Mercurial > hg > camir-aes2014

annotate toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/mlp_CPD.m @ 0:e9a9cd732c1e tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
rev   line source
wolffd@0 1 function CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped, max_iter, verbose, wthresh, llthresh)
wolffd@0 2 % MLP_CPD Make a CPD from a Multi Layer Perceptron (i.e., feedforward neural network)
wolffd@0 3 %
wolffd@0 4 % We use a different MLP for each discrete parent combination (if there are any discrete parents).
wolffd@0 5 % We currently assume this node (the child) is discrete.
wolffd@0 6 %
wolffd@0 7 % CPD = mlp_CPD(bnet, self, nhidden)
wolffd@0 8 % will create a CPD with random parameters, where self is the number of this node and nhidden the number of the hidden nodes.
wolffd@0 9 % The params are drawn from N(0, s*I), where s = 1/sqrt(n+1), n = length(X).
wolffd@0 10 %
wolffd@0 11 % CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2) allows you to specify the params, where
wolffd@0 12 % w1 = first-layer weight matrix
wolffd@0 13 % b1 = first-layer bias vector
wolffd@0 14 % w2 = second-layer weight matrix
wolffd@0 15 % b2 = second-layer bias vector
wolffd@0 16 % These are assumed to be the same for each discrete parent combination.
wolffd@0 17 % If any of these are [], random values will be created.
wolffd@0 18 %
wolffd@0 19 % CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped) allows you to prevent the params from being
wolffd@0 20 % updated during learning (if clamped = 1). Default: clamped = 0.
wolffd@0 21 %
wolffd@0 22 % CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped, max_iter, verbose, wthresh, llthresh)
wolffd@0 23 % alllows you to specify params that control the M step:
wolffd@0 24 % max_iter - the maximum number of steps to take (default: 10)
wolffd@0 25 % verbose - controls whether to print (default: 0 means silent).
wolffd@0 26 % wthresh - a measure of the precision required for the value of
wolffd@0 27 % the weights W at the solution. Default: 1e-2.
wolffd@0 28 % llthresh - a measure of the precision required of the objective
wolffd@0 29 % function (log-likelihood) at the solution. Both this and the previous condition must
wolffd@0 30 % be satisfied for termination. Default: 1e-2.
wolffd@0 31 %
wolffd@0 32 % For learning, we use a weighted version of scaled conjugated gradient in the M step.
wolffd@0 33
wolffd@0 34 if nargin==0
wolffd@0 35 % This occurs if we are trying to load an object from a file.
wolffd@0 36 CPD = init_fields;
wolffd@0 37 CPD = class(CPD, 'mlp_CPD', discrete_CPD(0,[]));
wolffd@0 38 return;
wolffd@0 39 elseif isa(bnet, 'mlp_CPD')
wolffd@0 40 % This might occur if we are copying an object.
wolffd@0 41 CPD = bnet;
wolffd@0 42 return;
wolffd@0 43 end
wolffd@0 44 CPD = init_fields;
wolffd@0 45
wolffd@0 46 assert(myismember(self, bnet.dnodes));
wolffd@0 47 ns = bnet.node_sizes;
wolffd@0 48
wolffd@0 49 ps = parents(bnet.dag, self);
wolffd@0 50 dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes);
wolffd@0 51 dps = myintersect(ps, dnodes);
wolffd@0 52 cps = myintersect(ps, bnet.cnodes);
wolffd@0 53 dpsz = prod(ns(dps));
wolffd@0 54 cpsz = sum(ns(cps));
wolffd@0 55 self_size = ns(self);
wolffd@0 56
wolffd@0 57 % discrete/cts parent index - which ones of my parents are discrete/cts?
wolffd@0 58 CPD.dpndx = find_equiv_posns(dps, ps);
wolffd@0 59 CPD.cpndx = find_equiv_posns(cps, ps);
wolffd@0 60
wolffd@0 61 CPD.mlp = cell(1,dpsz);
wolffd@0 62 for i=1:dpsz
wolffd@0 63 CPD.mlp{i} = mlp(cpsz, nhidden, self_size, 'softmax');
wolffd@0 64 if nargin >=4 & ~isempty(w1)
wolffd@0 65 CPD.mlp{i}.w1 = w1;
wolffd@0 66 end
wolffd@0 67 if nargin >=5 & ~isempty(b1)
wolffd@0 68 CPD.mlp{i}.b1 = b1;
wolffd@0 69 end
wolffd@0 70 if nargin >=6 & ~isempty(w2)
wolffd@0 71 CPD.mlp{i}.w2 = w2;
wolffd@0 72 end
wolffd@0 73 if nargin >=7 & ~isempty(b2)
wolffd@0 74 CPD.mlp{i}.b2 = b2;
wolffd@0 75 end
wolffd@0 76 W1app(:,:,i)=CPD.mlp{i}.w1;
wolffd@0 77 W2app(:,:,i)=CPD.mlp{i}.w2;
wolffd@0 78 b1app(i,:)=CPD.mlp{i}.b1;
wolffd@0 79 b2app(i,:)=CPD.mlp{i}.b2;
wolffd@0 80 end
wolffd@0 81 if nargin < 8, clamped = 0; end
wolffd@0 82 if nargin < 9, max_iter = 10; end
wolffd@0 83 if nargin < 10, verbose = 0; end
wolffd@0 84 if nargin < 11, wthresh = 1e-2; end
wolffd@0 85 if nargin < 12, llthresh = 1e-2; end
wolffd@0 86
wolffd@0 87 CPD.self = self;
wolffd@0 88 CPD.max_iter = max_iter;
wolffd@0 89 CPD.verbose = verbose;
wolffd@0 90 CPD.wthresh = wthresh;
wolffd@0 91 CPD.llthresh = llthresh;
wolffd@0 92
wolffd@0 93 % sufficient statistics
wolffd@0 94 % Since MLP is not in the exponential family, we must store all the raw data.
wolffd@0 95 %
wolffd@0 96 CPD.W1=W1app; % Extract all the parameters of the node for handling discrete obs parents
wolffd@0 97 CPD.W2=W2app; %
wolffd@0 98 nparaW=[size(W1app) size(W2app)]; %
wolffd@0 99 CPD.b1=b1app; %
wolffd@0 100 CPD.b2=b2app; %
wolffd@0 101 nparab=[size(b1app) size(b2app)]; %
wolffd@0 102
wolffd@0 103 CPD.sizes=bnet.node_sizes(:); % used in CPD_to_table to pump up the node sizes
wolffd@0 104
wolffd@0 105 CPD.parent_vals = []; % X(l,:) = value of cts parents in l'th example
wolffd@0 106
wolffd@0 107 CPD.eso_weights=[]; % weights used by the SCG algorithm
wolffd@0 108
wolffd@0 109 CPD.self_vals = []; % Y(l,:) = value of self in l'th example
wolffd@0 110
wolffd@0 111 % For BIC
wolffd@0 112 CPD.nsamples = 0;
wolffd@0 113 CPD.nparams=prod(nparaW)+prod(nparab);
wolffd@0 114 CPD = class(CPD, 'mlp_CPD', discrete_CPD(clamped, ns([ps self])));
wolffd@0 115
wolffd@0 116 %%%%%%%%%%%
wolffd@0 117
wolffd@0 118 function CPD = init_fields()
wolffd@0 119 % This ensures we define the fields in the same order
wolffd@0 120 % no matter whether we load an object from a file,
wolffd@0 121 % or create it from scratch. (Matlab requires this.)
wolffd@0 122
wolffd@0 123 CPD.mlp = {};
wolffd@0 124 CPD.self = [];
wolffd@0 125 CPD.max_iter = [];
wolffd@0 126 CPD.verbose = [];
wolffd@0 127 CPD.wthresh = [];
wolffd@0 128 CPD.llthresh = [];
wolffd@0 129 CPD.approx_hess = [];
wolffd@0 130 CPD.W1 = [];
wolffd@0 131 CPD.W2 = [];
wolffd@0 132 CPD.b1 = [];
wolffd@0 133 CPD.b2 = [];
wolffd@0 134 CPD.sizes = [];
wolffd@0 135 CPD.parent_vals = [];
wolffd@0 136 CPD.eso_weights=[];
wolffd@0 137 CPD.self_vals = [];
wolffd@0 138 CPD.nsamples = [];
wolffd@0 139 CPD.nparams = [];