mvpa2.clfs.base

Plumbing for all learners (classifiers and regressions)

Functions

accepts_dataset_as_samples(fx)	Decorator to extract samples from Datasets.
accepts_samples_as_dataset(fx)	Decorator to wrap samples into a Dataset.
deepcopy(x[, memo, _nil])	Deep copy operation on arbitrary Python objects.
idhash(val)	Craft unique id+hash for an object
is_datasetlike(obj)	Check if an object looks like a Dataset.

Classes

AttributeMap([map, mapnumeric, ...])	Map to translate literal values to numeric ones (and back).
Classifier([space])	Abstract classifier class to be inherited by all classifiers
ConditionalAttribute([enabled])	Simple container intended to conditionally store the value
ConfusionMatrix([labels, labels_map])	Class to contain information and display confusion matrix.
Dataset(samples[, sa, fa, a])	Generic storage class for datasets with multiple attributes.
Learner([auto_train, force_train])	Common trainable processing object.
Measure([null_dist])	A measure computed from a Dataset
Parameter(default[, constraints, ro, index, ...])	This class shall serve as a representation of a parameter.
RegressionStatistics(\*\*kwargs)	Class to contain information and display on regression results.

Exceptions

AttributeMap([map, mapnumeric, ...])	Map to translate literal values to numeric ones (and back).
Classifier([space])	Abstract classifier class to be inherited by all classifiers
ConditionalAttribute([enabled])	Simple container intended to conditionally store the value
ConfusionMatrix([labels, labels_map])	Class to contain information and display confusion matrix.
Dataset(samples[, sa, fa, a])	Generic storage class for datasets with multiple attributes.
Learner([auto_train, force_train])	Common trainable processing object.
Measure([null_dist])	A measure computed from a Dataset
Parameter(default[, constraints, ro, index, ...])	This class shall serve as a representation of a parameter.
RegressionStatistics(\*\*kwargs)	Class to contain information and display on regression results.