% ani
% trance
% house
% jungle
% ambient

% assemble input

np = [];
nt = [];

load ani
np = [np p]; [h w] = size(p); nt = [nt repmat(t,1,w)];

load ambient
np = [np p]; [h w] = size(p); nt = [nt repmat(t,1,w)];

load trance
np = [np p]; [h w] = size(p); nt = [nt repmat(t,1,w)];

load house
np = [np p]; [h w] = size(p); nt = [nt repmat(t,1,w)];

load jungle
np = [np p]; [h w] = size(p); nt = [nt repmat(t,1,w)];

p = []; t = [];

size(np)
size(nt)
t



% params
spectrumkeep = 24;
lumpkeep = 32;


% randomize input set
[h w] = size(np);
perm = randperm(w);
np = np(:,perm);
nt = nt(:,perm);


% create network
numinputs = lumpkeep*spectrumkeep;
pr = repmat([ 0 1 ], numinputs, 1);

net = newff( pr, [ numinputs, 350, 5 ], {'tansig','tansig','satlins'}, 'traingdx');
net = init(net);

net.trainParam.show = 1;
net.trainParam.lr = .3;
net.trainParam.mc = .4;
net.trainParam.epochs = 500;
net.trainParam.goal = .01;

% train
net = train(net,np,nt);

save net1 net;



net.trainParam.lr = .2;
net.trainParam.lr = .3;
net.trainParam.lr = .4;