Ejemplo n.º 1
0
static void gradCov(omxFitFunction *oo, FitContext *fc)
{
	const double Scale = Global->llScale;
	omxExpectation *expectation = oo->expectation;
	BA81FitState *state = (BA81FitState*) oo->argStruct;
	BA81Expect *estate = (BA81Expect*) expectation->argStruct;
	if (estate->verbose >= 1) mxLog("%s: cross product approximation", oo->name());

	estate->grp.ba81OutcomeProb(estate->itemParam->data, FALSE);

	const int numThreads = Global->numThreads;
	const int numUnique = estate->getNumUnique();
	ba81NormalQuad &quad = estate->getQuad();
	const int numSpecific = quad.numSpecific;
	const int maxDims = quad.maxDims;
	const int pDims = numSpecific? maxDims-1 : maxDims;
	const int maxAbilities = quad.maxAbilities;
	Eigen::MatrixXd icovMat(pDims, pDims);
	if (maxAbilities) {
		Eigen::VectorXd mean;
		Eigen::MatrixXd srcMat;
		estate->getLatentDistribution(fc, mean, srcMat);
		icovMat = srcMat.topLeftCorner(pDims, pDims);
		Matrix tmp(icovMat.data(), pDims, pDims);
		int info = InvertSymmetricPosDef(tmp, 'U');
		if (info) {
			omxRaiseErrorf("%s: latent covariance matrix is not positive definite", oo->name());
			return;
		}
		icovMat.triangularView<Eigen::Lower>() = icovMat.transpose().triangularView<Eigen::Lower>();
	}
	std::vector<int> &rowMap = estate->grp.rowMap;
	double *rowWeight = estate->grp.rowWeight;
	std::vector<bool> &rowSkip = estate->grp.rowSkip;
	const int totalQuadPoints = quad.totalQuadPoints;
	omxMatrix *itemParam = estate->itemParam;
	omxBuffer<double> patternLik(numUnique);

	const int priDerivCoef = pDims + triangleLoc1(pDims);
	const int numLatents = maxAbilities + triangleLoc1(maxAbilities);
	const int thrDerivSize = itemParam->cols * state->itemDerivPadSize;
	const int totalOutcomes = estate->totalOutcomes();
	const int numItems = state->freeItemParams? estate->numItems() : 0;
	const size_t numParam = fc->varGroup->vars.size();
	std::vector<double> thrGrad(numThreads * numParam);
	std::vector<double> thrMeat(numThreads * numParam * numParam);
	const double *wherePrep = quad.wherePrep.data();

	if (numSpecific == 0) {
		omxBuffer<double> thrLxk(totalQuadPoints * numThreads);
		omxBuffer<double> derivCoef(totalQuadPoints * priDerivCoef);

		if (state->freeLatents) {
#pragma omp parallel for num_threads(numThreads)
			for (int qx=0; qx < totalQuadPoints; qx++) {
				const double *where = wherePrep + qx * maxDims;
				calcDerivCoef(fc, state, estate, icovMat.data(), where,
					      derivCoef.data() + qx * priDerivCoef);
			}
		}

#pragma omp parallel for num_threads(numThreads)
		for (int px=0; px < numUnique; px++) {
			if (rowSkip[px]) continue;
			int thrId = omx_absolute_thread_num();
			double *lxk = thrLxk.data() + thrId * totalQuadPoints;
			omxBuffer<double> expected(totalOutcomes); // can use maxOutcomes instead TODO
			std::vector<double> deriv0(thrDerivSize);
			std::vector<double> latentGrad(numLatents);
			std::vector<double> patGrad(numParam);
			double *grad = thrGrad.data() + thrId * numParam;
			double *meat = thrMeat.data() + thrId * numParam * numParam;
			estate->grp.ba81LikelihoodSlow2(px, lxk);

			// If patternLik is already valid, maybe could avoid this loop TODO
			double patternLik1 = 0;
			for (int qx=0; qx < totalQuadPoints; qx++) {
				patternLik1 += lxk[qx];
			}
			patternLik[px] = patternLik1;

			// if (!validPatternLik(state, patternLik1))  complain, TODO

			for (int qx=0; qx < totalQuadPoints; qx++) {
				double tmp = lxk[qx];
				mapLatentDeriv(state, estate, tmp, derivCoef.data() + qx * priDerivCoef,
					       latentGrad.data());

				for (int ix=0; ix < numItems; ++ix) {
					int pick = estate->grp.dataColumns[ix][rowMap[px]];
					if (pick == NA_INTEGER) continue;
					OMXZERO(expected.data(), estate->itemOutcomes(ix));
					expected[pick-1] = tmp;
					const double *spec = estate->itemSpec(ix);
					double *iparam = omxMatrixColumn(itemParam, ix);
					const int id = spec[RPF_ISpecID];
					double *myDeriv = deriv0.data() + ix * state->itemDerivPadSize;
					(*Glibrpf_model[id].dLL1)(spec, iparam, wherePrep + qx * maxDims,
							      expected.data(), myDeriv);
				}
			}

			gradCov_finish_1pat(1 / patternLik1, rowWeight[px], numItems, numLatents, numParam,
					state, estate, itemParam, deriv0, latentGrad, Scale, patGrad, grad, meat);
		}
	} else {
		const int totalPrimaryPoints = quad.totalPrimaryPoints;
		const int specificPoints = quad.quadGridSize;
		omxBuffer<double> thrLxk(totalQuadPoints * numSpecific * numThreads);
		omxBuffer<double> thrEi(totalPrimaryPoints * numThreads);
		omxBuffer<double> thrEis(totalPrimaryPoints * numSpecific * numThreads);
		const int derivPerPoint = priDerivCoef + 2 * numSpecific;
		omxBuffer<double> derivCoef(totalQuadPoints * derivPerPoint);

		if (state->freeLatents) {
#pragma omp parallel for num_threads(numThreads)
			for (int qx=0; qx < totalQuadPoints; qx++) {
				const double *where = wherePrep + qx * maxDims;
				calcDerivCoef(fc, state, estate, icovMat.data(), where,
					      derivCoef.data() + qx * derivPerPoint);
				for (int Sgroup=0; Sgroup < numSpecific; ++Sgroup) {
					calcDerivCoef1(fc, state, estate, where, Sgroup,
						       derivCoef.data() + qx * derivPerPoint + priDerivCoef + 2 * Sgroup);
				}
			}
		}

#pragma omp parallel for num_threads(numThreads)
		for (int px=0; px < numUnique; px++) {
			if (rowSkip[px]) continue;
			int thrId = omx_absolute_thread_num();
			double *lxk = thrLxk.data() + totalQuadPoints * numSpecific * thrId;
			double *Ei = thrEi.data() + totalPrimaryPoints * thrId;
			double *Eis = thrEis.data() + totalPrimaryPoints * numSpecific * thrId;
			omxBuffer<double> expected(totalOutcomes); // can use maxOutcomes instead TODO
			std::vector<double> deriv0(thrDerivSize);
			std::vector<double> latentGrad(numLatents);
			std::vector<double> patGrad(numParam);
			double *grad = thrGrad.data() + thrId * numParam;
			double *meat = thrMeat.data() + thrId * numParam * numParam;
			estate->grp.cai2010EiEis(px, lxk, Eis, Ei);

			for (int qx=0, qloc = 0; qx < totalPrimaryPoints; qx++) {
				for (int sgroup=0; sgroup < numSpecific; ++sgroup) {
					Eis[qloc] = Ei[qx] / Eis[qloc];
					++qloc;
				}
			}

			for (int qloc=0, eisloc=0, qx=0; eisloc < totalPrimaryPoints * numSpecific; eisloc += numSpecific) {
				for (int sx=0; sx < specificPoints; sx++) {
					mapLatentDeriv(state, estate, Eis[eisloc] * lxk[qloc],
						       derivCoef.data() + qx * derivPerPoint,
						       latentGrad.data());

					for (int Sgroup=0; Sgroup < numSpecific; Sgroup++) {
						double lxk1 = lxk[qloc];
						double Eis1 = Eis[eisloc + Sgroup];
						double tmp = Eis1 * lxk1;
						mapLatentDerivS(state, estate, Sgroup, tmp,
								derivCoef.data() + qx * derivPerPoint + priDerivCoef + 2 * Sgroup,
								latentGrad.data());

						for (int ix=0; ix < numItems; ++ix) {
							if (estate->grp.Sgroup[ix] != Sgroup) continue;
							int pick = estate->grp.dataColumns[ix][rowMap[px]];
							if (pick == NA_INTEGER) continue;
							OMXZERO(expected.data(), estate->itemOutcomes(ix));
							expected[pick-1] = tmp;
							const double *spec = estate->itemSpec(ix);
							double *iparam = omxMatrixColumn(itemParam, ix);
							const int id = spec[RPF_ISpecID];
							const int dims = spec[RPF_ISpecDims];
							double *myDeriv = deriv0.data() + ix * state->itemDerivPadSize;
							const double *where = wherePrep + qx * maxDims;
							Eigen::VectorXd ptheta(dims);
							for (int dx=0; dx < dims; dx++) {
								ptheta[dx] = where[std::min(dx, maxDims-1)];
							}
							(*Glibrpf_model[id].dLL1)(spec, iparam, ptheta.data(),
									      expected.data(), myDeriv);
						}
						++qloc;
					}
					++qx;
				}
			}

			// If patternLik is already valid, maybe could avoid this loop TODO
			double patternLik1 = 0;
			for (int qx=0; qx < totalPrimaryPoints; ++qx) {
				patternLik1 += Ei[qx];
			}
			patternLik[px] = patternLik1;

			gradCov_finish_1pat(1 / patternLik1, rowWeight[px], numItems, numLatents, numParam,
					state, estate, itemParam, deriv0, latentGrad, Scale, patGrad, grad, meat);
		}
	}

	for (int tx=1; tx < numThreads; ++tx) {
		double *th = thrGrad.data() + tx * numParam;
		for (size_t en=0; en < numParam; ++en) {
			thrGrad[en] += th[en];
		}
	}
	for (int tx=1; tx < numThreads; ++tx) {
		double *th = thrMeat.data() + tx * numParam * numParam;
		for (size_t en=0; en < numParam * numParam; ++en) {
			thrMeat[en] += th[en];
		}
	}
	for (size_t d1=0; d1 < numParam; ++d1) {
		fc->grad(d1) += thrGrad[d1];
	}
	if (fc->infoB) {
		for (size_t d1=0; d1 < numParam; ++d1) {
			for (size_t d2=0; d2 < numParam; ++d2) {
				int cell = d1 * numParam + d2;
				fc->infoB[cell] += thrMeat[cell];
			}
		}
	}
}
Ejemplo n.º 2
0
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);
	}
}