Detailed Description

Definition at line 18 of file LogNorm.m.

Static Public Member Functions
static function [ aln , times , rowvec < integer > st_sizes ] =	compareSimTransJac_FullJac (models.BaseFullModel m,rowvec< integer > st_sizes)
	% Comparison of similarity transformed jacobian log norms to full log norms Computes logarithmic norms of similarity transformed jacobians using the model's offline data, containing \(N\) trajectory samples. More...

static function pm =	compareSimTransJac_FullJac_plots (m, aln, times, st_sizes, pm)

static function [ aln , times , jtimes , rowvec < integer > deim_orders , rowvec < integer > st_sizes ] =	compareSimTransDEIMJac_FullJac (models.BaseFullModel m,rowvec< integer > deim_orders,rowvec< integer > st_sizes)
	% Comparison of similarity transformed DEIM-approximated jacobian log norms to full log norms Computes logarithmic norms of similarity transformed AND matrix DEIM approximated jacobians using the model's offline data, containing \(N\) trajectory samples. More...

static function pm =	compareSimTransDEIMJac_FullJac_plots (m, aln, times, jtimes, deim_orders, st_sizes, pm)
	See WSH12 tests_burgers for likely better plot routine. More...

static function struct res =	getApproxLogNormsAtPos (models.BaseFullModel mo,colvec< double > x,double t,matrix< double > mui)
	Computes logarithmic norms of similarity transformed AND matrix DEIM approximated jacobians at the position given by (x,t) over all given parameters mui. More...

static function	getApproxLogNormsAtPos_plots (res, pm)

static function [ res , mScale , MScale , pos , l , sel , seli ] =	CompLogNorms (m, numt)
	LogNorm: More...

Member Function Documentation

function [ aln , times , jtimes , rowvec< integer > deim_orders , rowvec< integer > st_sizes ] = testing.LogNorm.compareSimTransDEIMJac_FullJac	(	models.BaseFullModel	m,
		rowvec< integer >	deim_orders,
		rowvec< integer >	st_sizes
	)

static

% Comparison of similarity transformed DEIM-approximated jacobian log norms to full log norms Computes logarithmic norms of similarity transformed AND matrix DEIM approximated jacobians using the model's offline data, containing \(N\) trajectory samples.

Parameters

m	A BaseFullModel whos offlineGenerations have been run and that uses a DEIMEstimator.
deim_orders	The DEIM orders \(d_1,\ldots,d_m\) to set for the matrix DEIM of the jacobian.
st_sizes	The sizes \(s_1,\ldots,s_n\) to use for the similarity transformation. If left empty, all from one to the models ErrorEstimator.JacSimTransMaxSize are used (extensive!)

Return values

aln	A \(m \times n \times N\) matrix with the approximated logarithmic norms in rows for each sim. trans. size.
times	A \(m \times n \times N\) matrix with the computation times for the logarithmic norm
jtimes	A \(m \times n \times N\) matrix with the computation times for the sim. trans. DEIM approximated jacobians
deim_orders	The effectively used DEIM orders (If given as parameter, it is looped through)
st_sizes	The effectively used sizes (If given as parameter, it is looped through)

Required fields of m:

Definition at line 137 of file LogNorm.m.

References models.BaseFullModel.Data, models.BaseFullModel.ErrorEstimator, data.ModelData.JacobianTrainData, Utils.logNorm(), and t.

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function pm = testing.LogNorm.compareSimTransDEIMJac_FullJac_plots	(	m,
		aln,
		times,
		jtimes,
		deim_orders,
		st_sizes,
		pm
	)

static

See WSH12 tests_burgers for likely better plot routine.

Required fields of m:

Required fields of pm:

Definition at line 211 of file LogNorm.m.

References LogPlot.logsurf(), LogPlot.nicesurf(), X, and Y.

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function [ aln , times , rowvec< integer > st_sizes ] = testing.LogNorm.compareSimTransJac_FullJac	(	models.BaseFullModel	m,
		rowvec< integer >	st_sizes
	)

static

% Comparison of similarity transformed jacobian log norms to full log norms Computes logarithmic norms of similarity transformed jacobians using the model's offline data, containing \(N\) trajectory samples.

Parameters

m	A BaseFullModel whos offlineGenerations have been run and that uses a DEIMEstimator.
st_sizes	The sizes \(s_1,\ldots,s_n\) to use for the similarity transformation. If left empty, all from one to the models ErrorEstimator.JacSimTransMaxSize are used (extensive!)

Return values

aln	A \(n \times N\) matrix with the approximated logarithmic norms in rows for each sim. trans. size.
times	A \(n \times N\) matrix with the computation time for the logarithmic norm
st_sizes	The effectively used sizes (If st_sizes is given as parameter, it is looped through)

Required fields of m:

Definition at line 28 of file LogNorm.m.

References models.BaseFullModel.Data, models.BaseFullModel.ErrorEstimator, models.BaseFirstOrderSystem.f, data.ModelData.JacobianTrainData, Utils.logNorm(), models.BaseModel.System, and t.

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static function pm = testing.LogNorm.compareSimTransJac_FullJac_plots	(	m,
		aln,
		times,
		st_sizes,
		pm
	)

static

Definition at line 87 of file LogNorm.m.

References LogPlot.logsurf(), LogPlot.nicesurf(), X, and Y.

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function [ res , mScale , MScale , pos , l , sel , seli ] = testing.LogNorm.CompLogNorms	(	m,
		numt
	)

static

LogNorm:

Author: Daniel Wirtz

Date: 2012-05-08

New in 0.6:: (Daniel Wirtz, 2012-05-08) Added this function.

This class is part of the framework

KerMor - Model Order Reduction using Kernels

Homepage http://www.morepas.org/software/index.html
Documentation http://www.morepas.org/software/kermor/index.html
License KerMor license conditions

Required fields of m:

Generated fields of res:

Definition at line 400 of file LogNorm.m.

References l, and t.

function struct res = testing.LogNorm.getApproxLogNormsAtPos	(	models.BaseFullModel	mo,
		colvec< double >	x,
		double	t,
		matrix< double >	mui
	)

static

Computes logarithmic norms of similarity transformed AND matrix DEIM approximated jacobians at the position given by (x,t) over all given parameters mui.

Parameters

mo	A BaseFullModel whos offlineGenerations have been run and that uses a DEIMEstimator.
x	The state space location to use.
t	The time t that belongs to the state space vector x.
mui	A matrix of \(\mu\) values to compute the approximated logarithmic norms for. If left empty, the model's ParamSamples are used.

Return values

res	A struct with multiple fields.
aln	The approximate log norms
times	The computation times for the log norm
jtimes	The computation times for the jacobian
mJ	The MDEIM order used
k	The similarity transformation size used

Required fields of mo:

Generated fields of res:

Definition at line 277 of file LogNorm.m.

References models.BaseFullModel.ErrorEstimator, Utils.logNorm(), models.BaseFirstOrderSystem.Params, models.BaseModel.System, and t.

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static function testing.LogNorm.getApproxLogNormsAtPos_plots	(	res,
		pm
	)

static

Definition at line 354 of file LogNorm.m.

References LogPlot.cleverPlot(), X, and Y.

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The documentation for this class was generated from the following file:

core/+testing/LogNorm.m

Detailed Description

Static Public Member Functions

Member Function Documentation