@@ -148,7 +148,7 @@ bool TikhonovAlgoAbstractBase::checkInputMatrixSizes( const AlgorithmInput & inp
148148 // check source regularization matrix sizes
149149 if (sourceWeighting_)
150150 {
151- if ( get (regularizationSolutionSubcase).toInt ()==solution_constrained )
151+ if ( get (Parameters:: regularizationSolutionSubcase).toInt ()==solution_constrained )
152152 {
153153 // check that the matrix is of appropriate size (equal number of rows as columns in fwd matrix)
154154 if ( N != sourceWeighting_->ncols () )
@@ -158,7 +158,7 @@ bool TikhonovAlgoAbstractBase::checkInputMatrixSizes( const AlgorithmInput & inp
158158 }
159159 }
160160 // otherwise, if the source regularization is provided as the squared version (RR^T)
161- else if ( get (regularizationSolutionSubcase).toInt ()==solution_constrained_squared )
161+ else if ( get (Parameters:: regularizationSolutionSubcase).toInt ()==solution_constrained_squared )
162162 {
163163 // check that the matrix is of appropriate size and squared (equal number of rows as columns in fwd matrix)
164164 if ( ( N != sourceWeighting_->nrows () ) || ( N != sourceWeighting_->ncols () ) )
@@ -172,7 +172,7 @@ bool TikhonovAlgoAbstractBase::checkInputMatrixSizes( const AlgorithmInput & inp
172172 // check measurement regularization matrix sizes
173173 if (sensorWeighting_)
174174 {
175- if (get (regularizationResidualSubcase).toInt () == residual_constrained)
175+ if (get (Parameters:: regularizationResidualSubcase).toInt () == residual_constrained)
176176 {
177177 // check that the matrix is of appropriate size (equal number of rows as rows in fwd matrix)
178178 if (M != sensorWeighting_->ncols ())
@@ -182,7 +182,7 @@ bool TikhonovAlgoAbstractBase::checkInputMatrixSizes( const AlgorithmInput & inp
182182 }
183183 }
184184 // otherwise if the source covariance matrix is provided in squared form
185- else if ( get (regularizationResidualSubcase).toInt () == residual_constrained_squared )
185+ else if ( get (Parameters:: regularizationResidualSubcase).toInt () == residual_constrained_squared )
186186 {
187187 // check that the matrix is of appropriate size and squared (equal number of rows as rows in fwd matrix)
188188 if ( (M != sensorWeighting_->nrows ()) || (M != sensorWeighting_->ncols ()) )
@@ -210,8 +210,8 @@ AlgorithmOutput TikhonovAlgoAbstractBase::run(const AlgorithmInput & input) cons
210210 auto sensorWeighting_ = input.get <Matrix>(TikhonovAlgoAbstractBase::WeightingInSensorSpace);
211211
212212 // get Parameters
213- auto RegularizationMethod_gotten = get ( RegularizationMethod). toString ( );
214- auto TikhonovImplementation_gotten = get (TikhonovImplementation).toString ();
213+ auto RegularizationMethod_gotten = getOption (Parameters:: RegularizationMethod);
214+ auto TikhonovImplementation_gotten = get (Parameters:: TikhonovImplementation).toString ();
215215
216216 // Alocate Variable
217217 DenseMatrix solution;
@@ -223,18 +223,18 @@ AlgorithmOutput TikhonovAlgoAbstractBase::run(const AlgorithmInput & input) cons
223223
224224 // Determine specific Tikhonov Implementation
225225 TikhonovImpl *algoImpl;
226- if ( get (RegularizationMethod).toInt () == standardTikhonov ){
226+ if ( get (Parameters:: RegularizationMethod).toInt () == standardTikhonov ){
227227 // get Parameters
228- int regularizationChoice_ = get (regularizationChoice).toInt ();
229- int regularizationSolutionSubcase_ = get (regularizationSolutionSubcase).toInt ();
230- int regularizationResidualSubcase_ = get (regularizationResidualSubcase).toInt ();
228+ int regularizationChoice_ = get (Parameters:: regularizationChoice).toInt ();
229+ int regularizationSolutionSubcase_ = get (Parameters:: regularizationSolutionSubcase).toInt ();
230+ int regularizationResidualSubcase_ = get (Parameters:: regularizationResidualSubcase).toInt ();
231231
232232 algoImpl = new SolveInverseProblemWithStandardTikhonovImpl ( *castMatrix::toDense (forwardMatrix_), *castMatrix::toDense (measuredData_), *castMatrix::toDense (sourceWeighting_), *castMatrix::toDense (sensorWeighting_), regularizationChoice_, regularizationSolutionSubcase_, regularizationResidualSubcase_);
233233 }
234- else if ( get (RegularizationMethod).toInt () == TikhonovSVD ){
234+ else if ( get (Parameters:: RegularizationMethod).toInt () == TikhonovSVD ){
235235 // algoImpl = new SolveInverseProblemWithTikhonovSVD_impl( *castMatrix::toDense(forwardMatrix_), *castMatrix::toDense(measuredData_), *castMatrix::toDense(sourceWeighting_), *castMatrix::toDense(sensorWeighting_));
236236 }
237- else if ( get (RegularizationMethod).toInt () == TikhonovTSVD ){
237+ else if ( get (Parameters:: RegularizationMethod).toInt () == TikhonovTSVD ){
238238 THROW_ALGORITHM_PROCESSING_ERROR (" Tikhonov TSVD not implemented yet" );
239239 }
240240 else {
@@ -249,12 +249,12 @@ AlgorithmOutput TikhonovAlgoAbstractBase::run(const AlgorithmInput & input) cons
249249 if (RegularizationMethod_gotten == " single" )
250250 {
251251 // Use single fixed lambda value, entered in UI
252- lambda_ = get (LambdaFromDirectEntry).toDouble ();
252+ lambda_ = get (Parameters:: LambdaFromDirectEntry).toDouble ();
253253 }
254254 else if (RegularizationMethod_gotten == " slider" )
255255 {
256256 // Use single fixed lambda value, select via slider
257- lambda_ = get (LambdaSliderValue).toDouble ();
257+ lambda_ = get (Parameters:: LambdaSliderValue).toDouble ();
258258 }
259259 }
260260 else if (RegularizationMethod_gotten == " lcurve" )
@@ -308,9 +308,9 @@ double TikhonovAlgoAbstractBase::computeLcurve( const SCIRun::Core::Algorithms::
308308 auto sensorWeighting_ = input.get <Matrix>(TikhonovAlgoAbstractBase::WeightingInSensorSpace);
309309
310310 // define the step size of the lambda vector to be computed (distance between min and max divided by number of desired lambdas in log scale)
311- const int nLambda = get (LambdaNum).toInt ();
312- const int lambdaMin_ = get (LambdaMin).toDouble ();
313- const int lambdaMax_ = get (LambdaMax).toDouble ();
311+ const int nLambda = get (Parameters:: LambdaNum).toInt ();
312+ const int lambdaMin_ = get (Parameters:: LambdaMin).toDouble ();
313+ const int lambdaMax_ = get (Parameters:: LambdaMax).toDouble ();
314314 const double lam_step = pow (10.0 , lambdaMax_ / lambdaMin_) / (nLambda-1 );
315315 double lambda = 0 ;
316316
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