wolffd@0: function errstring = consist(model, type, inputs, outputs)
wolffd@0: %CONSIST Check that arguments are consistent.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %
wolffd@0: %	ERRSTRING = CONSIST(NET, TYPE, INPUTS) takes a network data structure
wolffd@0: %	NET together with a string TYPE containing the correct network type,
wolffd@0: %	a matrix INPUTS of input vectors and checks that the data structure
wolffd@0: %	is consistent with the other arguments.  An empty string is returned
wolffd@0: %	if there is no error, otherwise the string contains the relevant
wolffd@0: %	error message.  If the TYPE string is empty, then any type of network
wolffd@0: %	is allowed.
wolffd@0: %
wolffd@0: %	ERRSTRING = CONSIST(NET, TYPE) takes a network data structure NET
wolffd@0: %	together with a string TYPE containing the correct  network type, and
wolffd@0: %	checks that the two types match.
wolffd@0: %
wolffd@0: %	ERRSTRING = CONSIST(NET, TYPE, INPUTS, OUTPUTS) also checks that the
wolffd@0: %	network has the correct number of outputs, and that the number of
wolffd@0: %	patterns in the INPUTS and OUTPUTS is the same.  The fields in NET
wolffd@0: %	that are used are
wolffd@0: %	  type
wolffd@0: %	  nin
wolffd@0: %	  nout
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	MLPFWD
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: % Assume that all is OK as default
wolffd@0: errstring = '';
wolffd@0: 
wolffd@0: % If type string is not empty
wolffd@0: if ~isempty(type)
wolffd@0:   % First check that model has type field
wolffd@0:   if ~isfield(model, 'type')
wolffd@0:     errstring = 'Data structure does not contain type field';
wolffd@0:     return
wolffd@0:   end
wolffd@0:   % Check that model has the correct type
wolffd@0:   s = model.type;
wolffd@0:   if ~strcmp(s, type)
wolffd@0:     errstring = ['Model type ''', s, ''' does not match expected type ''',...
wolffd@0: 	type, ''''];
wolffd@0:     return
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: % If inputs are present, check that they have correct dimension
wolffd@0: if nargin > 2
wolffd@0:   if ~isfield(model, 'nin')
wolffd@0:     errstring = 'Data structure does not contain nin field';
wolffd@0:     return
wolffd@0:   end
wolffd@0: 
wolffd@0:   data_nin = size(inputs, 2);
wolffd@0:   if model.nin ~= data_nin
wolffd@0:     errstring = ['Dimension of inputs ', num2str(data_nin), ...
wolffd@0: 	' does not match number of model inputs ', num2str(model.nin)];
wolffd@0:     return
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: % If outputs are present, check that they have correct dimension
wolffd@0: if nargin > 3
wolffd@0:   if ~isfield(model, 'nout')
wolffd@0:     errstring = 'Data structure does not conatin nout field';
wolffd@0:     return
wolffd@0:   end
wolffd@0:   data_nout = size(outputs, 2);
wolffd@0:   if model.nout ~= data_nout
wolffd@0:     errstring = ['Dimension of outputs ', num2str(data_nout), ...
wolffd@0: 	' does not match number of model outputs ', num2str(model.nout)];
wolffd@0:     return
wolffd@0:   end
wolffd@0: 
wolffd@0: % Also check that number of data points in inputs and outputs is the same
wolffd@0:   num_in = size(inputs, 1);
wolffd@0:   num_out = size(outputs, 1);
wolffd@0:   if num_in ~= num_out
wolffd@0:     errstring = ['Number of input patterns ', num2str(num_in), ...
wolffd@0: 	' does not match number of output patterns ', num2str(num_out)];
wolffd@0:     return
wolffd@0:   end
wolffd@0: end