0.19   0.41
+ -0.41   0.57
+  0.77   0.79
+ -0.53   0.53
+ -0.90   0.85
+  0.54   0.38
+ -0.42   0.77
+  0.02   0.19
+ -0.68   0.34
+ -0.87   0.48
+ -0.37   0.60
+ -0.46   0.47
+  0.08  -0.01
+  0.51   0.64
+ -0.53   0.06
+  0.87   0.82
+ -0.25   0.41
+ -0.46  -0.06
+  0.33   0.36
+ -0.05   0.01
+ -1.00   0.66
+ -0.42   0.10
+  0.08   0.53
+ -0.98   0.72
+  0.24   0.41
+  0.18   0.36
+ -0.24   0.62
+  0.56   0.35
+  0.39   0.18
+  0.76   0.27
+  1.00   0.68
+ -0.52   0.39
+ -0.66   0.75
+  0.53   0.67
+  0.77   0.62
+  0.84   0.70
+  0.07  -0.28
+ -0.67   0.86
+ -0.75   0.92
+  0.42   0.48
+ -0.32   0.59
+  0.39   0.43
+  0.79   1.31
+  0.34   0.43
+  0.48   0.86
+ -0.50   0.81
+  0.94   1.67
+  0.66   0.02
+ -0.28   0.02
+  0.89   1.28
+ -0.74   1.16
+  0.81   0.75
+  0.96   0.12
+ -0.63   0.53
+ -0.86   1.20
+  0.61   0.56
+  0.53   0.95
+  0.20   0.50
+ -0.07  -0.15
+  0.28  -0.11
+ -0.23   0.47
+  0.02   0.29
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_graddesc.m b/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_graddesc.m
new file mode 100644
index 00000000..534d9e58
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_graddesc.m
@@ -0,0 +1,51 @@
+
+%%%%%%%%%%
+
+function [theta, eta] = mixture_of_experts(q, data, num_iter, theta, eta)
+% MIXTURE_OF_EXPERTS Fit a piecewise linear regression model using stochastic gradient descent.
+% [theta, eta] = mixture_of_experts(q, data, num_iter)
+%
+% Inputs:
+% q = number of pieces (experts)
+% data(l,:) = input example l 
+% 
+% Outputs:
+% theta(i,:) = regression vector for expert i
+% eta(i,:) = softmax (gating) params for expert i
+
+[num_cases dim] = size(data);
+data = [ones(num_cases,1) data]; % prepend with offset
+mu = 0.5; % step size
+sigma = 1; % variance of noise
+
+if nargin < 4
+  theta = 0.1*rand(q, dim);
+  eta = 0.1*rand(q, dim);
+end
+
+for t=1:num_iter
+  for iter=1:num_cases
+    x = data(iter, 1:dim);
+    ystar = data(iter, dim+1); % target
+    % yhat(i) = E[y | Q=i, x] = prediction of i'th expert
+    yhat = theta * x'; 
+    % gate_prior(i,:) = Pr(Q=i | x)
+    gate_prior = exp(eta * x');
+    gate_prior = gate_prior / sum(gate_prior);
+    % lik(i) = Pr(y | Q=i, x)
+    lik = (1/(sqrt(2*pi)*sigma)) * exp(-(0.5/sigma^2) * ((ystar - yhat) .* (ystar - yhat)));
+    % gate_posterior(i,:) = Pr(Q=i | x, y)
+    gate_posterior = gate_prior .* lik;
+    gate_posterior = gate_posterior / sum(gate_posterior);
+    % Update
+    eta = eta + mu*(gate_posterior - gate_prior)*x;
+    theta = theta + mu*(gate_posterior .* (ystar - yhat))*x;
+  end
+
+  if mod(t,100)==0
+    fprintf(1, 'iter %d\n', t);
+  end
+
+end
+fprintf(1, '\n');
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_plot.m b/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_plot.m
new file mode 100644
index 00000000..bb2a2fec
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/mixexp_plot.m
@@ -0,0 +1,49 @@
+function plot_mixexp(theta, eta, data)
+% PLOT_MIXEXP  Plot the results for a piecewise linear regression model
+% plot_mixexp(theta, eta, data)
+% 
+% data(l,:) = [x y] for example l
+% theta(i,:) = regression vector for expert i
+% eta(i,:) = softmax (gating) params for expert i
+
+numexp = size(theta, 1);
+
+mn = min(data);
+mx = max(data);
+xa = mn(1):0.01:mx(1);
+x = [ones(length(xa),1) xa'];
+% pr(i,l) = posterior probability of expert i on example l
+pr = exp(eta * x');
+pr = pr ./ (ones(numexp,1) * sum(pr));
+% y(i,l) = prediction of expert i for example l
+y = theta * x';
+% yg(l) = weighted prediction  for example l
+yg = sum(y .* pr)';
+
+subplot(3,2,1);
+plot(xa, y(1,:));
+title('expert 1');
+
+subplot(3,2,2);
+plot(xa, y(2,:));
+title('expert 2');
+
+subplot(3,2,3);
+plot(xa, pr(1,:));
+title('gating 1');
+
+subplot(3,2,4);
+plot(xa, pr(2,:));
+title('gating 2');
+
+subplot(3,2,5);
+plot(xa, yg);
+axis([-1 1 -1 2])
+title('prediction');
+
+subplot(3,2,6);
+title('data');
+hold on
+plot(data(:,1), data(:,2), '+');
+hold off
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/Misc/sprinkler.bif b/sourcecodes/bnt-master/BNT/examples/static/Misc/sprinkler.bif
new file mode 100644
index 00000000..8925e79c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/Misc/sprinkler.bif
@@ -0,0 +1,18 @@
+network Grass
+    {}
+variable Cloudy
+    { type discrete[2] {false true}; }
+variable Sprinkler
+    { type discrete[2] {false true}; }
+variable Rain
+    { type discrete[2] {false true}; }
+variable WetGrass
+    { type discrete[2] {false true}; }
+probability (Cloudy)
+    { table 0.5 0.5; }
+probability (Sprinkler | Cloudy)
+    { table 0.5 0.9 0.5 0.1; }
+probability (Rain | Cloudy)
+    { table 0.8 0.2 0.2 0.8; }
+probability (WetGrass | Rain Sprinkler)
+    { table 1.0 0.1 0.1 0.01 0.0 0.9 0.9 0.99; }