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Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.clfs.kernel:
Kernels for Gaussian Process Regression and Classification.
Bases: object
Kernel function base class.
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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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.
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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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.
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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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.
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.