display_results

DISPLAY_RESULTS displays the desired and actual spike times

Description:
Function to display the actual and desired spike times to the Matlab
command window.

Syntax:
DISPLAY_RESULTS(ThNN,Mode,Input,tdAll,[tiOrder]);

Input Parameters:
o ThNN: An object of the theta neuron network class
o Mode: A string with possible values 'Inputs' or 'Outputs'. If the string
    is 'Inputs', then Inputsis interpreted to be input spike times, from
    which the output spike times must be calculated.  If Mode is 'Outputs'
    then Inputs is interpreted to be output spike times.
o Input: Either a cell array of length NumNeurons containing in each cell 
    an array of length r_j of neuron j's output spike times (This format 
    is the one produced from the run_network function) or a cell array of 
    length a, the number of input patterns, of which each cell is a qx2 
    array that contains the neuron indices for each spike time and the 
    input spike times (q may vary from cell to cell).  See Mode above.
o tdAll: A cell array of length a, the number of input patterns, of which 
    each cell is a (qo x 2) array that contains the output neuron indices 
    for each desired output spike time and the desired output spike times.
    qo may vary from cell to cell.
o tiOrder: Optional input array containing the order in which the input 
    patterns were processed during this epoch.  The order is random when 
    using online learning.

Output Parameters:
o N/A

Examples:
>> %Display from input spike times
>> ThNN = theta_neuron_network;
>> display_results(ThNN, 'Inputs', {[2 3], [2 6]}, {[3 25], [3 20]});

>> %Display from output spike times
>> ThNN = theta_neuron_network;
>> ts{1} = run_network(ThNN, [2 3]);
>> ts{2} = run_network(ThNN, [2 6]);
>> display_results(ThNN, 'Outputs', ts, {[3 25], [3 20]});

See also theta_neuron_network

0001 %DISPLAY_RESULTS displays the desired and actual spike times
0002 %
0003 %Description:
0004 %Function to display the actual and desired spike times to the Matlab
0005 %command window.
0006 %
0007 %Syntax:
0008 %DISPLAY_RESULTS(ThNN,Mode,Input,tdAll,[tiOrder]);
0009 %
0010 %Input Parameters:
0011 %o ThNN: An object of the theta neuron network class
0012 %o Mode: A string with possible values 'Inputs' or 'Outputs'. If the string
0013 %    is 'Inputs', then Inputsis interpreted to be input spike times, from
0014 %    which the output spike times must be calculated.  If Mode is 'Outputs'
0015 %    then Inputs is interpreted to be output spike times.
0016 %o Input: Either a cell array of length NumNeurons containing in each cell
0017 %    an array of length r_j of neuron j's output spike times (This format
0018 %    is the one produced from the run_network function) or a cell array of
0019 %    length a, the number of input patterns, of which each cell is a qx2
0020 %    array that contains the neuron indices for each spike time and the
0021 %    input spike times (q may vary from cell to cell).  See Mode above.
0022 %o tdAll: A cell array of length a, the number of input patterns, of which
0023 %    each cell is a (qo x 2) array that contains the output neuron indices
0024 %    for each desired output spike time and the desired output spike times.
0025 %    qo may vary from cell to cell.
0026 %o tiOrder: Optional input array containing the order in which the input
0027 %    patterns were processed during this epoch.  The order is random when
0028 %    using online learning.
0029 %
0030 %Output Parameters:
0031 %o N/A
0032 %
0033 %Examples:
0034 %>> %Display from input spike times
0035 %>> ThNN = theta_neuron_network;
0036 %>> display_results(ThNN, 'Inputs', {[2 3], [2 6]}, {[3 25], [3 20]});
0037 %
0038 %>> %Display from output spike times
0039 %>> ThNN = theta_neuron_network;
0040 %>> ts{1} = run_network(ThNN, [2 3]);
0041 %>> ts{2} = run_network(ThNN, [2 6]);
0042 %>> display_results(ThNN, 'Outputs', ts, {[3 25], [3 20]});
0043 %
0044 %See also theta_neuron_network
0045 
0046 %Copyright (C) 2008 Sam McKennoch <Samuel.McKennoch@loria.fr>
0047 
0048 
0049 function display_results(ThNN,Mode,Input,tdAll,tiOrder)
0050 
0051 if nargin==4
0052     tiOrder=1:length(tdAll);
0053 end
0054 if nargin<4
0055     disp('Error in display_results: Not enough input arguements');
0056     disp(['Needed at least 4 inputs but only got ' num2str(nargin)]);
0057     return;
0058 end
0059 
0060 if strcmp(Mode,'Inputs')
0061     tiAll=Input;
0062 else
0063     tsAll=Input;
0064 end
0065 
0066 %If ts is not, but rather tiAll instead, make sure we can handle this...
0067 
0068 for k=1:length(tiOrder)
0069     if strcmp(Mode,'Inputs')
0070         ti=tiAll{tiOrder(k)};
0071         ts = run_network(ThNN,ti);
0072     else
0073         ts = tsAll{tiOrder(k)};
0074     end
0075     td=tdAll{k};
0076     
0077     OutputNeurons=get_output_neurons(ThNN);
0078 
0079     disp('Desired Firing Times: ');
0080     for j=1:length(OutputNeurons)
0081         disp(['Neuron ', num2str(OutputNeurons(j)), ': ', num2str(sort(td(find(td(:,1)==OutputNeurons(j)),2)'))]);
0082     end
0083     disp('Actual Firing Times: ');
0084     for j=1:length(OutputNeurons)
0085         disp(['Neuron ', num2str(OutputNeurons(j)), ': ', num2str(ts{OutputNeurons(j)})]);
0086     end
0087 end