void CovarianceModel::addAppearance(cv::Mat & img, const cv::Rect &detBox)
{
cv::Mat newCovMat = cv::Mat::zeros(5,5, CV_64FC1);
cv::Mat target = (detBox == cv::Rect()) ? img : img(detBox);
cv::cvtColor(target, target, CV_RGB2GRAY);
const int totalPoints = target.rows * target.cols;
_Ix.create(target.rows, target.cols, CV_64FC1);
_Iy.create(target.rows, target.cols, CV_64FC1);
_Ixx.create(target.rows, target.cols, CV_64FC1);
_Iyy.create(target.rows, target.cols, CV_64FC1);
_Ixy.create(target.rows, target.cols, CV_64FC1);
cv::filter2D(target, _Ix, CV_64FC1, _kernelX);
cv::filter2D(target, _Iy, CV_64FC1, _kernelY);
cv::filter2D(target, _Ixx, CV_64FC1, _kernelXX);
cv::filter2D(target, _Iyy, CV_64FC1, _kernelYY);
cv::Sobel(target, _Ixy, CV_64FC1, 1, 1);
cv::Mat meanVec(5, 1, CV_64FC1);
meanVec.at<double>(0,0) = cv::mean(_Ix)(0); // cv::Scalar s; s(0) will give the first element in the scalar
meanVec.at<double>(1,0) = cv::mean(_Iy)(0);
meanVec.at<double>(2,0) = cv::mean(_Ixx)(0);
meanVec.at<double>(3,0) = cv::mean(_Iyy)(0);
meanVec.at<double>(4,0) = cv::mean(_Ixy)(0);
cv::Mat pix(5, 1, CV_64FC1);
for(int i=0; i<target.rows; ++i)
{
for(int j=0; j<target.cols; ++j)
{
pix.at<double>(0,0) = _Ix.at<double>(i,j);
pix.at<double>(1,0) = _Iy.at<double>(i,j);
pix.at<double>(2,0) = _Ixx.at<double>(i,j);
pix.at<double>(3,0) = _Iyy.at<double>(i,j);
pix.at<double>(4,0) = _Ixy.at<double>(i,j);
const cv::Mat pixMinMeanVec = pix-meanVec;
const cv::Mat currCovMat = (pixMinMeanVec * pixMinMeanVec.t());
newCovMat += currCovMat;
}
}
newCovMat /= (totalPoints-1);
_covMatrices.push_back(newCovMat);
if(_covMatrices.size() > 2)
_updateGaussian();
}
// covariance matrix
// WARNING: This is NOT the unbiased covariance in which
// you divide by (n - 1)! In this case we divide by n.
//
// This is essentially: (1/maxr) * m.Tdot(m) - meanT . mean
// and so is twice as slow as it needs to be.
// WARNING: allocates new matrix for answer
Matrix &Matrix::cov()
{
assertDefined("cov");
// OK, so this does twice the work it should... zzz but it was easy :-(
Matrix mean;
Matrix *out;
out = &Tdot(*this); // Tdot with yourself
mean = meanVec();
out->scalarMult(1.0/maxr);
out->sub(mean.Tdot(mean));
out->defined = true;
return *out;
}
void ifaGroup::import(SEXP Rlist)
{
SEXP argNames;
Rf_protect(argNames = Rf_getAttrib(Rlist, R_NamesSymbol));
if (Rf_length(Rlist) != Rf_length(argNames)) {
mxThrow("All list elements must be named");
}
std::vector<const char *> dataColNames;
paramRows = -1;
int pmatCols=-1;
int mips = 1;
int dataRows = 0;
SEXP Rmean=0, Rcov=0;
for (int ax=0; ax < Rf_length(Rlist); ++ax) {
const char *key = R_CHAR(STRING_ELT(argNames, ax));
SEXP slotValue = VECTOR_ELT(Rlist, ax);
if (strEQ(key, "spec")) {
importSpec(slotValue);
} else if (strEQ(key, "param")) {
if (!Rf_isReal(slotValue)) mxThrow("'param' must be a numeric matrix of item parameters");
param = REAL(slotValue);
getMatrixDims(slotValue, ¶mRows, &pmatCols);
SEXP dimnames;
Rf_protect(dimnames = Rf_getAttrib(slotValue, R_DimNamesSymbol));
if (!Rf_isNull(dimnames) && Rf_length(dimnames) == 2) {
SEXP names;
Rf_protect(names = VECTOR_ELT(dimnames, 0));
int nlen = Rf_length(names);
factorNames.resize(nlen);
for (int nx=0; nx < nlen; ++nx) {
factorNames[nx] = CHAR(STRING_ELT(names, nx));
}
Rf_protect(names = VECTOR_ELT(dimnames, 1));
nlen = Rf_length(names);
itemNames.resize(nlen);
for (int nx=0; nx < nlen; ++nx) {
itemNames[nx] = CHAR(STRING_ELT(names, nx));
}
}
} else if (strEQ(key, "mean")) {
Rmean = slotValue;
if (!Rf_isReal(slotValue)) mxThrow("'mean' must be a numeric vector or matrix");
mean = REAL(slotValue);
} else if (strEQ(key, "cov")) {
Rcov = slotValue;
if (!Rf_isReal(slotValue)) mxThrow("'cov' must be a numeric matrix");
cov = REAL(slotValue);
} else if (strEQ(key, "data")) {
Rdata = slotValue;
dataRows = Rf_length(VECTOR_ELT(Rdata, 0));
SEXP names;
Rf_protect(names = Rf_getAttrib(Rdata, R_NamesSymbol));
int nlen = Rf_length(names);
dataColNames.reserve(nlen);
for (int nx=0; nx < nlen; ++nx) {
dataColNames.push_back(CHAR(STRING_ELT(names, nx)));
}
Rf_protect(dataRowNames = Rf_getAttrib(Rdata, R_RowNamesSymbol));
} else if (strEQ(key, "weightColumn")) {
if (Rf_length(slotValue) != 1) {
mxThrow("You can only have one %s", key);
}
weightColumnName = CHAR(STRING_ELT(slotValue, 0));
} else if (strEQ(key, "freqColumn")) {
if (Rf_length(slotValue) != 1) {
mxThrow("You can only have one %s", key);
}
freqColumnName = CHAR(STRING_ELT(slotValue, 0));
} else if (strEQ(key, "qwidth")) {
qwidth = Rf_asReal(slotValue);
} else if (strEQ(key, "qpoints")) {
qpoints = Rf_asInteger(slotValue);
} else if (strEQ(key, "minItemsPerScore")) {
mips = Rf_asInteger(slotValue);
} else {
// ignore
}
}
learnMaxAbilities();
if (itemDims < (int) factorNames.size())
factorNames.resize(itemDims);
if (int(factorNames.size()) < itemDims) {
factorNames.reserve(itemDims);
const int SMALLBUF = 24;
char buf[SMALLBUF];
while (int(factorNames.size()) < itemDims) {
snprintf(buf, SMALLBUF, "s%d", int(factorNames.size()) + 1);
factorNames.push_back(CHAR(Rf_mkChar(buf)));
}
}
if (Rmean) {
if (Rf_isMatrix(Rmean)) {
int nrow, ncol;
getMatrixDims(Rmean, &nrow, &ncol);
if (!(nrow * ncol == itemDims && (nrow==1 || ncol==1))) {
mxThrow("mean must be a column or row matrix of length %d", itemDims);
}
} else {
if (Rf_length(Rmean) != itemDims) {
mxThrow("mean must be a vector of length %d", itemDims);
}
}
verifyFactorNames(Rmean, "mean");
}
if (Rcov) {
if (Rf_isMatrix(Rcov)) {
int nrow, ncol;
getMatrixDims(Rcov, &nrow, &ncol);
if (nrow != itemDims || ncol != itemDims) {
mxThrow("cov must be %dx%d matrix", itemDims, itemDims);
}
} else {
if (Rf_length(Rcov) != 1) {
mxThrow("cov must be %dx%d matrix", itemDims, itemDims);
}
}
verifyFactorNames(Rcov, "cov");
}
setLatentDistribution(mean, cov);
setMinItemsPerScore(mips);
if (numItems() != pmatCols) {
mxThrow("item matrix implies %d items but spec is length %d",
pmatCols, numItems());
}
if (Rdata) {
if (itemNames.size() == 0) mxThrow("Item matrix must have colnames");
for (int ix=0; ix < numItems(); ++ix) {
bool found=false;
for (int dc=0; dc < int(dataColNames.size()); ++dc) {
if (strEQ(itemNames[ix], dataColNames[dc])) {
SEXP col = VECTOR_ELT(Rdata, dc);
if (!Rf_isFactor(col)) {
if (TYPEOF(col) == INTSXP) {
mxThrow("Column '%s' is an integer but "
"not an ordered factor",
dataColNames[dc]);
} else {
mxThrow("Column '%s' is of type %s; expecting an "
"ordered factor (integer)",
dataColNames[dc], Rf_type2char(TYPEOF(col)));
}
}
dataColumns.push_back(INTEGER(col));
found=true;
break;
}
}
if (!found) {
mxThrow("Cannot find item '%s' in data", itemNames[ix]);
}
}
if (weightColumnName) {
for (int dc=0; dc < int(dataColNames.size()); ++dc) {
if (strEQ(weightColumnName, dataColNames[dc])) {
SEXP col = VECTOR_ELT(Rdata, dc);
if (TYPEOF(col) != REALSXP) {
mxThrow("Column '%s' is of type %s; expecting type numeric (double)",
dataColNames[dc], Rf_type2char(TYPEOF(col)));
}
rowWeight = REAL(col);
break;
}
}
if (!rowWeight) {
mxThrow("Cannot find weight column '%s'", weightColumnName);
}
}
if (freqColumnName) {
for (int dc=0; dc < int(dataColNames.size()); ++dc) {
if (strEQ(freqColumnName, dataColNames[dc])) {
SEXP col = VECTOR_ELT(Rdata, dc);
if (TYPEOF(col) != INTSXP) {
mxThrow("Column '%s' is of type %s; expecting type integer",
dataColNames[dc], Rf_type2char(TYPEOF(col)));
}
rowFreq = INTEGER(col);
break;
}
}
if (!rowFreq) {
mxThrow("Cannot find frequency column '%s'", freqColumnName);
}
}
rowMap.reserve(dataRows);
for (int rx=0; rx < dataRows; ++rx) rowMap.push_back(rx);
}
Eigen::Map< Eigen::ArrayXXd > Eparam(param, paramRows, numItems());
Eigen::Map< Eigen::VectorXd > meanVec(mean, itemDims);
Eigen::Map< Eigen::MatrixXd > covMat(cov, itemDims, itemDims);
quad.setStructure(qwidth, qpoints, Eparam, meanVec, covMat);
if (paramRows < impliedParamRows) {
mxThrow("At least %d rows are required in the item parameter matrix, only %d found",
impliedParamRows, paramRows);
}
quad.refresh(meanVec, covMat);
}
/*******************************************************************************
* data - p x n column matrix of data points
* p - dimension of data points
* n - number of data points
* radius - radius of search windo
* rate - gradient descent proportionality factor
* maxIter - max allowed number of iterations
* blur - specifies algorithm mode
* labels - labels for each cluster
* means - output (final clusters)
*******************************************************************************/
void meanShift( double data[], int p, int n, double radius, double rate,
int maxIter, bool blur, double labels[], double *means ) {
double radius2; /* radius^2 */
int iter; /* number of iterations */
double *mean; /* mean vector */
int i, j, o, m; /* looping and temporary variables */
int delta = 1; /* indicator if change occurred between iterations */
int *deltas; /* indicator if change occurred between iterations per point */
double *meansCur; /* calculated means for current iter */
double *meansNxt; /* calculated means for next iter */
double *data1; /* If blur data1 points to meansCur else it points to data */
int *consolidated; /* Needed in the assignment of cluster labels */
int nLabels = 1; /* Needed in the assignment of cluster labels */
/* initialization */
meansCur = (double*) malloc( sizeof(double)*p*n );
meansNxt = (double*) malloc( sizeof(double)*p*n );
mean = (double*) malloc( sizeof(double)*p );
consolidated = (int*) malloc( sizeof(int)*n );
deltas = (int*) malloc( sizeof(int)*n );
for(i=0; i<n; i++) deltas[i] = 1;
radius2 = radius * radius;
meansCur = (double*) memcpy(meansCur, data, p*n*sizeof(double) );
if( blur ) data1=meansCur; else data1=data;
/* main loop */
mexPrintf("Progress: 0.000000"); mexEvalString("drawnow;");
for(iter=0; iter<maxIter; iter++) {
delta = 0;
for( i=0; i<n; i++ ) {
if( deltas[i] ) {
/* shift meansNxt in direction of mean (if m>0) */
o=i*p; m=meanVec( meansCur+o, data1, p, n, radius2, mean );
if( m ) {
for( j=0; j<p; j++ ) meansNxt[o+j] = (1-rate)*meansCur[o+j] + rate*mean[j];
if( dist(meansNxt+o, meansCur+o, p)>0.001) delta=1; else deltas[i]=0;
} else {
for( j=0; j<p; j++ ) meansNxt[o+j] = meansCur[o+j]; deltas[i]=0;
}
}
}
mexPrintf( "\b\b\b\b\b\b\b\b%f", (float)(iter+1)/maxIter ); mexEvalString("drawnow;");
memcpy( meansCur, meansNxt, p*n*sizeof(double) ); if(!delta) break;
}
mexPrintf( "\n" );
/* Consolidate: assign all points that are within radius2 to same cluster. */
for( i=0; i<n; i++ ) { consolidated[i]=0; labels[i]=0; }
for( i=0; i<n; i++ ) if( !consolidated[i]) {
for( j=0; j<n; j++ ) if( !consolidated[j]) {
if( dist(meansCur+i*p, meansCur+j*p, p) < radius2) {
labels[j]=nLabels; consolidated[j]=1;
}
}
nLabels++;
}
nLabels--; memcpy( means, meansCur, p*n*sizeof(double) );
/* free memory */
free(meansNxt); free(meansCur); free(mean); free(consolidated); free(deltas);
}
void compute(const QUESO::FullEnvironment& env) {
struct timeval timevalNow;
gettimeofday(&timevalNow, NULL);
std::cout << std::endl << "Beginning run of 'Hysteretic' example at "
<< ctime(&timevalNow.tv_sec);
//------------------------------------------------------
// Step 1 of 5: Instantiate the parameter space
//------------------------------------------------------
QUESO::VectorSpace<> paramSpaceA(env, "paramA_", 1, NULL);
QUESO::VectorSpace<> paramSpaceB(env, "paramB_", 14, NULL);
QUESO::VectorSpace<> paramSpace (env, "param_", 15, NULL);
//------------------------------------------------------
// Step 2 of 5: Instantiate the parameter domain
//------------------------------------------------------
QUESO::GslVector paramMinsA(paramSpaceA.zeroVector());
paramMinsA.cwSet(0);
QUESO::GslVector paramMaxsA(paramSpaceA.zeroVector());
paramMaxsA.cwSet(5);
QUESO::BoxSubset<> paramDomainA("paramA_",paramSpaceA,paramMinsA,paramMaxsA);
QUESO::GslVector paramMinsB(paramSpaceB.zeroVector());
paramMinsB.cwSet(-INFINITY);
QUESO::GslVector paramMaxsB(paramSpaceB.zeroVector());
paramMaxsB.cwSet( INFINITY);
QUESO::BoxSubset<> paramDomainB("paramB_",paramSpaceB,paramMinsB,paramMaxsB);
QUESO::ConcatenationSubset<> paramDomain("",paramSpace,paramDomainA,paramDomainB);
//------------------------------------------------------
// Step 3 of 5: Instantiate the likelihood function object
//------------------------------------------------------
std::cout << "\tInstantiating the Likelihood; calling internally the hysteretic model"
<< std::endl;
Likelihood<> likelihood("like_", paramDomain);
likelihood.floor.resize(4,NULL);
unsigned int numTimeSteps = 401;
for (unsigned int i = 0; i < 4; ++i) {
likelihood.floor[i] = new std::vector<double>(numTimeSteps,0.);
}
likelihood.accel.resize(numTimeSteps,0.);
FILE *inp;
inp = fopen("an.txt","r");
unsigned int numObservations = 0;
double tmpA;
while (fscanf(inp,"%lf",&tmpA) != EOF) {
likelihood.accel[numObservations] = tmpA;
numObservations++;
}
numObservations=1;
FILE *inp1_1;
inp1_1=fopen("measured_data1_1.txt","r");
while (fscanf(inp1_1,"%lf",&tmpA) != EOF) {
(*likelihood.floor[0])[numObservations]=tmpA;
numObservations++;
}
numObservations=0;
FILE *inp1_2;
inp1_2=fopen("measured_data1_2.txt","r");
while (fscanf(inp1_2,"%lf",&tmpA) != EOF) {
(*likelihood.floor[1])[numObservations]=tmpA;
numObservations++;
}
numObservations=0;
FILE *inp1_3;
inp1_3=fopen("measured_data1_3.txt","r");
while (fscanf(inp1_3,"%lf",&tmpA) != EOF) {
(*likelihood.floor[2])[numObservations]=tmpA;
numObservations++;
}
numObservations=0;
FILE *inp1_4;
inp1_4=fopen("measured_data1_4.txt","r");
while (fscanf(inp1_4,"%lf",&tmpA) != EOF) {
(*likelihood.floor[3])[numObservations]=tmpA;
numObservations++;
}
//------------------------------------------------------
// Step 4 of 5: Instantiate the inverse problem
//------------------------------------------------------
std::cout << "\tInstantiating the SIP" << std::endl;
QUESO::UniformVectorRV<> priorRvA("priorA_", paramDomainA);
QUESO::GslVector meanVec(paramSpaceB.zeroVector());
QUESO::GslVector diagVec(paramSpaceB.zeroVector());
diagVec.cwSet(0.6*0.6);
QUESO::GslMatrix covMatrix(diagVec);
QUESO::GaussianVectorRV<> priorRvB("priorB_", paramDomainB,meanVec,covMatrix);
QUESO::ConcatenatedVectorRV<> priorRv("prior_", priorRvA, priorRvB, paramDomain);
QUESO::GenericVectorRV<> postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<> ip("", NULL, priorRv, likelihood, postRv);
//------------------------------------------------------
// Step 5 of 5: Solve the inverse problem
//------------------------------------------------------
std::cout << "\tSolving the SIP with Multilevel method" << std::endl;
ip.solveWithBayesMLSampling();
gettimeofday(&timevalNow, NULL);
std::cout << "Ending run of 'Hysteretic' example at "
<< ctime(&timevalNow.tv_sec) << std::endl;
return;
}
void omxInitExpectationBA81(omxExpectation* oo) {
omxState* currentState = oo->currentState;
SEXP rObj = oo->rObj;
SEXP tmp;
if(OMX_DEBUG) {
mxLog("Initializing %s.", oo->name);
}
if (!Glibrpf_model) {
#if USE_EXTERNAL_LIBRPF
get_librpf_t get_librpf = (get_librpf_t) R_GetCCallable("rpf", "get_librpf_model_GPL");
(*get_librpf)(LIBIFA_RPF_API_VERSION, &Glibrpf_numModels, &Glibrpf_model);
#else
// if linking against included source code
Glibrpf_numModels = librpf_numModels;
Glibrpf_model = librpf_model;
#endif
}
BA81Expect *state = new BA81Expect;
// These two constants should be as identical as possible
state->name = oo->name;
if (0) {
state->LogLargestDouble = 0.0;
state->LargestDouble = 1.0;
} else {
state->LogLargestDouble = log(std::numeric_limits<double>::max()) - 1;
state->LargestDouble = exp(state->LogLargestDouble);
ba81NormalQuad &quad = state->getQuad();
quad.setOne(state->LargestDouble);
}
state->expectedUsed = false;
state->estLatentMean = NULL;
state->estLatentCov = NULL;
state->type = EXPECTATION_OBSERVED;
state->itemParam = NULL;
state->EitemParam = NULL;
state->itemParamVersion = 0;
state->latentParamVersion = 0;
oo->argStruct = (void*) state;
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("data")));
state->data = omxDataLookupFromState(tmp, currentState);
}
if (strcmp(omxDataType(state->data), "raw") != 0) {
omxRaiseErrorf("%s unable to handle data type %s", oo->name, omxDataType(state->data));
return;
}
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("verbose")));
state->verbose = Rf_asInteger(tmp);
}
int targetQpoints;
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("qpoints")));
targetQpoints = Rf_asInteger(tmp);
}
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("qwidth")));
state->grp.setGridFineness(Rf_asReal(tmp), targetQpoints);
}
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("ItemSpec")));
state->grp.importSpec(tmp);
if (state->verbose >= 2) mxLog("%s: found %d item specs", oo->name, state->numItems());
}
state->_latentMeanOut = omxNewMatrixFromSlot(rObj, currentState, "mean");
state->_latentCovOut = omxNewMatrixFromSlot(rObj, currentState, "cov");
state->itemParam = omxNewMatrixFromSlot(rObj, currentState, "item");
state->grp.param = state->itemParam->data; // algebra not allowed yet TODO
const int numItems = state->itemParam->cols;
if (state->numItems() != numItems) {
omxRaiseErrorf("ItemSpec length %d must match the number of item columns (%d)",
state->numItems(), numItems);
return;
}
if (state->itemParam->rows != state->grp.impliedParamRows) {
omxRaiseErrorf("item matrix must have %d rows", state->grp.impliedParamRows);
return;
}
state->grp.paramRows = state->itemParam->rows;
// for algebra item param, will need to defer until later?
state->grp.learnMaxAbilities();
int maxAbilities = state->grp.itemDims;
state->grp.setFactorNames(state->itemParam->rownames);
{
ProtectedSEXP tmp2(R_do_slot(rObj, Rf_install(".detectIndependence")));
state->grp.detectIndependence = Rf_asLogical(tmp2);
}
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("EstepItem")));
if (!Rf_isNull(tmp)) {
int rows, cols;
getMatrixDims(tmp, &rows, &cols);
if (rows != state->itemParam->rows || cols != state->itemParam->cols) {
Rf_error("EstepItem must have the same dimensions as the item MxMatrix");
}
state->EitemParam = REAL(tmp);
}
}
oo->computeFun = ba81compute;
oo->setVarGroup = ignoreSetVarGroup;
oo->destructFun = ba81Destroy;
oo->populateAttrFun = ba81PopulateAttributes;
oo->componentFun = getComponent;
oo->canDuplicate = false;
// TODO: Exactly identical rows do not contribute any information.
// The sorting algorithm ought to remove them so we get better cache behavior.
// The following summary stats would be cheaper to calculate too.
omxData *data = state->data;
if (data->hasDefinitionVariables()) Rf_error("%s: not implemented yet", oo->name);
std::vector<int> &rowMap = state->grp.rowMap;
int weightCol;
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("weightColumn")));
weightCol = INTEGER(tmp)[0];
}
if (weightCol == NA_INTEGER) {
// Should rowMap be part of omxData? This is essentially a
// generic compression step that shouldn't be specific to IFA models.
state->grp.rowWeight = (double*) R_alloc(data->rows, sizeof(double));
rowMap.resize(data->rows);
int numUnique = 0;
for (int rx=0; rx < data->rows; ) {
int rw = 1;
state->grp.rowWeight[numUnique] = rw;
rowMap[numUnique] = rx;
rx += rw;
++numUnique;
}
rowMap.resize(numUnique);
state->weightSum = state->data->rows;
}
else {
if (omxDataColumnIsFactor(data, weightCol)) {
omxRaiseErrorf("%s: weightColumn %d is a factor", oo->name, 1 + weightCol);
return;
}
state->grp.rowWeight = omxDoubleDataColumn(data, weightCol);
state->weightSum = 0;
for (int rx=0; rx < data->rows; ++rx) { state->weightSum += state->grp.rowWeight[rx]; }
rowMap.resize(data->rows);
for (size_t rx=0; rx < rowMap.size(); ++rx) {
rowMap[rx] = rx;
}
}
// complain about non-integral rowWeights (EAP can't work) TODO
auto colMap = oo->getDataColumns();
for (int cx = 0; cx < numItems; cx++) {
int *col = omxIntDataColumnUnsafe(data, colMap[cx]);
state->grp.dataColumns.push_back(col);
}
// sanity check data
for (int cx = 0; cx < numItems; cx++) {
if (!omxDataColumnIsFactor(data, colMap[cx])) {
data->omxPrintData("diagnostic", 3);
omxRaiseErrorf("%s: column %d is not a factor", oo->name, int(1 + colMap[cx]));
return;
}
}
// TODO the max outcome should be available from omxData
for (int rx=0; rx < data->rows; rx++) {
int cols = 0;
for (int cx = 0; cx < numItems; cx++) {
const int *col = state->grp.dataColumns[cx];
int pick = col[rx];
if (pick == NA_INTEGER) continue;
++cols;
const int no = state->grp.itemOutcomes[cx];
if (pick > no) {
Rf_error("Data for item '%s' has at least %d outcomes, not %d",
state->itemParam->colnames[cx], pick, no);
}
}
if (cols == 0) {
Rf_error("Row %d has all NAs", 1+rx);
}
}
if (state->_latentMeanOut && state->_latentMeanOut->rows * state->_latentMeanOut->cols != maxAbilities) {
Rf_error("The mean matrix '%s' must be a row or column vector of size %d",
state->_latentMeanOut->name(), maxAbilities);
}
if (state->_latentCovOut && (state->_latentCovOut->rows != maxAbilities ||
state->_latentCovOut->cols != maxAbilities)) {
Rf_error("The cov matrix '%s' must be %dx%d",
state->_latentCovOut->name(), maxAbilities, maxAbilities);
}
state->grp.setLatentDistribution(state->_latentMeanOut? state->_latentMeanOut->data : NULL,
state->_latentCovOut? state->_latentCovOut->data : NULL);
{
EigenArrayAdaptor Eparam(state->itemParam);
Eigen::Map< Eigen::VectorXd > meanVec(state->grp.mean, maxAbilities);
Eigen::Map< Eigen::MatrixXd > covMat(state->grp.cov, maxAbilities, maxAbilities);
state->grp.quad.setStructure(state->grp.qwidth, state->grp.qpoints,
Eparam, meanVec, covMat);
}
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("minItemsPerScore")));
state->grp.setMinItemsPerScore(Rf_asInteger(tmp));
}
state->grp.buildRowSkip();
if (isErrorRaised()) return;
{ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("debugInternal")));
state->debugInternal = Rf_asLogical(tmp);
}
state->ElatentVersion = 0;
if (state->_latentMeanOut) {
state->estLatentMean = omxInitMatrix(maxAbilities, 1, TRUE, currentState);
omxCopyMatrix(state->estLatentMean, state->_latentMeanOut); // rename matrices TODO
}
if (state->_latentCovOut) {
state->estLatentCov = omxInitMatrix(maxAbilities, maxAbilities, TRUE, currentState);
omxCopyMatrix(state->estLatentCov, state->_latentCovOut);
}
}
