Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.clfs.kernel:
Kernels for Gaussian Process Regression and Classification.
Bases: object
Kernel function base class.
Resets the kernel dropping internal variables to the original values
Bases: mvpa.clfs.kernel.Kernel
The constant kernel class.
Initialize the constant kernel instance.
Parameters: | sigma_0 (float) – standard deviation of the Gaussian prior probability N(0,sigma_0**2) of the intercept of the constant regression. (Defaults to 1.0) |
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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.
Parameters: |
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Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD) BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS.
Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD). BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS.
Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.
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.
Parameters: |
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Compute kernel matrix.
Parameters: |
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Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.
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.
Parameters: | length_scale (float OR numpy.ndarray) – the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0) |
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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.
Parameters: |
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Compute kernel matrix.
Parameters: |
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Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data.
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.
Parameters: |
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Compute kernel matrix.
Parameters: |
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Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD).
Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Hyperparameters are in log scale which is sometimes more stable. Shorter formula. Allows vector of lengthscales (ARD).
Set hyperaparmeters from a vector.
Used by model selection.