Gaussian Process Regression (GPR).
Functions
NLAcholesky(a) | Cholesky decomposition. |
NLAsolve(a, b) | Solve the equation a x = b for x. |
Ndiag(v[, k]) | Extract a diagonal or construct a diagonal array. |
Ndot(a, b) | Dot product of two arrays. |
SLcho_solve(clow, b) | Solve a previously factored symmetric system of equations. |
SLcholesky(a[, lower, overwrite_a]) | 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(**kwargs[, space]) | Abstract classifier class to be inherited by all classifiers .. |
ConditionalAttribute(*args, **kwargs[, enabled]) | Simple container intended to conditionally store the value |
Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
GPR(**kwargs[, kernel]) | Gaussian Process Regression (GPR). |
GPRLinearWeights(clf, **kwargs[, 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, **kwargs[, ro, index, ...]) | This class shall serve as a representation of a parameter. |
Sensitivity(clf, **kwargs[, force_train]) | Sensitivities of features for a given Classifier. |
SquaredExponentialKernel(**kwargs[, ...]) | The Squared Exponential kernel class. |
Exceptions
Classifier(**kwargs[, space]) | Abstract classifier class to be inherited by all classifiers .. |
ConditionalAttribute(*args, **kwargs[, enabled]) | Simple container intended to conditionally store the value |
Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
GPR(**kwargs[, kernel]) | Gaussian Process Regression (GPR). |
GPRLinearWeights(clf, **kwargs[, 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, **kwargs[, ro, index, ...]) | This class shall serve as a representation of a parameter. |
Sensitivity(clf, **kwargs[, force_train]) | Sensitivities of features for a given Classifier. |
SquaredExponentialKernel(**kwargs[, ...]) | The Squared Exponential kernel class. |