template<typename MatrixType> void matrixRedux(const MatrixType& m)
{
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols);
// The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
// failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + Scalar(0.2) * m1;
VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
Scalar s(0), p(1), minc(internal::real(m1.coeff(0))), maxc(internal::real(m1.coeff(0)));
for(int j = 0; j < cols; j++)
for(int i = 0; i < rows; i++)
{
s += m1(i,j);
p *= m1_for_prod(i,j);
minc = (std::min)(internal::real(minc), internal::real(m1(i,j)));
maxc = (std::max)(internal::real(maxc), internal::real(m1(i,j)));
}
const Scalar mean = s/Scalar(RealScalar(rows*cols));
VERIFY_IS_APPROX(m1.sum(), s);
VERIFY_IS_APPROX(m1.mean(), mean);
VERIFY_IS_APPROX(m1_for_prod.prod(), p);
VERIFY_IS_APPROX(m1.real().minCoeff(), internal::real(minc));
VERIFY_IS_APPROX(m1.real().maxCoeff(), internal::real(maxc));
// test slice vectorization assuming assign is ok
Index r0 = internal::random<Index>(0,rows-1);
Index c0 = internal::random<Index>(0,cols-1);
Index r1 = internal::random<Index>(r0+1,rows)-r0;
Index c1 = internal::random<Index>(c0+1,cols)-c0;
VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
// test empty objects
VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
}
cv::Mat performSuperpixelSegmentation_VariableSandM(std::vector<std::vector<double> > &kseeds, std::vector<double> &kseedsx, std::vector<double> &kseedsy, const std::vector<cv::Mat> &vec, const cv::Size &size, const int &STEP, const int &NUMITR = 10) {
int sz = size.width*size.height;
const int numk = (int) kseedsx.size();
int numitr = 0;
int offset = (STEP < 10) ? STEP*1.5 : STEP;
cv::Mat klabels = cv::Mat(size, cv::DataType<int>::type);
klabels = -1;
std::vector<std::vector<double> > sigmac(0);
for (int c = 0; c < vec.size(); c++) {
sigmac.push_back(std::vector<double>(numk, 0));
}
std::vector<double> sigmax(numk, 0);
std::vector<double> sigmay(numk, 0);
std::vector<int> clustersize(numk, 0);
std::vector<double> inv(numk, 0);//to store 1/clustersize[k] values
std::vector<double> distxy(sz, DBL_MAX);
std::vector<double> distc(sz, DBL_MAX);
std::vector<double> distvec(sz, DBL_MAX);
std::vector<double> maxc(numk, 10*10);//THIS IS THE VARIABLE VALUE OF M, just start with 10
std::vector<double> maxxy(numk, STEP*STEP);//THIS IS THE VARIABLE VALUE OF M, just start with 10
double invxywt = 1.0/(STEP*STEP);//NOTE: this is different from how usual SLIC/LKM works
while( numitr < NUMITR )
{
//------
//cumerr = 0;
numitr++;
//------
distvec.assign(sz, DBL_MAX);
for( int n = 0; n < numk; n++ )
{
int y1 = std::max(double(0), kseedsy[n]-offset);
int y2 = std::min(double(size.height), kseedsy[n]+offset);
int x1 = std::max(double(0), kseedsx[n]-offset);
int x2 = std::min(double(size.width), kseedsx[n]+offset);
for( int y = y1; y < y2; y++ )
{
for( int x = x1; x < x2; x++ )
{
int i = y*size.width + x;
if( !(y < size.height && x < size.width && y >= 0 && x >= 0) ) {
throw cv::Exception();
}
distc[i] = 0;
for (int c = 0; c < vec.size(); c++) {
distc[i] += (vec[c].ptr<double>()[i] - kseeds[c][n]) * (vec[c].ptr<double>()[i] - kseeds[c][n]);
}
distxy[i] = (x - kseedsx[n])*(x - kseedsx[n]) + (y - kseedsy[n])*(y - kseedsy[n]);
double dist = distc[i]/maxc[n] + distxy[i]*invxywt;//only varying m, prettier superpixels
if( dist < distvec[i] )
{
distvec[i] = dist;
klabels.ptr<int>()[i] = n;
}
}
}
}
//-----------------------------------------------------------------
// Assign the max color distance for a cluster
//-----------------------------------------------------------------
if(0 == numitr)
{
maxc.assign(numk,1);
maxxy.assign(numk,1);
}
{for( int i = 0; i < sz; i++ )
{
if(maxc[klabels.ptr<int>()[i]] < distc[i]) maxc[klabels.ptr<int>()[i]] = distc[i];
if(maxxy[klabels.ptr<int>()[i]] < distxy[i]) maxxy[klabels.ptr<int>()[i]] = distxy[i];
}}
//-----------------------------------------------------------------
// Recalculate the centroid and store in the seed values
//-----------------------------------------------------------------
for (int c = 0; c < sigmac.size(); c++) {
sigmac[c].assign(numk, 0);
}
sigmax.assign(numk, 0);
sigmay.assign(numk, 0);
clustersize.assign(numk, 0);
for( int j = 0; j < sz; j++ )
{
if(!(klabels.ptr<int>()[j] >= 0))
throw cv::Exception();
for (int c = 0; c < sigmac.size(); c++) {
sigmac[c][klabels.ptr<int>()[j]] += vec[c].ptr<double>()[j];
}
sigmax[klabels.ptr<int>()[j]] += (j%size.width);
sigmay[klabels.ptr<int>()[j]] += (j/size.width);
clustersize[klabels.ptr<int>()[j]]++;
}
{for( int k = 0; k < numk; k++ )
{
if( clustersize[k] <= 0 ) clustersize[k] = 1;
inv[k] = 1.0/double(clustersize[k]);//computing inverse now to multiply, than divide later
}}
{for( int k = 0; k < numk; k++ )
{
for (int c = 0; c < kseeds.size(); c++) {
kseeds[c][k] = sigmac[c][k] * inv[k];
}
kseedsx[k] = sigmax[k]*inv[k];
kseedsy[k] = sigmay[k]*inv[k];
}}
}
return klabels;
}
TEST(AggregateTests, SortedSumMaxGroupByTest) {
/*
* SELECT a, SUM(b), MAX(c) from table GROUP BY a;
*/
const int tuple_count = TESTS_TUPLES_PER_TILEGROUP;
// Create a table and wrap it in logical tiles
auto &txn_manager = concurrency::TransactionManager::GetInstance();
auto txn = txn_manager.BeginTransaction();
auto txn_id = txn->GetTransactionId();
std::unique_ptr<storage::DataTable> data_table(
ExecutorTestsUtil::CreateTable(tuple_count, false));
ExecutorTestsUtil::PopulateTable(txn, data_table.get(), 2 * tuple_count, false,
false, true);
txn_manager.CommitTransaction();
std::unique_ptr<executor::LogicalTile> source_logical_tile1(
executor::LogicalTileFactory::WrapTileGroup(data_table->GetTileGroup(0),
txn_id));
std::unique_ptr<executor::LogicalTile> source_logical_tile2(
executor::LogicalTileFactory::WrapTileGroup(data_table->GetTileGroup(1),
txn_id));
// (1-5) Setup plan node
// 1) Set up group-by columns
std::vector<oid_t> group_by_columns = {0};
// 2) Set up project info
planner::ProjectInfo::DirectMapList direct_map_list = {
{0, {0, 0}}, {1, {1, 0}}, {2, {1, 1}}};
auto proj_info = new planner::ProjectInfo(planner::ProjectInfo::TargetList(),
std::move(direct_map_list));
// 3) Set up unique aggregates
std::vector<planner::AggregatePlan::AggTerm> agg_terms;
planner::AggregatePlan::AggTerm sumb(EXPRESSION_TYPE_AGGREGATE_SUM,
expression::TupleValueFactory(0, 1));
planner::AggregatePlan::AggTerm maxc(EXPRESSION_TYPE_AGGREGATE_MAX,
expression::TupleValueFactory(0, 2));
agg_terms.push_back(sumb);
agg_terms.push_back(maxc);
// 4) Set up predicate (empty)
expression::AbstractExpression* predicate = nullptr;
// 5) Create output table schema
auto data_table_schema = data_table.get()->GetSchema();
std::vector<oid_t> set = {0, 1, 2};
std::vector<catalog::Column> columns;
for (auto column_index : set) {
columns.push_back(data_table_schema->GetColumn(column_index));
}
auto output_table_schema = new catalog::Schema(columns);
// OK) Create the plan node
planner::AggregatePlan node(proj_info, predicate, std::move(agg_terms),
std::move(group_by_columns), output_table_schema,
AGGREGATE_TYPE_SORTED);
// Create and set up executor
auto txn2 = txn_manager.BeginTransaction();
std::unique_ptr<executor::ExecutorContext> context(
new executor::ExecutorContext(txn2));
executor::AggregateExecutor executor(&node, context.get());
MockExecutor child_executor;
executor.AddChild(&child_executor);
EXPECT_CALL(child_executor, DInit()).WillOnce(Return(true));
EXPECT_CALL(child_executor, DExecute())
.WillOnce(Return(true))
.WillOnce(Return(true))
.WillOnce(Return(false));
EXPECT_CALL(child_executor, GetOutput())
.WillOnce(Return(source_logical_tile1.release()))
.WillOnce(Return(source_logical_tile2.release()));
EXPECT_TRUE(executor.Init());
EXPECT_TRUE(executor.Execute());
txn_manager.CommitTransaction();
/* Verify result */
std::unique_ptr<executor::LogicalTile> result_tile(executor.GetOutput());
EXPECT_TRUE(result_tile.get() != nullptr);
EXPECT_TRUE(result_tile->GetValue(0, 0)
.OpEquals(ValueFactory::GetIntegerValue(0))
.IsTrue());
EXPECT_TRUE(result_tile->GetValue(0, 1)
.OpEquals(ValueFactory::GetIntegerValue(105))
.IsTrue());
EXPECT_TRUE(result_tile->GetValue(0, 2)
.OpEquals(ValueFactory::GetDoubleValue(42))
.IsTrue());
EXPECT_TRUE(result_tile->GetValue(1, 0)
.OpEquals(ValueFactory::GetIntegerValue(10))
.IsTrue());
}
template<typename VectorType> void vectorRedux(const VectorType& w)
{
typedef typename VectorType::Index Index;
typedef typename VectorType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
Index size = w.size();
VectorType v = VectorType::Random(size);
VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
for(int i = 1; i < size; i++)
{
Scalar s(0), p(1);
RealScalar minc(internal::real(v.coeff(0))), maxc(internal::real(v.coeff(0)));
for(int j = 0; j < i; j++)
{
s += v[j];
p *= v_for_prod[j];
minc = (std::min)(minc, internal::real(v[j]));
maxc = (std::max)(maxc, internal::real(v[j]));
}
VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.head(i).sum()), Scalar(1));
VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
}
for(int i = 0; i < size-1; i++)
{
Scalar s(0), p(1);
RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i)));
for(int j = i; j < size; j++)
{
s += v[j];
p *= v_for_prod[j];
minc = (std::min)(minc, internal::real(v[j]));
maxc = (std::max)(maxc, internal::real(v[j]));
}
VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.tail(size-i).sum()), Scalar(1));
VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
}
for(int i = 0; i < size/2; i++)
{
Scalar s(0), p(1);
RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i)));
for(int j = i; j < size-i; j++)
{
s += v[j];
p *= v_for_prod[j];
minc = (std::min)(minc, internal::real(v[j]));
maxc = (std::max)(maxc, internal::real(v[j]));
}
VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
}
// test empty objects
VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
VERIFY_RAISES_ASSERT(v.head(0).mean());
VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
}
