Multivariate Pattern Analysis in Python

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# mvpa2.kernels.np

Kernels for Gaussian Process Regression and Classification.

Functions

`squared_euclidean_distance`(data1[, data2, ...]) | Compute weighted euclidean distance matrix between two datasets.

Classes

`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`ConstantKernel`(*args, **kwargs) | The constant kernel class.
`ExponentialKernel`(*args, **kwargs) | The Exponential kernel class.
`GeneralizedLinearKernel`(*args, **kwargs) | The linear kernel class.
`LinearKernel`(*args, **kwargs) | Simple linear kernel: K(a,b) = a*b.T ..
`Matern_3_2Kernel`([length_scale, sigma_f, ...]) | The Matern kernel class for the case ni=3/2 or ni=5/2.
`Matern_5_2Kernel`(**kwargs) | The Matern kernel class for the case ni=5/2.
`NumpyKernel`(*args, **kwargs) | A Kernel object with internal representation as a 2d numpy array ..
`Parameter`(default[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter.
`PolyKernel`(*args, **kwargs) | Polynomial kernel: K(a,b) = (gamma*a*b.T+coef0)**degree ..
`RationalQuadraticKernel`([length_scale, ...]) | The Rational Quadratic (RQ) kernel class.
`RbfKernel`(*args, **kwargs) | Radial basis function (aka Gausian, aka ) kernel K(a,b) = exp(-||a-b||**2/sigma) ..
`SquaredExponentialKernel`([length_scale, sigma_f]) | The Squared Exponential kernel class.

Exceptions

`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`ConstantKernel`(*args, **kwargs) | The constant kernel class.
`ExponentialKernel`(*args, **kwargs) | The Exponential kernel class.
`GeneralizedLinearKernel`(*args, **kwargs) | The linear kernel class.
`LinearKernel`(*args, **kwargs) | Simple linear kernel: K(a,b) = a*b.T ..
`Matern_3_2Kernel`([length_scale, sigma_f, ...]) | The Matern kernel class for the case ni=3/2 or ni=5/2.
`Matern_5_2Kernel`(**kwargs) | The Matern kernel class for the case ni=5/2.
`NumpyKernel`(*args, **kwargs) | A Kernel object with internal representation as a 2d numpy array ..
`Parameter`(default[, ro, index, value, name, doc]) | This class shall serve as a representation of a parameter.
`PolyKernel`(*args, **kwargs) | Polynomial kernel: K(a,b) = (gamma*a*b.T+coef0)**degree ..
`RationalQuadraticKernel`([length_scale, ...]) | The Rational Quadratic (RQ) kernel class.
`RbfKernel`(*args, **kwargs) | Radial basis function (aka Gausian, aka ) kernel K(a,b) = exp(-||a-b||**2/sigma) ..
`SquaredExponentialKernel`([length_scale, sigma_f]) | The Squared Exponential kernel class.

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