% Supervised learning Example   file name: bp_ex1.m  Date:01-10-09.
% Here is a problem consisting of inputs P and targets T that we would
% like to solve with a network.
  P = [0 1 2 3 4 5 6 7 8 9 10];
  T = [0 1 2 3 4 3 2 1 2 3 4];
% Here a two-layer feed-forward network is created.  The network's
% input ranges from [0 to 10].  The first layer has six TANSIG
% neurons, the second layer has one PURELIN neuron.  The TRAINLM
% network training function is to be used.
  net = newff([0 10],[6 1],{'tansig' 'purelin'});
%Here the network is simulated and its output plotted against
%the targets.
  Y = sim(net,P);
  figure(1);
  plot(P,T,'+',P,Y,'o'); title('network before training, + is teacher');
% Here the network is trained for 80 epochs.  Again the network's
% output is plotted.
 net.trainParam.epochs = 80;
 net = train(net,P,T);
 Y = sim(net,P);
 figure(2)
 plot(P,T,'+',P,Y,'o'); title('Supervised output (+), Network output (o)');
 xlabel('x');ylabel('y');
 whos           % Which varibles do I have?
 net            % Gives structure information of trained Net
 input_weights = net.iw{1}
 input_bias = net.b{1}
 layer_weights = net.lw{2}
 layer_bias = net.b{2}

% SOM Example  2-dim File name: som2d_1.m Date:01-10-09
 % NEWSOM   - Creates a self-organizing map.
 % TRAIN  - Trains a neural network.
 % SIM   - Simulates a neural network.
 % This self-organizing map will learn to represent different regions of the input space where 
 % input vectors occur.  In this demo, however, the neurons will arrange themselves in a two-
 % dimensional grid, rather than a line. We would like to classify 1000 two-element vectors
 % occuring in a rectangular shaped vector space.
    P = rand(2,1000);       % here input is random (0 to 1) 
  % We will use a 5 by 6 layer of neurons to classify the vectors above. We would like each 
  % neuron to respond to a different region of the rectangle, and neighboring neurons to 
  % respond to adjacent regions.  We create a layer of 30 neurons spread out in a 5 by 6 grid:
    net=newsom([0 1; 0 1],[5 6]); 
   % Initially all the neurons have the same weights in the middle of the vectors,
   % so only one dot appears.;
   % Now we train the map on the 1000 vectors for 1 epoch and replot the network weights.  
   % Please wait for it. Blue lines show neurons with distance =1.
   net.trainParam.epochs=2; 
   net.trainParam.show=1;
   net=train(net,P);
   plotsom(net.iw{1,1},net.layers{1}.distances)
   %Note that the layer of neurons has begun to self-organize so that each neuron now 
   % classifies a different region of the input space, and adjacent (connected) 
   % neurons respond to adjacent regions.
   % We can now use SIM to classify vectors by giving them to the network and seeing 
   % which neuron responds.
   p = [0.5; 0.3];
   a=0;
   a = sim(net,p)
   % The neuron in parenthesis above responded with a "1", so p belongs to that class.

>>help plotsomhits

load iris_dataset
net = newsom(irisInputs,[5 5]);
[net,tr] = train(net,irisInputs);
plotsomhits(net,irisInputs);