TMultiDimFit

class description - source file - inheritance tree

class TMultiDimFit : public TNamed


    protected:

      virtual Double_t EvalControl(Int_t* powers)
      virtual Double_t EvalFactor(Int_t p, Double_t x)
          virtual void MakeCandidates()
          virtual void MakeCoefficientErrors()
          virtual void MakeCoefficients()
          virtual void MakeCorrelation()
      virtual Double_t MakeGramSchmidt(Int_t function)
          virtual void MakeNormalized()
          virtual void MakeParameterization()
          virtual void MakeRealCode(const char* filename, const char* classname, Option_t* option)
        virtual Bool_t Select(Int_t* iv)
        virtual Bool_t TestFunction(Double_t squareResidual, Double_t dResidur)

    public:

              TMultiDimFit TMultiDimFit()
              TMultiDimFit TMultiDimFit(Int_t dimension, TMultiDimFit::EMDFPolyType type = kMonomials, Option_t* option)
              TMultiDimFit TMultiDimFit(TMultiDimFit&)
              virtual void ~TMultiDimFit()
              virtual void AddRow(Double_t* x, Double_t D, Double_t E = 0)
              virtual void AddTestRow(Double_t* x, Double_t D, Double_t E = 0)
              virtual void Browse(TBrowser* b)
            static TClass* Class()
              virtual void Clear(Option_t* option)
              virtual void Draw(Option_t* option = d)
          virtual Double_t Eval(Double_t* x, Double_t* coeff = 0)
              virtual void FindParameterization(Option_t* option)
              virtual void Fit(Option_t* option)
                  Double_t GetChi2() const
                  Double_t GetError() const
                    Int_t* GetFunctionCodes() const
           const TMatrixD* GetFunctions() const
            virtual TList* GetHistograms() const
                  Double_t GetMaxAngle() const
                     Int_t GetMaxFunctions() const
                    Int_t* GetMaxPowers() const
                  Double_t GetMaxQuantity() const
                     Int_t GetMaxStudy() const
                     Int_t GetMaxTerms() const
           const TVectorD* GetMaxVariables() const
                  Double_t GetMeanQuantity() const
           const TVectorD* GetMeanVariables() const
                  Double_t GetMinAngle() const
                  Double_t GetMinQuantity() const
                  Double_t GetMinRelativeError() const
           const TVectorD* GetMinVariables() const
                     Int_t GetNCoefficients() const
                     Int_t GetNVariables() const
                    Int_t* GetPowerIndex() const
                  Double_t GetPowerLimit() const
              const Int_t* GetPowers() const
                  Double_t GetPrecision() const
           const TVectorD* GetQuantity() const
                  Double_t GetResidualMax() const
                     Int_t GetResidualMaxRow() const
                  Double_t GetResidualMin() const
                     Int_t GetResidualMinRow() const
                  Double_t GetResidualSumSq() const
                  Double_t GetRMS() const
                     Int_t GetSampleSize() const
           const TVectorD* GetSqError() const
                  Double_t GetSumSqAvgQuantity() const
                  Double_t GetSumSqQuantity() const
                  Double_t GetTestError() const
                  Double_t GetTestPrecision() const
           const TVectorD* GetTestQuantity() const
                     Int_t GetTestSampleSize() const
           const TVectorD* GetTestSqError() const
           const TVectorD* GetTestVariables() const
           const TVectorD* GetVariables() const
      static TMultiDimFit* Instance()
           virtual TClass* IsA() const
            virtual Bool_t IsFolder() const
          virtual Double_t MakeChi2(Double_t* coeff = 0)
              virtual void MakeCode(const char* functionName = MDF, Option_t* option)
              virtual void MakeHistograms(Option_t* option = A)
              virtual void MakeMethod(const Char_t* className = MDF, Option_t* option)
              virtual void Print(Option_t* option = ps) const
                      void SetMaxAngle(Double_t angle = 0)
                      void SetMaxFunctions(Int_t n)
                      void SetMaxPowers(Int_t* powers)
                      void SetMaxStudy(Int_t n)
                      void SetMaxTerms(Int_t terms)
                      void SetMinAngle(Double_t angle = 1)
                      void SetMinRelativeError(Double_t error)
                      void SetPowerLimit(Double_t limit = 1e-3)
              virtual void SetPowers(Int_t* powers, Int_t terms)
              virtual void ShowMembers(TMemberInspector& insp, char* parent)
              virtual void Streamer(TBuffer& b)
                      void StreamerNVirtual(TBuffer& b)


  Data Members

    private:

      static TMultiDimFit* fgInstance  Static instance 

    protected:

                        TVectorD fQuantity              Training sample, dependent quantity 
                        TVectorD fSqError               Training sample, error in quantity 
                        Double_t fMeanQuantity          Mean of dependent quantity 
                        Double_t fMaxQuantity           Max value of dependent quantity 
                        Double_t fMinQuantity           Min value of dependent quantity 
                        Double_t fSumSqQuantity         SumSquare of dependent quantity 
                        Double_t fSumSqAvgQuantity      Sum of squares away from mean
                        TVectorD fVariables             Training sample, independent variables
                           Int_t fNVariables            Number of independent variables
                        TVectorD fMeanVariables         mean value of independent variables
                        TVectorD fMaxVariables          max value of independent variables
                        TVectorD fMinVariables          min value of independent variables
                           Int_t fSampleSize            Size of training sample
                        TVectorD fTestQuantity          Test sample, dependent quantity  
                        TVectorD fTestSqError           Test sample, Error in quantity  
                        TVectorD fTestVariables         Test sample, independent variables
                           Int_t fTestSampleSize        Size of test sample 
                        Double_t fMinAngle              Min angle for acepting new function
                        Double_t fMaxAngle              Max angle for acepting new function
                           Int_t fMaxTerms              Max terms expected in final expr. 
                        Double_t fMinRelativeError      Min relative error accepted
                          Int_t* fMaxPowers             [fNVariables] maximum powers
                        Double_t fPowerLimit            Control parameter
                        TMatrixD fFunctions             Functions evaluated over sample
                           Int_t fMaxFunctions          max number of functions 
                          Int_t* fFunctionCodes         [fMaxFunctions] acceptance code
                           Int_t fMaxStudy              max functions to study
                        TMatrixD fOrthFunctions         As above, but orthogonalised
                        TVectorD fOrthFunctionNorms     Norm of the evaluated functions 
                          Int_t* fMaxPowersFinal        [fNVariables] maximum powers from fit;
                          Int_t* fPowers                [fMaxFunctions*fNVariables]
                          Int_t* fPowerIndex            [fMaxTerms] Index of accepted powers
                        TVectorD fResiduals             Vector of the final residuals
                        Double_t fMaxResidual           Max redsidual value
                        Double_t fMinResidual           Min redsidual value
                           Int_t fMaxResidualRow        Row giving max residual
                           Int_t fMinResidualRow        Row giving min residual
                        Double_t fSumSqResidual         Sum of Square residuals
                           Int_t fNCoefficients         Dimension of model coefficients
                        TVectorD fOrthCoefficients      The model coefficients
                        TMatrixD fOrthCurvatureMatrix   Model matrix  
                        TVectorD fCoefficients          Vector of the final coefficients 
                        TVectorD fCoefficientsRMS       Vector of RMS of coefficients 
                        Double_t fRMS                   Root mean square of fit
                        Double_t fChi2                  Chi square of fit
                           Int_t fParameterisationCode  Exit code of parameterisation
                        Double_t fError                 Error from parameterization
                        Double_t fTestError             Error from test
                        Double_t fPrecision             Relative precision of param
                        Double_t fTestPrecision         Relative precision of test
                        Double_t fCorrelationCoeff      Multi Correlation coefficient 
                        TMatrixD fCorrelationMatrix     Correlation matrix
                        Double_t fTestCorrelationCoeff  Multi Correlation coefficient
                          TList* fHistograms            List of histograms 
                          Byte_t fHistogramMask         Bit pattern of hisograms used 
                 TVirtualFitter* fFitter                ! Fit object (MINUIT)
      TMultiDimFit::EMDFPolyType fPolyType              Type of polynomials to use 
                          Bool_t fShowCorrelation       print correlation matrix
                          Bool_t fIsUserFunction        Flag for user defined function 
                          Bool_t fIsVerbose             

    public:

      static const TMultiDimFit::EMDFPolyType kMonomials  
      static const TMultiDimFit::EMDFPolyType kChebyshev  
      static const TMultiDimFit::EMDFPolyType kLegendre

Class Description


 
/*

Multidimensional Fits in ROOT


Overview

A common problem encountered in different fields of applied science is to find an expression for one physical quantity in terms of several others, which are directly measurable.

An example in high energy physics is the evaluation of the momentum of a charged particle from the observation of its trajectory in a magnetic field. The problem is to relate the momentum of the particle to the observations, which may consists of of positional measurements at intervals along the particle trajectory.

The exact functional relationship between the measured quantities (e.g., the space-points) and the dependent quantity (e.g., the momentum) is in general not known, but one possible way of solving the problem, is to find an expression which reliably approximates the dependence of the momentum on the observations.

This explicit function of the observations can be obtained by a least squares fitting procedure applied to a representive sample of the data, for which the dependent quantity (e.g., momentum) and the independent observations are known. The function can then be used to compute the quantity of interest for new observations of the independent variables.

This class TMultiDimFit implements such a procedure in ROOT. It is largely based on the CERNLIB MUDIFI package [2]. Though the basic concepts are still sound, and therefore kept, a few implementation details have changed, and this class can take advantage of MINUIT [4] to improve the errors of the fitting, thanks to the class TMinuit.

In [5] and [6] H. Wind demonstrates the utility of this procedure in the context of tracking, magnetic field parameterisation, and so on. The outline of the method used in this class is based on Winds discussion, and I refer these two excellents text for more information.

And example of usage is given in $ROOTSYS/tutorials/multidimfit.C.


The Method

Let $ D$ by the dependent quantity of interest, which depends smoothly on the observable quantities $ x_1, \ldots, x_N$, which we'll denote by $ \mathbf{x}$. Given a training sample of $ M$ tuples of the form, (TMultiDimFit::AddRow)

$\displaystyle \left(\mathbf{x}_j, D_j, E_j\right)\quad, $

where $ \mathbf{x}_j = (x_{1,j},\ldots,x_{N,j})$ are $ N$ independent variables, $ D_j$ is the known, quantity dependent at $ \mathbf{x}_j$, and $ E_j$ is the square error in $ D_j$, the class TMultiDimFit will try to find the parameterization

$\displaystyle D_p(\mathbf{x}) = \sum_{l=1}^{L} c_l \prod_{i=1}^{N} p_{li}\left(x_i\right) = \sum_{l=1}^{L} c_l F_l(\mathbf{x})$ (1)

such that

$\displaystyle S \equiv \sum_{j=1}^{M} \left(D_j - D_p\left(\mathbf{x}_j\right)\right)^2$ (2)

is minimal. Here $ p_{li}(x_i)$ are monomials, or Chebyshev or Legendre polynomials, labelled $ l = 1, \ldots, L$, in each variable $ x_i$, $ i=1, \ldots, N$.

So what TMultiDimFit does, is to determine the number of terms $ L$, and then $ L$ terms (or functions) $ F_l$, and the $ L$ coefficients $ c_l$, so that $ S$ is minimal (TMultiDimFit::FindParameterization).

Of course it's more than a little unlikely that $ S$ will ever become exact zero as a result of the procedure outlined below. Therefore, the user is asked to provide a minimum relative error $ \epsilon$ (TMultiDimFit::SetMinRelativeError), and $ S$ will be considered minimized when

$\displaystyle R = \frac{S}{\sum_{j=1}^M D_j^2} < \epsilon $

Optionally, the user may impose a functional expression by specifying the powers of each variable in $ L$ specified functions $ F_1, \ldots, F_L$ (TMultiDimFit::SetPowers). In that case, only the coefficients $ c_l$ is calculated by the class.


Limiting the Number of Terms

As always when dealing with fits, there's a real chance of over fitting. As is well-known, it's always possible to fit an $ N-1$ polynomial in $ x$ to $ N$ points $ (x,y)$ with $ \chi^2 = 0$, but the polynomial is not likely to fit new data at all [1]. Therefore, the user is asked to provide an upper limit, $ L_{max}$ to the number of terms in $ D_p$ (TMultiDimFit::SetMaxTerms).

However, since there's an infinite number of $ F_l$ to choose from, the user is asked to give the maximum power. $ P_{max,i}$, of each variable $ x_i$ to be considered in the minimization of $ S$ (TMultiDimFit::SetMaxPowers).

One way of obtaining values for the maximum power in variable $ i$, is to perform a regular fit to the dependent quantity $ D$, using a polynomial only in $ x_i$. The maximum power is $ P_{max,i}$ is then the power that does not significantly improve the one-dimensional least-square fit over $ x_i$ to $ D$ [5].

There are still a huge amount of possible choices for $ F_l$; in fact there are $ \prod_{i=1}^{N} (P_{max,i} + 1)$ possible choices. Obviously we need to limit this. To this end, the user is asked to set a power control limit, $ Q$ (TMultiDimFit::SetPowerLimit), and a function $ F_l$ is only accepted if

$\displaystyle Q_l = \sum_{i=1}^{N} \frac{P_{li}}{P_{max,i}} < Q $

where $ P_{li}$ is the leading power of variable $ x_i$ in function $ F_l$. (TMultiDimFit::MakeCandidates). So the number of functions increase with $ Q$ (1, 2 is fine, 5 is way out).

Gram-Schmidt Orthogonalisation

To further reduce the number of functions in the final expression, only those functions that significantly reduce $ S$ is chosen. What `significant' means, is chosen by the user, and will be discussed below (see 2.3).

The functions $ F_l$ are generally not orthogonal, which means one will have to evaluate all possible $ F_l$'s over all data-points before finding the most significant [1]. We can, however, do better then that. By applying the modified Gram-Schmidt orthogonalisation algorithm [5] [3] to the functions $ F_l$, we can evaluate the contribution to the reduction of $ S$ from each function in turn, and we may delay the actual inversion of the curvature-matrix (TMultiDimFit::MakeGramSchmidt).

So we are let to consider an $ M\times L$ matrix $ \mathsf{F}$, an element of which is given by

$\displaystyle f_{jl} = F_j\left(x_{1j} , x_{2j}, \ldots, x_{Nj}\right) = F_l(\mathbf{x}_j) $   with$\displaystyle  j=1,2,\ldots,M,$ (3)

where $ j$ labels the $ M$ rows in the training sample and $ l$ labels $ L$ functions of $ N$ variables, and $ L \leq M$. That is, $ f_{jl}$ is the term (or function) numbered $ l$ evaluated at the data point $ j$. We have to normalise $ \mathbf{x}_j$ to $ [-1,1]$ for this to succeed [5] (TMultiDimFit::MakeNormalized). We then define a matrix $ \mathsf{W}$ of which the columns $ \mathbf{w}_j$ are given by
$\displaystyle \mathbf{w}_1$ $\displaystyle =$ $\displaystyle \mathbf{f}_1 = F_1\left(\mathbf x_1\right)$ (4)
$\displaystyle \mathbf{w}_l$ $\displaystyle =$ $\displaystyle \mathbf{f}_l - \sum^{l-1}_{k=1} \frac{\mathbf{f}_l \bullet \mathbf{w}_k}{\mathbf{w}_k^2}\mathbf{w}_k .$ (5)

and $ \mathbf{w}_{l}$ is the component of $ \mathbf{f}_{l}$ orthogonal to $ \mathbf{w}_{1}, \ldots, \mathbf{w}_{l-1}$. Hence we obtain [3],

$\displaystyle \mathbf{w}_k\bullet\mathbf{w}_l = 0$   if$\displaystyle  k \neq l\quad.$ (6)

We now take as a new model $ \mathsf{W}\mathbf{a}$. We thus want to minimize

$\displaystyle S\equiv \left(\mathbf{D} - \mathsf{W}\mathbf{a}\right)^2\quad,$ (7)

where $ \mathbf{D} = \left(D_1,\ldots,D_M\right)$ is a vector of the dependent quantity in the sample. Differentiation with respect to $ a_j$ gives, using (6),

$\displaystyle \mathbf{D}\bullet\mathbf{w}_l - a_l\mathbf{w}_l^2 = 0$ (8)

or

$\displaystyle a_l = \frac{\mathbf{D}_l\bullet\mathbf{w}_l}{\mathbf{w}_l^2}$ (9)

Let $ S_j$ be the sum of squares of residuals when taking $ j$ functions into account. Then

$\displaystyle S_l = \left[\mathbf{D} - \sum^l_{k=1} a_k\mathbf{w}_k\right]^2 = ... ...2 - 2\mathbf{D} \sum^l_{k=1} a_k\mathbf{w}_k + \sum^l_{k=1} a_k^2\mathbf{w}_k^2$ (10)

Using (9), we see that
$\displaystyle S_l$ $\displaystyle =$ $\displaystyle \mathbf{D}^2 - 2 \sum^l_{k=1} a_k^2\mathbf{w}_k^2 + \sum^j_{k=1} a_k^2\mathbf{w}_k^2$  
  $\displaystyle =$ $\displaystyle \mathbf{D}^2 - \sum^l_{k=1} a_k^2\mathbf{w}_k^2$  
  $\displaystyle =$ $\displaystyle \mathbf{D}^2 - \sum^l_{k=1} \frac{\left(\mathbf D\bullet \mathbf w_k\right)}{\mathbf w_k^2}$ (11)

So for each new function $ F_l$ included in the model, we get a reduction of the sum of squares of residuals of $ a_l^2\mathbf{w}_l^2$, where $ \mathbf{w}_l$ is given by (4) and $ a_l$ by (9). Thus, using the Gram-Schmidt orthogonalisation, we can decide if we want to include this function in the final model, before the matrix inversion.


Function Selection Based on Residual

Supposing that $ L-1$ steps of the procedure have been performed, the problem now is to consider the $ L^{\mbox{th}}$ function.

The sum of squares of residuals can be written as

$\displaystyle S_L = \textbf{D}^T\bullet\textbf{D} - \sum^L_{l=1}a^2_l\left(\textbf{w}_l^T\bullet\textbf{w}_l\right)$ (12)

where the relation (9) have been taken into account. The contribution of the $ L^{\mbox{th}}$ function to the reduction of S, is given by

$\displaystyle \Delta S_L = a^2_L\left(\textbf{w}_L^T\bullet\textbf{w}_L\right)$ (13)

Two test are now applied to decide whether this $ L^{\mbox{th}}$ function is to be included in the final expression, or not.


Test 1

Denoting by $ H_{L-1}$ the subspace spanned by $ \textbf{w}_1,\ldots,\textbf{w}_{L-1}$ the function $ \textbf {w}_L$ is by construction (see (4)) the projection of the function $ F_L$ onto the direction perpendicular to $ H_{L-1}$. Now, if the length of $ \textbf {w}_L$ (given by $ \textbf{w}_L\bullet\textbf{w}_L$) is very small compared to the length of $ \textbf {f}_L$ this new function can not contribute much to the reduction of the sum of squares of residuals. The test consists then in calculating the angle $ \theta $ between the two vectors $ \textbf {w}_L$ and $ \textbf {f}_L$ (see also figure 1) and requiring that it's greater then a threshold value which the user must set (TMultiDimFit::SetMinAngle).

Figure 1: (a) Angle $ \theta $ between $ \textbf {w}_l$ and $ \textbf {f}_L$, (b) angle $ \phi $ between $ \textbf {w}_L$ and $ \textbf {D}$
\begin{figure}\begin{center} \begin{tabular}{p{.4\textwidth}p{.4\textwidth}} \... ... \put(80,100){$\mathbf{D}$} \end{picture} \end{tabular} \end{center}\end{figure}


Test 2

Let $ \textbf {D}$ be the data vector to be fitted. As illustrated in figure 1, the $ L^{\mbox{th}}$ function $ \textbf {w}_L$ will contribute significantly to the reduction of $ S$, if the angle $ \phi^\prime$ between $ \textbf {w}_L$ and $ \textbf {D}$ is smaller than an upper limit $ \phi $, defined by the user (TMultiDimFit::SetMaxAngle)

However, the method automatically readjusts the value of this angle while fitting is in progress, in order to make the selection criteria less and less difficult to be fulfilled. The result is that the functions contributing most to the reduction of $ S$ are chosen first (TMultiDimFit::TestFunction).

In case $ \phi $ isn't defined, an alternative method of performing this second test is used: The $ L^{\mbox{th}}$ function $ \textbf {f}_L$ is accepted if (refer also to equation (13))

$\displaystyle \Delta S_L > \frac{S_{L-1}}{L_{max}-L}$ (14)

where $ S_{L-1}$ is the sum of the $ L-1$ first residuals from the $ L-1$ functions previously accepted; and $ L_{max}$ is the total number of functions allowed in the final expression of the fit (defined by user).

From this we see, that by restricting $ L_{max}$ -- the number of terms in the final model -- the fit is more difficult to perform, since the above selection criteria is more limiting.

The more coefficients we evaluate, the more the sum of squares of residuals $ S$ will be reduced. We can evaluate $ S$ before inverting $ \mathsf{B}$ as shown below.

Coefficients and Coefficient Errors

Having found a parameterization, that is the $ F_l$'s and $ L$, that minimizes $ S$, we still need to determine the coefficients $ c_l$. However, it's a feature of how we choose the significant functions, that the evaluation of the $ c_l$'s becomes trivial [5]. To derive $ \mathbf{c}$, we first note that equation (4) can be written as

$\displaystyle \mathsf{F} = \mathsf{W}\mathsf{B}$ (15)

where

$\displaystyle b_{ij} = \left\{\begin{array}{rcl} \frac{\mathbf{f}_j \bullet \ma... ...f} & i < j\ 1 & \mbox{if} & i = j\ 0 & \mbox{if} & i > j \end{array}\right.$ (16)

Consequently, $ \mathsf{B}$ is an upper triangle matrix, which can be readily inverted. So we now evaluate

$\displaystyle \mathsf{F}\mathsf{B}^{-1} = \mathsf{W}$ (17)

The model $ \mathsf{W}\mathbf{a}$ can therefore be written as

$\displaystyle (\mathsf{F}\mathsf{B}^{-1})\mathbf{a} = \mathsf{F}(\mathsf{B}^{-1}\mathbf{a}) . $

The original model $ \mathsf{F}\mathbf{c}$ is therefore identical with this if

$\displaystyle \mathbf{c} = \left(\mathsf{B}^{-1}\mathbf{a}\right) = \left[\mathbf{a}^T\left(\mathsf{B}^{-1}\right)^T\right]^T .$ (18)

The reason we use $ \left(\mathsf{B}^{-1}\right)^T$ rather then $ \mathsf{B}^{-1}$ is to save storage, since $ \left(\mathsf{B}^{-1}\right)^T$ can be stored in the same matrix as $ \mathsf{B}$ (TMultiDimFit::MakeCoefficients). The errors in the coefficients is calculated by inverting the curvature matrix of the non-orthogonal functions $ f_{lj}$ [1] (TMultiDimFit::MakeCoefficientErrors).


Considerations

It's important to realize that the training sample should be representive of the problem at hand, in particular along the borders of the region of interest. This is because the algorithm presented here, is a interpolation, rahter then a extrapolation [5].

Also, the independent variables $ x_{i}$ need to be linear independent, since the procedure will perform poorly if they are not [5]. One can find an linear transformation from ones original variables $ \xi_{i}$ to a set of linear independent variables $ x_{i}$, using a Principal Components Analysis (see TPrincipal), and then use the transformed variable as input to this class [5] [6].

H. Wind also outlines a method for parameterising a multidimensional dependence over a multidimensional set of variables. An example of the method from [5], is a follows (please refer to [5] for a full discussion):

  1. Define $ \mathbf{P} = (P_1, \ldots, P_5)$ are the 5 dependent quantities that define a track.
  2. Compute, for $ M$ different values of $ \mathbf{P}$, the tracks through the magnetic field, and determine the corresponding $ \mathbf{x} = (x_1, \ldots, x_N)$.
  3. Use the simulated observations to determine, with a simple approximation, the values of $ \mathbf{P}_j$. We call these values $ \mathbf{P}^\prime_j, j = 1, \ldots, M$.
  4. Determine from $ \mathbf{x}$ a set of at least five relevant coordinates $ \mathbf{x}^\prime$, using contrains, or alternative:
  5. Perform a Principal Component Analysis (using TPrincipal), and use to get a linear transformation $ \mathbf{x} \rightarrow \mathbf{x}^\prime$, so that $ \mathbf{x}^\prime$ are constrained and linear independent.
  6. Perform a Principal Component Analysis on $ Q_i = P_i / P^prime_i i = 1, \ldots, 5$, to get linear indenpendent (among themselves, but not independent of $ \mathbf{x}$) quantities $ \mathbf{Q}^\prime$
  7. For each component $ Q^\prime_i$ make a mutlidimensional fit, using $ \mathbf{x}^\prime$ as the variables, thus determing a set of coefficents $ \mathbf{c}_i$.

To process data, using this parameterisation, do

  1. Test wether the observation $ \mathbf{x}$ within the domain of the parameterization, using the result from the Principal Component Analysis.
  2. Determine $ \mathbf{P}^\prime$ as before.
  3. Detetmine $ \mathbf{x}^\prime$ as before.
  4. Use the result of the fit to determind $ \mathbf{Q}^\prime$.
  5. Transform back to $ \mathbf{P}$ from $ \mathbf{Q}^\prime$, using the result from the Principal Component Analysis.


Testing the parameterization

The class also provides functionality for testing the, over the training sample, found parameterization (TMultiDimFit::Fit). This is done by passing the class a test sample of $ M_t$ tuples of the form $ (\mathbf{x}_{t,j}, D_{t,j}, E_{t,j})$, where $ \mathbf{x}_{t,j}$ are the independent variables, $ D_{t,j}$ the known, dependent quantity, and $ E_{t,j}$ is the square error in $ D_{t,j}$ (TMultiDimFit::AddTestRow).

The parameterization is then evaluated at every $ \mathbf{x}_t$ in the test sample, and

$\displaystyle S_t \equiv \sum_{j=1}^{M_t} \left(D_{t,j} - D_p\left(\mathbf{x}_{t,j}\right)\right)^2 $

is evaluated. The relative error over the test sample

$\displaystyle R_t = \frac{S_t}{\sum_{j=1}^{M_t} D_{t,j}^2} $

should not be to low or high compared to $ R$ from the training sample. Also, multiple correlation coefficient from both samples should be fairly close, otherwise one of the samples is not representive of the problem. A large difference in the reduced $ \chi^2$ over the two samples indicate an over fit, and the maximum number of terms in the parameterisation should be reduced.

It's possible to use Minuit [4] to further improve the fit, using the test sample.

Christian Holm
November 2000, NBI

Bibliography

1
Philip R. Bevington and D. Keith Robinson.
Data Reduction and Error Analysis for the Physical Sciences.
McGraw-Hill, 2 edition, 1992.

2
René Brun et al.
Mudifi.
Long writeup DD/75-23, CERN, 1980.

3
Gene H. Golub and Charles F. van Loan.
Matrix Computations.
John Hopkins Univeristy Press, Baltimore, 3 edition, 1996.

4
F. James.
Minuit.
Long writeup D506, CERN, 1998.

5
H. Wind.
Function parameterization.
In Proceedings of the 1972 CERN Computing and Data Processing School, volume 72-21 of Yellow report. CERN, 1972.

6
H. Wind.
1. principal component analysis, 2. pattern recognition for track finding, 3. interpolation and functional representation.
Yellow report EP/81-12, CERN, 1981. 
 */
TMultiDimFit() 
 Empty CTOR. Do not use
TMultiDimFit(Int_t dimension, EMDFPolyType type, Option_t *option) : TNamed("multidimfit","Multi-dimensional fit object"), fQuantity(dimension), fSqError(dimension), fVariables(dimension*100), fMeanVariables(dimension), fMaxVariables(dimension), fMinVariables(dimension) 
 Constructor
 Second argument is the type of polynomials to use in
 parameterisation, one of:
      TMultiDimFit::kMonomials
      TMultiDimFit::kChebyshev
      TMultiDimFit::kLegendre

 Options:
   K      Compute (k)correlation matrix
   V      Be verbose

 Default is no options.
~TMultiDimFit() 
 DTOR
void AddRow(Double_t *x, Double_t D, Double_t E) 
 Add a row consisting of fNVariables independent variables, the
 known, dependent quantity, and optionally, the square error in
 the dependent quantity, to the training sample to be used for the
 parameterization.
 The mean of the variables and quantity is calculated on the fly,
 as outlined in TPrincipal::AddRow.
 This sample should be representive of the problem at hand.
 Please note, that if no error is given Poisson statistics is
 assumed and the square error is set to the value of dependent
 quantity.  See also the
 class description
void AddTestRow(Double_t *x, Double_t D, Double_t E) 
 Add a row consisting of fNVariables independent variables, the
 known, dependent quantity, and optionally, the square error in
 the dependent quantity, to the test sample to be used for the
 test of the parameterization.
 This sample needn't be representive of the problem at hand.
 Please note, that if no error is given Poisson statistics is
 assumed and the square error is set to the value of dependent
 quantity.  See also the
 class description
void Browse(TBrowser* b) 
 Browse the TMultiDimFit object in the TBrowser.
void Clear(Option_t *option) 
 Clear internal structures and variables
Double_t Eval(Double_t *x, Double_t* coeff) 
 Evaluate parameterization at point x. Optional argument coeff is
 a vector of coefficients for the parameterisation, fNCoefficients
 elements long.
Double_t EvalControl(Int_t *iv) 
 PRIVATE METHOD:
 Calculate the control parameter from the passed powers
Double_t EvalFactor(Int_t p, Double_t x) 
 PRIVATE METHOD:
 Evaluate function with power p at variable value x
void FindParameterization(Option_t *option) 
 Find the parameterization

 Options:
     None so far

 For detailed description of what this entails, please refer to the
 class description
void Fit(Option_t *option) 
 Try to fit the found parameterisation to the test sample.

 Options
     M     use Minuit to improve coefficients

 Also, refer to
 class description
void MakeCandidates() 
 PRIVATE METHOD:
 Create list of candidate functions for the parameterisation. See
 also
 class description
Double_t MakeChi2(Double_t* coeff) 
 Calculate Chi square over either the test sample. The optional
 argument coeff is a vector of coefficients to use in the
 evaluation of the parameterisation. If coeff == 0, then the found
 coefficients is used.
 Used my MINUIT for fit (see TMultDimFit::Fit)
void MakeCode(const char* filename, Option_t *option) 
 Generate the file <filename> with .C appended if argument doesn't
 end in .cxx or .C. The contains the implementation of the
 function:

   Double_t <funcname>(Double_t *x)

 which does the same as TMultiDimFit::Eval. Please refer to this
 method.

 Further, the static variables:

     Int_t    gNVariables
     Int_t    gNCoefficients
     Double_t gDMean
     Double_t gXMean[]
     Double_t gXMin[]
     Double_t gXMax[]
     Double_t gCoefficient[]
     Int_t    gPower[]

 are initialized. The only ROOT header file needed is Rtypes.h

 See TMultiDimFit::MakeRealCode for a list of options
void MakeCoefficientErrors() 
 PRIVATE METHOD:
 Compute the errors on the coefficients. For this to be done, the
 curvature matrix of the non-orthogonal functions, is computed.
void MakeCoefficients() 
 PRIVATE METHOD:
 Invert the model matrix B, and compute final coefficients. For a
 more thorough discussion of what this means, please refer to the
 class description

 First we invert the lower triangle matrix fOrthCurvatureMatrix
 and store the inverted matrix in the upper triangle.
void MakeCorrelation() 
 PRIVATE METHOD:
 Compute the correlation matrix
Double_t MakeGramSchmidt(Int_t function) 
 PRIVATE METHOD:
 Make Gram-Schmidt orthogonalisation. The class description gives
 a thorough account of this algorithm, as well as
 references. Please refer to the
 class description
void MakeHistograms(Option_t *option) 
 Make histograms of the result of the analysis. This message
 should be sent after having read all data points, but before
 finding the parameterization

 Options:
     A         All the below
     X         Original independent variables
     D         Original dependent variables
     N         Normalised independent variables
     S         Shifted dependent variables
     R1        Residuals versus normalised independent variables
     R2        Residuals versus dependent variable
     R3        Residuals computed on training sample
     R4        Residuals computed on test sample

 For a description of these quantities, refer to
 class description
void MakeMethod(const Char_t* classname, Option_t* option) 
 Generate the file <classname>MDF.cxx which contains the
 implementation of the method:

   Double_t <classname>::MDF(Double_t *x)

 which does the same as  TMultiDimFit::Eval. Please refer to this
 method.

 Further, the public static members:

   Int_t    <classname>::fgNVariables
   Int_t    <classname>::fgNCoefficients
   Double_t <classname>::fgDMean
   Double_t <classname>::fgXMean[]       //[fgNVariables]
   Double_t <classname>::fgXMin[]        //[fgNVariables]
   Double_t <classname>::fgXMax[]        //[fgNVariables]
   Double_t <classname>::fgCoefficient[] //[fgNCoeffficents]
   Int_t    <classname>::fgPower[]       //[fgNCoeffficents*fgNVariables]

 are initialized, and assumed to exist. The class declaration is
 assumed to be in <classname>.h and assumed to be provided by the
 user.

 See TMultiDimFit::MakeRealCode for a list of options

 The minimal class definition is:

   class <classname> {
   public:
     Int_t    <classname>::fgNVariables;     // Number of variables
     Int_t    <classname>::fgNCoefficients;  // Number of terms
     Double_t <classname>::fgDMean;          // Mean from training sample
     Double_t <classname>::fgXMean[];        // Mean from training sample
     Double_t <classname>::fgXMin[];         // Min from training sample
     Double_t <classname>::fgXMax[];         // Max from training sample
     Double_t <classname>::fgCoefficient[];  // Coefficients
     Int_t    <classname>::fgPower[];        // Function powers

     Double_t Eval(Double_t *x);
   };

 Whether the method <classname>::Eval should be static or not, is
 up to the user.
void MakeNormalized() 
 PRIVATE METHOD:
 Normalize data to the interval [-1;1]. This is needed for the
 classes method to work.
void MakeParameterization() 
 PRIVATE METHOD:
 Find the parameterization over the training sample. A full account
 of the algorithm is given in the
 class description
void MakeRealCode(const char *filename, const char *classname, Option_t *opt) 
 PRIVATE METHOD:
 This is the method that actually generates the code for the
 evaluation the parameterization on some point.
 It's called by TMultiDimFit::MakeCode and TMultiDimFit::MakeMethod.

 The options are: NONE so far
void Print(Option_t *option) const 
 Print statistics etc.
 Options are
   P        Parameters
   S        Statistics
   C        Coefficients
   R        Result of parameterisation
   F        Result of fit
Bool_t Select(Int_t *iv) 
 Selection method. User can override this method for specialized
 selection of acceptable functions in fit. Default is to select
 all. This message is sent during the build-up of the function
 candidates table once for each set of powers in
 variables. Notice, that the argument array contains the powers
 PLUS ONE. For example, to De select the function
     f = x1^2 * x2^4 * x3^5,
 this method should return kFALSE if given the argument
     { 3, 4, 6 }
void SetMaxAngle(Double_t ang) 
 Set the max angle (in degrees) between the initial data vector to
 be fitted, and the new candidate function to be included in the
 fit.  By default it is 0, which automatically chooses another
 selection criteria. See also
 class description
void SetMinAngle(Double_t ang) 
 Set the min angle (in degrees) between a new candidate function
 and the subspace spanned by the previously accepted
 functions. See also
 class description
void SetPowers(Int_t* powers, Int_t terms) 
 Define a user function. The input array must be of the form
 (p11, ..., p1N, ... ,pL1, ..., pLN)
 Where N is the dimension of the data sample, L is the number of
 terms (given in terms) and the first number, labels the term, the
 second the variable.  More information is given in the
 class description
void SetPowerLimit(Double_t limit) 
 Set the user parameter for the function selection. The bigger the
 limit, the more functions are used. The meaning of this variable
 is defined in the
 class description
void SetMaxPowers(Int_t* powers) 
 Set the maximum power to be considered in the fit for each
 variable. See also
 class description
void SetMinRelativeError(Double_t error) 
 Set the acceptable relative error for when sum of square
 residuals is considered minimized. For a full account, refer to
 the
 class description
Bool_t TestFunction(Double_t squareResidual, Double_t dResidur) 
 PRIVATE METHOD:
 Test whether the currently considered function contributes to the
 fit. See also
 class description



Inline Functions

                   void Draw(Option_t* option = d)
               Double_t GetChi2() const
               Double_t GetError() const
                 Int_t* GetFunctionCodes() const
        const TMatrixD* GetFunctions() const
                 TList* GetHistograms() const
               Double_t GetMaxAngle() const
                  Int_t GetMaxFunctions() const
                 Int_t* GetMaxPowers() const
               Double_t GetMaxQuantity() const
                  Int_t GetMaxStudy() const
                  Int_t GetMaxTerms() const
        const TVectorD* GetMaxVariables() const
               Double_t GetMeanQuantity() const
        const TVectorD* GetMeanVariables() const
               Double_t GetMinAngle() const
               Double_t GetMinQuantity() const
               Double_t GetMinRelativeError() const
        const TVectorD* GetMinVariables() const
                  Int_t GetNVariables() const
                  Int_t GetNCoefficients() const
                 Int_t* GetPowerIndex() const
               Double_t GetPowerLimit() const
           const Int_t* GetPowers() const
               Double_t GetPrecision() const
        const TVectorD* GetQuantity() const
               Double_t GetResidualMax() const
               Double_t GetResidualMin() const
                  Int_t GetResidualMaxRow() const
                  Int_t GetResidualMinRow() const
               Double_t GetResidualSumSq() const
               Double_t GetRMS() const
                  Int_t GetSampleSize() const
        const TVectorD* GetSqError() const
               Double_t GetSumSqAvgQuantity() const
               Double_t GetSumSqQuantity() const
               Double_t GetTestError() const
               Double_t GetTestPrecision() const
        const TVectorD* GetTestQuantity() const
                  Int_t GetTestSampleSize() const
        const TVectorD* GetTestSqError() const
        const TVectorD* GetTestVariables() const
        const TVectorD* GetVariables() const
          TMultiDimFit* Instance()
                 Bool_t IsFolder() const
                   void SetMaxFunctions(Int_t n)
                   void SetMaxStudy(Int_t n)
                   void SetMaxTerms(Int_t terms)
                TClass* Class()
                TClass* IsA() const
                   void ShowMembers(TMemberInspector& insp, char* parent)
                   void Streamer(TBuffer& b)
                   void StreamerNVirtual(TBuffer& b)
           TMultiDimFit TMultiDimFit(TMultiDimFit&)

Author: Christian Holm Christensen 07/11/2000
Last update: root/hist:$Name: $:$Id: TMultiDimFit.cxx,v 1.3 2000/12/13 15:13:51 brun Exp $

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