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Multivariate Pattern Analysis in Python |
Kernels for Gaussian Process Regression and Classification.
The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.clfs.kernel (for developers).
Bases: object
Kernel function base class.
See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the Kernel documentation.
Full API documentation of Kernel in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.Kernel
The constant kernel class.
Initialize the constant kernel instance.
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Compute kernel matrix.
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See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelConstant documentation.
Full API documentation of KernelConstant in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.Kernel
The Exponential kernel class.
Note that it can handle a length scale for each dimension for Automtic Relevance Determination.
Initialize an Exponential kernel instance.
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Compute kernel matrix.
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Set hyperaparmeters from a vector.
Used by model selection.
See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelExponential documentation.
Full API documentation of KernelExponential in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.Kernel
The linear kernel class.
Initialize the linear kernel instance.
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Compute kernel matrix. Set Sigma_p to correct dimensions and default value if necessary.
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See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelLinear documentation.
Full API documentation of KernelLinear in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.Kernel
The Matern kernel class for the case ni=3/2 or ni=5/2.
Note that it can handle a length scale for each dimension for Automtic Relevance Determination.
Initialize a Squared Exponential kernel instance.
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Compute kernel matrix.
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Set hyperaparmeters from a vector.
Used by model selection. Note: ‘numerator’ is not considered as an hyperparameter.
See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelMatern_3_2 documentation.
Full API documentation of KernelMatern_3_2 in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.KernelMatern_3_2
The Matern kernel class for the case ni=5/2.
This kernel is just KernelMatern_3_2(numerator=5.0).
Initialize a Squared Exponential kernel instance.
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See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelMatern_5_2 documentation.
Full API documentation of KernelMatern_5_2 in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.Kernel
The Rational Quadratic (RQ) kernel class.
Note that it can handle a length scale for each dimension for Automtic Relevance Determination.
Initialize a Squared Exponential kernel instance.
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Compute kernel matrix.
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Set hyperaparmeters from a vector.
Used by model selection. Note: ‘alpha’ is not considered as an hyperparameter.
See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelRationalQuadratic documentation.
Full API documentation of KernelRationalQuadratic in module mvpa.clfs.kernel.
Bases: mvpa.clfs.kernel.Kernel
The Squared Exponential kernel class.
Note that it can handle a length scale for each dimension for Automtic Relevance Determination.
Initialize a Squared Exponential kernel instance.
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Compute kernel matrix.
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Set hyperaparmeters from a vector.
Used by model selection.
See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the KernelSquaredExponential documentation.
Full API documentation of KernelSquaredExponential in module mvpa.clfs.kernel.