KernelMatrix class
====================

``KernelMatrix`` is a self-contained class for the Gram matrix induced by a kernel function on a given sample. This class defines the central data structure for all kernel methods, as it acts a *key bridge* between input data space and the learning algorithms.

The class is designed in such a way that

 - computation is **on-demand**, meaning it only computes elements of the kernel matrix (KM) as needed, and nothing more. This can save a lot computation and storage, for large samples.
 - supports both callable as well as attribute access. This allows easy access to *partial* or *random* portions of the KM. Indexing the KM is aimed to be compliant that of numpy arrays as much as possible.
 - allows **parallel** computation of different part of the KM to speed up computation when ``N`` is large
 - allows setting of *user-defined* **attributes** to allow easy identification and differentiation among a collection of KMs when working in applications such as Multiple Kernel Learning (MKL)
 - implements basic operations such as centering and normalization (whose implementation differs from that of manipulating regular matrices)
 - exposes several convenience attributes to make advanced development a breeze

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This library also provides the following convenience wrappers (see below for the API):

 - ``KernelMatrixPrecomputed`` turns a precomputed kernel matrix into a ``KernelMatrix`` class with all its attractive properties
 - ``ConstantKernelMatrix`` that defines a ``KernelMatrix`` with a constant everywhere
 - custom exception ``KMAccessError`` to identify invalid access of the KM


API for ``KernelMatrix``
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.. autoclass:: kernelmethods.KernelMatrix
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Special Kernel Matrices
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.. autoclass:: kernelmethods.base.ConstantKernelMatrix
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.. autoclass:: kernelmethods.base.KernelMatrixPrecomputed
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Exceptions
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.. autoclass:: kernelmethods.KMAccessError
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