void XEMModel::FixKnownPartition(XEMPartition *& knownPartition) {
// update Nk if knownLabel
if (knownPartition!=NULL) {
int64_t ** knownPartitionValue = knownPartition->_tabValue;
int64_t * knownPartition_i;
double ** p_cik = _tabCik;
int64_t ** p_zikKnown = _tabZikKnown;
//double ** p_tik = _tabTik;
int64_t k;
int64_t i;
double sumLabel = 0.0;
for (i=0; i<_nbSample; i++) {
sumLabel = 0.0;
knownPartition_i = *knownPartitionValue;
for (k=0; k<_nbCluster; k++) {
sumLabel += knownPartition_i[k];
}
if (sumLabel != 0.0) {
_tabZiKnown[i] = true;
recopyTab(knownPartition_i,*p_cik,_nbCluster);
recopyTab(knownPartition_i,*p_zikKnown,_nbCluster);
//recopyTab(knownLabel_i,*p_tik,_nbCluster);
}
knownPartitionValue++;
p_cik++;
//p_tik++;
p_zikKnown++;
}
//
computeNk();
}//endif
}
/*--------------------------------------------------------------------------------------------
C step
------
Classification Step
Note : Cstep follows an Estep
----
already updated :
- _tabFik, _tabCik, _tabTik, _tabNk, _parameter
updated in this method :
- _tabCik, _tabNk
--------------------------------------------------------------------------------------------*/
void XEMModel::Cstep() {
int64_t k, kMax;
int64_t i;
double tikMax = 0;
for (i=0; i<_nbSample; i++) {
if (!_tabZiKnown[i]) {
kMax = 0;
tikMax = _tabTik[i][0];
for (k=1; k<_nbCluster; k++) {
if (_tabTik[i][k] > tikMax) {
tikMax = _tabTik[i][k];
kMax = k;
}
}
for (k=0; k<_nbCluster; k++) {
_tabCik[i][k] = 0;
}
_tabCik[i][kMax] = 1;
}
}
//update _tabNk
if(_algoName == UNKNOWN_ALGO_NAME)
throw;
if(_algoName != MAP) {
computeNk();
}
}
/*--------------------------------------------------------------------------------------------
E step : Expectation
------
Note : Estep follows a Mstep or an initialsation (so _parameter is updated)
----
already updated :
- _parameter
updated in this method :
- _tabFik, _tabCik, _tabTik, _tabNk
--------------------------------------------------------------------------------------------*/
void XEMModel::Estep() {
// 1. compute fik
//---------------
computeFik();
//2. compute tik (conditional probabilities (posteriori probabilities))
//---------------
int64_t k;
int64_t i;
for (i=0; i<_nbSample; i++) {
if (_tabSumF[i]==0.0) {
_parameter->computeTikUnderflow(i, _tabTik);
}
else {
for (k=0; k<_nbCluster; k++) {
_tabTik[i][k] = _tabFik[i][k] / _tabSumF[i] ;
}
}
// 3. compute cik
//---------------
if (!_tabZiKnown[i]) {
for (k=0; k<_nbCluster; k++) {
_tabCik[i][k] = _tabTik[i][k];
}
}
}
//4. compute nk
//------------
computeNk();
}
/*!
Perform the Hinkley test. A downward jump is detected if \f$ M_k - S_k >
\alpha \f$. An upward jump is detected if \f$ T_k - N_k > \alpha \f$.
\param signal : Observed signal \f$ s(t) \f$.
\sa setDelta(), setAlpha(), testDownwardJump(), testUpwardJump()
*/
vpHinkley::vpHinkleyJumpType vpHinkley::testDownUpwardJump(double signal)
{
vpHinkleyJumpType jump = noJump;
nsignal ++; // Signal length
if (nsignal == 1) mean = signal;
// Calcul des variables cumulees
computeSk(signal);
computeTk(signal);
computeMk();
computeNk();
vpCDEBUG(2) << "alpha: " << alpha << " dmin2: " << dmin2
<< " signal: " << signal
<< " Sk: " << Sk << " Mk: " << Mk
<< " Tk: " << Tk << " Nk: " << Nk << std::endl;
// teste si les variables cumulees excedent le seuil
if ((Mk - Sk) > alpha)
jump = downwardJump;
else if ((Tk - Nk) > alpha)
jump = upwardJump;
#ifdef VP_DEBUG
if (VP_DEBUG_MODE >= 2) {
switch(jump) {
case noJump:
std::cout << "noJump " << std::endl;
break;
case downwardJump:
std::cout << "downWardJump " << std::endl;
break;
case upwardJump:
std::cout << "upwardJump " << std::endl;
break;
}
}
#endif
computeMean(signal);
if ((jump == upwardJump) || (jump == downwardJump)) {
vpCDEBUG(2) << "\n*** Reset the Hinkley test ***\n";
Sk = 0; Mk = 0; Tk = 0; Nk = 0; nsignal = 0;
// Debut modif FS le 03/09/2003
mean = signal;
// Fin modif FS le 03/09/2003
}
return (jump);
}
/*--------------------------------------------------------------------------------------------
S step
------
// S Step : Stochastic Classification
Note : Sstep follows an Estep
----
already updated :
- _tabFik, _tabCik, _tabTik, _tabNk, _parameter
updated in this method :
- _tabCik, _tabNk
--------------------------------------------------------------------------------------------*/
void XEMModel::Sstep() {
int64_t i;
int64_t k;
double ** cumTabT = new double*[_nbSample];
for (i=0; i<_nbSample; i++) {
cumTabT[i] = new double[_nbCluster];
cumTabT[i][0] = _tabTik[i][0];
}
for (k=1; k<_nbCluster; k++) {
for (i=0; i<_nbSample; i++) {
cumTabT[i][k] = _tabTik[i][k] + cumTabT[i][k-1];
}
}
double * tabRnd = new double[_nbSample];
for (i=0; i<_nbSample; i++) {
tabRnd[i] = rnd();
}
for (i=0; i<_nbSample; i++) {
if (!_tabZiKnown[i]) {
for (k=0; k<_nbCluster; k++) {
_tabCik[i][k] = 0;
}
k=0;
while ((k<_nbCluster) && (tabRnd[i] > cumTabT[i][k])) {
k++;
}
if (tabRnd[i] <= cumTabT[i][k]) {
_tabCik[i][k] = 1;
}
else {
throw internalMixmodError;
}
}
}
for (i=0; i<_nbSample; i++) {
delete[] cumTabT[i];
}
delete[] cumTabT;
delete[] tabRnd;
//update _tabNk
computeNk();
}
/*!
Perform the Hinkley test. An upward jump is detected if \f$ T_k - N_k >
\alpha \f$.
\param signal : Observed signal \f$ s(t) \f$.
\sa setDelta(), setAlpha(), testDownwardJump()
*/
vpHinkley::vpHinkleyJumpType vpHinkley::testUpwardJump(double signal)
{
vpHinkleyJumpType jump = noJump;
nsignal ++; // Signal length
if (nsignal == 1) mean = signal;
// Calcul des variables cumulees
computeTk(signal);
computeNk();
vpCDEBUG(2) << "alpha: " << alpha << " dmin2: " << dmin2
<< " signal: " << signal << " Tk: " << Tk << " Nk: " << Nk;
// teste si les variables cumulees excedent le seuil
if ((Tk - Nk) > alpha)
jump = upwardJump;
#ifdef VP_DEBUG
if (VP_DEBUG_MODE >= 2) {
switch(jump) {
case noJump:
std::cout << "noJump " << std::endl;
break;
case downwardJump:
std::cout << "downWardJump " << std::endl;
break;
case upwardJump:
std::cout << "upWardJump " << std::endl;
break;
}
}
#endif
computeMean(signal);
if (jump == upwardJump) {
vpCDEBUG(2) << "\n*** Reset the Hinkley test ***\n";
Tk = 0; Nk = 0; nsignal = 0;
}
return (jump);
}
