mvpa2.clfs.gprΒΆ

Gaussian Process Regression (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.