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 |
GeneralizedLinearKernel(\*args, \*\*kwargs) |
The linear kernel class. |
LinearKernel(\*args, \*\*kwargs) |
Simple linear kernel: K(a,b) = a*b.T |
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 |
GeneralizedLinearKernel(\*args, \*\*kwargs) |
The linear kernel class. |
LinearKernel(\*args, \*\*kwargs) |
Simple linear kernel: K(a,b) = a*b.T |
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. |
