inline size_t idx2idx_shift(const size_t idx,
struct shift_t delta,
const struct bbox_t *bbox) {
return shift2idx(shiftdelta(idx2shift(idx,bbox),delta),bbox);
}
bool RtDesignMatrix::buildDesignMatrix() {
numAddedColumns = 0;
// TODO this will need to get called by unserializeXML as well
// fill the rest of the measurements as periodic stim
if (loadDesignMatrixFromFile) {
if (padConditionWithZeros) {
// TODO
}
else {
unsigned int i = blockLen - 1;
for (; i < numMeas; i++) {
inputConditions.put(
i, numInputConditions - 1, inputConditions.get(
i % blockLen, numInputConditions - 1));
}
}
}
// the rest was originally in build()
// setup the size
unsigned int numConditions = getNumConditionBases();
unsigned int numNuisance = getNumNuisanceBases();
columnNames.clear();
columnNames.reserve(numConditions + numNuisance);
set_size(numMeas, numConditions + numNuisance);
// set neural condition regressors
buildHrf();
// store the new condition names as we go along
vector<string> inputConditionNames = conditionNames;
// build shifted delta for potential condition shifting
vnl_vector<double> shiftdelta(numMeas, 0.0);
if (conditionShift > 0 && conditionShift < numMeas) {
shiftdelta.put(conditionShift, 1);
}
else {
shiftdelta.put(0, 1);
}
// consider each input condition individually
for (unsigned int cond = 0; cond < numConditions; cond++) {
vnl_vector<double> basis = inputConditions.get_column(cond);
// incorporate condition shift via convolution with shifted delta
if (conditionShift > 0 && conditionShift < numMeas) {
vnl_vector<double> shiftcol = vnl_convolve(basis, shiftdelta);
basis.update(shiftcol.extract(basis.size()));
}
// build block-wise condition vectors if required
if (modelEachBlock) {
unsigned int numBlocks =
ceil((double) ((numMeas - conditionShift) / blockLen));
// create a design matrix column per block
for (unsigned int block = 0; block <= numBlocks; block++) {
vnl_vector<double> blockCol(numMeas, 0);
// copy the entire block from the input condition vector
for (unsigned int meas = conditionShift + block * blockLen;
meas < conditionShift + (block + 1) * blockLen && meas < numMeas;
meas++) {
blockCol.put(meas, basis[meas]);
}
string blockStr;
RtConfigVal::convertToString<unsigned int>(blockStr, block);
addColumn(convolveVecWithHrf(blockCol),
inputConditionNames[cond] + "_block" + blockStr,
conditionInterestIndicator[cond]);
// add columns for temporal derivatives if required
if (modelTemporalDerivatives) {
addColumn(convolveVecWithTemporalDerivative(blockCol),
inputConditionNames[cond] + "_block" +
blockStr + "_deriv",
conditionInterestIndicator[cond]);
}
}
}
else { // no block splitting necessary
addColumn(convolveVecWithHrf(basis),
inputConditionNames[cond],
conditionInterestIndicator[cond]);
// add columns for temporal derivatives if required
if (modelTemporalDerivatives) {
addColumn(convolveVecWithTemporalDerivative(basis),
inputConditionNames[cond] + "_deriv",
conditionInterestIndicator[cond]);
}
}
}
// build the nuisance columns
// fill the trend colums
for (unsigned int i = 0; i <= maxTrendOrder; i++) {
vnl_vector<double> trend(numMeas, 0);
for (unsigned int j = 0; j < numMeas; j++) {
switch (i) {
case 0: // mean
trend.put(j, 1.0);
break;
case 1: // linear
trend.put(j, j + 1);
break;
default: // higher
trend.put(j, pow((double) j - numMeas / 2, (int) i));
break;
}
}
string order;
RtConfigVal::convertToString(order, i);
addColumn(trend, string("trend_order_") + order, false);
}
// name the motion columns
vnl_vector<double> motStub(numMeas, 0);
if (modelMotionParameters) {
addColumn(motStub, "motion_tx", false);
addColumn(motStub, "motion_ty", false);
addColumn(motStub, "motion_tz", false);
addColumn(motStub, "motion_rx", false);
addColumn(motStub, "motion_ry", false);
addColumn(motStub, "motion_rz", false);
}
// event and artifact columns will be left blank until they are received
// check number of conditions against filled columns
if (numAddedColumns != numNuisance + numConditions) {
cerr << "WARNING: number of columns generated ("
<< numAddedColumns << ") is different from that expected ("
<< numNuisance + numConditions
<< "). further calculations are suspect."
<< endl;
}
if (DEBUG_LEVEL & MODERATE) {
print();
}
built = true;
return true;
}
inline size_t gid2idx_shift(const size_t gid,
struct shift_t delta,
const struct bbox_t *bbox) {
return shift2idx(shiftdelta(gid2shift(gid,bbox),delta),bbox);
}
