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PyMVPA: Python MultiVariate Pattern Analysis
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Class Hierarchy
mvpa.clfs.libsvm._svm.SVMModel
mvpa.clfs.libsvm._svm.SVMProblem
mvpa.misc.copy._EmptyClass
object
:
The most base type
exceptions.BaseException
:
Common base class for all exceptions
exceptions.Exception
:
Common base class for all non-exit exceptions.
mvpa.misc.exceptions.ConvergenceError
:
Thrown if some algorithm does not converge to a solution.
mvpa.misc.exceptions.DatasetError
:
Thrown if there is an internal problem with a Dataset.
mvpa.misc.copy.Error
mvpa.misc.exceptions.UnknownStateError
:
Thrown if the internal state of the class is not yet defined.
mvpa.featsel.helpers.BestDetector
:
Determine whether the last value in a sequence is the best one given some criterion.
mvpa.misc.state.CollectableAttribute
:
Base class for any custom behaving attribute intended to become part of a collection
mvpa.misc.param.Parameter
:
This class shall serve as a representation of a parameter.
mvpa.misc.param.KernelParameter
:
Just that it is different beast
mvpa.misc.state.StateVariable
:
Simple container intended to conditionally store the value
mvpa.misc.state.Collection
:
Container of some CollectableAttributes.
mvpa.misc.state.ParameterCollection
:
Container of Parameters for a stateful object.
mvpa.misc.state.StateCollection
:
Container of StateVariables for a stateful object.
mvpa.clfs.transerror.ConfusionMatrix
:
Simple class for confusion matrix computation / display.
mvpa.datasets.base.Dataset
:
This class provides a container to store all necessary data to perform MVPA analyses.
mvpa.datasets.mappeddataset.MappedDataset
:
A
Dataset
which is created by applying a
Mapper
to the data.
mvpa.datasets.maskeddataset.MaskedDataset
:
Helper class which is
MappedDataset
with using
MaskMapper
.
mvpa.clfs.stats.Distribution
mvpa.clfs.stats.FixedDist
:
Proxy/Adaptor class for SciPy distributions.
mvpa.clfs.stats.MCNullDist
:
Class to determine the distribution of a measure under the NULL distribution (no signal).
mvpa.misc.errorfx.ErrorFunctionBase
:
Dummy error function base class
mvpa.misc.support.Harvester
:
World domination helper: do whatever it is asked and accumulate results
mvpa.misc.support.HarvesterCall
mvpa.clfs.kernel.Kernel
:
Kernel function base class.
mvpa.clfs.kernel.KernelConstant
:
The constant kernel class.
mvpa.clfs.kernel.KernelLinear
:
The linear kernel class.
mvpa.clfs.kernel.KernelMatern
:
The Matern kernel class.
mvpa.clfs.kernel.KernelSquaredExponential
:
The Squared Exponential kernel class.
mvpa.misc.verbosity.Logger
:
Base class to provide logging
mvpa.misc.verbosity.LevelLogger
:
Logger which prints based on level -- ie everything which is smaller than specified level
mvpa.misc.verbosity.OnceLogger
:
Logger which prints a message for a given ID just once.
mvpa.misc.WarningLog
mvpa.misc.verbosity.SetLogger
:
Logger which prints based on defined sets identified by Id.
mvpa.misc.verbosity.DebugLogger
:
Logger for debugging purposes.
mvpa.misc.support.MapOverlap
:
Compute some overlap stats from a sequence of binary maps.
mvpa.mappers.base.Mapper
:
Methods are prefixed correspondingly.
mvpa.mappers.metric.MetricMapper
:
Mapper which has information about the metrics of the dataspace it is mapping.
mvpa.mappers.mask.MaskMapper
:
Mapper which uses a binary mask to select "Features"
mvpa.mappers.pca.PCAMapper
:
Mapper to project data onto PCA components estimated from some dataset.
mvpa.mappers.svd.SVDMapper
:
Mapper to project data onto SVD components estimated from some dataset.
mvpa.misc.fsl.melodic.MelodicResults
:
Easy access to MELODIC output.
mvpa.datasets.metric.Metric
:
Abstract class for any finder.
mvpa.datasets.metric.DescreteMetric
:
Find neighboring points in descretized space
mvpa.mappers.metric.MetricMapper
:
Mapper which has information about the metrics of the dataspace it is mapping.
mvpa.mappers.mask.MaskMapper
:
Mapper which uses a binary mask to select "Features"
mvpa.clfs.model_selector.ModelSelector
:
Model selection facility.
mvpa.misc.verbosity.RelativeTime
:
Simple helper class to provide relative time it took from previous invocation
mvpa.clfs.libsvm._svm.SVMParameter
:
SVMParameter class safe to be deepcopied.
mvpa.datasets.splitter.Splitter
:
Base class of dataset splitters.
mvpa.datasets.splitter.CustomSplitter
:
Split a dataset using an arbitrary custom rule.
mvpa.datasets.splitter.HalfSplitter
:
Split a dataset into two halves of the sample attribute.
mvpa.datasets.splitter.NFoldSplitter
:
Generic N-fold data splitter.
mvpa.datasets.splitter.NoneSplitter
:
This is a dataset splitter that does
not
split.
mvpa.datasets.splitter.OddEvenSplitter
:
Split a dataset into odd and even values of the sample attribute.
mvpa.misc.state.Stateful
:
Base class for stateful objects.
mvpa.measures.base.DatasetMeasure
:
A measure computed from a `Dataset`
mvpa.measures.base.FeaturewiseDatasetMeasure
:
A per-feature-measure computed from a
Dataset
(base class).
mvpa.measures.base.CombinedFeaturewiseDatasetMeasure
:
Set sensitivity analyzers to be merged into a single output
mvpa.measures.noiseperturbation.NoisePerturbationSensitivity
:
This is a
FeaturewiseDatasetMeasure
that uses a scalar
DatasetMeasure
and selective noise perturbation to compute a sensitivity map.
mvpa.measures.anova.OneWayAnova
:
FeaturewiseDatasetMeasure
that performs a univariate ANOVA.
mvpa.measures.base.Sensitivity
mvpa.measures.base.BoostedClassifierSensitivityAnalyzer
:
Set sensitivity analyzers to be merged into a single output
mvpa.measures.base.ProxyClassifierSensitivityAnalyzer
:
Set sensitivity analyzer output just to pass through
mvpa.measures.splitmeasure.SplitFeaturewiseMeasure
:
This is a
FeaturewiseDatasetMeasure
that uses another
FeaturewiseDatasetMeasure
and runs it multiple times on differents splits of a
Dataset
.
mvpa.measures.searchlight.Searchlight
:
Runs a scalar `DatasetMeasure` on all possible spheres of a certain size within a dataset.
mvpa.measures.base.StaticDatasetMeasure
:
A static (assigned) sensitivity measure.
mvpa.featsel.helpers.ElementSelector
:
Base class to implement functors to select some elements based on a sequence of values.
mvpa.featsel.helpers.RangeElementSelector
:
Select elements based on specified range of values
mvpa.featsel.helpers.TailSelector
:
Select elements from a tail of a distribution.
mvpa.featsel.helpers.FixedNElementTailSelector
:
Given a sequence, provide set of IDs for a fixed number of to be selected elements.
mvpa.featsel.helpers.FractionTailSelector
:
Given a sequence, provide Ids for a fraction of elements
mvpa.featsel.base.FeatureSelection
:
Base class for any feature selection
mvpa.featsel.base.FeatureSelectionPipeline
:
Feature elimination through the list of FeatureSelection's.
mvpa.featsel.ifs.IFS
:
Incremental feature search.
mvpa.featsel.base.SensitivityBasedFeatureSelection
:
Feature elimination.
mvpa.misc.state.Harvestable
:
Classes inherited from this class intend to collect attributes within internal processing.
mvpa.misc.state.Parametrized
:
Base class for all classes which have collected parameters
mvpa.featsel.helpers.StoppingCriterion
:
Base class for all functors to decide when to stop RFE (or may be general optimization...
mvpa.featsel.helpers.FixedErrorThresholdStopCrit
:
Stop computation if the latest error drops below a certain threshold.
mvpa.featsel.helpers.MultiStopCrit
:
Stop computation if the latest error drops below a certain threshold.
mvpa.featsel.helpers.NBackHistoryStopCrit
:
Stop computation if for a number of steps error was increasing
mvpa.featsel.helpers.NStepsStopCrit
:
Stop computation after a certain number of steps.
mvpa.misc.vproperty.VProperty
:
Provides "virtual" property: uses derived class's method
mvpa.clfs.warehouse.Warehouse
:
Class to keep known instantiated classifiers
mvpa.clfs.libsvm._svm.SVMParameter._SVMCParameter
:
Internal class to to avoid memory leaks returning away svmc's params
mvpa.misc.__Singleton
dict
:
dict() -> new empty dictionary.
mvpa.misc.iohelpers.ColumnData
:
Read data that is stored in columns of text files.
mvpa.misc.iohelpers.FslEV3
:
IO helper to read FSL's EV3 files.
mvpa.misc.iohelpers.McFlirtParams
:
Read and write McFlirt's motion estimation parameters from and to text files.
mvpa.misc.iohelpers.SampleAttributes
:
Read and write PyMVPA sample attribute definitions from and to text files.
type
:
type(object) -> the object's type type(name, bases, dict) -> a new type
mvpa.misc._SingletonType
:
Simple singleton implementation adjusted from
http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/412551
mvpa.misc.state.collector
:
Intended to collect and compose StateCollection for any child class of this metaclass
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PyMVPA: Python MultiVariate Pattern Analysis
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