function errstring = consist(model, type, inputs, outputs)%CONSIST Check that arguments are consistent.%%	Description%%	ERR_STRING = CONSIST(NET, TYPE, INPUTS) takes a network data%	structure NET together with a string TYPE containing the correct%	network type, a matrix INPUTS of input vectors and checks that the%	data structure is consistent with the other arguments.  An empty%	string is returned if there is no error, otherwise the string%	contains the relevant error message.  If the TYPE string is empty,%	then any type of network is allowed.%%	ERR_STRING = CONSIST(NET, TYPE) takes a network data structure NET%	together with a string TYPE containing the correct  network type, and%	checks that the two types match.%%	ERR_STRING = CONSIST(NET, TYPE, INPUTS, OUTPUTS) also checks that the%	network has the correct number of outputs, and that the number of%	patterns in the INPUTS and OUTPUTS is the same.  The fields in NET%	that are used are%	  type%	  nin%	  nout%%	See also%	MLPFWD%%	Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)% Assume that all is OK as defaulterrstring = '';% If type string is not emptyif ~isempty(type)  % First check that model has type field  if ~isfield(model, 'type')    errstring = 'Data structure does not contain type field';    return  end  % Check that model has the correct type  s = model.type;  if ~strcmp(s, type)    errstring = ['Model type ''', s, ''' does not match expected type ''',...	type, ''''];    return  endend% If inputs are present, check that they have correct dimensionif nargin > 2  if ~isfield(model, 'nin')    errstring = 'Data structure does not contain nin field';    return  end  data_nin = size(inputs, 2);  if model.nin ~= data_nin    errstring = ['Dimension of inputs ', num2str(data_nin), ...	' does not match number of model inputs ', num2str(model.nin)];    return  endend% If outputs are present, check that they have correct dimensionif nargin > 3  if ~isfield(model, 'nout')    errstring = 'Data structure does not conatin nout field';    return  end  data_nout = size(outputs, 2);  if model.nout ~= data_nout    errstring = ['Dimension of outputs ', num2str(data_nout), ...	' does not match number of model outputs ', num2str(model.nout)];    return  end% Also check that number of data points in inputs and outputs is the same  num_in = size(inputs, 1);  num_out = size(outputs, 1);  if num_in ~= num_out    errstring = ['Number of input patterns ', num2str(num_in), ...	' does not match number of output patterns ', num2str(num_out)];    return  endend