mvpa2.clfs.baseΒΆ

Base class for all XXX learners: classifiers and regressions.

Inheritance diagram of mvpa2.clfs.base

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 All dataset measures support arbitrary transformation of the measure after it has been computed.
Parameter(default[, ro, index, value, name, doc]) 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 All dataset measures support arbitrary transformation of the measure after it has been computed.
Parameter(default[, ro, index, value, name, doc]) This class shall serve as a representation of a parameter.
RegressionStatistics(**kwargs) Class to contain information and display on regression results.

NeuroDebian

NITRC-listed