Name
match_funct_1d_transT_match_funct_1d_transMatchFunct1dTransmatch_funct_1d_transMatchFunct1dTransMatchFunct1dTrans — Calculate transformation parameters between two functions.
Herror match_funct_1d_trans(const HTuple& Function1, const HTuple& Function2, const HTuple& Border, const HTuple& ParamsConst, const HTuple& UseParams, HTuple* Params, HTuple* ChiSquare, HTuple* Covar)
HTuple HFunction1D::MatchFunct1dTrans(const HTuple& Function2, const HTuple& Border, const HTuple& ParamsConst, const HTuple& UseParams, HTuple* ChiSquare, HTuple* Covar) const
void MatchFunct1dTrans(const HTuple& Function1, const HTuple& Function2, const HTuple& Border, const HTuple& ParamsConst, const HTuple& UseParams, HTuple* Params, HTuple* ChiSquare, HTuple* Covar)
HTuple HFunction1D::MatchFunct1dTrans(const HFunction1D& Function2, const HString& Border, const HTuple& ParamsConst, const HTuple& UseParams, double* ChiSquare, HTuple* Covar) const
HTuple HFunction1D::MatchFunct1dTrans(const HFunction1D& Function2, const char* Border, const HTuple& ParamsConst, const HTuple& UseParams, double* ChiSquare, HTuple* Covar) const
void HOperatorSetX.MatchFunct1dTrans(
[in] VARIANT Function1, [in] VARIANT Function2, [in] VARIANT Border, [in] VARIANT ParamsConst, [in] VARIANT UseParams, [out] VARIANT* Params, [out] VARIANT* ChiSquare, [out] VARIANT* Covar)
VARIANT HFunction1DX.MatchFunct1dTrans(
[in] IHFunction1DX* Function2, [in] BSTR Border, [in] VARIANT ParamsConst, [in] VARIANT UseParams, [out] double* ChiSquare, [out] VARIANT* Covar)
static void HOperatorSet.MatchFunct1dTrans(HTuple function1, HTuple function2, HTuple border, HTuple paramsConst, HTuple useParams, out HTuple paramsVal, out HTuple chiSquare, out HTuple covar)
HTuple HFunction1D.MatchFunct1dTrans(HFunction1D function2, string border, HTuple paramsConst, HTuple useParams, out double chiSquare, out HTuple covar)
match_funct_1d_transmatch_funct_1d_transMatchFunct1dTransmatch_funct_1d_transMatchFunct1dTransMatchFunct1dTrans calculates the transformation parameters
between two functions given as the tuples Function1Function1Function1Function1Function1function1 and
Function2Function2Function2Function2Function2function2 (see create_funct_1d_arraycreate_funct_1d_arrayCreateFunct1dArraycreate_funct_1d_arrayCreateFunct1dArrayCreateFunct1dArray and
create_funct_1d_pairscreate_funct_1d_pairsCreateFunct1dPairscreate_funct_1d_pairsCreateFunct1dPairsCreateFunct1dPairs). The following model is used for the
transformation between the two functions:
y1(x) = a1*y2(a3*x+a4)+a2 .
The transformation parameters are determined by a least-squares
minimization of the following function:
n-1
----
\ 2
/ (y1(x[i])-a1*y2(a3*x[i]+a4)+a2) .
----
i=0
The values of the function y2 are obtained by
linear interpolation. The parameter BorderBorderBorderBorderBorderborder determines
the values of the function Function2Function2Function2Function2Function2function2 outside of its domain.
For BorderBorderBorderBorderBorderborder='zero'"zero""zero""zero""zero""zero" these values are set to 0, for
BorderBorderBorderBorderBorderborder='constant'"constant""constant""constant""constant""constant" they are set to the
corresponding value at the border, for
BorderBorderBorderBorderBorderborder='mirror'"mirror""mirror""mirror""mirror""mirror" they are mirrored at the border,
and for BorderBorderBorderBorderBorderborder='cyclic'"cyclic""cyclic""cyclic""cyclic""cyclic" they are continued
cyclically. The calculated transformation parameters are returned
as a 4-tuple [a1, a2, a3, a4]
in ParamsParamsParamsParamsParamsparamsVal. If some of the parameter values are
known, the respective parameters can be excluded from the
least-squares adjustment by setting the corresponding value in the
tuple UseParamsUseParamsUseParamsUseParamsUseParamsuseParams to the value 'false'"false""false""false""false""false". In this
case, the tuple ParamsConstParamsConstParamsConstParamsConstParamsConstparamsConst must contain the known value of
the respective parameter. If a parameter is used for the adjustment
(UseParamsUseParamsUseParamsUseParamsUseParamsuseParams = 'true'"true""true""true""true""true"), the corresponding parameter
in ParamsConstParamsConstParamsConstParamsConstParamsConstparamsConst is ignored. On output,
match_funct_1d_transmatch_funct_1d_transMatchFunct1dTransmatch_funct_1d_transMatchFunct1dTransMatchFunct1dTrans additionally returns the sum of the
squared errors ChiSquareChiSquareChiSquareChiSquareChiSquarechiSquare of the resulting function, i.e.,
the function obtained by transforming the input function with the
transformation parameters, as well as the covariance matrix
CovarCovarCovarCovarCovarcovar of the transformation parameters ParamsParamsParamsParamsParamsparamsVal.
These parameters can be used to decide whether a successful matching
of the functions was possible.
Note that in case that there is no unique solution for the
transformation parameters, match_funct_1d_transmatch_funct_1d_transMatchFunct1dTransmatch_funct_1d_transMatchFunct1dTransMatchFunct1dTrans either
returns one selected single solution or returns the error 9205
(Matrix is singular).
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
Border treatment for function 2.
Default value:
'constant'
"constant"
"constant"
"constant"
"constant"
"constant"
List of values: 'constant'"constant""constant""constant""constant""constant", 'cyclic'"cyclic""cyclic""cyclic""cyclic""cyclic", 'mirror'"mirror""mirror""mirror""mirror""mirror", 'zero'"zero""zero""zero""zero""zero"
Values of the parameters to remain constant.
Number of elements: 4
Default value: [1.0,0.0,1.0,0.0]
Should a parameter be adapted for it?
Number of elements: 4
Default value:
['true','true','true','true']
["true","true","true","true"]
["true","true","true","true"]
["true","true","true","true"]
["true","true","true","true"]
["true","true","true","true"]
List of values: 'false'"false""false""false""false""false", 'true'"true""true""true""true""true"
Transformation parameters between the functions.
Number of elements: 4
Quadratic error of the output function.
Covariance Matrix of the transformation parameters.
Number of elements: 16
create_funct_1d_arraycreate_funct_1d_arrayCreateFunct1dArraycreate_funct_1d_arrayCreateFunct1dArrayCreateFunct1dArray,
create_funct_1d_pairscreate_funct_1d_pairsCreateFunct1dPairscreate_funct_1d_pairsCreateFunct1dPairsCreateFunct1dPairs
gray_projectionsgray_projectionsGrayProjectionsgray_projectionsGrayProjectionsGrayProjections
Foundation