static void setLatentStartingValues(omxFitFunction *oo, FitContext *fc) //remove? TODO
{
	BA81FitState *state = (BA81FitState*) oo->argStruct;
	BA81Expect *estate = (BA81Expect*) oo->expectation->argStruct;
	std::vector<int> &latentMap = state->latentMap;
	ba81NormalQuad &quad = estate->getQuad();
	int maxAbilities = quad.maxAbilities;
	omxMatrix *estMean = estate->estLatentMean;
	omxMatrix *estCov = estate->estLatentCov;

	for (int a1 = 0; a1 < maxAbilities; ++a1) {
		if (latentMap[a1] >= 0) {
			int to = latentMap[a1];
			fc->est[to] = omxVectorElement(estMean, a1);
		}

		for (int a2 = 0; a2 <= a1; ++a2) {
			int to = latentMap[maxAbilities + triangleLoc1(a1) + a2];
			if (to < 0) continue;
			fc->est[to] = omxMatrixElement(estCov, a1, a2);
		}
	}

	if (estate->verbose >= 1) {
		mxLog("%s: set latent parameters for version %d",
		      oo->name(), estate->ElatentVersion);
	}
}
Exemple #2
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void ba81AggregateDistributions(std::vector<struct omxExpectation *> &expectation,
				int *version, omxMatrix *meanMat, omxMatrix *covMat)
{
	int allVer = 0;
	for (size_t ex=0; ex < expectation.size(); ++ex) {
		BA81Expect *ba81 = (BA81Expect *) expectation[ex]->argStruct;
		allVer += ba81->ElatentVersion;
	}
	if (*version == allVer) return;
	*version = allVer;

	BA81Expect *exemplar = (BA81Expect *) expectation[0]->argStruct;
	ba81NormalQuad &quad = exemplar->getQuad();
	ba81NormalQuad combined(quad);

	int got = 0;
	for (size_t ex=0; ex < expectation.size(); ++ex) {
		BA81Expect *ba81 = (BA81Expect *) expectation[ex]->argStruct;
		// double weight = 1/ba81->weightSum; ?
		combined.addSummary(ba81->grp.quad);
		++got;
	}
	if (got == 0) return;

	int dim = quad.abilities();
	int numLatents = dim + triangleLoc1(dim);
	Eigen::ArrayXd latentDist(numLatents);
	combined.EAP(got, latentDist);
	for (int d1=quad.abilities(); d1 < numLatents; d1++) {
		latentDist[d1] *= got / (got - 1.0);
	}
	exportLatentDistToOMX(quad, latentDist.data(), meanMat, covMat);
}
Exemple #3
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/**
 * MAP is not affected by the number of items. EAP is. Likelihood can
 * get concentrated in a single quadrature ordinate. For 3PL, response
 * patterns can have a bimodal likelihood. This will confuse MAP and
 * is a key advantage of EAP (Thissen & Orlando, 2001, p. 136).
 *
 * Thissen, D. & Orlando, M. (2001). IRT for items scored in two
 * categories. In D. Thissen & H. Wainer (Eds.), \emph{Test scoring}
 * (pp 73-140). Lawrence Erlbaum Associates, Inc.
 */
static void
ba81PopulateAttributes(omxExpectation *oo, SEXP robj)
{
	BA81Expect *state = (BA81Expect *) oo->argStruct;
	if (!state->debugInternal) return;

	ba81NormalQuad &quad = state->getQuad();
	int maxAbilities = quad.abilities();
	const int numUnique = state->getNumUnique();

	const double LogLargest = state->LogLargestDouble;
	SEXP Rlik;

	if (state->grp.patternLik.size() != numUnique) {
		refreshPatternLikelihood(state, oo->dynamicDataSource);
	}

	Rf_protect(Rlik = Rf_allocVector(REALSXP, numUnique));
	memcpy(REAL(Rlik), state->grp.patternLik.data(), sizeof(double) * numUnique);
	double *lik_out = REAL(Rlik);
	for (int px=0; px < numUnique; ++px) {
		// Must return value in log units because it may not be representable otherwise
		lik_out[px] = log(lik_out[px]) - LogLargest;
	}

	MxRList dbg;
	dbg.add("patternLikelihood", Rlik);

	if (quad.getEstepTableSize(0)) {
		SEXP Rexpected;
		Rf_protect(Rexpected = Rf_allocVector(REALSXP, quad.getEstepTableSize(0)));
		Eigen::Map< Eigen::ArrayXd > box(REAL(Rexpected), quad.getEstepTableSize(0));
		quad.exportEstepTable(0, box);
		dbg.add("em.expected", Rexpected);
	}

	SEXP Rmean, Rcov;
	if (state->estLatentMean) {
		Rf_protect(Rmean = Rf_allocVector(REALSXP, maxAbilities));
		memcpy(REAL(Rmean), state->estLatentMean->data, maxAbilities * sizeof(double));
		dbg.add("mean", Rmean);
	}
	if (state->estLatentCov) {
		Rf_protect(Rcov = Rf_allocMatrix(REALSXP, maxAbilities, maxAbilities));
		memcpy(REAL(Rcov), state->estLatentCov->data, maxAbilities * maxAbilities * sizeof(double));
		dbg.add("cov", Rcov);
	}

	Rf_setAttrib(robj, Rf_install("debug"), dbg.asR());
}
Exemple #4
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// Attempt G-H grid? http://dbarajassolano.wordpress.com/2012/01/26/on-sparse-grid-quadratures/
void ba81RefreshQuadrature(omxExpectation* oo)
{
	BA81Expect *state = (BA81Expect *) oo->argStruct;
	ba81NormalQuad &quad = state->getQuad();

	Eigen::VectorXd mean;
	Eigen::MatrixXd fullCov;
	state->getLatentDistribution(NULL, mean, fullCov);

	if (state->verbose >= 1) {
		mxLog("%s: refresh quadrature", oo->name);
		if (state->verbose >= 2) {
			int dim = mean.rows();
			pda(mean.data(), 1, dim);
			pda(fullCov.data(), dim, dim);
		}
	}

	quad.refresh(mean, fullCov);
}
void omxInitFitFunctionBA81(omxFitFunction* oo)
{
	if (!oo->argStruct) { // ugh!
		BA81FitState *state = new BA81FitState;
		oo->argStruct = state;
	}
	omxState *currentState = oo->matrix->currentState;
	BA81FitState *state = (BA81FitState*) oo->argStruct;

	omxExpectation *expectation = oo->expectation;
	BA81Expect *estate = (BA81Expect*) expectation->argStruct;
	estate->fit = oo;

	oo->computeFun = ba81Compute;
	oo->setVarGroup = ba81SetFreeVarGroup;
	oo->destructFun = ba81Destroy;
	oo->gradientAvailable = TRUE;
	oo->hessianAvailable = TRUE;

	int maxParam = estate->itemParam->rows;
	state->itemDerivPadSize = maxParam + triangleLoc1(maxParam);

	int numItems = estate->itemParam->cols;
	for (int ix=0; ix < numItems; ix++) {
		const double *spec = estate->itemSpec(ix);
		int id = spec[RPF_ISpecID];
		if (id < 0 || id >= Glibrpf_numModels) {
			Rf_error("ItemSpec %d has unknown item model %d", ix, id);
		}
	}

	state->itemParam = omxInitMatrix(0, 0, TRUE, currentState);
	state->latentMean = omxInitMatrix(0, 0, TRUE, currentState);
	state->latentCov = omxInitMatrix(0, 0, TRUE, currentState);
	state->copyEstimates(estate);

	state->returnRowLikelihoods = Rf_asInteger(R_do_slot(oo->rObj, Rf_install("vector")));
}
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];
			}
		}
	}
}
static void sandwich(omxFitFunction *oo, FitContext *fc)
{
	const double abScale = fabs(Global->llScale);
	omxExpectation *expectation = oo->expectation;
	BA81FitState *state = (BA81FitState*) oo->argStruct;
	BA81Expect *estate = (BA81Expect*) expectation->argStruct;
	if (estate->verbose >= 1) mxLog("%s: sandwich", 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;
	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);

	std::vector<const double*> &itemSpec = estate->grp.spec;
	const int totalOutcomes = estate->totalOutcomes();
	const int numItems = estate->grp.numItems();
	const size_t numParam = fc->varGroup->vars.size();
	const double *wherePrep = quad.wherePrep.data();
	std::vector<double> thrBreadG(numThreads * numParam * numParam);
	std::vector<double> thrBreadH(numThreads * numParam * numParam);
	std::vector<double> thrMeat(numThreads * numParam * numParam);

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

#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> itemDeriv(state->itemDerivPadSize);
			omxBuffer<double> expected(totalOutcomes); // can use maxOutcomes instead TODO
			double *breadG = thrBreadG.data() + thrId * numParam * numParam; //a
			double *breadH = thrBreadH.data() + thrId * numParam * numParam; //a
			double *meat = thrMeat.data() + thrId * numParam * numParam;   //b
			std::vector<double> patGrad(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

			double weight = 1 / patternLik[px];
			for (int qx=0; qx < totalQuadPoints; qx++) {
				double tmp = lxk[qx] * weight;
				double sqrtTmp = sqrt(tmp);

				std::vector<double> gradBuf(numParam);
				int gradOffset = 0;

				for (int ix=0; ix < numItems; ++ix) {
					if (ix) gradOffset += state->paramPerItem[ix-1];
					int pick = estate->grp.dataColumns[ix][rowMap[px]];
					if (pick == NA_INTEGER) continue;
					pick -= 1;

					const int iOutcomes = estate->itemOutcomes(ix);
					OMXZERO(expected.data(), iOutcomes);
					expected[pick] = 1;
					const double *spec = itemSpec[ix];
					double *iparam = omxMatrixColumn(itemParam, ix);
					const int id = spec[RPF_ISpecID];
					OMXZERO(itemDeriv.data(), state->itemDerivPadSize);
					(*Glibrpf_model[id].dLL1)(spec, iparam, wherePrep + qx * maxDims,
							      expected.data(), itemDeriv.data());
					(*Glibrpf_model[id].dLL2)(spec, iparam, itemDeriv.data());

					for (int par = 0; par < state->paramPerItem[ix]; ++par) {
						int to = state->itemGradMap[gradOffset + par];
						if (to >= 0) {
							gradBuf[to] -= itemDeriv[par] * sqrtTmp;
							patGrad[to] -= itemDeriv[par] * tmp;
						}
					}
					int derivBase = ix * state->itemDerivPadSize;
					for (int ox=0; ox < state->itemDerivPadSize; ox++) {
						int to = state->paramMap[derivBase + ox];
						if (to >= int(numParam)) {
							int Hto = to - numParam;
							breadH[Hto] += abScale * itemDeriv[ox] * tmp * rowWeight[px];
						}
					}
				}
				addSymOuterProd(abScale * rowWeight[px], gradBuf.data(), numParam, breadG);
			}
			addSymOuterProd(abScale * rowWeight[px], patGrad.data(), numParam, meat);
		}

	} else {
		Rf_error("Sandwich information matrix method is not implemented for bifactor models");
		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);

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

			// 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;

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

			// WARNING: I didn't work out the math. I just coded this the way
			// it seems to make sense.
			for (int qloc=0, eisloc=0, qx=0; eisloc < totalPrimaryPoints * numSpecific; eisloc += numSpecific) {
				for (int sx=0; sx < specificPoints; sx++) {
					for (int Sgroup=0; Sgroup < numSpecific; Sgroup++) {
						std::vector<double> gradBuf(numParam);
						int gradOffset = 0;
						double lxk1 = lxk[qloc + Sgroup];
						double Eis1 = Eis[eisloc + Sgroup];
						double tmp = Eis1 * lxk1 / patternLik1;
						double sqrtTmp = sqrt(tmp);
						for (int ix=0; ix < numItems; ++ix) {
							if (ix) gradOffset += state->paramPerItem[ix-1];
							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] = 1;
							const double *spec = itemSpec[ix];
							double *iparam = omxMatrixColumn(itemParam, ix);
							const int id = spec[RPF_ISpecID];
							const int dims = spec[RPF_ISpecDims];
							OMXZERO(itemDeriv.data(), 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(), itemDeriv.data());
							(*Glibrpf_model[id].dLL2)(spec, iparam, itemDeriv.data());

							for (int par = 0; par < state->paramPerItem[ix]; ++par) {
								int to = state->itemGradMap[gradOffset + par];
								if (to >= 0) {
									gradBuf[to] -= itemDeriv[par] * sqrtTmp;
									patGrad[to] -= itemDeriv[par] * tmp;
								}
							}
							int derivBase = ix * state->itemDerivPadSize;
							for (int ox=0; ox < state->itemDerivPadSize; ox++) {
								int to = state->paramMap[derivBase + ox];
								if (to >= int(numParam)) {
									int Hto = to - numParam;
									breadH[Hto] += (abScale * itemDeriv[ox] *
											tmp * rowWeight[px]);
								}
							}
						}
						addSymOuterProd(abScale * rowWeight[px], gradBuf.data(), numParam, breadG);
					}
					qloc += numSpecific;
					++qx;
				}
			}
			addSymOuterProd(abScale * rowWeight[px], patGrad.data(), numParam, meat);
		}
	}

	// only need upper triangle TODO
	for (int tx=1; tx < numThreads; ++tx) {
		double *th = thrBreadG.data() + tx * numParam * numParam;
		for (size_t en=0; en < numParam * numParam; ++en) {
			thrBreadG[en] += th[en];
		}
	}
	for (int tx=1; tx < numThreads; ++tx) {
		double *th = thrBreadH.data() + tx * numParam * numParam;
		for (size_t en=0; en < numParam * numParam; ++en) {
			thrBreadH[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];
		}
	}
	//pda(thrBreadG.data(), numParam, numParam);
	//pda(thrBreadH.data(), numParam, numParam);
	//pda(thrMeat.data(), numParam, numParam);
	if (fc->infoA) {
		for (size_t d1=0; d1 < numParam; ++d1) {
			for (size_t d2=0; d2 < numParam; ++d2) {
				int cell = d1 * numParam + d2;
				fc->infoA[cell] += thrBreadH[cell] - thrBreadG[cell] + thrMeat[cell];
			}
		}
	}
	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];
			}
		}
	}
}
static double
ba81ComputeEMFit(omxFitFunction* oo, int want, FitContext *fc)
{
	const double Scale = Global->llScale;
	BA81FitState *state = (BA81FitState*) oo->argStruct;
	BA81Expect *estate = (BA81Expect*) oo->expectation->argStruct;
	omxMatrix *itemParam = estate->itemParam;
	std::vector<const double*> &itemSpec = estate->grp.spec;
        std::vector<int> &cumItemOutcomes = estate->grp.cumItemOutcomes;
	ba81NormalQuad &quad = estate->getQuad();
	const int maxDims = quad.maxDims;
	const size_t numItems = itemSpec.size();
	const int do_fit = want & FF_COMPUTE_FIT;
	const int do_deriv = want & (FF_COMPUTE_GRADIENT | FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN);

	if (do_deriv && !state->freeItemParams) {
		omxRaiseErrorf("%s: no free parameters", oo->name());
		return NA_REAL;
	}

	if (state->returnRowLikelihoods) {
		omxRaiseErrorf("%s: vector=TRUE not implemented", oo->name());
		return NA_REAL;
	}

	if (estate->verbose >= 3) mxLog("%s: complete data fit(want fit=%d deriv=%d)", oo->name(), do_fit, do_deriv);

	if (do_fit) estate->grp.ba81OutcomeProb(itemParam->data, TRUE);

	const int thrDerivSize = itemParam->cols * state->itemDerivPadSize;
	std::vector<double> thrDeriv(thrDerivSize * Global->numThreads);
	double *wherePrep = quad.wherePrep.data();

	double ll = 0;
#pragma omp parallel for num_threads(Global->numThreads) reduction(+:ll)
	for (size_t ix=0; ix < numItems; ix++) {
		const int thrId = omx_absolute_thread_num();
		const double *spec = itemSpec[ix];
		const int id = spec[RPF_ISpecID];
		const int dims = spec[RPF_ISpecDims];
		Eigen::VectorXd ptheta(dims);
		const rpf_dLL1_t dLL1 = Glibrpf_model[id].dLL1;
		const int iOutcomes = estate->grp.itemOutcomes[ix];
		const int outcomeBase = cumItemOutcomes[ix] * quad.totalQuadPoints;
		const double *weight = estate->expected + outcomeBase;
                const double *oProb = estate->grp.outcomeProb + outcomeBase;
		const double *iparam = omxMatrixColumn(itemParam, ix);
		double *myDeriv = thrDeriv.data() + thrDerivSize * thrId + ix * state->itemDerivPadSize;

		for (int qx=0; qx < quad.totalQuadPoints; qx++) {
			if (do_fit) {
				for (int ox=0; ox < iOutcomes; ox++) {
					ll += weight[ox] * oProb[ox];
				}
			}
			if (do_deriv) {
				double *where = wherePrep + qx * maxDims;
				for (int dx=0; dx < dims; dx++) {
					ptheta[dx] = where[std::min(dx, maxDims-1)];
				}

				(*dLL1)(spec, iparam, ptheta.data(), weight, myDeriv);
			}
			weight += iOutcomes;
			oProb += iOutcomes;
		}
	}

	size_t excluded = 0;

	if (do_deriv) {
		double *deriv0 = thrDeriv.data();

		int perThread = itemParam->cols * state->itemDerivPadSize;
		for (int th=1; th < Global->numThreads; th++) {
			double *thrD = thrDeriv.data() + th * perThread;
			for (int ox=0; ox < perThread; ox++) deriv0[ox] += thrD[ox];
		}

		int numFreeParams = int(state->numFreeParam);
		int ox=-1;
		for (size_t ix=0; ix < numItems; ix++) {
			const double *spec = itemSpec[ix];
			int id = spec[RPF_ISpecID];
			double *iparam = omxMatrixColumn(itemParam, ix);
			double *pad = deriv0 + ix * state->itemDerivPadSize;
			(*Glibrpf_model[id].dLL2)(spec, iparam, pad);

			HessianBlock *hb = state->hBlocks[ix].clone();
			hb->mat.triangularView<Eigen::Upper>().setZero();

			for (int dx=0; dx < state->itemDerivPadSize; ++dx) {
				int to = state->paramMap[++ox];
				if (to == -1) continue;

				// Need to check because this can happen if
				// lbounds/ubounds are not set appropriately.
				if (0 && !std::isfinite(deriv0[ox])) {
					int item = ox / itemParam->rows;
					mxLog("item parameters:\n");
					const double *spec = itemSpec[item];
					int id = spec[RPF_ISpecID];
					int numParam = (*Glibrpf_model[id].numParam)(spec);
					double *iparam = omxMatrixColumn(itemParam, item);
					pda(iparam, numParam, 1);
					// Perhaps bounds can be pulled in from librpf? TODO
					Rf_error("Deriv %d for item %d is %f; are you missing a lbound/ubound?",
						 ox, item, deriv0[ox]);
				}

				if (to < numFreeParams) {
					if (want & FF_COMPUTE_GRADIENT) {
						fc->grad(to) -= Scale * deriv0[ox];
					}
				} else {
					if (want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)) {
						int Hto = state->hbMap[ox];
						if (Hto >= 0) hb->mat.data()[Hto] -= Scale * deriv0[ox];
					}
				}
			}
			fc->queue(hb);
		}
	}

	if (excluded && estate->verbose >= 1) {
		mxLog("%s: Hessian not positive definite for %d/%d items",
		      oo->name(), (int) excluded, (int) numItems);
	}
	if (excluded == numItems) {
		omxRaiseErrorf("Hessian not positive definite for %d/%d items",
			       (int) excluded, (int) numItems);
	}

	return Scale * ll;
}
static void
ba81ComputeFit(omxFitFunction* oo, int want, FitContext *fc)
{
	BA81FitState *state = (BA81FitState*) oo->argStruct;
	BA81Expect *estate = (BA81Expect*) oo->expectation->argStruct;
	if (fc) state->numFreeParam = fc->varGroup->vars.size();

	if (want & FF_COMPUTE_INITIAL_FIT) return;

	if (estate->type == EXPECTATION_AUGMENTED) {
		buildItemParamMap(oo, fc);

		if (want & FF_COMPUTE_PARAMFLAVOR) {
			for (size_t px=0; px < state->numFreeParam; ++px) {
				if (state->paramFlavor[px] == NULL) continue;
				fc->flavor[px] = state->paramFlavor[px];
			}
			return;
		}

		if (want & FF_COMPUTE_PREOPTIMIZE) {
			omxExpectationCompute(fc, oo->expectation, NULL);
			return;
		}

		if (want & FF_COMPUTE_INFO) {
			buildLatentParamMap(oo, fc);
			if (!state->freeItemParams) {
				omxRaiseErrorf("%s: no free parameters", oo->name());
				return;
			}
			ba81SetupQuadrature(oo->expectation);

			if (fc->infoMethod == INFO_METHOD_HESSIAN) {
				ba81ComputeEMFit(oo, FF_COMPUTE_HESSIAN, fc);
			} else {
				omxRaiseErrorf("Information matrix approximation method %d is not available",
					       fc->infoMethod);
				return;
			}
			return;
		}

		double got = ba81ComputeEMFit(oo, want, fc);
		oo->matrix->data[0] = got;
		return;
	} else if (estate->type == EXPECTATION_OBSERVED) {

		if (want == FF_COMPUTE_STARTING) {
			buildLatentParamMap(oo, fc);
			if (state->freeLatents) setLatentStartingValues(oo, fc);
			return;
		}

		if (want & (FF_COMPUTE_INFO | FF_COMPUTE_GRADIENT)) {
			buildLatentParamMap(oo, fc); // only to check state->freeLatents
			buildItemParamMap(oo, fc);
			if (!state->freeItemParams && !state->freeLatents) {
				omxRaiseErrorf("%s: no free parameters", oo->name());
				return;
			}
			ba81SetupQuadrature(oo->expectation);

			if (want & FF_COMPUTE_GRADIENT ||
			    (want & FF_COMPUTE_INFO && fc->infoMethod == INFO_METHOD_MEAT)) {
				gradCov(oo, fc);
			} else {
				if (state->freeLatents) {
					omxRaiseErrorf("Information matrix approximation method %d is not available",
						       fc->infoMethod);
					return;
				}
				if (!state->freeItemParams) {
					omxRaiseErrorf("%s: no free parameters", oo->name());
					return;
				}
				sandwich(oo, fc);
			}
		}
		if (want & (FF_COMPUTE_HESSIAN | FF_COMPUTE_IHESSIAN)) {
			omxRaiseErrorf("%s: Hessian is not available for observed data", oo->name());
		}

		if (want & FF_COMPUTE_MAXABSCHANGE) {
			double mac = std::max(omxMaxAbsDiff(state->itemParam, estate->itemParam),
					      omxMaxAbsDiff(state->latentMean, estate->_latentMeanOut));
			fc->mac = std::max(mac, omxMaxAbsDiff(state->latentCov, estate->_latentCovOut));
			state->copyEstimates(estate);
		}

		if (want & FF_COMPUTE_FIT) {
			omxExpectationCompute(fc, oo->expectation, NULL);

			Eigen::ArrayXd &patternLik = estate->grp.patternLik;
			const int numUnique = estate->getNumUnique();
			if (state->returnRowLikelihoods) {
				const double OneOverLargest = estate->grp.quad.getReciprocalOfOne();
				omxData *data = estate->data;
				for (int rx=0; rx < numUnique; rx++) {
					int dups = omxDataNumIdenticalRows(data, estate->grp.rowMap[rx]);
					for (int dup=0; dup < dups; dup++) {
						int dest = omxDataIndex(data, estate->grp.rowMap[rx]+dup);
						oo->matrix->data[dest] = patternLik[rx] * OneOverLargest;
					}
				}
			} else {
				double *rowWeight = estate->grp.rowWeight;
				const double LogLargest = estate->LogLargestDouble;
				double got = 0;
#pragma omp parallel for num_threads(Global->numThreads) reduction(+:got)
				for (int ux=0; ux < numUnique; ux++) {
					if (patternLik[ux] == 0) continue;
					got += rowWeight[ux] * (log(patternLik[ux]) - LogLargest);
				}
				double fit = nan("infeasible");
				if (estate->grp.excludedPatterns < numUnique) {
					fit = Global->llScale * got;
					// add in some badness for excluded patterns
					fit += fit * estate->grp.excludedPatterns;
				}
				if (estate->verbose >= 1) mxLog("%s: observed fit %.4f (%d/%d excluded)",
								oo->name(), fit, estate->grp.excludedPatterns, numUnique);
				oo->matrix->data[0] = fit;
			}
		}
	} else {
		Rf_error("%s: Predict nothing or scores before computing %d", oo->name(), want);
	}
}
Exemple #10
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);
	}
}
Exemple #11
0
static void
ba81compute(omxExpectation *oo, FitContext *fc, const char *what, const char *how)
{
	BA81Expect *state = (BA81Expect *) oo->argStruct;

	if (what) {
		if (strcmp(what, "latentDistribution")==0 && how && strcmp(how, "copy")==0) {
			omxCopyMatrix(state->_latentMeanOut, state->estLatentMean);
			omxCopyMatrix(state->_latentCovOut, state->estLatentCov);

			double sampleSizeAdj = (state->weightSum - 1.0) / state->weightSum;
			int covSize = state->_latentCovOut->rows * state->_latentCovOut->cols;
			for (int cx=0; cx < covSize; ++cx) {
				state->_latentCovOut->data[cx] *= sampleSizeAdj;
			}
			return;
		}

		if (strcmp(what, "scores")==0) {
			state->expectedUsed = true;
			state->type = EXPECTATION_AUGMENTED;
		} else if (strcmp(what, "nothing")==0) {
			state->type = EXPECTATION_OBSERVED;
		} else {
			omxRaiseErrorf("%s: don't know how to predict '%s'",
				       oo->name, what);
		}

		if (state->verbose >= 1) {
			mxLog("%s: predict %s", oo->name, what);
		}
		return;
	}

	bool latentClean = state->latentParamVersion == getLatentVersion(state);
	bool itemClean = state->itemParamVersion == omxGetMatrixVersion(state->itemParam) && latentClean;

	ba81NormalQuad &quad = state->getQuad();

	if (state->verbose >= 1) {
		mxLog("%s: Qinit %d itemClean %d latentClean %d (1=clean) expectedUsed=%d",
		      oo->name, (int)quad.isAllocated(), itemClean, latentClean, state->expectedUsed);
	}

	if (!latentClean) {
		ba81RefreshQuadrature(oo);
		state->latentParamVersion = getLatentVersion(state);
	}

	if (!itemClean) {
		double *param = state->EitemParam? state->EitemParam : state->itemParam->data;
		state->grp.quad.cacheOutcomeProb(param, FALSE);

		bool estep = state->expectedUsed;
		if (estep) {
			if (oo->dynamicDataSource) {
				BA81Engine<BA81Expect*, BA81LatentSummary, BA81Estep> engine;
				engine.ba81Estep1(&state->grp, state);
			} else {
				BA81Engine<BA81Expect*, BA81LatentFixed, BA81Estep> engine;
				engine.ba81Estep1(&state->grp, state);
			}
		} else {
			state->grp.quad.releaseEstep();
			refreshPatternLikelihood(state, oo->dynamicDataSource);
		}
		if (oo->dynamicDataSource && state->verbose >= 2) {
			mxLog("%s: empirical distribution mean and cov:", state->name);
			omxPrint(state->estLatentMean, "mean");
			omxPrint(state->estLatentCov, "cov");
		}
		if (state->verbose >= 1) {
			const int numUnique = state->getNumUnique();
			mxLog("%s: estep<%s, %s> %d/%d rows excluded",
			      state->name,
			      (estep && oo->dynamicDataSource? "summary":"fixed"),
			      (estep? "estep":"omitEstep"),
			      state->grp.excludedPatterns, numUnique);
		}
	}

	state->itemParamVersion = omxGetMatrixVersion(state->itemParam);
}