double NeuralNet::train(cube x,cube y, double alpha, long int epoch){
double cst = 0 ;
int rnd = 0;
cube sample_x;
cube sample_y;
for(int i=0; i < epoch; i++){
rnd = rand() % (x.n_slices - 1);
sample_x =(x.slices(rnd, rnd));
sample_y =(y.slices(rnd, rnd));
cst = computeCost(sample_x, sample_y);
backprop(sample_x, sample_y);
updateWeights(alpha);
cout << "Epoch[" << i << "]: " << cst << endl;
}
double total = 0;
double ctotal = 0;
for(int i=0; i < x.n_slices; i++){
sample_x = (x.slices(i,i));
sample_y = (y.slices(i,i));
cst = computeCost(sample_x, sample_y);
total += cst;
// classification error:
uword rid, cid, sid;
cube p = pred(sample_x);
p.max(rid,cid,sid);
if(sample_y(rid,cid,sid) == 1)
ctotal++;
}
total /= x.n_slices;
ctotal /= x.n_slices;
cout << "Mean Training Error: " << total << endl;
cout << "Classification Error: " << ctotal << endl;
return 0;
}
void white(const mat& x1, const mat& x2, const uvec& sti, const cube& matphi1, const cube& matphi2, cube& wX, const uvec& End, const uvec & wEnd){
int T = x1.n_rows, L = matphi1.n_slices, P = x1.n_cols, m = End.n_elem-1;
mat multiply(P,P);
cube X(3*P,P,T);
for(int t=0;t<T;t++){
X.slice(t)=join_cols(join_cols(diagmat(x1.row(t)),diagmat(x2.row(t))),diagmat(ones(1,P)));
}
for(int run=0;run<m;run++){
wX.slices(wEnd[run],wEnd[run+1]-1) = X.slices(End[run]+L,End[run+1]-1);
if(L>0){
for(unsigned int t=End[run]+L;t<End[run+1];t++){
for(int l=0;l<L;l++){
multiply = sti[t-l-1]?(matphi1.slice(l)):(matphi2.slice(l));
wX.slice(t-L*(run+1)) -= X.slice(t-l-1)*multiply;
}
}
}
}
}
void white(const mat& Hc, const rowvec vec_beta, const uvec& sti, const cube& matphi1, const cube& matphi2, cube& wHX, const uvec& End, const uvec & wEnd){
int T = Hc.n_rows, J = Hc.n_cols/2, L = matphi1.n_slices, P = matphi1.n_rows, m = End.n_elem-1;
mat multiply(P,P);
cube X = zeros(J*P,P,T);
for(int t=0;t<T;t++){
for(int p=0; p<P;p++){
X.slice(t).col(p).rows(J*p, J*(p+1)-1) = trans(Hc.submat(t, 0, t, J-1))*vec_beta[p] + trans(Hc.submat(t, J, t, 2*J-1))*vec_beta[P+p];
}
}
for(int run=0;run<m;run++){
wHX.slices(wEnd[run],wEnd[run+1]-1) = X.slices(End[run]+L,End[run+1]-1);
if(L>0){
for(unsigned int t=End[run]+L;t<End[run+1];t++){
for(int l=0;l<L;l++){
multiply = sti[t-l-1]?(matphi1.slice(l)):(matphi2.slice(l));
wHX.slice(t-L*(run+1)) -= X.slice(t-l-1)*multiply;
}
}
}
}
}
