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[diderot] Diff of /trunk/doc/report/types.tex
 [diderot] / trunk / doc / report / types.tex

# Diff of /trunk/doc/report/types.tex

revision 101, Fri May 28 15:16:21 2010 UTC revision 161, Tue Jul 20 14:08:09 2010 UTC
# Line 30  Line 30
30  \end{center}%  \end{center}%
31
32  \section{Images}  \section{Images}
33    Images are rectangular arrays of tensor data that are used to represent the data sets
34    that Diderot programs are analysing, as well as other data.
35    The syntax of an image type is
36    \begin{center}
37      \kw{image}\kw{(} $n$ \kw{)} \kw{[} $d_1,\ldots{},d_n$ \kw{]}
38    \end{center}%
39    where $n$ is the dimension of the field (typically 2 or 3) and $d_1,\ldots{},d_n$ is the
40    shape of the tensor data (\ie{}, the elements of the image are tensors
41    of type \kw{tensor[}$d_1,\ldots{},d_n$\kw{]}).
42
43  \section{Fields}  \section{Fields}
44    Fields are functions from some $n$-dimensional vector space to some tensor type.
45    The syntax of a field type is
46    \begin{center}
47      \kw{field}\kw{\#} $k$ \kw{(} $n$ \kw{)} \kw{[} $d_1,\ldots{},d_n$ \kw{]}
48    \end{center}%
49    where $k \geq 0$ is the number of levels of differentiation supported by the field,
50    $n$ is the dimension of the field (typically 2 or 3), and $d_1,\ldots{},d_n$ is the shape of the field.
51    Probing the field will produce a tensor of type \kw{tensor[}$d_1,\ldots{},d_n$\kw{]}.
52
53  \section{Kernels}  \section{Kernels}
54  Kernels are abstract types that represent the \emph{convolution kernels} used  Kernels are abstract types that represent the \emph{convolution kernels} used

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