#include <itkGaussianDistribution.h>
Inheritance diagram for itk::Statistics::GaussianDistribution:
GaussianDistribution provides access to the probability density function (pdf), the cumulative distribution function (cdf), and the inverse cumulative distribution function for a Gaussian 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.
GaussianDistributions 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).
GaussianDistributions can be used for Z-score statistical tests.
Definition at line 53 of file itkGaussianDistribution.h.
Public Types | |
| typedef SmartPointer< const Self > | ConstPointer |
| typedef Array< double > | ParametersType |
| typedef SmartPointer< Self > | Pointer |
| typedef GaussianDistribution | Self |
| typedef ProbabilityDistribution | Superclass |
Public Member Functions | |
| virtual LightObject::Pointer | CreateAnother () const |
| virtual void | DebugOff () const |
| virtual void | DebugOn () const |
| virtual void | Delete () |
| virtual double | EvaluateCDF (double x, double mean, double variance) const |
| virtual double | EvaluateCDF (double x, const ParametersType &) const |
| virtual double | EvaluateCDF (double x) const |
| virtual double | EvaluateInverseCDF (double p, double mean, double variance) const |
| virtual double | EvaluateInverseCDF (double p, const ParametersType &) const |
| virtual double | EvaluateInverseCDF (double p) const |
| virtual double | EvaluatePDF (double x, double mean, double variance) const |
| virtual double | EvaluatePDF (double x, const ParametersType &) const |
| virtual double | EvaluatePDF (double x) const |
| Command * | GetCommand (unsigned long tag) |
| bool | GetDebug () const |
| virtual double | GetMean () const |
| const MetaDataDictionary & | GetMetaDataDictionary (void) const |
| MetaDataDictionary & | GetMetaDataDictionary (void) |
| virtual unsigned long | GetMTime () const |
| virtual const char * | GetNameOfClass () const |
| virtual unsigned long | GetNumberOfParameters () const |
| virtual const ParametersType & | GetParameters () |
| virtual int | GetReferenceCount () const |
| virtual double | GetVariance () const |
| virtual bool | HasMean () const |
| bool | HasObserver (const EventObject &event) const |
| virtual bool | HasVariance () const |
| void | InvokeEvent (const EventObject &) const |
| void | InvokeEvent (const EventObject &) |
| virtual void | Modified () const |
| void | Print (std::ostream &os, Indent indent=0) const |
| Static method to evaluate the inverse cumulative distribution *function of a and Mathematical Tables John Wiley &Sons *New York Equation pg **Since the initial approximation only provides an estimate within *E of the true Newton Raphson interations are used *to refine the approximation Accuracy is approximately *this function computes x such that | Q (x) |
| Static method to evaluate the inverse cumulative distribution *function of a and Mathematical Tables John Wiley &Sons *New York Equation pg **Since the initial approximation only provides an estimate within *E of the true Newton Raphson interations are used *to refine the approximation Accuracy is approximately * | Q (x) |
| virtual void | Register () const |
| void | RemoveAllObservers () |
| void | RemoveObserver (unsigned long tag) |
| void | SetDebug (bool debugFlag) const |
| Set the parameters of the distribution See concrete subclasses *for the order of the parameters Subclasses may provide convenience *methods for setting i e | SetDegreesOfFreedom () |
| virtual void | SetMean (double) |
| void | SetMetaDataDictionary (const MetaDataDictionary &rhs) |
| Set the parameters of the distribution See concrete subclasses *for the order of the parameters Subclasses may provide convenience *methods for setting i e etc *virtual void | SetParameters (const ParametersType ¶ms) |
| virtual void | SetReferenceCount (int) |
| virtual void | SetVariance (double) |
| Static method to evaluate the inverse cumulative distribution *function of a | standardized (mean zero, unit variance) Gaussian.*The static method provides optimized access without requiring an *instance of the class.Parameter p must be between 0.0 and 1.0.**THis implementation was provided by Robert W.Cox from the *Biophysics Research Institute at the Medical College of *Wisconsin.This function is based off of a rational polynomial *approximation to the inverse Gaussian CDF which can be found in *M.Abramowitz and I.A.Stegun.Handbook of Mathematical Functions *with Formulas |
| virtual void | UnRegister () const |
Static Public Member Functions | |
| static void | BreakOnError () |
| static double | CDF (double x, double mean, double variance) |
| static double | CDF (double x, const ParametersType &) |
| static double | CDF (double x) |
| static bool | GetGlobalWarningDisplay () |
| static void | GlobalWarningDisplayOff () |
| static void | GlobalWarningDisplayOn () |
| static Pointer | New () |
| static double | PDF (double x, double mean, double variance) |
| static double | PDF (double x, const ParametersType &) |
| static double | PDF (double x) |
| This is a global flag that controls whether any warning *or error messages are displayed *static void | SetGlobalWarningDisplay (bool flag) |
Public Attributes | |
| Allow people to add remove invoke observers(callbacks) to any ITK *object.This is an implementation of the subject/observer design *pattern.An observer is added by specifying an event to respond to *and an itk unsigned lon | AddObserver )(const EventObject &event, Command *) const |
| This is a global flag that controls whether any | debug |
| Static method to evaluate the inverse cumulative distribution *function of a | Graphs |
| Static method to evaluate the inverse cumulative distribution *function of a and Mathematical Tables John Wiley &Sons *New York Equation pg **Since the initial approximation only provides an estimate within *E of the true Newton Raphson interations are used *to refine the approximation Accuracy is approximately ** | Let |
| Set the parameters of the distribution See concrete subclasses *for the order of the parameters Subclasses may provide convenience *methods for setting | parameters |
| Static method to evaluate the inverse cumulative distribution *function of a and Mathematical Tables John Wiley &Sons *New York Equation pg **Since the initial approximation only provides an estimate within *E of the true | value |
Protected Member Functions | |
| bool | PrintObservers (std::ostream &os, Indent indent) const |
| void | PrintSelf (std::ostream &os, Indent indent) const |
| virtual void | PrintTrailer (std::ostream &os, Indent indent) const |
Protected Attributes | |
| ParametersType | m_Parameters |
| int | m_ReferenceCount |
| SimpleFastMutexLock | m_ReferenceCountLock |
| Methods invoked by virtual 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.*/virtual void PrintSelf(std voi | PrintHeader )(std::ostream &os, Indent indent) const |
|
|
Reimplemented from itk::Statistics::ProbabilityDistribution. Definition at line 61 of file itkGaussianDistribution.h. |
|
|
Type of the parameter vector. Definition at line 78 of file itkProbabilityDistribution.h. |
|
|
Reimplemented from itk::Statistics::ProbabilityDistribution. Definition at line 60 of file itkGaussianDistribution.h. |
|
|
Standard class typedefs Reimplemented from itk::Statistics::ProbabilityDistribution. Definition at line 58 of file itkGaussianDistribution.h. |
|
|
Reimplemented from itk::Statistics::ProbabilityDistribution. Definition at line 59 of file itkGaussianDistribution.h. |
|
|
This method is called when itkExceptionMacro executes. It allows the debugger to break on error. |
|
||||||||||||||||
|
Static method to evaluate the cumulative distribution function (cdf) of a Gaussian. The parameters of the distribution are passed as separate values. The static method provides optimized access without requiring an instance of the class. |
|
||||||||||||
|
Static method to evaluate the cumulative distribution function (cdf) of a Gaussian. The parameters of the distribution are passed as a parameter vector. The ordering of the parameters is (mean, variance). The static method provides optimized access without requiring an instance of the class. |
|
|
Static method to evaluate the cumulative distribution function (cdf) of a standardized (mean zero, unit variance) Gaussian. The static method provides optimized access without requiring an instance of the class. Accuracy is approximately 10^-8. |
|
|
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::LightObject. |
|
|
Turn debugging output off. |
|
|
Turn debugging output on. |
|
|
Delete an itk object. This method should always be used to delete an object when the new operator was used to create it. Using the C delete method will not work with reference counting. |
|
||||||||||||||||
|
Evaluate the cumulative distribution function (cdf). The parameters of the distribution are passed as separate parameters. |
|
||||||||||||
|
Evaluate the cumulative distribution function (cdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (mean, variance). Implements itk::Statistics::ProbabilityDistribution. |
|
|
Evaluate the cumulative distribution function (cdf). The parameters of the distribution are assigned via SetParameters(). Implements itk::Statistics::ProbabilityDistribution. |
|
||||||||||||||||
|
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. |
|
||||||||||||
|
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 (mean, variance). Implements itk::Statistics::ProbabilityDistribution. |
|
|
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. |
|
||||||||||||||||
|
Evaluate the probability density function (pdf). The parameters of the distribution are passed as separate parameters. |
|
||||||||||||
|
Evaluate the probability density function (pdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (mean, variance). Implements itk::Statistics::ProbabilityDistribution. |
|
|
Evaluate the probability density function (pdf). The parameters of the distribution are assigned via SetParameters(). Implements itk::Statistics::ProbabilityDistribution. |
|
|
Get the command associated with the given tag. NOTE: This returns a pointer to a Command, but it is safe to asign this to a Command::Pointer. Since Command inherits from LightObject, at this point in the code, only a pointer or a reference to the Command can be used. |
|
|
Get the value of the debug flag. |
|
|
|
|
|
Get the mean of the Gaussian distribution. Defaults to 0.0. The mean is stored in position 0 of the parameters vector. Implements itk::Statistics::ProbabilityDistribution. |
|
|
|
|
|
|
|
|
|
Strandard macros Reimplemented from itk::Statistics::ProbabilityDistribution. |
|
|
Return the number of parameters. For a univariate Gaussian, this is 2 (mean, variance). Implements itk::Statistics::ProbabilityDistribution. Definition at line 71 of file itkGaussianDistribution.h. |
|
|
Get the parameters of the distribution. See concrete subclasses for the order of parameters. Subclasses may provide convenience methods for setting parameters, i.e. SetDegreesOfFreedom(), etc. |
|
|
Gets the reference count on this object. Definition at line 98 of file itkLightObject.h. |
|
|
Get the variance of the Gaussian distribution. Defaults to 1.0. The variance is stored in position 1 of the parameters vector. Implements itk::Statistics::ProbabilityDistribution. |
|
|
Definition at line 100 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay(). |
|
|
Definition at line 98 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay(). |
|
|
Does this distribution have a mean? Implements itk::Statistics::ProbabilityDistribution. Definition at line 126 of file itkGaussianDistribution.h. |
|
|
Return true if an observer is registered for this event. |
|
|
Does this distribution have a variance? Implements itk::Statistics::ProbabilityDistribution. Definition at line 138 of file itkGaussianDistribution.h. |
|
|
Call Execute on all the Commands observing this event id. The actions triggered by this call doesn't modify this object. |
|
|
Call Execute on all the Commands observing this event id. |
|
|
|
Method for creation through the object factory. Reimplemented from itk::Object. |
|
||||||||||||||||
|
Static method to evaluate the probability density function (pdf) of a Gaussian. The parameters of the distribution are passed as separate values. The static method provides optimized access without requiring an instance of the class. |
|
||||||||||||
|
Static method to evaluate the probability density function (pdf) of a Gaussian. The parameters of the distribution are passed as a parameter vector. The ordering of the parameters is (mean, variance). The static method provides optimized access without requiring an instance of the class. |
|
|
Static method to evaluate the probability density function (pdf) of a standardized (mean zero, unit variance) Gaussian. The static method provides optimized access without requiring an instance of the class. |
|
||||||||||||
|
Cause the object to print itself out. |
|
||||||||||||
|
|
|
||||||||||||
|
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::Object. Reimplemented in itk::Statistics::ChiSquareDistribution, and itk::Statistics::TDistribution. Definition at line 154 of file itkProbabilityDistribution.h. References HardConnectedComponentImageFilter::PrintSelf(). |
|
||||||||||||
|
|
|
|
|
|
|
|
|
|
Increase the reference count (mark as used by another object). Reimplemented from itk::LightObject. |
|
|
Remove all observers . |
|
|
Remove the observer with this tag value. |
|
|
Set the value of the debug flag. A non-zero value turns debugging on. |
|
|
|
|
|
Referenced by itk::Object::GlobalWarningDisplayOff(), and itk::Object::GlobalWarningDisplayOn(). |
|
|
Set the mean of the Gaussian distribution. Defaults to 0.0. The mean is stored in position 0 of the parameters vector. |
|
|
|
|
|
Definition at line 96 of file itkProbabilityDistribution.h. |
|
|
Sets the reference count (use with care) Reimplemented from itk::LightObject. |
|
|
Set the variance of the Gaussian distribution. Defaults to 1.0. The variance is stored in position 1 of the parameters vector. |
|
||||||||||||
|
|
|
|
Decrease the reference count (release by another object). Reimplemented from itk::LightObject. |
|
|
|
|
|
Definition at line 94 of file itkObject.h. |
|
|
Definition at line 188 of file itkGaussianDistribution.h. |
|
|
Definition at line 188 of file itkGaussianDistribution.h. |
|
|
Definition at line 158 of file itkProbabilityDistribution.h. |
|
|
Number of uses of this object by other objects. Definition at line 119 of file itkLightObject.h. |
|
|
Mutex lock to protect modification to the reference count Definition at line 122 of file itkLightObject.h. |
|
|
Definition at line 91 of file itkProbabilityDistribution.h. |
|
|
|
|
|
Definition at line 188 of file itkGaussianDistribution.h. |
1.4.2 written by Dimitri van Heesch,
© 1997-2000