C++ (Cpp) GMatrix::heap Examples

Example #1

File: main.cpp Project: litaoshao/waffles

void splitClass(GArgReader& args)
{
	const char* filename = args.pop_string();
	GMatrix* pData = loadData(filename);
	Holder<GMatrix> hData(pData);
	size_t classAttr = args.pop_uint();
	
	bool dropClass = false;
	while(args.size() > 0)
	{
		if(args.if_pop("-dropclass"))
			dropClass = true;
		else
			ThrowError("Invalid option: ", args.peek());
	}

	for(size_t i = 0; i < pData->relation()->valueCount(classAttr); i++)
	{
		GMatrix tmp(pData->relation(), pData->heap());
		pData->splitByNominalValue(&tmp, classAttr, i);
		std::ostringstream oss;
		PathData pd;
		GFile::parsePath(filename, &pd);
		string fn;
		fn.assign(filename + pd.fileStart, pd.extStart - pd.fileStart);
		oss << fn << "_";
		pData->relation()->printAttrValue(oss, classAttr, (double)i);
		oss << ".arff";
		string s = oss.str();
		if(dropClass)
			tmp.deleteColumn(classAttr);
		tmp.saveArff(s.c_str());
	}
}

Example #2

File: GEnsemble.cpp Project: har777/tapkee_benchmarks

// virtual
void GBag::trainInnerInner(GMatrix& features, GMatrix& labels)
{
	// Train all the models
	size_t nLearnerCount = m_models.size();
	size_t nDrawSize = size_t(m_trainSize * features.rows());
	GMatrix drawnFeatures(features.relation(), features.heap());
	GMatrix drawnLabels(labels.relation(), labels.heap());
	drawnFeatures.reserve(nDrawSize);
	drawnLabels.reserve(nDrawSize);
	{
		for(size_t i = 0; i < nLearnerCount; i++)
		{
			if(m_pCB)
				m_pCB(m_pThis, i, nLearnerCount);

			// Randomly draw some data (with replacement)
			GReleaseDataHolder hDrawnFeatures(&drawnFeatures);
			GReleaseDataHolder hDrawnLabels(&drawnLabels);
			for(size_t j = 0; j < nDrawSize; j++)
			{
				size_t r = (size_t)m_rand.next(features.rows());
				drawnFeatures.takeRow(features[r]);
				drawnLabels.takeRow(labels[r]);
			}

			// Train the learner with the drawn data
			m_models[i]->m_pModel->train(drawnFeatures, drawnLabels);
		}
		if(m_pCB)
			m_pCB(m_pThis, nLearnerCount, nLearnerCount);
	}

	// Determine the weights
	determineWeights(features, labels);
	normalizeWeights();
}