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1 function net = glm(nin, nout, outfunc, prior, beta)
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2 %GLM Create a generalized linear model.
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3 %
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4 % Description
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5 %
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6 % NET = GLM(NIN, NOUT, FUNC) takes the number of inputs and outputs for
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7 % a generalized linear model, together with a string FUNC which
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8 % specifies the output unit activation function, and returns a data
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9 % structure NET. The weights are drawn from a zero mean, isotropic
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10 % Gaussian, with variance scaled by the fan-in of the output units.
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11 % This makes use of the Matlab function RANDN and so the seed for the
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12 % random weight initialization can be set using RANDN('STATE', S)
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13 % where S is the seed value. The optional argument ALPHA sets the
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14 % inverse variance for the weight initialization.
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15 %
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16 % The fields in NET are
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17 % type = 'glm'
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18 % nin = number of inputs
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19 % nout = number of outputs
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20 % nwts = total number of weights and biases
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21 % actfn = string describing the output unit activation function:
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22 % 'linear'
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23 % 'logistic'
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24 % 'softmax'
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25 % w1 = first-layer weight matrix
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26 % b1 = first-layer bias vector
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27 %
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28 % NET = GLM(NIN, NOUT, FUNC, PRIOR), in which PRIOR is a scalar, allows
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29 % the field NET.ALPHA in the data structure NET to be set,
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30 % corresponding to a zero-mean isotropic Gaussian prior with inverse
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31 % variance with value PRIOR. Alternatively, PRIOR can consist of a data
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32 % structure with fields ALPHA and INDEX, allowing individual Gaussian
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33 % priors to be set over groups of weights in the network. Here ALPHA is
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34 % a column vector in which each element corresponds to a separate
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35 % group of weights, which need not be mutually exclusive. The
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36 % membership of the groups is defined by the matrix INDEX in which the
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37 % columns correspond to the elements of ALPHA. Each column has one
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38 % element for each weight in the matrix, in the order defined by the
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39 % function GLMPAK, and each element is 1 or 0 according to whether the
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40 % weight is a member of the corresponding group or not.
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41 %
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42 % NET = GLM(NIN, NOUT, FUNC, PRIOR, BETA) also sets the additional
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43 % field NET.BETA in the data structure NET, where beta corresponds to
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44 % the inverse noise variance.
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45 %
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46 % See also
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47 % GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMGRAD, GLMTRAIN
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48 %
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49
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50 % Copyright (c) Ian T Nabney (1996-2001)
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51
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52 net.type = 'glm';
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53 net.nin = nin;
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54 net.nout = nout;
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55 net.nwts = (nin + 1)*nout;
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56
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57 outtfns = {'linear', 'logistic', 'softmax'};
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58
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59 if sum(strcmp(outfunc, outtfns)) == 0
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60 error('Undefined activation function. Exiting.');
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61 else
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62 net.outfn = outfunc;
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63 end
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64
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65 if nargin > 3
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66 if isstruct(prior)
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67 net.alpha = prior.alpha;
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68 net.index = prior.index;
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69 elseif size(prior) == [1 1]
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70 net.alpha = prior;
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71 else
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72 error('prior must be a scalar or structure');
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73 end
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74 end
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75
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76 net.w1 = randn(nin, nout)/sqrt(nin + 1);
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77 net.b1 = randn(1, nout)/sqrt(nin + 1);
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78
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79 if nargin == 5
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80 net.beta = beta;
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81 end
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82
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