mvpa2.clfs.gprΒΆ

Gaussian Process Regression (GPR).

Inheritance diagram of mvpa2.clfs.gpr

Functions

NLAcholesky(a) Cholesky decomposition.
NLAsolve(a, b) Solve a linear matrix equation, or system of linear scalar equations.
Ndiag(v[, k]) Extract a diagonal or construct a diagonal array.
Ndot(a, b[, out]) Dot product of two arrays.
SLcho_solve(c_and_lower, b[, overwrite_b, ...]) Solve the linear equations A x = b, given the Cholesky factorization of A.
SLcholesky(a[, lower, overwrite_a, check_finite]) Compute the Cholesky decomposition of a matrix.
accepts_dataset_as_samples(fx) Decorator to extract samples from Datasets.
array(object[, dtype, copy, order, subok, ndmin]) Create an array.
asarray(a[, dtype, order]) Convert the input to an array.

Classes

Classifier([space]) Abstract classifier class to be inherited by all classifiers
ConditionalAttribute([enabled]) Simple container intended to conditionally store the value
Dataset(samples[, sa, fa, a]) Generic storage class for datasets with multiple attributes.
EnsureFloat() Ensure that an input (or several inputs) are of a data type ‘float’.
EnsureNone Ensure an input is of value None
EnsureRange([min, max]) Ensure an input is within a particular range
GPR([kernel]) Gaussian Process Regression (GPR).
GPRLinearWeights(clf[, force_train]) SensitivityAnalyzer that reports the weights GPR trained
GPRWeights(clf[, force_train]) SensitivityAnalyzer that reports the weights GPR trained
GeneralizedLinearKernel(\*args, \*\*kwargs) The linear kernel class.
LinearKernel(\*args, \*\*kwargs) Simple linear kernel: K(a,b) = a*b.T
ModelSelector(parametric_model, dataset) Model selection facility.
Parameter(default[, constraints, ro, index, ...]) This class shall serve as a representation of a parameter.
Sensitivity(clf[, force_train]) Sensitivities of features for a given Classifier.
SquaredExponentialKernel([length_scale, sigma_f]) The Squared Exponential kernel class.

Exceptions

Classifier([space]) Abstract classifier class to be inherited by all classifiers
ConditionalAttribute([enabled]) Simple container intended to conditionally store the value
Dataset(samples[, sa, fa, a]) Generic storage class for datasets with multiple attributes.
EnsureFloat() Ensure that an input (or several inputs) are of a data type ‘float’.
EnsureNone Ensure an input is of value None
EnsureRange([min, max]) Ensure an input is within a particular range
GPR([kernel]) Gaussian Process Regression (GPR).
GPRLinearWeights(clf[, force_train]) SensitivityAnalyzer that reports the weights GPR trained
GPRWeights(clf[, force_train]) SensitivityAnalyzer that reports the weights GPR trained
GeneralizedLinearKernel(\*args, \*\*kwargs) The linear kernel class.
LinearKernel(\*args, \*\*kwargs) Simple linear kernel: K(a,b) = a*b.T
ModelSelector(parametric_model, dataset) Model selection facility.
Parameter(default[, constraints, ro, index, ...]) This class shall serve as a representation of a parameter.
Sensitivity(clf[, force_train]) Sensitivities of features for a given Classifier.
SquaredExponentialKernel([length_scale, sigma_f]) The Squared Exponential kernel class.