inline
typename ICR::EnsembleLearning::Gamma<T>::vector_data_t
ICR::EnsembleLearning::Gamma<T>::CalcMean(NP_parameter NP)
{
const data_t shape = NP[1]+1.0;
const data_t iscale = -NP[0];
vector_data_t vmean(1);
vmean[0] = shape/iscale;
return vmean;
}
/* Subroutine to compute the Covariance of two vectors */
double vcovar(vector *X, vector *Y)
{
int i;
double mean_x, mean_y, sum=0, result;
mean_x = vmean(X);
mean_y = vmean(Y);
if (X->l != Y->l) {
printf(" Warning: cov() is failed since the lenth of vector X and Y are different. \n");
return 0;
}
for (i=0; i<(X->l); i++) {
sum += (*(X->pr+i)-mean_x)*(*(Y->pr+i)-mean_y);
}
result = sum/(X->l-1);
return result;
}
inline
typename ICR::EnsembleLearning::Discrete<T>::vector_data_t
ICR::EnsembleLearning::Discrete<T>::CalcMean(NP_parameter NP)
{
//Get the probabilities from the moments
moments_t moments= CalcMoments(NP);
data_t mean = 0;
for(size_t i=0;i<moments.size();++i){
mean += i*moments[i];
}
std::vector<data_t> vmean(1);
vmean[0]=mean;
return vmean;
}
/*******************************************************************
Subroutine to do the EM algorithm
matrix *D: the pointer to the matrix data
matrix *mean0_x: the pointer to a matrix containing the initial Means of clusters
vector *w0: the pointer to a vector containing the initial mixing proportion of clusters
double vv: the value for initializing the Covariance matrix of clusters
double error: the error threshold
vector *Zjk_up: the pointer to a vector containing Posterior probabilities of the up-level
cluster samples
matrix *mean1_x: the pointer to a matrix containing the Means of clusters in t-space
vector *w0_t: the pointer to a vector containing the mixing proportions of the identified
clusters in t-space
matrix *cov_mat: the pointer to a group of matrixs containing the Covariance
matrix of clusters in t-space
matrix *Zjk: the pointer to a matrix containing Posterior probabilities of all samples
belonging to all the sub-level clusters, each column is for one cluster.
return value: '1' - successfully exit
'0' - exit with waring/error
*******************************************************************/
int veSubEM(matrix *D, matrix *mean0_x, vector *w0, double vv, double error, vector *Zjk_up, //input
matrix *mean1_x, vector *w0_t, matrix *cov_mat, matrix *Zjk) //output
{
int k0, kc, n, p;
int i, j, k, u, s;
matrix *Var0;
matrix Gxn;
vector Fx;
matrix MUK;
matrix MU1;
int zeroFx_num = 1;
//double error = 0.01;
double err = error + (double)1;
vector Zjk_temp;
n = D->m;
p = D->n;
k0 = mean0_x->m;
kc = mean0_x->n;
Var0 = new matrix[k0];
for(i=0; i<k0; i++) {
mnew(Var0+i, p, p);
}
mnew(&Gxn, n, k0);
vnew(&Fx, n);
vnew(&Zjk_temp, n);
mnew(&MUK, k0, p);
mcopy(mean0_x, &MUK);
mnew(&MU1, k0, p);
vector D_j;
vector Zjk_k;
double sum_tmp = 0;
matrix Ck;
vector D_i;
vector MUK_k;
vector cen_D_i;
matrix mtmp;
vector vtmp;
vnew(&D_j, n);
vnew(&Zjk_k, n);
mnew(&Ck, p, p);
vnew(&D_i, p);
vnew(&MUK_k, p);
vnew(&cen_D_i, p);
mnew(&mtmp, p, p);
vnew(&vtmp, n);
//Initializing the parameters of mixture of Gaussians
//Initinalize the covariance matrix
//Use EM algorithm to perform the local training.
//Test intialization of covarinace matrix
//printf("Testing covariance matrix initialization... \n");
while (zeroFx_num != 0) {
for(i=0; i<k0; i++) {
meye(Var0+i);
for (j=0; j<p; j++) {
*((Var0+i)->pr+j*p+j) = vv;
}
}
veModel(D, mean0_x, Var0, w0, &Gxn, &Fx);
//printf("\n Gxn = :\n");
//mprint(&Gxn);
//printf("\n Fx = :\n");
//vprint(&Fx);
zeroFx_num = 0;
for (i=0; i<n; i++) {
if (*(Fx.pr+i) == 0) {
zeroFx_num++;
}
}
vv *= 2;
}
vones(&Zjk_temp);
//printf("\n EM in t-space starts ... \n");
//printf("\n Data = \n");
//mprint(D);
int l = 0;
while (err > error) {
#ifdef _DEBUG
printf(" \n...... in EM loop %d ......\n", ++l);
printf("\n L%d: w0 = \n", l);
vprint(w0);
printf("\n L%d: MUK = \n", l);
mprint(&MUK);
printf("\n L%d: Var0 = \n", l);
for(i=0; i<k0; i++) {
mprint(Var0+i);
printf("\n");
}
printf("\n L%d: Zjk = \n", l);
mprint(Zjk);
#endif
veModel(D, &MUK, Var0, w0, &Gxn, &Fx);
#ifdef _DEBUG
printf("\n L%d: Gxn = \n", l);
mprint(&Gxn);
printf("\n L%d: Fx = \n", l);
vprint(&Fx);
#endif
for (k=0; k<k0; k++) {
u = k*p;
double zz = 0;
double zz_up = 0;
for (i=0; i<n; i++) {
*(Zjk->pr+i*k0+k) = (*(w0->pr+k)) * Zjk_up->pr[i] * (*(Gxn.pr+i*k0+k)) / (*(Fx.pr+i));
zz += *(Zjk->pr+i*k0+k);
zz_up += Zjk_up->pr[i];
}
*(w0->pr+k) = zz/zz_up;
for (j=0; j<p; j++) {
getcolvec(D, j, &D_j);
getcolvec(Zjk, k, &Zjk_k);
sum_tmp = 0;
for (i=0; i<n; i++) {
sum_tmp += (*(Zjk_k.pr+i)) * (*(D_j.pr+i));
}
*(MU1.pr+u+j) = sum_tmp / zz;
}
mzero(&Ck);
for (i=0; i<n; i++) {
getrowvec(D, i, &D_i);
getrowvec(&MUK, k, &MUK_k);
for (j=0; j<p; j++) {
*(cen_D_i.pr+j) = *(D_i.pr+j) - *(MUK_k.pr+j);
}
vvMul(&cen_D_i, &cen_D_i, &mtmp);
for (j=0; j<p; j++) {
for (s=0; s<p; s++) {
*(Ck.pr+j*p+s) += (*(Zjk->pr+i*k0+k)) * (*(mtmp.pr+j*p+s));
}
}
}
for (j=0; j<p; j++) {
for (s=0; s<p; s++) {
*(Var0[k].pr+j*p+s) = (*(Ck.pr+j*p+s)) / zz;
}
}
} // for (k...
mcopy(&MU1, &MUK);
for (i=0; i<n; i++) {
*(vtmp.pr+i) = fabs(*(Zjk_k.pr+i) - *(Zjk_temp.pr+i));
}
err = vmean(&vtmp);
vcopy(&Zjk_k, &Zjk_temp);
} // while
vcopy(w0, w0_t);
mcopy(&MUK, mean1_x);
for(i=0; i<k0; i++) {
mcopy(Var0+i, cov_mat+i);
}
for(i=0; i<k0; i++) {
mdelete(Var0+i);
}
mdelete(&Gxn);
vdelete(&Fx);
vdelete(&Zjk_temp);
mdelete(&MUK);
mdelete(&MU1);
vdelete(&D_j);
vdelete(&Zjk_k);
mdelete(&Ck);
vdelete(&D_i);
vdelete(&MUK_k);
vdelete(&cen_D_i);
mdelete(&mtmp);
vdelete(&vtmp);
return 1;
}
