train_bootstrap_snn.m

神经网络的工具箱, 神经网络的工具箱,

function [net, tr_info, dataset] = train_bootstrap_snn(net, data, blocksize)
%TRAIN_BOOTSTRAP_SNN Train network on bootstrapped sample of input data
%
%  Syntax
%
%   [net, tr_info, dataset] = train_bootstrap_snn(net, wcf_data)
%
%  Description
%
%   TRAIN_BOOTSTRAP_SNN takes
%     net       - a net_struct with cost function WCF_SNN.
%     wcf_data  - a wcfdata_struct containing the input data set.
%   and returns
%     net       - a net_struct containing a network trained on 
%                 training and validation data, which are obtained by
%                 bootstrapping on the input data set.
%     tr_info   - a structure containing information about the
%                 training process.
%     dataset   - a structure with information about the subdivision 
%                 of input data in training and validation set.
%
%  See also
%
%   TRAIN_HALFOUT_SNN
%

if (nargin < 3)
   blocksize = 1;
end

if strcmp(net.costFcn.name, 'wcf_snn')
   q = size(data.P,2)/blocksize;
   [ind_block_LV, ind_block_VV] = randboot_snn(q);
   n_block_LV = prod(size(ind_block_LV));
   n_block_VV = prod(size(ind_block_VV));
   ind_LV = blocksize * (reshape(...
               repmat(reshape(ind_block_LV,1,n_block_LV),blocksize,1), ...
               1, n_block_LV*blocksize) - 1) ...
            + repmat([1:blocksize], 1, n_block_LV);
   ind_VV = blocksize * (reshape(...
               repmat(reshape(ind_block_VV,1,n_block_VV),blocksize,1), ...
               1, n_block_VV*blocksize) - 1) ...
            + repmat([1:blocksize], 1, n_block_VV);
   dataLV = subset_wcfdata_snn(data, ind_LV);
   dataVV = subset_wcfdata_snn(data, ind_VV);
   dataset.trg_ind = ind_LV;
   dataset.val_ind = ind_VV;
   dataset.data = data;
   [net, tr_info] = train_snn(net, dataLV, dataVV);
else
   errtext = sprintf(...
          'TRAIN_BOOTSTRAP_SNN: Do not know how to subdivide data for %s.', ...
          net.costFcn.name);
   error(errtext);
end