TEST(SmCommonTestSuite,testAssertMacros)
{
SM_DEFINE_EXCEPTION(Exception, std::runtime_error);
{
double* val = new double;
EXPECT_NO_THROW( SM_ASSERT_TRUE(Exception, true, "") );
EXPECT_NO_THROW( SM_ASSERT_FALSE(Exception, false, "") );
EXPECT_NO_THROW( SM_ASSERT_GE_LT(Exception, 0.0, 0.0, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_GT_LE(Exception, 0.1, 0.0, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_GE_LE(Exception, 0.0, 0.0, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_GE_LE(Exception, 1.0, 0.0, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_LT(Exception, 0.0, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_GT(Exception, 1.0, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_POSITIVE(Exception, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NONNEGATIVE(Exception, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NEGATIVE(Exception, -1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NONPOSITIVE(Exception, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_ZERO(Exception, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NOTNULL(Exception, val, "") );
EXPECT_NO_THROW( SM_ASSERT_LE(Exception, 0.0, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_GE(Exception, 0.0, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NE(Exception, 0.0, 1.0, "") );
EXPECT_NO_THROW( SM_ASSERT_EQ(Exception, 0.0, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NEAR(Exception, 0.0, 1.0, 2.0, "") );
EXPECT_NO_THROW( SM_ASSERT_FINITE(Exception, 0.0, "") );
EXPECT_NO_THROW( SM_ASSERT_NOTNAN(Exception, 0.0, "") );
delete val;
}
{
double* val = NULL;
EXPECT_THROW( SM_ASSERT_TRUE(Exception, false, ""), Exception);
EXPECT_THROW( SM_ASSERT_FALSE(Exception, true, ""), Exception );
EXPECT_THROW( SM_ASSERT_GE_LT(Exception, 1.0, 0.0, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_GT_LE(Exception, 0.0, 0.0, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_GE_LE(Exception, -0.1, 0.0, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_GE_LE(Exception, 1.1, 0.0, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_LT(Exception, 1.0, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_GT(Exception, 0.0, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_POSITIVE(Exception, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_NONNEGATIVE(Exception, -1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_NEGATIVE(Exception, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_NONPOSITIVE(Exception, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_ZERO(Exception, 1.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_NOTNULL(Exception, val, ""), Exception );
EXPECT_THROW( SM_ASSERT_LE(Exception, 1.0, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_GE(Exception, -1.0, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_NE(Exception, 0.0, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_EQ(Exception, 1.0, 0.0, ""), Exception );
EXPECT_THROW( SM_ASSERT_NEAR(Exception, 0.0, 1.0, 0.5, ""), Exception );
EXPECT_THROW( SM_ASSERT_FINITE(Exception, std::numeric_limits<float>::infinity(), ""), Exception );
EXPECT_THROW( SM_ASSERT_NOTNAN(Exception, std::numeric_limits<float>::signaling_NaN(), ""), Exception );
}
}
void SerializedMap<T,A>::validateTableName()
{
SM_ASSERT_GE(InvalidTableNameException,_tableName.size(),1,"Table name \"" << _tableName << "\" is invalid.");
SM_ASSERT_TRUE(InvalidTableNameException,isalpha(_tableName[0]), "Table name \"" << _tableName << "\" is invalid.");
for(size_t i = 1; i < _tableName.size(); i++)
{
SM_ASSERT_TRUE(InvalidTableNameException,isalnum(_tableName[i]), "Table name \"" << _tableName << "\" is invalid. Character " << i << " is not alphanumeric.");
}
}
void marginalize(
std::vector<aslam::backend::DesignVariable*>& inDesignVariables,
std::vector<aslam::backend::ErrorTerm*>& inErrorTerms,
int numberOfInputDesignVariablesToRemove,
bool useMEstimator,
boost::shared_ptr<aslam::backend::MarginalizationPriorErrorTerm>& outPriorErrorTermPtr,
Eigen::MatrixXd& outCov,
std::vector<aslam::backend::DesignVariable*>& outDesignVariablesInRTop,
size_t numTopRowsInCov,
size_t /* numThreads */)
{
SM_WARN_STREAM_COND(inDesignVariables.size() == 0, "Zero input design variables in the marginalizer!");
// check for duplicates!
std::unordered_set<aslam::backend::DesignVariable*> inDvSetHT;
for(auto it = inDesignVariables.begin(); it != inDesignVariables.end(); ++it)
{
auto ret = inDvSetHT.insert(*it);
SM_ASSERT_TRUE(aslam::Exception, ret.second, "Error! Duplicate design variables in input list!");
}
std::unordered_set<aslam::backend::ErrorTerm*> inEtSetHT;
for(auto it = inErrorTerms.begin(); it != inErrorTerms.end(); ++it)
{
auto ret = inEtSetHT.insert(*it);
SM_ASSERT_TRUE(aslam::Exception, ret.second, "Error! Duplicate error term in input list!");
}
SM_DEBUG_STREAM("NO duplicates in input design variables or input error terms found.");
// Partition the design varibles into removed/remaining.
int dimOfDesignVariablesToRemove = 0;
std::vector<aslam::backend::DesignVariable*> remainingDesignVariables;
int k = 0;
size_t dimOfDvsInTopBlock = 0;
for(std::vector<aslam::backend::DesignVariable*>::const_iterator it = inDesignVariables.begin(); it != inDesignVariables.end(); ++it)
{
if (k < numberOfInputDesignVariablesToRemove)
{
dimOfDesignVariablesToRemove += (*it)->minimalDimensions();
} else
{
remainingDesignVariables.push_back(*it);
}
if(dimOfDvsInTopBlock < numTopRowsInCov)
{
outDesignVariablesInRTop.push_back(*it);
}
dimOfDvsInTopBlock += (*it)->minimalDimensions();
k++;
}
// store original block indices to prevent side effects
std::vector<int> originalBlockIndices;
std::vector<int> originalColumnBase;
// assign block indices
int columnBase = 0;
for (size_t i = 0; i < inDesignVariables.size(); ++i) {
originalBlockIndices.push_back(inDesignVariables[i]->blockIndex());
originalColumnBase.push_back(inDesignVariables[i]->columnBase());
inDesignVariables[i]->setBlockIndex(i);
inDesignVariables[i]->setColumnBase(columnBase);
columnBase += inDesignVariables[i]->minimalDimensions();
}
int dim = 0;
std::vector<size_t> originalRowBase;
for(std::vector<aslam::backend::ErrorTerm*>::iterator it = inErrorTerms.begin(); it != inErrorTerms.end(); ++it)
{
originalRowBase.push_back((*it)->rowBase());
(*it)->setRowBase(dim);
dim += (*it)->dimension();
}
aslam::backend::DenseQrLinearSystemSolver qrSolver;
qrSolver.initMatrixStructure(inDesignVariables, inErrorTerms, false);
SM_INFO_STREAM("Marginalization optimization problem initialized with " << inDesignVariables.size() << " design variables and " << inErrorTerms.size() << " error terrms");
SM_INFO_STREAM("The Jacobian matrix is " << dim << " x " << columnBase);
qrSolver.evaluateError(1, useMEstimator);
qrSolver.buildSystem(1, useMEstimator);
const Eigen::MatrixXd& jacobian = qrSolver.getJacobian();
const Eigen::VectorXd& b = qrSolver.e();
// check dimension of jacobian
int jrows = jacobian.rows();
int jcols = jacobian.cols();
int dimOfRemainingDesignVariables = jcols - dimOfDesignVariablesToRemove;
//int dimOfPriorErrorTerm = jrows;
// check the rank
Eigen::FullPivLU<Eigen::MatrixXd> lu_decomp(jacobian);
//lu_decomp.setThreshold(1e-20);
double threshold = lu_decomp.threshold();
int rank = lu_decomp.rank();
int fullRank = std::min(jacobian.rows(), jacobian.cols());
SM_DEBUG_STREAM("Rank of jacobian: " << rank << " (full rank: " << fullRank << ", threshold: " << threshold << ")");
bool rankDeficient = rank < fullRank;
if(rankDeficient)
{
SM_WARN("Marginalization jacobian is rank deficient!");
}
//SM_ASSERT_FALSE(aslam::Exception, rankDeficient, "Right now, we don't want the jacobian to be rank deficient - ever...");
Eigen::MatrixXd R_reduced;
Eigen::VectorXd d_reduced;
if (jrows < jcols)
{
SM_THROW(aslam::Exception, "underdetermined LSE!");
// underdetermined LSE, don't do QR
R_reduced = jacobian.block(0, dimOfDesignVariablesToRemove, jrows, jcols - dimOfDesignVariablesToRemove);
d_reduced = b;
} else {
// PTF: Do we know what will happen when the jacobian matrix is rank deficient?
// MB: yes, bad things!
// do QR decomposition
sm::timing::Timer myTimer("QR Decomposition");
Eigen::HouseholderQR<Eigen::MatrixXd> qr(jacobian);
Eigen::MatrixXd Q = qr.householderQ();
Eigen::MatrixXd R = qr.matrixQR().triangularView<Eigen::Upper>();
Eigen::VectorXd d = Q.transpose()*b;
myTimer.stop();
if(numTopRowsInCov > 0)
{
sm::timing::Timer myTimer("Covariance computation");
Eigen::FullPivLU<Eigen::MatrixXd> lu_decomp(R);
Eigen::MatrixXd Rinv = lu_decomp.inverse();
Eigen::MatrixXd covariance = Rinv * Rinv.transpose();
outCov = covariance.block(0, 0, numTopRowsInCov, numTopRowsInCov);
myTimer.stop();
}
// size_t numRowsToKeep = rank - dimOfDesignVariablesToRemove;
// SM_ASSERT_TRUE_DBG(aslam::Exception, rankDeficient || (numRowsToKeep == dimOfRemainingDesignVariables), "must be the same if full rank!");
// get the top left block
SM_ASSERT_GE(aslam::Exception, R.rows(), numTopRowsInCov, "Cannot extract " << numTopRowsInCov << " rows of R because it only has " << R.rows() << " rows.");
SM_ASSERT_GE(aslam::Exception, R.cols(), numTopRowsInCov, "Cannot extract " << numTopRowsInCov << " cols of R because it only has " << R.cols() << " cols.");
//outRtop = R.block(0, 0, numTopRowsInRtop, numTopRowsInRtop);
// cut off the zero rows at the bottom
R_reduced = R.block(dimOfDesignVariablesToRemove, dimOfDesignVariablesToRemove, dimOfRemainingDesignVariables, dimOfRemainingDesignVariables);
//R_reduced = R.block(dimOfDesignVariablesToRemove, dimOfDesignVariablesToRemove, numRowsToKeep, dimOfRemainingDesignVariables);
d_reduced = d.segment(dimOfDesignVariablesToRemove, dimOfRemainingDesignVariables);
//d_reduced = d.segment(dimOfDesignVariablesToRemove, numRowsToKeep);
//dimOfPriorErrorTerm = dimOfRemainingDesignVariables;
}
// now create the new error term
boost::shared_ptr<aslam::backend::MarginalizationPriorErrorTerm> err(new aslam::backend::MarginalizationPriorErrorTerm(remainingDesignVariables, d_reduced, R_reduced));
outPriorErrorTermPtr.swap(err);
// restore initial block indices to prevent side effects
for (size_t i = 0; i < inDesignVariables.size(); ++i) {
inDesignVariables[i]->setBlockIndex(originalBlockIndices[i]);
inDesignVariables[i]->setColumnBase(originalColumnBase[i]);
}
int index = 0;
for(std::vector<aslam::backend::ErrorTerm*>::iterator it = inErrorTerms.begin(); it != inErrorTerms.end(); ++it)
{
(*it)->setRowBase(originalRowBase[index++]);
}
}
