ITK
4.2.0
Insight Segmentation and Registration Toolkit
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#include <itkTDistribution.h>
Public Types | |
typedef SmartPointer< const Self > | ConstPointer |
typedef SmartPointer< Self > | Pointer |
typedef TDistribution | Self |
typedef ProbabilityDistribution | Superclass |
Public Types inherited from itk::Statistics::ProbabilityDistribution | |
typedef Array< double > | ParametersType |
Public Types inherited from itk::Object | |
Public Types inherited from itk::LightObject |
Public Member Functions | |
virtual ::itk::LightObject::Pointer | CreateAnother (void) const |
virtual double | EvaluateCDF (double x) const |
virtual double | EvaluateCDF (double x, const ParametersType &) const |
virtual double | EvaluateCDF (double x, SizeValueType degreesOfFreedom) const |
virtual double | EvaluateInverseCDF (double p) const |
virtual double | EvaluateInverseCDF (double p, const ParametersType &) const |
virtual double | EvaluateInverseCDF (double p, SizeValueType degreesOfFreedom) const |
virtual double | EvaluatePDF (double x) const |
virtual double | EvaluatePDF (double x, const ParametersType &) const |
virtual double | EvaluatePDF (double x, SizeValueType degreesOfFreedom) const |
virtual SizeValueType | GetDegreesOfFreedom () const |
virtual double | GetMean () const |
virtual const char * | GetNameOfClass () const |
virtual SizeValueType | GetNumberOfParameters () const |
virtual double | GetVariance () const |
virtual bool | HasMean () const |
virtual bool | HasVariance () const |
virtual void | SetDegreesOfFreedom (SizeValueType) |
Public Member Functions inherited from itk::Statistics::ProbabilityDistribution | |
virtual const ParametersType & | GetParameters () |
virtual void | SetParameters (const ParametersType ¶ms) |
Public Member Functions inherited from itk::Object | |
unsigned long | AddObserver (const EventObject &event, Command *) |
unsigned long | AddObserver (const EventObject &event, Command *) const |
virtual void | DebugOff () const |
virtual void | DebugOn () const |
Command * | GetCommand (unsigned long tag) |
bool | GetDebug () const |
MetaDataDictionary & | GetMetaDataDictionary (void) |
const MetaDataDictionary & | GetMetaDataDictionary (void) const |
virtual unsigned long | GetMTime () const |
virtual const TimeStamp & | GetTimeStamp () const |
bool | HasObserver (const EventObject &event) const |
void | InvokeEvent (const EventObject &) |
void | InvokeEvent (const EventObject &) const |
virtual void | Modified () const |
virtual void | Register () const |
void | RemoveAllObservers () |
void | RemoveObserver (unsigned long tag) |
void | SetDebug (bool debugFlag) const |
void | SetMetaDataDictionary (const MetaDataDictionary &rhs) |
virtual void | SetReferenceCount (int) |
virtual void | UnRegister () const |
Public Member Functions inherited from itk::LightObject | |
virtual void | Delete () |
virtual int | GetReferenceCount () const |
itkCloneMacro (Self) | |
void | Print (std::ostream &os, Indent indent=0) const |
Static Public Member Functions | |
static double | CDF (double x, const ParametersType &) |
static double | CDF (double x, SizeValueType degreesOfFreedom) |
static double | InverseCDF (double p, const ParametersType &) |
static double | InverseCDF (double p, SizeValueType degreesOfFreedom) |
static Pointer | New () |
static double | PDF (double x, const ParametersType &) |
static double | PDF (double x, SizeValueType degreesOfFreedom) |
Protected Member Functions | |
void | PrintSelf (std::ostream &os, Indent indent) const |
TDistribution (void) | |
virtual | ~TDistribution (void) |
Protected Member Functions inherited from itk::Statistics::ProbabilityDistribution | |
ProbabilityDistribution (void) | |
virtual | ~ProbabilityDistribution (void) |
Protected Member Functions inherited from itk::Object | |
Object () | |
bool | PrintObservers (std::ostream &os, Indent indent) const |
virtual void | SetTimeStamp (const TimeStamp &time) |
virtual | ~Object () |
Protected Member Functions inherited from itk::LightObject | |
virtual LightObject::Pointer | InternalClone () const |
LightObject () | |
virtual void | PrintHeader (std::ostream &os, Indent indent) const |
virtual void | PrintTrailer (std::ostream &os, Indent indent) const |
virtual | ~LightObject () |
Private Member Functions | |
void | operator= (const Self &) |
TDistribution (const Self &) |
Additional Inherited Members | |
Protected Attributes inherited from itk::Statistics::ProbabilityDistribution | |
ParametersType | m_Parameters |
TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.).
TDistribution provides access to the probability density function (pdf), the cumulative distribution function (cdf), and the inverse cumulative distribution function for a Student-t distribution.
The EvaluatePDF(), EvaluateCDF, EvaluateInverseCDF() methods are all virtual, allowing algorithms to be written with an abstract interface to a distribution (with said distribution provided to the algorithm at run-time). Static methods, not requiring an instance of the distribution, are also provided. The static methods allow for optimized access to distributions when the distribution is known a priori to the algorithm.
TDistributions are univariate. Multivariate versions may be provided under a separate superclass (since the parameters to the pdf and cdf would have to be vectors not scalars).
TDistributions can be used for t tests.
Definition at line 57 of file itkTDistribution.h.
typedef SmartPointer< const Self > itk::Statistics::TDistribution::ConstPointer |
Reimplemented from itk::Statistics::ProbabilityDistribution.
Definition at line 65 of file itkTDistribution.h.
Reimplemented from itk::Statistics::ProbabilityDistribution.
Definition at line 64 of file itkTDistribution.h.
Standard class typedefs
Reimplemented from itk::Statistics::ProbabilityDistribution.
Definition at line 62 of file itkTDistribution.h.
Reimplemented from itk::Statistics::ProbabilityDistribution.
Definition at line 63 of file itkTDistribution.h.
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inlineprotectedvirtual |
Definition at line 201 of file itkTDistribution.h.
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private |
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Static method to evaluate the cumulative distribution function (cdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. The degrees of freedom are passed as a parameters vector.
This is based on Abramowitz and Stegun 26.7.1. Accuracy is approximately 10^-14.
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Static method to evaluate the cumulative distribution function (cdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class.
This is based on Abramowitz and Stegun 26.7.1. Accuracy is approximately 10^-14.
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Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.
Reimplemented from itk::Object.
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Evaluate the cumulative distribution function (cdf). The parameters of the distribution are assigned via SetParameters().
Implements itk::Statistics::ProbabilityDistribution.
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Evaluate the cumulative distribution function (cdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (degreesOfFreedom).
Implements itk::Statistics::ProbabilityDistribution.
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Evaluate the cumulative distribution function (cdf). The parameters of the distribution are passed as separate parameters.
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Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters of the distribution are assigned via SetParameters().
Implements itk::Statistics::ProbabilityDistribution.
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Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (degrees of freedom).
Implements itk::Statistics::ProbabilityDistribution.
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Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters of the distribution are passed as separate parameters.
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Evaluate the probability density function (pdf). The parameters of the distribution are assigned via SetParameters().
Implements itk::Statistics::ProbabilityDistribution.
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Evaluate the probability density function (pdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (degrees of freedom).
Implements itk::Statistics::ProbabilityDistribution.
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Evaluate the probability density function (pdf). The parameters of the distribution are passed as separate parameters.
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Get the number of degrees of freedom in the t distribution. Defaults to 1
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Get the mean of the distribution.
Implements itk::Statistics::ProbabilityDistribution.
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Strandard macros
Reimplemented from itk::Statistics::ProbabilityDistribution.
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Return the number of parameters. For a univariate Student-t distribution, the number of parameters is 1 (degrees of freedom)
Implements itk::Statistics::ProbabilityDistribution.
Definition at line 75 of file itkTDistribution.h.
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Get the variance of the distribution. If the variance does not exist, then quiet_NaN is returned.
Implements itk::Statistics::ProbabilityDistribution.
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Does the Student-t distribution have a mean?
Implements itk::Statistics::ProbabilityDistribution.
Definition at line 128 of file itkTDistribution.h.
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Does the Student-t distribution have a variance? Variance is only defined for degrees of freedom greater than 2
Implements itk::Statistics::ProbabilityDistribution.
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Static method to evaluate the inverse cumulative distribution function of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0. The degrees of freedom are passed as a parameters vector.
This is based on Abramowitz and Stegun 26.7.5 followed by a few Newton iterations to improve the precision at low degrees of freedom. Accuracy is approximately 10^-10.
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Static method to evaluate the inverse cumulative distribution function of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0.
This is based on Abramowitz and Stegun 26.7.5 followed by a few Newton iterations to improve the precision at low degrees of freedom. Accuracy is approximately 10^-10.
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Method for creation through the object factory.
Reimplemented from itk::Object.
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Mutex lock to protect modification to the reference count
Reimplemented from itk::Statistics::ProbabilityDistribution.
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Static method to evaluate the probability density function (pdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. The degrees of freedom for the distribution are passed in a parameters vector.
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Static method to evaluate the probability density function (pdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class.
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Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.
Reimplemented from itk::Statistics::ProbabilityDistribution.
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Set the number of degrees of freedom in the Student-t distribution. Defaults to 1