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comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/gaussian_CPD.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function CPD = gaussian_CPD(bnet, self, varargin)
2 % GAUSSIAN_CPD Make a conditional linear Gaussian distrib.
3 %
4 % CPD = gaussian_CPD(bnet, node, ...) will create a CPD with random parameters,
5 % where node is the number of a node in this equivalence class.
6
7 % To define this CPD precisely, call the continuous (cts) parents (if any) X,
8 % the discrete parents (if any) Q, and this node Y. Then the distribution on Y is:
9 % - no parents: Y ~ N(mu, Sigma)
10 % - cts parents : Y|X=x ~ N(mu + W x, Sigma)
11 % - discrete parents: Y|Q=i ~ N(mu(i), Sigma(i))
12 % - cts and discrete parents: Y|X=x,Q=i ~ N(mu(i) + W(i) x, Sigma(i))
13 %
14 % The list below gives optional arguments [default value in brackets].
15 % (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y) and Q = prod(ns(Q)).)
16 % Parameters will be reshaped to the right size if necessary.
17 %
18 % mean - mu(:,i) is the mean given Q=i [ randn(Y,Q) ]
19 % cov - Sigma(:,:,i) is the covariance given Q=i [ repmat(100*eye(Y,Y), [1 1 Q]) ]
20 % weights - W(:,:,i) is the regression matrix given Q=i [ randn(Y,X,Q) ]
21 % cov_type - if 'diag', Sigma(:,:,i) is diagonal [ 'full' ]
22 % tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i [0]
23 % clamp_mean - if 1, we do not adjust mu(:,i) during learning [0]
24 % clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning [0]
25 % clamp_weights - if 1, we do not adjust W(:,:,i) during learning [0]
26 % cov_prior_weight - weight given to I prior for estimating Sigma [0.01]
27 % cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0]
28 %
29 % e.g., CPD = gaussian_CPD(bnet, i, 'mean', [0; 0], 'clamp_mean', 1)
30
31 if nargin==0
32 % This occurs if we are trying to load an object from a file.
33 CPD = init_fields;
34 clamp = 0;
35 CPD = class(CPD, 'gaussian_CPD', generic_CPD(clamp));
36 return;
37 elseif isa(bnet, 'gaussian_CPD')
38 % This might occur if we are copying an object.
39 CPD = bnet;
40 return;
41 end
42 CPD = init_fields;
43
44 CPD = class(CPD, 'gaussian_CPD', generic_CPD(0));
45
46 args = varargin;
47 ns = bnet.node_sizes;
48 ps = parents(bnet.dag, self);
49 dps = myintersect(ps, bnet.dnodes);
50 cps = myintersect(ps, bnet.cnodes);
51 fam_sz = ns([ps self]);
52
53 CPD.self = self;
54 CPD.sizes = fam_sz;
55
56 % Figure out which (if any) of the parents are discrete, and which cts, and how big they are
57 % dps = discrete parents, cps = cts parents
58 CPD.cps = find_equiv_posns(cps, ps); % cts parent index
59 CPD.dps = find_equiv_posns(dps, ps);
60 ss = fam_sz(end);
61 psz = fam_sz(1:end-1);
62 dpsz = prod(psz(CPD.dps));
63 cpsz = sum(psz(CPD.cps));
64
65 % set default params
66 CPD.mean = randn(ss, dpsz);
67 CPD.cov = 100*repmat(eye(ss), [1 1 dpsz]);
68 CPD.weights = randn(ss, cpsz, dpsz);
69 CPD.cov_type = 'full';
70 CPD.tied_cov = 0;
71 CPD.clamped_mean = 0;
72 CPD.clamped_cov = 0;
73 CPD.clamped_weights = 0;
74 CPD.cov_prior_weight = 0.01;
75 CPD.cov_prior_entropic = 0;
76 nargs = length(args);
77 if nargs > 0
78 CPD = set_fields(CPD, args{:});
79 end
80
81 % Make sure the matrices have 1 dimension per discrete parent.
82 % Bug fix due to Xuejing Sun 3/6/01
83 CPD.mean = myreshape(CPD.mean, [ss ns(dps)]);
84 CPD.cov = myreshape(CPD.cov, [ss ss ns(dps)]);
85 CPD.weights = myreshape(CPD.weights, [ss cpsz ns(dps)]);
86
87 % Precompute indices into block structured matrices
88 % to speed up CPD_to_lambda_msg and CPD_to_pi
89 cpsizes = CPD.sizes(CPD.cps);
90 CPD.cps_block_ndx = cell(1, length(cps));
91 for i=1:length(cps)
92 CPD.cps_block_ndx{i} = block(i, cpsizes);
93 end
94
95 %%%%%%%%%%%
96 % Learning stuff
97
98 % expected sufficient statistics
99 CPD.Wsum = zeros(dpsz,1);
100 CPD.WYsum = zeros(ss, dpsz);
101 CPD.WXsum = zeros(cpsz, dpsz);
102 CPD.WYYsum = zeros(ss, ss, dpsz);
103 CPD.WXXsum = zeros(cpsz, cpsz, dpsz);
104 CPD.WXYsum = zeros(cpsz, ss, dpsz);
105
106 % For BIC
107 CPD.nsamples = 0;
108 switch CPD.cov_type
109 case 'full',
110 % since symmetric
111 %ncov_params = ss*(ss-1)/2;
112 ncov_params = ss*(ss+1)/2;
113 case 'diag',
114 ncov_params = ss;
115 otherwise
116 error(['unrecognized cov_type ' cov_type]);
117 end
118 % params = weights + mean + cov
119 if CPD.tied_cov
120 CPD.nparams = ss*cpsz*dpsz + ss*dpsz + ncov_params;
121 else
122 CPD.nparams = ss*cpsz*dpsz + ss*dpsz + dpsz*ncov_params;
123 end
124
125 % for speeding up maximize_params
126 CPD.useC = exist('rep_mult');
127
128 clamped = CPD.clamped_mean & CPD.clamped_cov & CPD.clamped_weights;
129 CPD = set_clamped(CPD, clamped);
130
131 %%%%%%%%%%%
132
133 function CPD = init_fields()
134 % This ensures we define the fields in the same order
135 % no matter whether we load an object from a file,
136 % or create it from scratch. (Matlab requires this.)
137
138 CPD.self = [];
139 CPD.sizes = [];
140 CPD.cps = [];
141 CPD.dps = [];
142 CPD.mean = [];
143 CPD.cov = [];
144 CPD.weights = [];
145 CPD.clamped_mean = [];
146 CPD.clamped_cov = [];
147 CPD.clamped_weights = [];
148 CPD.cov_type = [];
149 CPD.tied_cov = [];
150 CPD.Wsum = [];
151 CPD.WYsum = [];
152 CPD.WXsum = [];
153 CPD.WYYsum = [];
154 CPD.WXXsum = [];
155 CPD.WXYsum = [];
156 CPD.nsamples = [];
157 CPD.nparams = [];
158 CPD.cov_prior_weight = [];
159 CPD.cov_prior_entropic = [];
160 CPD.useC = [];
161 CPD.cps_block_ndx = [];