void flattenDataToVector(omxMatrix* cov, omxMatrix* means, omxMatrix *obsThresholdMat,
std::vector< omxThresholdColumn > &thresholds, omxMatrix* vector) {
// TODO: vectorize data flattening
// if(OMX_DEBUG) { mxLog("Flattening out data vectors: cov 0x%x, mean 0x%x, thresh 0x%x[n=%d] ==> 0x%x",
// cov, means, thresholds, nThresholds, vector); }
int nextLoc = 0;
for(int j = 0; j < cov->rows; j++) {
for(int k = j; k < cov->rows; k++) {
omxSetVectorElement(vector, nextLoc, omxMatrixElement(cov, j, k)); // Use upper triangle in case of SYMM-style mat.
nextLoc++;
}
}
if (means != NULL) {
for(int j = 0; j < cov->rows; j++) {
omxSetVectorElement(vector, nextLoc, omxVectorElement(means, j));
nextLoc++;
}
}
for(int j = 0; j < int(thresholds.size()); j++) {
omxThresholdColumn* thresh = &thresholds[j];
for(int k = 0; k < thresh->numThresholds; k++) {
omxSetVectorElement(vector, nextLoc, omxMatrixElement(obsThresholdMat, k, thresh->column));
nextLoc++;
}
}
}
static void exportLatentDistToOMX(ba81NormalQuad &quad, double *latentDist1, omxMatrix *meanOut, omxMatrix *covOut)
{
const int maxAbilities = quad.abilities();
if (meanOut) {
for (int d1=0; d1 < maxAbilities; d1++) {
omxSetVectorElement(meanOut, d1, latentDist1[d1]);
}
}
if (covOut) {
for (int d1=0; d1 < maxAbilities; d1++) {
int cx = maxAbilities + triangleLoc1(d1);
for (int d2=0; d2 <= d1; d2++) {
double cov = latentDist1[cx];
omxSetMatrixElement(covOut, d1, d2, cov);
if (d1 != d2) omxSetMatrixElement(covOut, d2, d1, cov);
++cx;
}
}
}
}
static void omxRowFitFunctionSingleIteration(omxFitFunction *localobj, omxFitFunction *sharedobj, int rowbegin, int rowcount,
FitContext *fc) {
omxRowFitFunction* oro = ((omxRowFitFunction*) localobj->argStruct);
omxRowFitFunction* shared_oro = ((omxRowFitFunction*) sharedobj->argStruct);
omxMatrix *rowAlgebra, *rowResults;
omxMatrix *filteredDataRow, *dataRow, *existenceVector;
omxMatrix *dataColumns;
omxData *data;
int isContiguous, contiguousStart, contiguousLength;
rowAlgebra = oro->rowAlgebra;
rowResults = shared_oro->rowResults;
data = oro->data;
dataColumns = oro->dataColumns;
dataRow = oro->dataRow;
filteredDataRow = oro->filteredDataRow;
existenceVector = oro->existenceVector;
isContiguous = oro->contiguous.isContiguous;
contiguousStart = oro->contiguous.start;
contiguousLength = oro->contiguous.length;
int *toRemove = (int*) malloc(sizeof(int) * dataColumns->cols);
int *zeros = (int*) calloc(dataColumns->cols, sizeof(int));
for(int row = rowbegin; row < data->rows && (row - rowbegin) < rowcount; row++) {
mxLogSetCurrentRow(row);
data->loadDefVars(localobj->matrix->currentState, row);
// Populate data row
if (isContiguous) {
omxContiguousDataRow(data, row, contiguousStart, contiguousLength, dataRow);
} else {
omxDataRow(data, row, dataColumns, dataRow); // Populate data row
}
markDataRowDependencies(localobj->matrix->currentState, oro);
for(int j = 0; j < dataColumns->cols; j++) {
if(omxDataElementMissing(data, row, j)) {
toRemove[j] = 1;
omxSetVectorElement(existenceVector, j, 0);
} else {
toRemove[j] = 0;
omxSetVectorElement(existenceVector, j, 1);
}
}
omxCopyMatrix(filteredDataRow, dataRow);
omxRemoveRowsAndColumns(filteredDataRow, zeros, toRemove);
omxRecompute(rowAlgebra, fc);
omxCopyMatrixToRow(rowAlgebra, row, rowResults);
}
free(toRemove);
free(zeros);
}
static void omxRowFitFunctionSingleIteration(omxFitFunction *localobj, omxFitFunction *sharedobj, int rowbegin, int rowcount,
FitContext *fc) {
omxRowFitFunction* oro = ((omxRowFitFunction*) localobj->argStruct);
omxRowFitFunction* shared_oro = ((omxRowFitFunction*) sharedobj->argStruct);
omxMatrix *rowAlgebra, *rowResults;
omxMatrix *filteredDataRow, *dataRow, *existenceVector;
omxMatrix *dataColumns;
omxData *data;
int isContiguous, contiguousStart, contiguousLength;
int numCols, numRemoves;
rowAlgebra = oro->rowAlgebra;
rowResults = shared_oro->rowResults;
data = oro->data;
dataColumns = oro->dataColumns;
dataRow = oro->dataRow;
filteredDataRow = oro->filteredDataRow;
existenceVector = oro->existenceVector;
isContiguous = oro->contiguous.isContiguous;
contiguousStart = oro->contiguous.start;
contiguousLength = oro->contiguous.length;
Eigen::VectorXd oldDefs;
oldDefs.resize(data->defVars.size());
oldDefs.setConstant(NA_REAL);
numCols = dataColumns->cols;
int *toRemove = (int*) malloc(sizeof(int) * dataColumns->cols);
int *zeros = (int*) calloc(dataColumns->cols, sizeof(int));
for(int row = rowbegin; row < data->rows && (row - rowbegin) < rowcount; row++) {
data->handleDefinitionVarList(localobj->matrix->currentState, row, oldDefs.data());
omxStateNextRow(localobj->matrix->currentState); // Advance row
// Populate data row
numRemoves = 0;
if (isContiguous) {
omxContiguousDataRow(data, row, contiguousStart, contiguousLength, dataRow);
} else {
omxDataRow(data, row, dataColumns, dataRow); // Populate data row
}
markDataRowDependencies(localobj->matrix->currentState, oro);
for(int j = 0; j < dataColumns->cols; j++) {
double dataValue = omxVectorElement(dataRow, j);
if(std::isnan(dataValue)) {
numRemoves++;
toRemove[j] = 1;
omxSetVectorElement(existenceVector, j, 0);
} else {
toRemove[j] = 0;
omxSetVectorElement(existenceVector, j, 1);
}
}
// TODO: Determine if this is the correct response.
if(numRemoves == numCols) {
char *errstr = (char*) calloc(250, sizeof(char));
sprintf(errstr, "Row %d completely missing. omxRowFitFunction cannot have completely missing rows.", omxDataIndex(data, row));
omxRaiseError(errstr);
free(errstr);
continue;
}
omxCopyMatrix(filteredDataRow, dataRow);
omxRemoveRowsAndColumns(filteredDataRow, 0, numRemoves, zeros, toRemove);
omxRecompute(rowAlgebra, fc);
omxCopyMatrixToRow(rowAlgebra, omxDataIndex(data, row), rowResults);
}
free(toRemove);
free(zeros);
}
