/*
</pre>
<H1><A NAME="SECTION00010000000000000000">
Multidimensional Fits in ROOT</A>
</H1>
<H1><A NAME="SECTION00020000000000000000"></A>
<A NAME="sec:overview"></A><BR>
Overview
</H1>
<P>
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.
<P>
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.
<P>
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.
<P>
This explicit function of the observations can be obtained by a
<I>least squares</I> 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.
<P>
This class <TT>TMultiDimFit</TT> implements such a procedure in
ROOT. It is largely based on the CERNLIB MUDIFI package
[<A
HREF="TMultiFimFit.html#mudifi">2</A>]. Though the basic concepts are still sound, and
therefore kept, a few implementation details have changed, and this
class can take advantage of MINUIT [<A
HREF="TMultiFimFit.html#minuit">4</A>] to improve the errors
of the fitting, thanks to the class <TT>TMinuit</TT>.
<P>
In [<A
HREF="TMultiFimFit.html#wind72">5</A>] and [<A
HREF="TMultiFimFit.html#wind81">6</A>] 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.
<P>
And example of usage is given in
<A NAME="tex2html1"
HREF="
./examples/multidimfit.C"><TT>$ROOTSYS/tutorials/fit/multidimfit.C</TT></A>.
<P>
<H1><A NAME="SECTION00030000000000000000"></A>
<A NAME="sec:method"></A><BR>
The Method
</H1>
<P>
Let <IMG
WIDTH="18" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img7.gif"
ALT="$ D$"> by the dependent quantity of interest, which depends smoothly
on the observable quantities <!-- MATH
$x_1, \ldots, x_N$
-->
<IMG
WIDTH="80" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img8.gif"
ALT="$ x_1, \ldots, x_N$">, which we'll denote by
<!-- MATH
$\mathbf{x}$
-->
<IMG
WIDTH="14" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img9.gif"
ALT="$ \mathbf{x}$">. Given a training sample of <IMG
WIDTH="21" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img10.gif"
ALT="$ M$"> tuples of the form,
(<A NAME="tex2html2"
HREF="
./TMultiDimFit.html#TMultiDimFit:AddRow"><TT>TMultiDimFit::AddRow</TT></A>)
<!-- MATH
\begin{displaymath}
\left(\mathbf{x}_j, D_j, E_j\right)\quad,
\end{displaymath}
-->
<P></P><DIV ALIGN="CENTER">
<IMG
WIDTH="108" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img11.gif"
ALT="$\displaystyle \left(\mathbf{x}_j, D_j, E_j\right)\quad,
$">
</DIV><P></P>
where <!-- MATH
$\mathbf{x}_j = (x_{1,j},\ldots,x_{N,j})$
-->
<IMG
WIDTH="148" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img12.gif"
ALT="$ \mathbf{x}_j = (x_{1,j},\ldots,x_{N,j})$"> are <IMG
WIDTH="19" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img13.gif"
ALT="$ N$"> independent
variables, <IMG
WIDTH="24" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img14.gif"
ALT="$ D_j$"> is the known, quantity dependent at <!-- MATH
$\mathbf{x}_j$
-->
<IMG
WIDTH="20" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img15.gif"
ALT="$ \mathbf{x}_j$">,
and <IMG
WIDTH="23" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img16.gif"
ALT="$ E_j$"> is the square error in <IMG
WIDTH="24" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img14.gif"
ALT="$ D_j$">, the class
<A NAME="tex2html3"
HREF="../TMultiDimFit.html"><TT>TMultiDimFit</TT></A>
will
try to find the parameterization
<P></P>
<DIV ALIGN="CENTER"><A NAME="Dp"></A><!-- MATH
\begin{equation}
D_p(\mathbf{x}) = \sum_{l=1}^{L} c_l \prod_{i=1}^{N} p_{li}\left(x_i\right)
= \sum_{l=1}^{L} c_l F_l(\mathbf{x})
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="274" HEIGHT="65" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img17.gif"
ALT="$\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})$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(1)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
such that
<P></P>
<DIV ALIGN="CENTER"><A NAME="S"></A><!-- MATH
\begin{equation}
S \equiv \sum_{j=1}^{M} \left(D_j - D_p\left(\mathbf{x}_j\right)\right)^2
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="172" HEIGHT="65" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img18.gif"
ALT="$\displaystyle S \equiv \sum_{j=1}^{M} \left(D_j - D_p\left(\mathbf{x}_j\right)\right)^2$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(2)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
is minimal. Here <!-- MATH
$p_{li}(x_i)$
-->
<IMG
WIDTH="48" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img19.gif"
ALT="$ p_{li}(x_i)$"> are monomials, or Chebyshev or Legendre
polynomials, labelled <!-- MATH
$l = 1, \ldots, L$
-->
<IMG
WIDTH="87" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img20.gif"
ALT="$ l = 1, \ldots, L$">, in each variable
<IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img21.gif"
ALT="$ x_i$">, <!-- MATH
$i=1, \ldots, N$
-->
<IMG
WIDTH="91" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img22.gif"
ALT="$ i=1, \ldots, N$">.
<P>
So what <TT>TMultiDimFit</TT> does, is to determine the number of
terms <IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img23.gif"
ALT="$ L$">, and then <IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img23.gif"
ALT="$ L$"> terms (or functions) <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$">, and the <IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img23.gif"
ALT="$ L$">
coefficients <IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img25.gif"
ALT="$ c_l$">, so that <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> is minimal
(<A NAME="tex2html4"
HREF="
./TMultiDimFit.html#TMultiDimFit:FindParameterization"><TT>TMultiDimFit::FindParameterization</TT></A>).
<P>
Of course it's more than a little unlikely that <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> will ever become
exact zero as a result of the procedure outlined below. Therefore, the
user is asked to provide a minimum relative error <IMG
WIDTH="11" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img27.gif"
ALT="$ \epsilon$">
(<A NAME="tex2html5"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetMinRelativeError"><TT>TMultiDimFit::SetMinRelativeError</TT></A>), and <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$">
will be considered minimized when
<!-- MATH
\begin{displaymath}
R = \frac{S}{\sum_{j=1}^M D_j^2} < \epsilon
\end{displaymath}
-->
<P></P><DIV ALIGN="CENTER">
<IMG
WIDTH="132" HEIGHT="51" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img28.gif"
ALT="$\displaystyle R = \frac{S}{\sum_{j=1}^M D_j^2} < \epsilon
$">
</DIV><P></P>
<P>
Optionally, the user may impose a functional expression by specifying
the powers of each variable in <IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img23.gif"
ALT="$ L$"> specified functions <!-- MATH
$F_1, \ldots,
F_L$
-->
<IMG
WIDTH="79" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img29.gif"
ALT="$ F_1, \ldots,
F_L$"> (<A NAME="tex2html6"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetPowers"><TT>TMultiDimFit::SetPowers</TT></A>). In that case, only the
coefficients <IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img25.gif"
ALT="$ c_l$"> is calculated by the class.
<P>
<H2><A NAME="SECTION00031000000000000000"></A>
<A NAME="sec:selection"></A><BR>
Limiting the Number of Terms
</H2>
<P>
As always when dealing with fits, there's a real chance of
<I>over fitting</I>. As is well-known, it's always possible to fit an
<IMG
WIDTH="46" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img30.gif"
ALT="$ N-1$"> polynomial in <IMG
WIDTH="13" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img31.gif"
ALT="$ x$"> to <IMG
WIDTH="19" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img13.gif"
ALT="$ N$"> points <IMG
WIDTH="41" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img32.gif"
ALT="$ (x,y)$"> with <!-- MATH
$\chi^2 = 0$
-->
<IMG
WIDTH="50" HEIGHT="33" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img33.gif"
ALT="$ \chi^2 = 0$">, but
the polynomial is not likely to fit new data at all
[<A
HREF="TMultiFimFit.html#bevington">1</A>]. Therefore, the user is asked to provide an upper
limit, <IMG
WIDTH="41" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img34.gif"
ALT="$ L_{max}$"> to the number of terms in <IMG
WIDTH="25" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img35.gif"
ALT="$ D_p$">
(<A NAME="tex2html7"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetMaxTerms"><TT>TMultiDimFit::SetMaxTerms</TT></A>).
<P>
However, since there's an infinite number of <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$"> to choose from, the
user is asked to give the maximum power. <IMG
WIDTH="49" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img36.gif"
ALT="$ P_{max,i}$">, of each variable
<IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img21.gif"
ALT="$ x_i$"> to be considered in the minimization of <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$">
(<A NAME="tex2html8"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetMaxPowers"><TT>TMultiDimFit::SetMaxPowers</TT></A>).
<P>
One way of obtaining values for the maximum power in variable <IMG
WIDTH="10" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img37.gif"
ALT="$ i$">, is
to perform a regular fit to the dependent quantity <IMG
WIDTH="18" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img7.gif"
ALT="$ D$">, using a
polynomial only in <IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img21.gif"
ALT="$ x_i$">. The maximum power is <IMG
WIDTH="49" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img36.gif"
ALT="$ P_{max,i}$"> is then the
power that does not significantly improve the one-dimensional
least-square fit over <IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img21.gif"
ALT="$ x_i$"> to <IMG
WIDTH="18" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img7.gif"
ALT="$ D$"> [<A
HREF="TMultiFimFit.html#wind72">5</A>].
<P>
There are still a huge amount of possible choices for <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$">; in fact
there are <!-- MATH
$\prod_{i=1}^{N} (P_{max,i} + 1)$
-->
<IMG
WIDTH="125" HEIGHT="39" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img38.gif"
ALT="$ \prod_{i=1}^{N} (P_{max,i} + 1)$"> possible
choices. Obviously we need to limit this. To this end, the user is
asked to set a <I>power control limit</I>, <IMG
WIDTH="17" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img39.gif"
ALT="$ Q$">
(<A NAME="tex2html9"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetPowerLimit"><TT>TMultiDimFit::SetPowerLimit</TT></A>), and a function
<IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$"> is only accepted if
<!-- MATH
\begin{displaymath}
Q_l = \sum_{i=1}^{N} \frac{P_{li}}{P_{max,i}} < Q
\end{displaymath}
-->
<P></P><DIV ALIGN="CENTER">
<IMG
WIDTH="151" HEIGHT="65" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img40.gif"
ALT="$\displaystyle Q_l = \sum_{i=1}^{N} \frac{P_{li}}{P_{max,i}} < Q
$">
</DIV><P></P>
where <IMG
WIDTH="24" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img41.gif"
ALT="$ P_{li}$"> is the leading power of variable <IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img21.gif"
ALT="$ x_i$"> in function
<IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$">. (<A NAME="tex2html10"
HREF="
./TMultiDimFit.html#TMultiDimFit:MakeCandidates"><TT>TMultiDimFit::MakeCandidates</TT></A>). So the number of
functions increase with <IMG
WIDTH="17" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img39.gif"
ALT="$ Q$"> (1, 2 is fine, 5 is way out).
<P>
<H2><A NAME="SECTION00032000000000000000">
Gram-Schmidt Orthogonalisation</A>
</H2>
<P>
To further reduce the number of functions in the final expression,
only those functions that significantly reduce <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> is chosen. What
`significant' means, is chosen by the user, and will be
discussed below (see <A HREF="TMultiFimFit.html#sec:selectiondetail">2.3</A>).
<P>
The functions <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$"> are generally not orthogonal, which means one will
have to evaluate all possible <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$">'s over all data-points before
finding the most significant [<A
HREF="TMultiFimFit.html#bevington">1</A>]. We can, however, do
better then that. By applying the <I>modified Gram-Schmidt
orthogonalisation</I> algorithm [<A
HREF="TMultiFimFit.html#wind72">5</A>] [<A
HREF="TMultiFimFit.html#golub">3</A>] to the
functions <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$">, we can evaluate the contribution to the reduction of
<IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> from each function in turn, and we may delay the actual inversion
of the curvature-matrix
(<A NAME="tex2html11"
HREF="
./TMultiDimFit.html#TMultiDimFit:MakeGramSchmidt"><TT>TMultiDimFit::MakeGramSchmidt</TT></A>).
<P>
So we are let to consider an <IMG
WIDTH="52" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img42.gif"
ALT="$ M\times L$"> matrix <!-- MATH
$\mathsf{F}$
-->
<IMG
WIDTH="13" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img43.gif"
ALT="$ \mathsf{F}$">, an
element of which is given by
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:Felem"></A><!-- MATH
\begin{equation}
f_{jl} = F_j\left(x_{1j} , x_{2j}, \ldots, x_{Nj}\right)
= F_l(\mathbf{x}_j)\, \quad\mbox{with}~j=1,2,\ldots,M,
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="260" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img44.gif"
ALT="$\displaystyle f_{jl} = F_j\left(x_{1j} , x_{2j}, \ldots, x_{Nj}\right) = F_l(\mathbf{x}_j) $"> with<IMG
WIDTH="120" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img45.gif"
ALT="$\displaystyle j=1,2,\ldots,M,$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(3)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
where <IMG
WIDTH="12" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img46.gif"
ALT="$ j$"> labels the <IMG
WIDTH="21" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img10.gif"
ALT="$ M$"> rows in the training sample and <IMG
WIDTH="9" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img47.gif"
ALT="$ l$"> labels
<IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img23.gif"
ALT="$ L$"> functions of <IMG
WIDTH="19" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img13.gif"
ALT="$ N$"> variables, and <IMG
WIDTH="53" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img48.gif"
ALT="$ L \leq M$">. That is, <IMG
WIDTH="23" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img49.gif"
ALT="$ f_{jl}$"> is
the term (or function) numbered <IMG
WIDTH="9" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img47.gif"
ALT="$ l$"> evaluated at the data point
<IMG
WIDTH="12" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img46.gif"
ALT="$ j$">. We have to normalise <!-- MATH
$\mathbf{x}_j$
-->
<IMG
WIDTH="20" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img15.gif"
ALT="$ \mathbf{x}_j$"> to <IMG
WIDTH="48" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img50.gif"
ALT="$ [-1,1]$"> for this to
succeed [<A
HREF="TMultiFimFit.html#wind72">5</A>]
(<A NAME="tex2html12"
HREF="
./TMultiDimFit.html#TMultiDimFit:MakeNormalized"><TT>TMultiDimFit::MakeNormalized</TT></A>). We then define a
matrix <!-- MATH
$\mathsf{W}$
-->
<IMG
WIDTH="19" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img51.gif"
ALT="$ \mathsf{W}$"> of which the columns <!-- MATH
$\mathbf{w}_j$
-->
<IMG
WIDTH="24" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img52.gif"
ALT="$ \mathbf{w}_j$"> are given by
<BR>
<DIV ALIGN="CENTER"><A NAME="eq:wj"></A><!-- MATH
\begin{eqnarray}
\mathbf{w}_1 &=& \mathbf{f}_1 = F_1\left(\mathbf x_1\right)\\
\mathbf{w}_l &=& \mathbf{f}_l - \sum^{l-1}_{k=1} \frac{\mathbf{f}_l \bullet
\mathbf{w}_k}{\mathbf{w}_k^2}\mathbf{w}_k\,.
\end{eqnarray}
-->
<TABLE CELLPADDING="0" ALIGN="CENTER" WIDTH="100%">
<TR VALIGN="MIDDLE"><TD NOWRAP ALIGN="RIGHT"><IMG
WIDTH="25" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img53.gif"
ALT="$\displaystyle \mathbf{w}_1$"></TD>
<TD WIDTH="10" ALIGN="CENTER" NOWRAP><IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img54.gif"
ALT="$\displaystyle =$"></TD>
<TD ALIGN="LEFT" NOWRAP><IMG
WIDTH="87" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img55.gif"
ALT="$\displaystyle \mathbf{f}_1 = F_1\left(\mathbf x_1\right)$"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
(4)</TD></TR>
<TR VALIGN="MIDDLE"><TD NOWRAP ALIGN="RIGHT"><IMG
WIDTH="22" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img56.gif"
ALT="$\displaystyle \mathbf{w}_l$"></TD>
<TD WIDTH="10" ALIGN="CENTER" NOWRAP><IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img54.gif"
ALT="$\displaystyle =$"></TD>
<TD ALIGN="LEFT" NOWRAP><IMG
WIDTH="138" HEIGHT="66" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img57.gif"
ALT="$\displaystyle \mathbf{f}_l - \sum^{l-1}_{k=1} \frac{\mathbf{f}_l \bullet
\mathbf{w}_k}{\mathbf{w}_k^2}\mathbf{w}_k .$"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
(5)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
and <!-- MATH
$\mathbf{w}_{l}$
-->
<IMG
WIDTH="22" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img58.gif"
ALT="$ \mathbf{w}_{l}$"> is the component of <!-- MATH
$\mathbf{f}_{l}$
-->
<IMG
WIDTH="15" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img59.gif"
ALT="$ \mathbf{f}_{l}$"> orthogonal
to <!-- MATH
$\mathbf{w}_{1}, \ldots, \mathbf{w}_{l-1}$
-->
<IMG
WIDTH="97" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img60.gif"
ALT="$ \mathbf{w}_{1}, \ldots, \mathbf{w}_{l-1}$">. Hence we obtain
[<A
HREF="TMultiFimFit.html#golub">3</A>],
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:worto"></A><!-- MATH
\begin{equation}
\mathbf{w}_k\bullet\mathbf{w}_l = 0\quad\mbox{if}~k \neq l\quad.
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="87" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img61.gif"
ALT="$\displaystyle \mathbf{w}_k\bullet\mathbf{w}_l = 0$"> if<IMG
WIDTH="65" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img62.gif"
ALT="$\displaystyle k \neq l\quad.$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(6)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
<P>
We now take as a new model <!-- MATH
$\mathsf{W}\mathbf{a}$
-->
<IMG
WIDTH="28" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img63.gif"
ALT="$ \mathsf{W}\mathbf{a}$">. We thus want to
minimize
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:S"></A><!-- MATH
\begin{equation}
S\equiv \left(\mathbf{D} - \mathsf{W}\mathbf{a}\right)^2\quad,
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="136" HEIGHT="38" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img64.gif"
ALT="$\displaystyle S\equiv \left(\mathbf{D} - \mathsf{W}\mathbf{a}\right)^2\quad,$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(7)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
where <!-- MATH
$\mathbf{D} = \left(D_1,\ldots,D_M\right)$
-->
<IMG
WIDTH="137" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img65.gif"
ALT="$ \mathbf{D} = \left(D_1,\ldots,D_M\right)$"> is a vector of the
dependent quantity in the sample. Differentiation with respect to
<IMG
WIDTH="19" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img66.gif"
ALT="$ a_j$"> gives, using (<A HREF="TMultiFimFit.html#eq:worto">6</A>),
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:dS"></A><!-- MATH
\begin{equation}
\mathbf{D}\bullet\mathbf{w}_l - a_l\mathbf{w}_l^2 = 0
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="134" HEIGHT="35" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img67.gif"
ALT="$\displaystyle \mathbf{D}\bullet\mathbf{w}_l - a_l\mathbf{w}_l^2 = 0$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(8)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
or
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:dS2"></A><!-- MATH
\begin{equation}
a_l = \frac{\mathbf{D}_l\bullet\mathbf{w}_l}{\mathbf{w}_l^2}
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="95" HEIGHT="51" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img68.gif"
ALT="$\displaystyle a_l = \frac{\mathbf{D}_l\bullet\mathbf{w}_l}{\mathbf{w}_l^2}$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(9)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
Let <IMG
WIDTH="21" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img69.gif"
ALT="$ S_j$"> be the sum of squares of residuals when taking <IMG
WIDTH="12" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img46.gif"
ALT="$ j$"> functions
into account. Then
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:Sj"></A><!-- MATH
\begin{equation}
S_l = \left[\mathbf{D} - \sum^l_{k=1} a_k\mathbf{w}_k\right]^2
= \mathbf{D}^2 - 2\mathbf{D} \sum^l_{k=1} a_k\mathbf{w}_k
+ \sum^l_{k=1} a_k^2\mathbf{w}_k^2
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="394" HEIGHT="72" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img70.gif"
ALT="$\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$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(10)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
Using (<A HREF="TMultiFimFit.html#eq:dS2">9</A>), we see that
<BR>
<DIV ALIGN="CENTER"><A NAME="eq:sj2"></A><!-- MATH
\begin{eqnarray}
S_l &=& \mathbf{D}^2 - 2 \sum^l_{k=1} a_k^2\mathbf{w}_k^2 +
\sum^j_{k=1} a_k^2\mathbf{w}_k^2\nonumber\\
&=& \mathbf{D}^2 - \sum^l_{k=1} a_k^2\mathbf{w}_k^2\nonumber\\
&=& \mathbf{D}^2 - \sum^l_{k=1} \frac{\left(\mathbf D\bullet \mathbf
w_k\right)}{\mathbf w_k^2}
\end{eqnarray}
-->
<TABLE CELLPADDING="0" ALIGN="CENTER" WIDTH="100%">
<TR VALIGN="MIDDLE"><TD NOWRAP ALIGN="RIGHT"><IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img71.gif"
ALT="$\displaystyle S_l$"></TD>
<TD WIDTH="10" ALIGN="CENTER" NOWRAP><IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img54.gif"
ALT="$\displaystyle =$"></TD>
<TD ALIGN="LEFT" NOWRAP><IMG
WIDTH="201" HEIGHT="67" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img72.gif"
ALT="$\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$"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
</TD></TR>
<TR VALIGN="MIDDLE"><TD NOWRAP ALIGN="RIGHT"> </TD>
<TD WIDTH="10" ALIGN="CENTER" NOWRAP><IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img54.gif"
ALT="$\displaystyle =$"></TD>
<TD ALIGN="LEFT" NOWRAP><IMG
WIDTH="108" HEIGHT="66" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img73.gif"
ALT="$\displaystyle \mathbf{D}^2 - \sum^l_{k=1} a_k^2\mathbf{w}_k^2$"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
</TD></TR>
<TR VALIGN="MIDDLE"><TD NOWRAP ALIGN="RIGHT"> </TD>
<TD WIDTH="10" ALIGN="CENTER" NOWRAP><IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img54.gif"
ALT="$\displaystyle =$"></TD>
<TD ALIGN="LEFT" NOWRAP><IMG
WIDTH="137" HEIGHT="66" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img74.gif"
ALT="$\displaystyle \mathbf{D}^2 - \sum^l_{k=1} \frac{\left(\mathbf D\bullet \mathbf
w_k\right)}{\mathbf w_k^2}$"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
(11)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
<P>
So for each new function <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$"> included in the model, we get a
reduction of the sum of squares of residuals of <!-- MATH
$a_l^2\mathbf{w}_l^2$
-->
<IMG
WIDTH="40" HEIGHT="33" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img75.gif"
ALT="$ a_l^2\mathbf{w}_l^2$">,
where <!-- MATH
$\mathbf{w}_l$
-->
<IMG
WIDTH="22" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img76.gif"
ALT="$ \mathbf{w}_l$"> is given by (<A HREF="TMultiFimFit.html#eq:wj">4</A>) and <IMG
WIDTH="17" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img77.gif"
ALT="$ a_l$"> by
(<A HREF="TMultiFimFit.html#eq:dS2">9</A>). Thus, using the Gram-Schmidt orthogonalisation, we
can decide if we want to include this function in the final model,
<I>before</I> the matrix inversion.
<P>
<H2><A NAME="SECTION00033000000000000000"></A>
<A NAME="sec:selectiondetail"></A><BR>
Function Selection Based on Residual
</H2>
<P>
Supposing that <IMG
WIDTH="42" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img78.gif"
ALT="$ L-1$"> steps of the procedure have been performed, the
problem now is to consider the <!-- MATH
$L^{\mbox{th}}$
-->
<IMG
WIDTH="31" HEIGHT="20" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img79.gif"
ALT="$ L^{\mbox{th}}$"> function.
<P>
The sum of squares of residuals can be written as
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:sums"></A><!-- MATH
\begin{equation}
S_L = \textbf{D}^T\bullet\textbf{D} -
\sum^L_{l=1}a^2_l\left(\textbf{w}_l^T\bullet\textbf{w}_l\right)
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="232" HEIGHT="65" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img80.gif"
ALT="$\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)$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(12)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
where the relation (<A HREF="TMultiFimFit.html#eq:dS2">9</A>) have been taken into account. The
contribution of the <!-- MATH
$L^{\mbox{th}}$
-->
<IMG
WIDTH="31" HEIGHT="20" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img79.gif"
ALT="$ L^{\mbox{th}}$"> function to the reduction of S, is
given by
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:dSN"></A><!-- MATH
\begin{equation}
\Delta S_L = a^2_L\left(\textbf{w}_L^T\bullet\textbf{w}_L\right)
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="154" HEIGHT="36" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img81.gif"
ALT="$\displaystyle \Delta S_L = a^2_L\left(\textbf{w}_L^T\bullet\textbf{w}_L\right)$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(13)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
<P>
Two test are now applied to decide whether this <!-- MATH
$L^{\mbox{th}}$
-->
<IMG
WIDTH="31" HEIGHT="20" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img79.gif"
ALT="$ L^{\mbox{th}}$">
function is to be included in the final expression, or not.
<P>
<H3><A NAME="SECTION00033100000000000000"></A>
<A NAME="testone"></A><BR>
Test 1
</H3>
<P>
Denoting by <IMG
WIDTH="43" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img82.gif"
ALT="$ H_{L-1}$"> the subspace spanned by
<!-- MATH
$\textbf{w}_1,\ldots,\textbf{w}_{L-1}$
-->
<IMG
WIDTH="102" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img83.gif"
ALT="$ \textbf{w}_1,\ldots,\textbf{w}_{L-1}$"> the function <!-- MATH
$\textbf{w}_L$
-->
<IMG
WIDTH="27" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img5.gif"
ALT="$ \textbf {w}_L$"> is
by construction (see (<A HREF="TMultiFimFit.html#eq:wj">4</A>)) the projection of the function
<IMG
WIDTH="24" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img84.gif"
ALT="$ F_L$"> onto the direction perpendicular to <IMG
WIDTH="43" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img82.gif"
ALT="$ H_{L-1}$">. Now, if the
length of <!-- MATH
$\textbf{w}_L$
-->
<IMG
WIDTH="27" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img5.gif"
ALT="$ \textbf {w}_L$"> (given by <!-- MATH
$\textbf{w}_L\bullet\textbf{w}_L$
-->
<IMG
WIDTH="65" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img85.gif"
ALT="$ \textbf{w}_L\bullet\textbf{w}_L$">)
is very small compared to the length of <!-- MATH
$\textbf{f}_L$
-->
<IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img3.gif"
ALT="$ \textbf {f}_L$"> this new
function can not contribute much to the reduction of the sum of
squares of residuals. The test consists then in calculating the angle
<IMG
WIDTH="12" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img1.gif"
ALT="$ \theta $"> between the two vectors <!-- MATH
$\textbf{w}_L$
-->
<IMG
WIDTH="27" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img5.gif"
ALT="$ \textbf {w}_L$"> and <!-- MATH
$\textbf{f}_L$
-->
<IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img3.gif"
ALT="$ \textbf {f}_L$">
(see also figure <A HREF="TMultiFimFit.html#fig:thetaphi">1</A>) and requiring that it's
<I>greater</I> then a threshold value which the user must set
(<A NAME="tex2html14"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetMinAngle"><TT>TMultiDimFit::SetMinAngle</TT></A>).
<P>
<P></P>
<DIV ALIGN="CENTER"><A NAME="fig:thetaphi"></A><A NAME="519"></A>
<TABLE>
<CAPTION ALIGN="BOTTOM"><STRONG>Figure 1:</STRONG>
(a) Angle <IMG
WIDTH="12" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img1.gif"
ALT="$ \theta $"> between <!-- MATH
$\textbf{w}_l$
-->
<IMG
WIDTH="22" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img2.gif"
ALT="$ \textbf {w}_l$"> and
<!-- MATH
$\textbf{f}_L$
-->
<IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img3.gif"
ALT="$ \textbf {f}_L$">, (b) angle <IMG
WIDTH="14" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img4.gif"
ALT="$ \phi $"> between <!-- MATH
$\textbf{w}_L$
-->
<IMG
WIDTH="27" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img5.gif"
ALT="$ \textbf {w}_L$"> and
<!-- MATH
$\textbf{D}$
-->
<IMG
WIDTH="18" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img6.gif"
ALT="$ \textbf {D}$"></CAPTION>
<TR><TD><IMG
WIDTH="466" HEIGHT="172" BORDER="0"
SRC="gif/multidimfit_img86.gif"
ALT="\begin{figure}\begin{center}
\begin{tabular}{p{.4\textwidth}p{.4\textwidth}}
\...
... \put(80,100){$\mathbf{D}$}
\end{picture} \end{tabular} \end{center}\end{figure}"></TD></TR>
</TABLE>
</DIV><P></P>
<P>
<H3><A NAME="SECTION00033200000000000000"></A> <A NAME="testtwo"></A><BR>
Test 2
</H3>
<P>
Let <!-- MATH
$\textbf{D}$
-->
<IMG
WIDTH="18" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img6.gif"
ALT="$ \textbf {D}$"> be the data vector to be fitted. As illustrated in
figure <A HREF="TMultiFimFit.html#fig:thetaphi">1</A>, the <!-- MATH
$L^{\mbox{th}}$
-->
<IMG
WIDTH="31" HEIGHT="20" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img79.gif"
ALT="$ L^{\mbox{th}}$"> function <!-- MATH
$\textbf{w}_L$
-->
<IMG
WIDTH="27" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img5.gif"
ALT="$ \textbf {w}_L$">
will contribute significantly to the reduction of <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$">, if the angle
<!-- MATH
$\phi^\prime$
-->
<IMG
WIDTH="18" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img87.gif"
ALT="$ \phi^\prime$"> between <!-- MATH
$\textbf{w}_L$
-->
<IMG
WIDTH="27" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img5.gif"
ALT="$ \textbf {w}_L$"> and <!-- MATH
$\textbf{D}$
-->
<IMG
WIDTH="18" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img6.gif"
ALT="$ \textbf {D}$"> is smaller than
an upper limit <IMG
WIDTH="14" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img4.gif"
ALT="$ \phi $">, defined by the user
(<A NAME="tex2html15"
HREF="
./TMultiDimFit.html#TMultiDimFit:SetMaxAngle"><TT>TMultiDimFit::SetMaxAngle</TT></A>)
<P>
However, the method automatically readjusts the value of this angle
while fitting is in progress, in order to make the selection criteria
less and less difficult to be fulfilled. The result is that the
functions contributing most to the reduction of <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> are chosen first
(<A NAME="tex2html16"
HREF="
./TMultiDimFit.html#TMultiDimFit:TestFunction"><TT>TMultiDimFit::TestFunction</TT></A>).
<P>
In case <IMG
WIDTH="14" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img4.gif"
ALT="$ \phi $"> isn't defined, an alternative method of
performing this second test is used: The <!-- MATH
$L^{\mbox{th}}$
-->
<IMG
WIDTH="31" HEIGHT="20" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img79.gif"
ALT="$ L^{\mbox{th}}$"> function
<!-- MATH
$\textbf{f}_L$
-->
<IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img3.gif"
ALT="$ \textbf {f}_L$"> is accepted if (refer also to equation (<A HREF="TMultiFimFit.html#eq:dSN">13</A>))
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:dSN2"></A><!-- MATH
\begin{equation}
\Delta S_L > \frac{S_{L-1}}{L_{max}-L}
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="129" HEIGHT="51" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img88.gif"
ALT="$\displaystyle \Delta S_L > \frac{S_{L-1}}{L_{max}-L}$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(14)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
where <IMG
WIDTH="40" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img89.gif"
ALT="$ S_{L-1}$"> is the sum of the <IMG
WIDTH="42" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img78.gif"
ALT="$ L-1$"> first residuals from the
<IMG
WIDTH="42" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img78.gif"
ALT="$ L-1$"> functions previously accepted; and <IMG
WIDTH="41" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img34.gif"
ALT="$ L_{max}$"> is the total number
of functions allowed in the final expression of the fit (defined by
user).
<P>
>From this we see, that by restricting <IMG
WIDTH="41" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img34.gif"
ALT="$ L_{max}$"> -- the number of
terms in the final model -- the fit is more difficult to perform,
since the above selection criteria is more limiting.
<P>
The more coefficients we evaluate, the more the sum of squares of
residuals <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> will be reduced. We can evaluate <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$"> before inverting
<!-- MATH
$\mathsf{B}$
-->
<IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img90.gif"
ALT="$ \mathsf{B}$"> as shown below.
<P>
<H2><A NAME="SECTION00034000000000000000">
Coefficients and Coefficient Errors</A>
</H2>
<P>
Having found a parameterization, that is the <IMG
WIDTH="19" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img24.gif"
ALT="$ F_l$">'s and <IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img23.gif"
ALT="$ L$">, that
minimizes <IMG
WIDTH="15" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img26.gif"
ALT="$ S$">, we still need to determine the coefficients
<IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img25.gif"
ALT="$ c_l$">. However, it's a feature of how we choose the significant
functions, that the evaluation of the <IMG
WIDTH="16" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img25.gif"
ALT="$ c_l$">'s becomes trivial
[<A
HREF="TMultiFimFit.html#wind72">5</A>]. To derive <!-- MATH
$\mathbf{c}$
-->
<IMG
WIDTH="12" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img91.gif"
ALT="$ \mathbf{c}$">, we first note that
equation (<A HREF="TMultiFimFit.html#eq:wj">4</A>) can be written as
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:FF"></A><!-- MATH
\begin{equation}
\mathsf{F} = \mathsf{W}\mathsf{B}
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="60" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img92.gif"
ALT="$\displaystyle \mathsf{F} = \mathsf{W}\mathsf{B}$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(15)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
where
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:bij"></A><!-- MATH
\begin{equation}
b_{ij} = \left\{\begin{array}{rcl}
\frac{\mathbf{f}_j \bullet \mathbf{w}_i}{\mathbf{w}_i^2}
& \mbox{if} & i < j\\
1 & \mbox{if} & i = j\\
0 & \mbox{if} & i > j
\end{array}\right.
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="187" HEIGHT="79" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img93.gif"
ALT="$\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.$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(16)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
Consequently, <!-- MATH
$\mathsf{B}$
-->
<IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img90.gif"
ALT="$ \mathsf{B}$"> is an upper triangle matrix, which can be
readily inverted. So we now evaluate
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:FFF"></A><!-- MATH
\begin{equation}
\mathsf{F}\mathsf{B}^{-1} = \mathsf{W}
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="77" HEIGHT="35" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img94.gif"
ALT="$\displaystyle \mathsf{F}\mathsf{B}^{-1} = \mathsf{W}$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(17)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
The model <!-- MATH
$\mathsf{W}\mathbf{a}$
-->
<IMG
WIDTH="28" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img63.gif"
ALT="$ \mathsf{W}\mathbf{a}$"> can therefore be written as
<!-- MATH
\begin{displaymath}
(\mathsf{F}\mathsf{B}^{-1})\mathbf{a} =
\mathsf{F}(\mathsf{B}^{-1}\mathbf{a})\,.
\end{displaymath}
-->
<P></P><DIV ALIGN="CENTER">
<IMG
WIDTH="148" HEIGHT="35" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img95.gif"
ALT="$\displaystyle (\mathsf{F}\mathsf{B}^{-1})\mathbf{a} =
\mathsf{F}(\mathsf{B}^{-1}\mathbf{a}) .
$">
</DIV><P></P>
The original model <!-- MATH
$\mathsf{F}\mathbf{c}$
-->
<IMG
WIDTH="21" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img96.gif"
ALT="$ \mathsf{F}\mathbf{c}$"> is therefore identical with
this if
<P></P>
<DIV ALIGN="CENTER"><A NAME="eq:id:cond"></A><!-- MATH
\begin{equation}
\mathbf{c} = \left(\mathsf{B}^{-1}\mathbf{a}\right) =
\left[\mathbf{a}^T\left(\mathsf{B}^{-1}\right)^T\right]^T\,.
\end{equation}
-->
<TABLE CELLPADDING="0" WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE">
<TD NOWRAP ALIGN="CENTER"><IMG
WIDTH="214" HEIGHT="51" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img97.gif"
ALT="$\displaystyle \mathbf{c} = \left(\mathsf{B}^{-1}\mathbf{a}\right) = \left[\mathbf{a}^T\left(\mathsf{B}^{-1}\right)^T\right]^T .$"></TD>
<TD NOWRAP WIDTH="10" ALIGN="RIGHT">
(18)</TD></TR>
</TABLE></DIV>
<BR CLEAR="ALL"><P></P>
The reason we use <!-- MATH
$\left(\mathsf{B}^{-1}\right)^T$
-->
<IMG
WIDTH="56" HEIGHT="42" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img98.gif"
ALT="$ \left(\mathsf{B}^{-1}\right)^T$"> rather then
<!-- MATH
$\mathsf{B}^{-1}$
-->
<IMG
WIDTH="32" HEIGHT="16" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img99.gif"
ALT="$ \mathsf{B}^{-1}$"> is to save storage, since
<!-- MATH
$\left(\mathsf{B}^{-1}\right)^T$
-->
<IMG
WIDTH="56" HEIGHT="42" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img98.gif"
ALT="$ \left(\mathsf{B}^{-1}\right)^T$"> can be stored in the same matrix as
<!-- MATH
$\mathsf{B}$
-->
<IMG
WIDTH="15" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img90.gif"
ALT="$ \mathsf{B}$">
(<A NAME="tex2html17"
HREF="
./TMultiDimFit.html#TMultiDimFit:MakeCoefficients"><TT>TMultiDimFit::MakeCoefficients</TT></A>). The errors in
the coefficients is calculated by inverting the curvature matrix
of the non-orthogonal functions <IMG
WIDTH="23" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img100.gif"
ALT="$ f_{lj}$"> [<A
HREF="TMultiFimFit.html#bevington">1</A>]
(<A NAME="tex2html18"
HREF="
./TMultiDimFit.html#TMultiDimFit:MakeCoefficientErrors"><TT>TMultiDimFit::MakeCoefficientErrors</TT></A>).
<P>
<H2><A NAME="SECTION00035000000000000000"></A>
<A NAME="sec:considerations"></A><BR>
Considerations
</H2>
<P>
It's important to realize that the training sample should be
representive of the problem at hand, in particular along the borders
of the region of interest. This is because the algorithm presented
here, is a <I>interpolation</I>, rahter then a <I>extrapolation</I>
[<A
HREF="TMultiFimFit.html#wind72">5</A>].
<P>
Also, the independent variables <IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img101.gif"
ALT="$ x_{i}$"> need to be linear
independent, since the procedure will perform poorly if they are not
[<A
HREF="TMultiFimFit.html#wind72">5</A>]. One can find an linear transformation from ones
original variables <IMG
WIDTH="16" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img102.gif"
ALT="$ \xi_{i}$"> to a set of linear independent variables
<IMG
WIDTH="18" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img101.gif"
ALT="$ x_{i}$">, using a <I>Principal Components Analysis</I>
<A NAME="tex2html19"
HREF="../TPrincipal.html">(see <TT>TPrincipal</TT>)</A>, and
then use the transformed variable as input to this class [<A
HREF="TMultiFimFit.html#wind72">5</A>]
[<A
HREF="TMultiFimFit.html#wind81">6</A>].
<P>
H. Wind also outlines a method for parameterising a multidimensional
dependence over a multidimensional set of variables. An example
of the method from [<A
HREF="TMultiFimFit.html#wind72">5</A>], is a follows (please refer to
[<A
HREF="TMultiFimFit.html#wind72">5</A>] for a full discussion):
<P>
<OL>
<LI>Define <!-- MATH
$\mathbf{P} = (P_1, \ldots, P_5)$
-->
<IMG
WIDTH="123" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img103.gif"
ALT="$ \mathbf{P} = (P_1, \ldots, P_5)$"> are the 5 dependent
quantities that define a track.
</LI>
<LI>Compute, for <IMG
WIDTH="21" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img10.gif"
ALT="$ M$"> different values of <!-- MATH
$\mathbf{P}$
-->
<IMG
WIDTH="17" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img104.gif"
ALT="$ \mathbf{P}$">, the tracks
through the magnetic field, and determine the corresponding
<!-- MATH
$\mathbf{x} = (x_1, \ldots, x_N)$
-->
<IMG
WIDTH="123" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img105.gif"
ALT="$ \mathbf{x} = (x_1, \ldots, x_N)$">.
</LI>
<LI>Use the simulated observations to determine, with a simple
approximation, the values of <!-- MATH
$\mathbf{P}_j$
-->
<IMG
WIDTH="23" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img106.gif"
ALT="$ \mathbf{P}_j$">. We call these values
<!-- MATH
$\mathbf{P}^\prime_j, j = 1, \ldots, M$
-->
<IMG
WIDTH="122" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img107.gif"
ALT="$ \mathbf{P}^\prime_j, j = 1, \ldots, M$">.
</LI>
<LI>Determine from <!-- MATH
$\mathbf{x}$
-->
<IMG
WIDTH="14" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img9.gif"
ALT="$ \mathbf{x}$"> a set of at least five relevant
coordinates <!-- MATH
$\mathbf{x}^\prime$
-->
<IMG
WIDTH="18" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img108.gif"
ALT="$ \mathbf{x}^\prime$">, using contrains, <I>or
alternative:</I>
</LI>
<LI>Perform a Principal Component Analysis (using
<A NAME="tex2html20"
HREF="../TPrincipal.html"><TT>TPrincipal</TT></A>), and use
to get a linear transformation
<!-- MATH
$\mathbf{x} \rightarrow \mathbf{x}^\prime$
-->
<IMG
WIDTH="53" HEIGHT="16" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img109.gif"
ALT="$ \mathbf{x} \rightarrow \mathbf{x}^\prime$">, so that
<!-- MATH
$\mathbf{x}^\prime$
-->
<IMG
WIDTH="18" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img108.gif"
ALT="$ \mathbf{x}^\prime$"> are constrained and linear independent.
</LI>
<LI>Perform a Principal Component Analysis on
<!-- MATH
$Q_i = P_i / P^\prime_i\, i = 1, \ldots, 5$
-->
<IMG
WIDTH="210" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img110.gif"
ALT="$ Q_i = P_i / P^prime_i i = 1, \ldots, 5$">, to get linear
indenpendent (among themselves, but not independent of
<!-- MATH
$\mathbf{x}$
-->
<IMG
WIDTH="14" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img9.gif"
ALT="$ \mathbf{x}$">) quantities <!-- MATH
$\mathbf{Q}^\prime$
-->
<IMG
WIDTH="22" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img111.gif"
ALT="$ \mathbf{Q}^\prime$">
</LI>
<LI>For each component <!-- MATH
$Q^\prime_i$
-->
<IMG
WIDTH="22" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img112.gif"
ALT="$ Q^\prime_i$"> make a mutlidimensional fit,
using <!-- MATH
$\mathbf{x}^\prime$
-->
<IMG
WIDTH="18" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img108.gif"
ALT="$ \mathbf{x}^\prime$"> as the variables, thus determing a set of
coefficents <!-- MATH
$\mathbf{c}_i$
-->
<IMG
WIDTH="17" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img113.gif"
ALT="$ \mathbf{c}_i$">.
</LI>
</OL>
<P>
To process data, using this parameterisation, do
<OL>
<LI>Test wether the observation <!-- MATH
$\mathbf{x}$
-->
<IMG
WIDTH="14" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img9.gif"
ALT="$ \mathbf{x}$"> within the domain of
the parameterization, using the result from the Principal Component
Analysis.
</LI>
<LI>Determine <!-- MATH
$\mathbf{P}^\prime$
-->
<IMG
WIDTH="21" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img114.gif"
ALT="$ \mathbf{P}^\prime$"> as before.
</LI>
<LI>Detetmine <!-- MATH
$\mathbf{x}^\prime$
-->
<IMG
WIDTH="18" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img108.gif"
ALT="$ \mathbf{x}^\prime$"> as before.
</LI>
<LI>Use the result of the fit to determind <!-- MATH
$\mathbf{Q}^\prime$
-->
<IMG
WIDTH="22" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img111.gif"
ALT="$ \mathbf{Q}^\prime$">.
</LI>
<LI>Transform back to <!-- MATH
$\mathbf{P}$
-->
<IMG
WIDTH="17" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img104.gif"
ALT="$ \mathbf{P}$"> from <!-- MATH
$\mathbf{Q}^\prime$
-->
<IMG
WIDTH="22" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img111.gif"
ALT="$ \mathbf{Q}^\prime$">, using
the result from the Principal Component Analysis.
</LI>
</OL>
<P>
<H2><A NAME="SECTION00036000000000000000"></A>
<A NAME="sec:testing"></A><BR>
Testing the parameterization
</H2>
<P>
The class also provides functionality for testing the, over the
training sample, found parameterization
(<A NAME="tex2html21"
HREF="
./TMultiDimFit.html#TMultiDimFit:Fit"><TT>TMultiDimFit::Fit</TT></A>). This is done by passing
the class a test sample of <IMG
WIDTH="25" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img115.gif"
ALT="$ M_t$"> tuples of the form <!-- MATH
$(\mathbf{x}_{t,j},
D_{t,j}, E_{t,j})$
-->
<IMG
WIDTH="111" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img116.gif"
ALT="$ (\mathbf{x}_{t,j},
D_{t,j}, E_{t,j})$">, where <!-- MATH
$\mathbf{x}_{t,j}$
-->
<IMG
WIDTH="29" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img117.gif"
ALT="$ \mathbf{x}_{t,j}$"> are the independent
variables, <IMG
WIDTH="33" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img118.gif"
ALT="$ D_{t,j}$"> the known, dependent quantity, and <IMG
WIDTH="31" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img119.gif"
ALT="$ E_{t,j}$"> is
the square error in <IMG
WIDTH="33" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img118.gif"
ALT="$ D_{t,j}$">
(<A NAME="tex2html22"
HREF="
./TMultiDimFit.html#TMultiDimFit:AddTestRow"><TT>TMultiDimFit::AddTestRow</TT></A>).
<P>
The parameterization is then evaluated at every <!-- MATH
$\mathbf{x}_t$
-->
<IMG
WIDTH="19" HEIGHT="28" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img120.gif"
ALT="$ \mathbf{x}_t$"> in the
test sample, and
<!-- MATH
\begin{displaymath}
S_t \equiv \sum_{j=1}^{M_t} \left(D_{t,j} -
D_p\left(\mathbf{x}_{t,j}\right)\right)^2
\end{displaymath}
-->
<P></P><DIV ALIGN="CENTER">
<IMG
WIDTH="194" HEIGHT="66" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img121.gif"
ALT="$\displaystyle S_t \equiv \sum_{j=1}^{M_t} \left(D_{t,j} -
D_p\left(\mathbf{x}_{t,j}\right)\right)^2
$">
</DIV><P></P>
is evaluated. The relative error over the test sample
<!-- MATH
\begin{displaymath}
R_t = \frac{S_t}{\sum_{j=1}^{M_t} D_{t,j}^2}
\end{displaymath}
-->
<P></P><DIV ALIGN="CENTER">
<IMG
WIDTH="118" HEIGHT="51" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img122.gif"
ALT="$\displaystyle R_t = \frac{S_t}{\sum_{j=1}^{M_t} D_{t,j}^2}
$">
</DIV><P></P>
should not be to low or high compared to <IMG
WIDTH="16" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
SRC="gif/multidimfit_img123.gif"
ALT="$ R$"> from the training
sample. Also, multiple correlation coefficient from both samples should
be fairly close, otherwise one of the samples is not representive of
the problem. A large difference in the reduced <IMG
WIDTH="21" HEIGHT="33" ALIGN="MIDDLE" BORDER="0"
SRC="gif/multidimfit_img124.gif"
ALT="$ \chi^2$"> over the two
samples indicate an over fit, and the maximum number of terms in the
parameterisation should be reduced.
<P>
It's possible to use <A NAME="tex2html23"
HREF="../TMinuit.html"><I>Minuit</I></A>
[<A
HREF="TMultiFimFit.html#minuit">4</A>] to further improve the fit, using the test sample.
<P>
<DIV ALIGN="RIGHT">
Christian Holm
<BR> November 2000, NBI
</DIV>
<P>
<H2><A NAME="SECTION00040000000000000000">
Bibliography</A>
</H2><DL COMPACT><DD><P></P><DT><A NAME="bevington">1</A>
<DD>
Philip R. Bevington and D. Keith Robinson.
<BR><EM>Data Reduction and Error Analysis for the Physical Sciences</EM>.
<BR>McGraw-Hill, 2 edition, 1992.
<P></P><DT><A NAME="mudifi">2</A>
<DD>
René Brun et al.
<BR>Mudifi.
<BR>Long writeup DD/75-23, CERN, 1980.
<P></P><DT><A NAME="golub">3</A>
<DD>
Gene H. Golub and Charles F. van Loan.
<BR><EM>Matrix Computations</EM>.
<BR>John Hopkins Univeristy Press, Baltimore, 3 edition, 1996.
<P></P><DT><A NAME="minuit">4</A>
<DD>
F. James.
<BR>Minuit.
<BR>Long writeup D506, CERN, 1998.
<P></P><DT><A NAME="wind72">5</A>
<DD>
H. Wind.
<BR>Function parameterization.
<BR>In <EM>Proceedings of the 1972 CERN Computing and Data Processing
School</EM>, volume 72-21 of <EM>Yellow report</EM>. CERN, 1972.
<P></P><DT><A NAME="wind81">6</A>
<DD>
H. Wind.
<BR>1. principal component analysis, 2. pattern recognition for track
finding, 3. interpolation and functional representation.
<BR>Yellow report EP/81-12, CERN, 1981.
</DL>
<pre>
*/
//End_Html
#include "Riostream.h"
#include "TMultiDimFit.h"
#include "TMath.h"
#include "TH1.h"
#include "TH2.h"
#include "TROOT.h"
#include "TBrowser.h"
#include "TDecompChol.h"
#define RADDEG (180. / TMath::Pi())
#define DEGRAD (TMath::Pi() / 180.)
#define HIST_XORIG 0
#define HIST_DORIG 1
#define HIST_XNORM 2
#define HIST_DSHIF 3
#define HIST_RX 4
#define HIST_RD 5
#define HIST_RTRAI 6
#define HIST_RTEST 7
#define PARAM_MAXSTUDY 1
#define PARAM_SEVERAL 2
#define PARAM_RELERR 3
#define PARAM_MAXTERMS 4
static void mdfHelper(int&, double*, double&, double*, int);
ClassImp(TMultiDimFit);
TMultiDimFit* TMultiDimFit::fgInstance = 0;
TMultiDimFit::TMultiDimFit()
{
fMeanQuantity = 0;
fMaxQuantity = 0;
fMinQuantity = 0;
fSumSqQuantity = 0;
fSumSqAvgQuantity = 0;
fPowerLimit = 1;
fMaxAngle = 0;
fMinAngle = 1;
fNVariables = 0;
fMaxVariables = 0;
fMinVariables = 0;
fSampleSize = 0;
fMaxAngle = 0;
fMinAngle = 0;
fPolyType = kMonomials;
fShowCorrelation = kFALSE;
fIsUserFunction = kFALSE;
fPowers = 0;
fMaxPowers = 0;
fMaxPowersFinal = 0;
fBinVarX = 100;
fBinVarY = 100;
fHistograms = 0;
fHistogramMask = 0;
fPowerIndex = 0;
fFunctionCodes = 0;
fFitter = 0;
}
TMultiDimFit::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)
{
fgInstance = this;
fMeanQuantity = 0;
fMaxQuantity = 0;
fMinQuantity = 0;
fSumSqQuantity = 0;
fSumSqAvgQuantity = 0;
fPowerLimit = 1;
fMaxAngle = 0;
fMinAngle = 1;
fNVariables = dimension;
fMaxVariables = 0;
fMinVariables = 0;
fSampleSize = 0;
fTestSampleSize = 0;
fMinRelativeError = 0.01;
fError = 0;
fTestError = 0;
fPrecision = 0;
fTestPrecision = 0;
fParameterisationCode = 0;
fPolyType = type;
fShowCorrelation = kFALSE;
fIsVerbose = kFALSE;
TString opt = option;
opt.ToLower();
if (opt.Contains("k")) fShowCorrelation = kTRUE;
if (opt.Contains("v")) fIsVerbose = kTRUE;
fIsUserFunction = kFALSE;
fBinVarX = 100;
fBinVarY = 100;
fHistograms = 0;
fHistogramMask = 0;
fPowerIndex = 0;
fFunctionCodes = 0;
fPowers = 0;
fMaxPowers = new Int_t[dimension];
fMaxPowersFinal = new Int_t[dimension];
fFitter = 0;
}
TMultiDimFit::~TMultiDimFit()
{
delete [] fPowers;
delete [] fMaxPowers;
delete [] fMaxPowersFinal;
delete [] fPowerIndex;
delete [] fFunctionCodes;
if (fHistograms) fHistograms->Clear("nodelete");
delete fHistograms;
}
void TMultiDimFit::AddRow(const Double_t *x, Double_t D, Double_t E)
{
if (!x)
return;
if (++fSampleSize == 1) {
fMeanQuantity = D;
fMaxQuantity = D;
fMinQuantity = D;
}
else {
fMeanQuantity *= 1 - 1./Double_t(fSampleSize);
fMeanQuantity += D / Double_t(fSampleSize);
fSumSqQuantity += D * D;
if (D >= fMaxQuantity) fMaxQuantity = D;
if (D <= fMinQuantity) fMinQuantity = D;
}
Int_t size = fQuantity.GetNrows();
if (fSampleSize > size) {
fQuantity.ResizeTo(size + size/2);
fSqError.ResizeTo(size + size/2);
}
fQuantity(fSampleSize-1) = D;
fSqError(fSampleSize-1) = (E == 0 ? D : E);
size = fVariables.GetNrows();
if (fSampleSize * fNVariables > size)
fVariables.ResizeTo(size + size/2);
Int_t i,j;
for (i = 0; i < fNVariables; i++) {
if (fSampleSize == 1) {
fMeanVariables(i) = x[i];
fMaxVariables(i) = x[i];
fMinVariables(i) = x[i];
}
else {
fMeanVariables(i) *= 1 - 1./Double_t(fSampleSize);
fMeanVariables(i) += x[i] / Double_t(fSampleSize);
if (x[i] >= fMaxVariables(i)) fMaxVariables(i) = x[i];
if (x[i] <= fMinVariables(i)) fMinVariables(i) = x[i];
}
j = (fSampleSize-1) * fNVariables + i;
fVariables(j) = x[i];
}
}
void TMultiDimFit::AddTestRow(const Double_t *x, Double_t D, Double_t E)
{
if (fTestSampleSize++ == 0) {
fTestQuantity.ResizeTo(fNVariables);
fTestSqError.ResizeTo(fNVariables);
fTestVariables.ResizeTo(fNVariables * 100);
}
Int_t size = fTestQuantity.GetNrows();
if (fTestSampleSize > size) {
fTestQuantity.ResizeTo(size + size/2);
fTestSqError.ResizeTo(size + size/2);
}
fTestQuantity(fTestSampleSize-1) = D;
fTestSqError(fTestSampleSize-1) = (E == 0 ? D : E);
size = fTestVariables.GetNrows();
if (fTestSampleSize * fNVariables > size)
fTestVariables.ResizeTo(size + size/2);
Int_t i,j;
for (i = 0; i < fNVariables; i++) {
j = fNVariables * (fTestSampleSize - 1) + i;
fTestVariables(j) = x[i];
if (x[i] > fMaxVariables(i))
Warning("AddTestRow", "variable %d (row: %d) too large: %f > %f",
i, fTestSampleSize, x[i], fMaxVariables(i));
if (x[i] < fMinVariables(i))
Warning("AddTestRow", "variable %d (row: %d) too small: %f < %f",
i, fTestSampleSize, x[i], fMinVariables(i));
}
}
void TMultiDimFit::Browse(TBrowser* b)
{
if (fHistograms) {
TIter next(fHistograms);
TH1* h = 0;
while ((h = (TH1*)next()))
b->Add(h,h->GetName());
}
if (fVariables.IsValid())
b->Add(&fVariables, "Variables (Training)");
if (fQuantity.IsValid())
b->Add(&fQuantity, "Quantity (Training)");
if (fSqError.IsValid())
b->Add(&fSqError, "Error (Training)");
if (fMeanVariables.IsValid())
b->Add(&fMeanVariables, "Mean of Variables (Training)");
if (fMaxVariables.IsValid())
b->Add(&fMaxVariables, "Mean of Variables (Training)");
if (fMinVariables.IsValid())
b->Add(&fMinVariables, "Min of Variables (Training)");
if (fTestVariables.IsValid())
b->Add(&fTestVariables, "Variables (Test)");
if (fTestQuantity.IsValid())
b->Add(&fTestQuantity, "Quantity (Test)");
if (fTestSqError.IsValid())
b->Add(&fTestSqError, "Error (Test)");
if (fFunctions.IsValid())
b->Add(&fFunctions, "Functions");
if(fCoefficients.IsValid())
b->Add(&fCoefficients,"Coefficients");
if(fCoefficientsRMS.IsValid())
b->Add(&fCoefficientsRMS,"Coefficients Errors");
if (fOrthFunctions.IsValid())
b->Add(&fOrthFunctions, "Orthogonal Functions");
if (fOrthFunctionNorms.IsValid())
b->Add(&fOrthFunctionNorms, "Orthogonal Functions Norms");
if (fResiduals.IsValid())
b->Add(&fResiduals, "Residuals");
if(fOrthCoefficients.IsValid())
b->Add(&fOrthCoefficients,"Orthogonal Coefficients");
if (fOrthCurvatureMatrix.IsValid())
b->Add(&fOrthCurvatureMatrix,"Orthogonal curvature matrix");
if(fCorrelationMatrix.IsValid())
b->Add(&fCorrelationMatrix,"Correlation Matrix");
if (fFitter)
b->Add(fFitter, fFitter->GetName());
}
void TMultiDimFit::Clear(Option_t *option)
{
Int_t i, j, n = fNVariables, m = fMaxFunctions;
fQuantity.Zero();
fSqError.Zero();
fMeanQuantity = 0;
fMaxQuantity = 0;
fMinQuantity = 0;
fSumSqQuantity = 0;
fSumSqAvgQuantity = 0;
fVariables.Zero();
fNVariables = 0;
fSampleSize = 0;
fMeanVariables.Zero();
fMaxVariables.Zero();
fMinVariables.Zero();
fTestQuantity.Zero();
fTestSqError.Zero();
fTestVariables.Zero();
fTestSampleSize = 0;
fFunctions.Zero();
fMaxFunctions = 0;
fMaxStudy = 0;
fOrthFunctions.Zero();
fOrthFunctionNorms.Zero();
fMinRelativeError = 0;
fMinAngle = 0;
fMaxAngle = 0;
fMaxTerms = 0;
for (i = 0; i < n; i++) {
fMaxPowers[i] = 0;
fMaxPowersFinal[i] = 0;
for (j = 0; j < m; j++)
fPowers[i * n + j] = 0;
}
fPowerLimit = 0;
fMaxResidual = 0;
fMinResidual = 0;
fMaxResidualRow = 0;
fMinResidualRow = 0;
fSumSqResidual = 0;
fNCoefficients = 0;
fOrthCoefficients = 0;
fOrthCurvatureMatrix = 0;
fRMS = 0;
fCorrelationMatrix.Zero();
fError = 0;
fTestError = 0;
fPrecision = 0;
fTestPrecision = 0;
fCoefficients.Zero();
fCoefficientsRMS.Zero();
fResiduals.Zero();
fHistograms->Clear(option);
fPolyType = kMonomials;
fShowCorrelation = kFALSE;
fIsUserFunction = kFALSE;
}
Double_t TMultiDimFit::Eval(const Double_t *x, const Double_t* coeff) const
{
Double_t returnValue = fMeanQuantity;
Double_t term = 0;
Int_t i, j;
for (i = 0; i < fNCoefficients; i++) {
term = (coeff ? coeff[i] : fCoefficients(i));
for (j = 0; j < fNVariables; j++) {
Int_t p = fPowers[fPowerIndex[i] * fNVariables + j];
Double_t y = 1 + 2. / (fMaxVariables(j) - fMinVariables(j))
* (x[j] - fMaxVariables(j));
term *= EvalFactor(p,y);
}
returnValue += term;
}
return returnValue;
}
Double_t TMultiDimFit::EvalError(const Double_t *x, const Double_t* coeff) const
{
Double_t returnValue = 0;
Double_t term = 0;
Int_t i, j;
for (i = 0; i < fNCoefficients; i++) {
}
for (i = 0; i < fNCoefficients; i++) {
term = (coeff ? coeff[i] : fCoefficientsRMS(i));
for (j = 0; j < fNVariables; j++) {
Int_t p = fPowers[fPowerIndex[i] * fNVariables + j];
Double_t y = 1 + 2. / (fMaxVariables(j) - fMinVariables(j))
* (x[j] - fMaxVariables(j));
term *= EvalFactor(p,y);
}
returnValue += term*term;
}
returnValue = sqrt(returnValue);
return returnValue;
}
Double_t TMultiDimFit::EvalControl(const Int_t *iv) const
{
Double_t s = 0;
Double_t epsilon = 1e-6;
for (Int_t i = 0; i < fNVariables; i++) {
if (fMaxPowers[i] != 1)
s += (epsilon + iv[i] - 1) / (epsilon + fMaxPowers[i] - 1);
}
return s;
}
Double_t TMultiDimFit::EvalFactor(Int_t p, Double_t x) const
{
Int_t i = 0;
Double_t p1 = 1;
Double_t p2 = 0;
Double_t p3 = 0;
Double_t r = 0;
switch(p) {
case 1:
r = 1;
break;
case 2:
r = x;
break;
default:
p2 = x;
for (i = 3; i <= p; i++) {
p3 = p2 * x;
if (fPolyType == kLegendre)
p3 = ((2 * i - 3) * p2 * x - (i - 2) * p1) / (i - 1);
else if (fPolyType == kChebyshev)
p3 = 2 * x * p2 - p1;
p1 = p2;
p2 = p3;
}
r = p3;
}
return r;
}
void TMultiDimFit::FindParameterization(Option_t *)
{
MakeNormalized();
MakeCandidates();
MakeParameterization();
MakeCoefficients();
MakeCoefficientErrors();
MakeCorrelation();
}
void TMultiDimFit::Fit(Option_t *option)
{
Int_t i, j;
Double_t* x = new Double_t[fNVariables];
Double_t sumSqD = 0;
Double_t sumD = 0;
Double_t sumSqR = 0;
Double_t sumR = 0;
for (i = 0; i < fTestSampleSize; i++) {
for (j = 0; j < fNVariables; j++)
x[j] = fTestVariables(i * fNVariables + j);
Double_t res = fTestQuantity(i) - Eval(x);
sumD += fTestQuantity(i);
sumSqD += fTestQuantity(i) * fTestQuantity(i);
sumR += res;
sumSqR += res * res;
if (TESTBIT(fHistogramMask,HIST_RTEST))
((TH1D*)fHistograms->FindObject("res_test"))->Fill(res);
}
Double_t dAvg = sumSqD - (sumD * sumD) / fTestSampleSize;
Double_t rAvg = sumSqR - (sumR * sumR) / fTestSampleSize;
fTestCorrelationCoeff = (dAvg - rAvg) / dAvg;
fTestError = sumSqR;
fTestPrecision = sumSqR / sumSqD;
TString opt(option);
opt.ToLower();
if (!opt.Contains("m"))
MakeChi2();
if (fNCoefficients * 50 > fTestSampleSize)
Warning("Fit", "test sample is very small");
if (!opt.Contains("m"))
return;
fFitter = TVirtualFitter::Fitter(0,fNCoefficients);
fFitter->SetFCN(mdfHelper);
const Int_t maxArgs = 16;
Int_t args = 1;
Double_t* arglist = new Double_t[maxArgs];
arglist[0] = -1;
fFitter->ExecuteCommand("SET PRINT",arglist,args);
for (i = 0; i < fNCoefficients; i++) {
Double_t startVal = fCoefficients(i);
Double_t startErr = fCoefficientsRMS(i);
fFitter->SetParameter(i, Form("coeff%02d",i),
startVal, startErr, 0, 0);
}
args = 1;
fFitter->ExecuteCommand("MIGRAD",arglist,args);
for (i = 0; i < fNCoefficients; i++) {
Double_t val = 0, err = 0, low = 0, high = 0;
fFitter->GetParameter(i, Form("coeff%02d",i),
val, err, low, high);
fCoefficients(i) = val;
fCoefficientsRMS(i) = err;
}
}
void TMultiDimFit::MakeCandidates()
{
Int_t i = 0;
Int_t j = 0;
Int_t k = 0;
fMaxFuncNV = fNVariables * fMaxFunctions;
Int_t *powers = new Int_t[fMaxFuncNV];
Double_t* control = new Double_t[fMaxFunctions];
Int_t *iv = new Int_t[fNVariables];
for (i = 0; i < fNVariables; i++)
iv[i] = 1;
if (!fIsUserFunction) {
Int_t numberFunctions = 0;
Int_t maxNumberFunctions = 1;
for (i = 0; i < fNVariables; i++)
maxNumberFunctions *= fMaxPowers[i];
while (kTRUE) {
Double_t s = EvalControl(iv);
if (s <= fPowerLimit) {
if (Select(iv)) {
numberFunctions++;
if (numberFunctions > fMaxFunctions)
break;
control[numberFunctions-1] = Int_t(1.0e+6*s);
for (i = 0; i < fNVariables; i++) {
j = (numberFunctions - 1) * fNVariables + i;
powers[j] = iv[i];
}
}
}
for (i = 0; i < fNVariables; i++)
if (iv[i] < fMaxPowers[i])
break;
if (i == fNVariables) {
fMaxFunctions = numberFunctions;
break;
}
iv[i]++;
for (j = 0; j < i; j++)
iv[j] = 1;
}
}
else {
for (i = 0; i < fMaxFunctions; i++) {
for (j = 0; j < fNVariables; j++) {
powers[i * fNVariables + j] = fPowers[i * fNVariables + j];
iv[j] = fPowers[i * fNVariables + j];
}
control[i] = Int_t(1.0e+6*EvalControl(iv));
}
}
Int_t *order = new Int_t[fMaxFunctions];
for (i = 0; i < fMaxFunctions; i++)
order[i] = i;
fMaxFuncNV = fMaxFunctions * fNVariables;
fPowers = new Int_t[fMaxFuncNV];
for (i = 0; i < fMaxFunctions; i++) {
Double_t x = control[i];
Int_t l = order[i];
k = i;
for (j = i; j < fMaxFunctions; j++) {
if (control[j] <= x) {
x = control[j];
l = order[j];
k = j;
}
}
if (k != i) {
control[k] = control[i];
control[i] = x;
order[k] = order[i];
order[i] = l;
}
}
for (i = 0; i < fMaxFunctions; i++)
for (j = 0; j < fNVariables; j++)
fPowers[i * fNVariables + j] = powers[order[i] * fNVariables + j];
delete [] control;
delete [] powers;
delete [] order;
delete [] iv;
}
Double_t TMultiDimFit::MakeChi2(const Double_t* coeff)
{
fChi2 = 0;
Int_t i, j;
Double_t* x = new Double_t[fNVariables];
for (i = 0; i < fTestSampleSize; i++) {
for (j = 0; j < fNVariables; j++)
x[j] = fTestVariables(i * fNVariables + j);
Double_t f = Eval(x,coeff);
fChi2 += 1. / TMath::Max(fTestSqError(i),1e-20)
* (fTestQuantity(i) - f) * (fTestQuantity(i) - f);
}
delete [] x;
return fChi2;
}
void TMultiDimFit::MakeCode(const char* filename, Option_t *option)
{
TString outName(filename);
if (!outName.EndsWith(".C") && !outName.EndsWith(".cxx"))
outName += ".C";
MakeRealCode(outName.Data(),"",option);
}
void TMultiDimFit::MakeCoefficientErrors()
{
Int_t i = 0;
Int_t j = 0;
Int_t k = 0;
TVectorD iF(fSampleSize);
TVectorD jF(fSampleSize);
fCoefficientsRMS.ResizeTo(fNCoefficients);
TMatrixDSym curvatureMatrix(fNCoefficients);
for (i = 0; i < fNCoefficients; i++) {
iF = TMatrixDRow(fFunctions,i);
for (j = 0; j <= i; j++) {
jF = TMatrixDRow(fFunctions,j);
for (k = 0; k < fSampleSize; k++)
curvatureMatrix(i,j) +=
1 / TMath::Max(fSqError(k), 1e-20) * iF(k) * jF(k);
curvatureMatrix(j,i) = curvatureMatrix(i,j);
}
}
fChi2 = 0;
for (i = 0; i < fSampleSize; i++) {
Double_t f = 0;
for (j = 0; j < fNCoefficients; j++)
f += fCoefficients(j) * fFunctions(j,i);
fChi2 += 1. / TMath::Max(fSqError(i),1e-20) * (fQuantity(i) - f)
* (fQuantity(i) - f);
}
const TVectorD diag = TMatrixDDiag_const(curvatureMatrix);
curvatureMatrix.NormByDiag(diag);
TDecompChol chol(curvatureMatrix);
if (!chol.Decompose())
Error("MakeCoefficientErrors", "curvature matrix is singular");
chol.Invert(curvatureMatrix);
curvatureMatrix.NormByDiag(diag);
for (i = 0; i < fNCoefficients; i++)
fCoefficientsRMS(i) = TMath::Sqrt(curvatureMatrix(i,i));
}
void TMultiDimFit::MakeCoefficients()
{
Int_t i = 0, j = 0;
Int_t col = 0, row = 0;
for (col = 1; col < fNCoefficients; col++) {
for (row = col - 1; row > -1; row--) {
fOrthCurvatureMatrix(row,col) = 0;
for (i = row; i <= col ; i++)
fOrthCurvatureMatrix(row,col) -=
fOrthCurvatureMatrix(i,row)
* fOrthCurvatureMatrix(i,col);
}
}
fCoefficients.ResizeTo(fNCoefficients);
for (i = 0; i < fNCoefficients; i++) {
Double_t sum = 0;
for (j = i; j < fNCoefficients; j++)
sum += fOrthCurvatureMatrix(i,j) * fOrthCoefficients(j);
fCoefficients(i) = sum;
}
fResiduals.ResizeTo(fSampleSize);
for (i = 0; i < fSampleSize; i++)
fResiduals(i) = fQuantity(i);
for (i = 0; i < fNCoefficients; i++)
for (j = 0; j < fSampleSize; j++)
fResiduals(j) -= fCoefficients(i) * fFunctions(i,j);
fMinResidual = 10e10;
fMaxResidual = -10e10;
Double_t sqRes = 0;
for (i = 0; i < fSampleSize; i++){
sqRes += fResiduals(i) * fResiduals(i);
if (fResiduals(i) <= fMinResidual) {
fMinResidual = fResiduals(i);
fMinResidualRow = i;
}
if (fResiduals(i) >= fMaxResidual) {
fMaxResidual = fResiduals(i);
fMaxResidualRow = i;
}
}
fCorrelationCoeff = fSumSqResidual / fSumSqAvgQuantity;
fPrecision = TMath::Sqrt(sqRes / fSumSqQuantity);
if (TESTBIT(fHistogramMask,HIST_RD) ||
TESTBIT(fHistogramMask,HIST_RTRAI) ||
TESTBIT(fHistogramMask,HIST_RX)) {
for (i = 0; i < fSampleSize; i++) {
if (TESTBIT(fHistogramMask,HIST_RD))
((TH2D*)fHistograms->FindObject("res_d"))->Fill(fQuantity(i),
fResiduals(i));
if (TESTBIT(fHistogramMask,HIST_RTRAI))
((TH1D*)fHistograms->FindObject("res_train"))->Fill(fResiduals(i));
if (TESTBIT(fHistogramMask,HIST_RX))
for (j = 0; j < fNVariables; j++)
((TH2D*)fHistograms->FindObject(Form("res_x_%d",j)))
->Fill(fVariables(i * fNVariables + j),fResiduals(i));
}
}
}
void TMultiDimFit::MakeCorrelation()
{
if (!fShowCorrelation)
return;
fCorrelationMatrix.ResizeTo(fNVariables,fNVariables+1);
Double_t d2 = 0;
Double_t ddotXi = 0;
Double_t xiNorm = 0;
Double_t xidotXj = 0;
Double_t xjNorm = 0;
Int_t i, j, k, l, m;
for (i = 0; i < fSampleSize; i++)
d2 += fQuantity(i) * fQuantity(i);
for (i = 0; i < fNVariables; i++) {
ddotXi = 0.;
xiNorm = 0.;
for (j = 0; j< fSampleSize; j++) {
k = j * fNVariables + i;
ddotXi += fQuantity(j) * (fVariables(k) - fMeanVariables(i));
xiNorm += (fVariables(k) - fMeanVariables(i))
* (fVariables(k) - fMeanVariables(i));
}
fCorrelationMatrix(i,0) = ddotXi / TMath::Sqrt(d2 * xiNorm);
for (j = 0; j < i; j++) {
xidotXj = 0.;
xjNorm = 0.;
for (k = 0; k < fSampleSize; k++) {
l = k * fNVariables + j;
m = k * fNVariables + i;
xidotXj += (fVariables(m) - fMeanVariables(i))
* (fVariables(l) - fMeanVariables(j));
xjNorm += (fVariables(l) - fMeanVariables(j))
* (fVariables(l) - fMeanVariables(j));
}
fCorrelationMatrix(i,j+1) = xidotXj / TMath::Sqrt(xiNorm * xjNorm);
}
}
}
Double_t TMultiDimFit::MakeGramSchmidt(Int_t function)
{
Double_t f2 = 0;
fOrthCoefficients(fNCoefficients) = 0;
fOrthFunctionNorms(fNCoefficients) = 0;
Int_t j = 0;
Int_t k = 0;
for (j = 0; j < fSampleSize; j++) {
fFunctions(fNCoefficients, j) = 1;
fOrthFunctions(fNCoefficients, j) = 0;
for (k = 0; k < fNVariables; k++) {
Int_t p = fPowers[function * fNVariables + k];
Double_t x = fVariables(j * fNVariables + k);
fFunctions(fNCoefficients, j) *= EvalFactor(p,x);
}
f2 += fFunctions(fNCoefficients,j) * fFunctions(fNCoefficients,j);
fOrthFunctions(fNCoefficients, j) = fFunctions(fNCoefficients, j);
}
for (j = 0; j < fNCoefficients; j++) {
Double_t fdw = 0;
for (k = 0; k < fSampleSize; k++) {
fdw += fFunctions(fNCoefficients, k) * fOrthFunctions(j,k)
/ fOrthFunctionNorms(j);
}
fOrthCurvatureMatrix(fNCoefficients,j) = fdw;
for (k = 0; k < fSampleSize; k++)
fOrthFunctions(fNCoefficients,k) -= fdw * fOrthFunctions(j,k);
}
for (j = 0; j < fSampleSize; j++) {
fOrthFunctionNorms(fNCoefficients) +=
fOrthFunctions(fNCoefficients,j)
* fOrthFunctions(fNCoefficients,j);
fOrthCoefficients(fNCoefficients) += fQuantity(j)
* fOrthFunctions(fNCoefficients, j);
}
if (!fIsUserFunction)
if (TMath::Sqrt(fOrthFunctionNorms(fNCoefficients) / (f2 + 1e-10))
< TMath::Sin(fMinAngle*DEGRAD))
return 0;
fOrthCurvatureMatrix(fNCoefficients,fNCoefficients) = 1;
Double_t b = fOrthCoefficients(fNCoefficients);
fOrthCoefficients(fNCoefficients) /= fOrthFunctionNorms(fNCoefficients);
Double_t dResidur = fOrthCoefficients(fNCoefficients) * b;
return dResidur;
}
void TMultiDimFit::MakeHistograms(Option_t *option)
{
TString opt(option);
opt.ToLower();
if (opt.Length() < 1)
return;
if (!fHistograms)
fHistograms = new TList;
Int_t i = 0;
if (opt.Contains("x") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_XORIG);
for (i = 0; i < fNVariables; i++)
if (!fHistograms->FindObject(Form("x_%d_orig",i)))
fHistograms->Add(new TH1D(Form("x_%d_orig",i),
Form("Original variable # %d",i),
fBinVarX, fMinVariables(i),
fMaxVariables(i)));
}
if (opt.Contains("d") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_DORIG);
if (!fHistograms->FindObject("d_orig"))
fHistograms->Add(new TH1D("d_orig", "Original Quantity",
fBinVarX, fMinQuantity, fMaxQuantity));
}
if (opt.Contains("n") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_XNORM);
for (i = 0; i < fNVariables; i++)
if (!fHistograms->FindObject(Form("x_%d_norm",i)))
fHistograms->Add(new TH1D(Form("x_%d_norm",i),
Form("Normalized variable # %d",i),
fBinVarX, -1,1));
}
if (opt.Contains("s") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_DSHIF);
if (!fHistograms->FindObject("d_shifted"))
fHistograms->Add(new TH1D("d_shifted", "Shifted Quantity",
fBinVarX, fMinQuantity - fMeanQuantity,
fMaxQuantity - fMeanQuantity));
}
if (opt.Contains("r1") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_RX);
for (i = 0; i < fNVariables; i++)
if (!fHistograms->FindObject(Form("res_x_%d",i)))
fHistograms->Add(new TH2D(Form("res_x_%d",i),
Form("Computed residual versus x_%d", i),
fBinVarX, -1, 1,
fBinVarY,
fMinQuantity - fMeanQuantity,
fMaxQuantity - fMeanQuantity));
}
if (opt.Contains("r2") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_RD);
if (!fHistograms->FindObject("res_d"))
fHistograms->Add(new TH2D("res_d",
"Computed residuals vs Quantity",
fBinVarX,
fMinQuantity - fMeanQuantity,
fMaxQuantity - fMeanQuantity,
fBinVarY,
fMinQuantity - fMeanQuantity,
fMaxQuantity - fMeanQuantity));
}
if (opt.Contains("r3") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_RTRAI);
if (!fHistograms->FindObject("res_train"))
fHistograms->Add(new TH1D("res_train",
"Computed residuals over training sample",
fBinVarX, fMinQuantity - fMeanQuantity,
fMaxQuantity - fMeanQuantity));
}
if (opt.Contains("r4") || opt.Contains("a")) {
SETBIT(fHistogramMask,HIST_RTEST);
if (!fHistograms->FindObject("res_test"))
fHistograms->Add(new TH1D("res_test",
"Distribution of residuals from test",
fBinVarX,fMinQuantity - fMeanQuantity,
fMaxQuantity - fMeanQuantity));
}
}
void TMultiDimFit::MakeMethod(const Char_t* classname, Option_t* option)
{
MakeRealCode(Form("%sMDF.cxx", classname), classname, option);
}
void TMultiDimFit::MakeNormalized()
{
Int_t i = 0;
Int_t j = 0;
Int_t k = 0;
for (i = 0; i < fSampleSize; i++) {
if (TESTBIT(fHistogramMask,HIST_DORIG))
((TH1D*)fHistograms->FindObject("d_orig"))->Fill(fQuantity(i));
fQuantity(i) -= fMeanQuantity;
fSumSqAvgQuantity += fQuantity(i) * fQuantity(i);
if (TESTBIT(fHistogramMask,HIST_DSHIF))
((TH1D*)fHistograms->FindObject("d_shifted"))->Fill(fQuantity(i));
for (j = 0; j < fNVariables; j++) {
Double_t range = 1. / (fMaxVariables(j) - fMinVariables(j));
k = i * fNVariables + j;
if (TESTBIT(fHistogramMask,HIST_XORIG))
((TH1D*)fHistograms->FindObject(Form("x_%d_orig",j)))
->Fill(fVariables(k));
fVariables(k) = 1 + 2 * range * (fVariables(k) - fMaxVariables(j));
if (TESTBIT(fHistogramMask,HIST_XNORM))
((TH1D*)fHistograms->FindObject(Form("x_%d_norm",j)))
->Fill(fVariables(k));
}
}
fMaxQuantity -= fMeanQuantity;
fMinQuantity -= fMeanQuantity;
for (i = 0; i < fNVariables; i++) {
Double_t range = 1. / (fMaxVariables(i) - fMinVariables(i));
fMeanVariables(i) = 1 + 2 * range * (fMeanVariables(i)
- fMaxVariables(i));
}
}
void TMultiDimFit::MakeParameterization()
{
Int_t i = -1;
Int_t j = 0;
Int_t k = 0;
Int_t maxPass = 3;
Int_t studied = 0;
Double_t squareResidual = fSumSqAvgQuantity;
fNCoefficients = 0;
fSumSqResidual = fSumSqAvgQuantity;
fFunctions.ResizeTo(fMaxTerms,fSampleSize);
fOrthFunctions.ResizeTo(fMaxTerms,fSampleSize);
fOrthFunctionNorms.ResizeTo(fMaxTerms);
fOrthCoefficients.ResizeTo(fMaxTerms);
fOrthCurvatureMatrix.ResizeTo(fMaxTerms,fMaxTerms);
fFunctions = 1;
fFunctionCodes = new Int_t[fMaxFunctions];
fPowerIndex = new Int_t[fMaxTerms];
Int_t l;
for (l=0;l<fMaxFunctions;l++) fFunctionCodes[l] = 0;
for (l=0;l<fMaxTerms;l++) fPowerIndex[l] = 0;
if (fMaxAngle != 0) maxPass = 100;
if (fIsUserFunction) maxPass = 1;
while(kTRUE) {
if (studied++ >= fMaxStudy) {
fParameterisationCode = PARAM_MAXSTUDY;
break;
}
if (k >= maxPass) {
fParameterisationCode = PARAM_SEVERAL;
break;
}
i++;
if (i == fMaxFunctions) {
if (fMaxAngle != 0)
fMaxAngle += (90 - fMaxAngle) / 2;
i = 0;
studied--;
k++;
continue;
}
if (studied == 1)
fFunctionCodes[i] = 0;
else if (fFunctionCodes[i] >= 2)
continue;
if (fIsVerbose && studied == 1)
cout << "Coeff SumSqRes Contrib Angle QM Func"
<< " Value W^2 Powers" << endl;
Double_t dResidur = MakeGramSchmidt(i);
if (dResidur == 0) {
fFunctionCodes[i] = 1;
continue;
}
if (!fIsUserFunction) {
fFunctionCodes[i] = 2;
if (!TestFunction(squareResidual, dResidur)) {
fFunctionCodes[i] = 1;
continue;
}
}
fFunctionCodes[i] = 3;
fPowerIndex[fNCoefficients] = i;
fNCoefficients++;
squareResidual -= dResidur;
for (j = 0; j < fNVariables; j++) {
if (fNCoefficients == 1
|| fMaxPowersFinal[j] <= fPowers[i * fNVariables + j] - 1)
fMaxPowersFinal[j] = fPowers[i * fNVariables + j] - 1;
}
Double_t s = EvalControl(&fPowers[i * fNVariables]);
if (fIsVerbose) {
cout << setw(5) << fNCoefficients << " "
<< setw(10) << setprecision(4) << squareResidual << " "
<< setw(10) << setprecision(4) << dResidur << " "
<< setw(7) << setprecision(3) << fMaxAngle << " "
<< setw(7) << setprecision(3) << s << " "
<< setw(5) << i << " "
<< setw(10) << setprecision(4)
<< fOrthCoefficients(fNCoefficients-1) << " "
<< setw(10) << setprecision(4)
<< fOrthFunctionNorms(fNCoefficients-1) << " "
<< flush;
for (j = 0; j < fNVariables; j++)
cout << " " << fPowers[i * fNVariables + j] - 1 << flush;
cout << endl;
}
if (fNCoefficients >= fMaxTerms ) {
fParameterisationCode = PARAM_MAXTERMS;
break;
}
Double_t err = TMath::Sqrt(TMath::Max(1e-20,squareResidual) /
fSumSqAvgQuantity);
if (err < fMinRelativeError) {
fParameterisationCode = PARAM_RELERR;
break;
}
}
fError = TMath::Max(1e-20,squareResidual);
fSumSqResidual -= fError;
fRMS = TMath::Sqrt(fError / fSampleSize);
}
void TMultiDimFit::MakeRealCode(const char *filename,
const char *classname,
Option_t *)
{
Int_t i, j;
Bool_t isMethod = (classname[0] == '\0' ? kFALSE : kTRUE);
const char *prefix = (isMethod ? Form("%s::", classname) : "");
const char *cv_qual = (isMethod ? "" : "static ");
ofstream outFile(filename,ios::out|ios::trunc);
if (!outFile) {
Error("MakeRealCode","couldn't open output file '%s'",filename);
return;
}
if (fIsVerbose)
cout << "Writing on file \"" << filename << "\" ... " << flush;
outFile << "// -*- mode: c++ -*-" << endl;
outFile << "// " << endl
<< "// File " << filename
<< " generated by TMultiDimFit::MakeRealCode" << endl;
TDatime date;
outFile << "// on " << date.AsString() << endl;
outFile << "// ROOT version " << gROOT->GetVersion()
<< endl << "//" << endl;
outFile << "// This file contains the function " << endl
<< "//" << endl
<< "// double " << prefix << "MDF(double *x); " << endl
<< "//" << endl
<< "// For evaluating the parameterization obtained" << endl
<< "// from TMultiDimFit and the point x" << endl
<< "// " << endl
<< "// See TMultiDimFit class documentation for more "
<< "information " << endl << "// " << endl;
if (isMethod)
outFile << "#include \"" << classname << ".h\"" << endl;
outFile << "//" << endl
<< "// Static data variables" << endl
<< "//" << endl;
outFile << cv_qual << "int " << prefix << "gNVariables = "
<< fNVariables << ";" << endl;
outFile << cv_qual << "int " << prefix << "gNCoefficients = "
<< fNCoefficients << ";" << endl;
outFile << cv_qual << "double " << prefix << "gDMean = "
<< fMeanQuantity << ";" << endl;
outFile << "// Assignment to mean vector." << endl;
outFile << cv_qual << "double " << prefix
<< "gXMean[] = {" << endl;
for (i = 0; i < fNVariables; i++)
outFile << (i != 0 ? ", " : " ") << fMeanVariables(i) << flush;
outFile << " };" << endl << endl;
outFile << "// Assignment to minimum vector." << endl;
outFile << cv_qual << "double " << prefix
<< "gXMin[] = {" << endl;
for (i = 0; i < fNVariables; i++)
outFile << (i != 0 ? ", " : " ") << fMinVariables(i) << flush;
outFile << " };" << endl << endl;
outFile << "// Assignment to maximum vector." << endl;
outFile << cv_qual << "double " << prefix
<< "gXMax[] = {" << endl;
for (i = 0; i < fNVariables; i++)
outFile << (i != 0 ? ", " : " ") << fMaxVariables(i) << flush;
outFile << " };" << endl << endl;
outFile << "// Assignment to coefficients vector." << endl;
outFile << cv_qual << "double " << prefix
<< "gCoefficient[] = {" << flush;
for (i = 0; i < fNCoefficients; i++)
outFile << (i != 0 ? "," : "") << endl
<< " " << fCoefficients(i) << flush;
outFile << endl << " };" << endl << endl;
outFile << "// Assignment to error coefficients vector." << endl;
outFile << cv_qual << "double " << prefix
<< "gCoefficientRMS[] = {" << flush;
for (i = 0; i < fNCoefficients; i++)
outFile << (i != 0 ? "," : "") << endl
<< " " << fCoefficientsRMS(i) << flush;
outFile << endl << " };" << endl << endl;
outFile << "// Assignment to powers vector." << endl
<< "// The powers are stored row-wise, that is" << endl
<< "// p_ij = " << prefix
<< "gPower[i * NVariables + j];" << endl;
outFile << cv_qual << "int " << prefix
<< "gPower[] = {" << flush;
for (i = 0; i < fNCoefficients; i++) {
for (j = 0; j < fNVariables; j++) {
if (j != 0) outFile << flush << " ";
else outFile << endl << " ";
outFile << fPowers[fPowerIndex[i] * fNVariables + j]
<< (i == fNCoefficients - 1 && j == fNVariables - 1 ? "" : ",")
<< flush;
}
}
outFile << endl << "};" << endl << endl;
outFile << "// " << endl
<< "// The "
<< (isMethod ? "method " : "function ")
<< " double " << prefix
<< "MDF(double *x)"
<< endl << "// " << endl;
outFile << "double " << prefix
<< "MDF(double *x) {" << endl
<< " double returnValue = " << prefix << "gDMean;" << endl
<< " int i = 0, j = 0, k = 0;" << endl
<< " for (i = 0; i < " << prefix << "gNCoefficients ; i++) {"
<< endl
<< " // Evaluate the ith term in the expansion" << endl
<< " double term = " << prefix << "gCoefficient[i];"
<< endl
<< " for (j = 0; j < " << prefix << "gNVariables; j++) {"
<< endl
<< " // Evaluate the polynomial in the jth variable." << endl
<< " int power = "<< prefix << "gPower["
<< prefix << "gNVariables * i + j]; " << endl
<< " double p1 = 1, p2 = 0, p3 = 0, r = 0;" << endl
<< " double v = 1 + 2. / ("
<< prefix << "gXMax[j] - " << prefix
<< "gXMin[j]) * (x[j] - " << prefix << "gXMax[j]);" << endl
<< " // what is the power to use!" << endl
<< " switch(power) {" << endl
<< " case 1: r = 1; break; " << endl
<< " case 2: r = v; break; " << endl
<< " default: " << endl
<< " p2 = v; " << endl
<< " for (k = 3; k <= power; k++) { " << endl
<< " p3 = p2 * v;" << endl;
if (fPolyType == kLegendre)
outFile << " p3 = ((2 * i - 3) * p2 * v - (i - 2) * p1)"
<< " / (i - 1);" << endl;
if (fPolyType == kChebyshev)
outFile << " p3 = 2 * v * p2 - p1; " << endl;
outFile << " p1 = p2; p2 = p3; " << endl << " }" << endl
<< " r = p3;" << endl << " }" << endl
<< " // multiply this term by the poly in the jth var" << endl
<< " term *= r; " << endl << " }" << endl
<< " // Add this term to the final result" << endl
<< " returnValue += term;" << endl << " }" << endl
<< " return returnValue;" << endl << "}" << endl << endl;
outFile << "// EOF for " << filename << endl;
outFile.close();
if (fIsVerbose)
cout << "done" << endl;
}
void TMultiDimFit::Print(Option_t *option) const
{
Int_t i = 0;
Int_t j = 0;
TString opt(option);
opt.ToLower();
if (opt.Contains("p")) {
cout << "User parameters:" << endl
<< "----------------" << endl
<< " Variables: " << fNVariables << endl
<< " Data points: " << fSampleSize << endl
<< " Max Terms: " << fMaxTerms << endl
<< " Power Limit Parameter: " << fPowerLimit << endl
<< " Max functions: " << fMaxFunctions << endl
<< " Max functions to study: " << fMaxStudy << endl
<< " Max angle (optional): " << fMaxAngle << endl
<< " Min angle: " << fMinAngle << endl
<< " Relative Error accepted: " << fMinRelativeError << endl
<< " Maximum Powers: " << flush;
for (i = 0; i < fNVariables; i++)
cout << " " << fMaxPowers[i] - 1 << flush;
cout << endl << endl
<< " Parameterisation will be done using " << flush;
if (fPolyType == kChebyshev)
cout << "Chebyshev polynomials" << endl;
else if (fPolyType == kLegendre)
cout << "Legendre polynomials" << endl;
else
cout << "Monomials" << endl;
cout << endl;
}
if (opt.Contains("s")) {
cout << "Sample statistics:" << endl
<< "------------------" << endl
<< " D" << flush;
for (i = 0; i < fNVariables; i++)
cout << " " << setw(10) << i+1 << flush;
cout << endl << " Max: " << setw(10) << setprecision(7)
<< fMaxQuantity << flush;
for (i = 0; i < fNVariables; i++)
cout << " " << setw(10) << setprecision(4)
<< fMaxVariables(i) << flush;
cout << endl << " Min: " << setw(10) << setprecision(7)
<< fMinQuantity << flush;
for (i = 0; i < fNVariables; i++)
cout << " " << setw(10) << setprecision(4)
<< fMinVariables(i) << flush;
cout << endl << " Mean: " << setw(10) << setprecision(7)
<< fMeanQuantity << flush;
for (i = 0; i < fNVariables; i++)
cout << " " << setw(10) << setprecision(4)
<< fMeanVariables(i) << flush;
cout << endl << " Function Sum Squares: " << fSumSqQuantity
<< endl << endl;
}
if (opt.Contains("r")) {
cout << "Results of Parameterisation:" << endl
<< "----------------------------" << endl
<< " Total reduction of square residuals "
<< fSumSqResidual << endl
<< " Relative precision obtained: "
<< fPrecision << endl
<< " Error obtained: "
<< fError << endl
<< " Multiple correlation coefficient: "
<< fCorrelationCoeff << endl
<< " Reduced Chi square over sample: "
<< fChi2 / (fSampleSize - fNCoefficients) << endl
<< " Maximum residual value: "
<< fMaxResidual << endl
<< " Minimum residual value: "
<< fMinResidual << endl
<< " Estimated root mean square: "
<< fRMS << endl
<< " Maximum powers used: " << flush;
for (j = 0; j < fNVariables; j++)
cout << fMaxPowersFinal[j] << " " << flush;
cout << endl
<< " Function codes of candidate functions." << endl
<< " 1: considered,"
<< " 2: too little contribution,"
<< " 3: accepted." << flush;
for (i = 0; i < fMaxFunctions; i++) {
if (i % 60 == 0)
cout << endl << " " << flush;
else if (i % 10 == 0)
cout << " " << flush;
cout << fFunctionCodes[i];
}
cout << endl << " Loop over candidates stopped because " << flush;
switch(fParameterisationCode){
case PARAM_MAXSTUDY:
cout << "max allowed studies reached" << endl; break;
case PARAM_SEVERAL:
cout << "all candidates considered several times" << endl; break;
case PARAM_RELERR:
cout << "wanted relative error obtained" << endl; break;
case PARAM_MAXTERMS:
cout << "max number of terms reached" << endl; break;
default:
cout << "some unknown reason" << endl;
break;
}
cout << endl;
}
if (opt.Contains("f")) {
cout << "Results of Fit:" << endl
<< "---------------" << endl
<< " Test sample size: "
<< fTestSampleSize << endl
<< " Multiple correlation coefficient: "
<< fTestCorrelationCoeff << endl
<< " Relative precision obtained: "
<< fTestPrecision << endl
<< " Error obtained: "
<< fTestError << endl
<< " Reduced Chi square over sample: "
<< fChi2 / (fSampleSize - fNCoefficients) << endl
<< endl;
if (fFitter) {
fFitter->PrintResults(1,1);
cout << endl;
}
}
if (opt.Contains("c")){
cout << "Coefficients:" << endl
<< "-------------" << endl
<< " # Value Error Powers" << endl
<< " ---------------------------------------" << endl;
for (i = 0; i < fNCoefficients; i++) {
cout << " " << setw(3) << i << " "
<< setw(12) << fCoefficients(i) << " "
<< setw(12) << fCoefficientsRMS(i) << " " << flush;
for (j = 0; j < fNVariables; j++)
cout << " " << setw(3)
<< fPowers[fPowerIndex[i] * fNVariables + j] - 1 << flush;
cout << endl;
}
cout << endl;
}
if (opt.Contains("k") && fCorrelationMatrix.IsValid()) {
cout << "Correlation Matrix:" << endl
<< "-------------------";
fCorrelationMatrix.Print();
}
if (opt.Contains("m")) {
cout << "Parameterization:" << endl
<< "-----------------" << endl
<< " Normalised variables: " << endl;
for (i = 0; i < fNVariables; i++)
cout << "\ty_" << i << "\t= 1 + 2 * (x_" << i << " - "
<< fMaxVariables(i) << ") / ("
<< fMaxVariables(i) << " - " << fMinVariables(i) << ")"
<< endl;
cout << endl
<< " f(";
for (i = 0; i < fNVariables; i++) {
cout << "y_" << i;
if (i != fNVariables-1) cout << ", ";
}
cout << ") = ";
for (Int_t i = 0; i < fNCoefficients; i++) {
if (i != 0)
cout << endl << "\t" << (fCoefficients(i) < 0 ? "- " : "+ ")
<< TMath::Abs(fCoefficients(i));
else
cout << fCoefficients(i);
for (Int_t j = 0; j < fNVariables; j++) {
Int_t p = fPowers[fPowerIndex[i] * fNVariables + j];
switch (p) {
case 1: break;
case 2: cout << " * y_" << j; break;
default:
switch(fPolyType) {
case kLegendre: cout << " * L_" << p-1 << "(y_" << j << ")"; break;
case kChebyshev: cout << " * C_" << p-1 << "(y_" << j << ")"; break;
default: cout << " * y_" << j << "^" << p-1; break;
}
}
}
}
cout << endl;
}
}
Bool_t TMultiDimFit::Select(const Int_t *)
{
return kTRUE;
}
void TMultiDimFit::SetMaxAngle(Double_t ang)
{
if (ang >= 90 || ang < 0) {
Warning("SetMaxAngle", "angle must be in [0,90)");
return;
}
fMaxAngle = ang;
}
void TMultiDimFit::SetMinAngle(Double_t ang)
{
if (ang > 90 || ang <= 0) {
Warning("SetMinAngle", "angle must be in [0,90)");
return;
}
fMinAngle = ang;
}
void TMultiDimFit::SetPowers(const Int_t* powers, Int_t terms)
{
fIsUserFunction = kTRUE;
fMaxFunctions = terms;
fMaxTerms = terms;
fMaxStudy = terms;
fMaxFuncNV = fMaxFunctions * fNVariables;
fPowers = new Int_t[fMaxFuncNV];
Int_t i, j;
for (i = 0; i < fMaxFunctions; i++)
for(j = 0; j < fNVariables; j++)
fPowers[i * fNVariables + j] = powers[i * fNVariables + j] + 1;
}
void TMultiDimFit::SetPowerLimit(Double_t limit)
{
fPowerLimit = limit;
}
void TMultiDimFit::SetMaxPowers(const Int_t* powers)
{
if (!powers)
return;
for (Int_t i = 0; i < fNVariables; i++)
fMaxPowers[i] = powers[i]+1;
}
void TMultiDimFit::SetMinRelativeError(Double_t error)
{
fMinRelativeError = error;
}
Bool_t TMultiDimFit::TestFunction(Double_t squareResidual,
Double_t dResidur)
{
if (fNCoefficients != 0) {
if (fMaxAngle == 0) {
if (dResidur <
squareResidual / (fMaxTerms - fNCoefficients + 1 + 1E-10)) {
return kFALSE;
}
}
else {
if (TMath::Sqrt(dResidur/fSumSqAvgQuantity) <
TMath::Cos(fMaxAngle*DEGRAD)) {
return kFALSE;
}
}
}
return kTRUE;
}
void mdfHelper(int& , double* , double& chi2,
double* coeffs, int )
{
TMultiDimFit* mdf = TMultiDimFit::Instance();
chi2 = mdf->MakeChi2(coeffs);
}
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