mvpa2.clfs.smlrΒΆ
Sparse Multinomial Logistic Regression classifier.
Functions
Doi(\*args, \*\*kwargs) |
Perform no good and no bad |
accepts_dataset_as_samples(fx) |
Decorator to extract samples from Datasets. |
expand_contraint_spec(spec) |
Helper to translate literal contraint specs into functional ones |
Classes
AltConstraints(\*constraints) |
Logical OR for constraints. |
Classifier([space]) |
Abstract classifier class to be inherited by all classifiers |
ConditionalAttribute([enabled]) |
Simple container intended to conditionally store the value |
Constraint |
Base class for input value conversion/validation. |
Constraints(\*constraints) |
Logical AND for constraints. |
Dataset(samples[, sa, fa, a]) |
Generic storage class for datasets with multiple attributes. |
EnsureBool |
Ensure that an input is a bool. |
EnsureChoice(\*values) |
Ensure an input is element of a set of possible values |
EnsureDType(dtype) |
Ensure that an input (or several inputs) are of a particular data type. |
EnsureFloat() |
Ensure that an input (or several inputs) are of a data type ‘float’. |
EnsureInt() |
Ensure that an input (or several inputs) are of a data type ‘int’. |
EnsureListOf(dtype) |
Ensure that an input is a list of a particular data type |
EnsureNone |
Ensure an input is of value None |
EnsureRange([min, max]) |
Ensure an input is within a particular range |
EnsureStr |
Ensure an input is a string. |
EnsureTupleOf(dtype) |
Ensure that an input is a tuple of a particular data type |
Parameter(default[, constraints, ro, index, ...]) |
This class shall serve as a representation of a parameter. |
SMLR(\*\*kwargs) |
Sparse Multinomial Logistic Regression Classifier. |
SMLRWeights(clf[, force_train]) |
SensitivityAnalyzer that reports the weights SMLR trained |
Sensitivity(clf[, force_train]) |
Sensitivities of features for a given Classifier. |
Exceptions
AltConstraints(\*constraints) |
Logical OR for constraints. |
Classifier([space]) |
Abstract classifier class to be inherited by all classifiers |
ConditionalAttribute([enabled]) |
Simple container intended to conditionally store the value |
Constraint |
Base class for input value conversion/validation. |
Constraints(\*constraints) |
Logical AND for constraints. |
Dataset(samples[, sa, fa, a]) |
Generic storage class for datasets with multiple attributes. |
EnsureBool |
Ensure that an input is a bool. |
EnsureChoice(\*values) |
Ensure an input is element of a set of possible values |
EnsureDType(dtype) |
Ensure that an input (or several inputs) are of a particular data type. |
EnsureFloat() |
Ensure that an input (or several inputs) are of a data type ‘float’. |
EnsureInt() |
Ensure that an input (or several inputs) are of a data type ‘int’. |
EnsureListOf(dtype) |
Ensure that an input is a list of a particular data type |
EnsureNone |
Ensure an input is of value None |
EnsureRange([min, max]) |
Ensure an input is within a particular range |
EnsureStr |
Ensure an input is a string. |
EnsureTupleOf(dtype) |
Ensure that an input is a tuple of a particular data type |
Parameter(default[, constraints, ro, index, ...]) |
This class shall serve as a representation of a parameter. |
SMLR(\*\*kwargs) |
Sparse Multinomial Logistic Regression Classifier. |
SMLRWeights(clf[, force_train]) |
SensitivityAnalyzer that reports the weights SMLR trained |
Sensitivity(clf[, force_train]) |
Sensitivities of features for a given Classifier. |
