本文整理汇总了C++中GMatrix::setRelation方法的典型用法代码示例。如果您正苦于以下问题：C++ GMatrix::setRelation方法的具体用法？C++ GMatrix::setRelation怎么用？C++ GMatrix::setRelation使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类GMatrix的用法示例。
在下文中一共展示了GMatrix::setRelation方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: fillMissingValues
void fillMissingValues(GArgReader& args)
{
unsigned int seed = getpid() * (unsigned int)time(NULL);
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else
ThrowError("Invalid option: ", args.peek());
}
// Load the data and the filter
GMatrix* pDataOrig = GMatrix::loadArff(args.pop_string());
Holder<GMatrix> hDataOrig(pDataOrig);
sp_relation pOrigRel = pDataOrig->relation();
GRand prng(seed);
GCollaborativeFilter* pModel = InstantiateAlgorithm(prng, args);
Holder<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
ThrowError("Superfluous argument: ", args.peek());
// Convert to all normalized real values
GNominalToCat* pNtc = new GNominalToCat();
GTwoWayTransformChainer filter(new GNormalize(), pNtc);
pNtc->preserveUnknowns();
filter.train(*pDataOrig);
GMatrix* pData = filter.transformBatch(*pDataOrig);
Holder<GMatrix> hData(pData);
hDataOrig.release();
pDataOrig = NULL;
// Convert to 3-column form
GMatrix* pMatrix = new GMatrix(0, 3);
Holder<GMatrix> hMatrix(pMatrix);
size_t dims = pData->cols();
for(size_t i = 0; i < pData->rows(); i++)
{
double* pRow = pData->row(i);
for(size_t j = 0; j < dims; j++)
{
if(*pRow != UNKNOWN_REAL_VALUE)
{
double* pVec = pMatrix->newRow();
pVec[0] = i;
pVec[1] = j;
pVec[2] = *pRow;
}
pRow++;
}
}
// Train the collaborative filter
pModel->train(*pMatrix);
hMatrix.release();
pMatrix = NULL;
// Predict values for missing elements
for(size_t i = 0; i < pData->rows(); i++)
{
double* pRow = pData->row(i);
for(size_t j = 0; j < dims; j++)
{
if(*pRow == UNKNOWN_REAL_VALUE)
*pRow = pModel->predict(i, j);
GAssert(*pRow != UNKNOWN_REAL_VALUE);
pRow++;
}
}
// Convert the data back to its original form
GMatrix* pOut = filter.untransformBatch(*pData);
pOut->setRelation(pOrigRel);
pOut->print(cout);
}示例2: fillMissingValues
void GRecommenderLib::fillMissingValues(GArgReader& args)
{
unsigned int seed = getpid() * (unsigned int)time(NULL);
bool normalize = true;
while(args.next_is_flag())
{
if(args.if_pop("-seed"))
seed = args.pop_uint();
else if(args.if_pop("-nonormalize"))
normalize = false;
else
throw Ex("Invalid option: ", args.peek());
}
// Load the data and the filter
GMatrix dataOrig;
dataOrig.loadArff(args.pop_string());
// Parse params
vector<size_t> ignore;
while(args.next_is_flag())
{
if(args.if_pop("-ignore"))
parseAttributeList(ignore, args, dataOrig.cols());
else
throw Ex("Invalid option: ", args.peek());
}
// Throw out the ignored attributes
std::sort(ignore.begin(), ignore.end());
for(size_t i = ignore.size() - 1; i < ignore.size(); i--)
dataOrig.deleteColumns(ignore[i], 1);
GRelation* pOrigRel = dataOrig.relation().clone();
std::unique_ptr<GRelation> hOrigRel(pOrigRel);
GCollaborativeFilter* pModel = InstantiateAlgorithm(args);
std::unique_ptr<GCollaborativeFilter> hModel(pModel);
if(args.size() > 0)
throw Ex("Superfluous argument: ", args.peek());
pModel->rand().setSeed(seed);
// Convert to all normalized real values
GNominalToCat* pNtc = new GNominalToCat();
GIncrementalTransform* pFilter = pNtc;
std::unique_ptr<GIncrementalTransformChainer> hChainer;
if(normalize)
{
GIncrementalTransformChainer* pChainer = new GIncrementalTransformChainer(new GNormalize(), pNtc);
hChainer.reset(pChainer);
pFilter = pChainer;
}
pNtc->preserveUnknowns();
pFilter->train(dataOrig);
GMatrix* pData = pFilter->transformBatch(dataOrig);
std::unique_ptr<GMatrix> hData(pData);
// Convert to 3-column form
GMatrix* pMatrix = new GMatrix(0, 3);
std::unique_ptr<GMatrix> hMatrix(pMatrix);
size_t dims = pData->cols();
for(size_t i = 0; i < pData->rows(); i++)
{
GVec& row = pData->row(i);
for(size_t j = 0; j < dims; j++)
{
if(row[j] != UNKNOWN_REAL_VALUE)
{
GVec& vec = pMatrix->newRow();
vec[0] = (double)i;
vec[1] = (double)j;
vec[2] = row[j];
}
}
}
// Train the collaborative filter
pModel->train(*pMatrix);
hMatrix.release();
pMatrix = NULL;
// Predict values for missing elements
for(size_t i = 0; i < pData->rows(); i++)
{
GVec& row = pData->row(i);
for(size_t j = 0; j < dims; j++)
{
if(row[j] == UNKNOWN_REAL_VALUE)
row[j] = pModel->predict(i, j);
GAssert(row[j] != UNKNOWN_REAL_VALUE);
}
}
// Convert the data back to its original form
GMatrix* pOut = pFilter->untransformBatch(*pData);
pOut->setRelation(hOrigRel.release());
pOut->print(cout);
}