/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef itkGaussianMembershipFunction_h
#define itkGaussianMembershipFunction_h

#include "itkMatrix.h"
#include "itkMembershipFunctionBase.h"

namespace itk
{
namespace Statistics
{
/** \class GaussianMembershipFunction
 * \brief GaussianMembershipFunction models class membership through a
 * multivariate Gaussian function.
 *
 * GaussianMembershipFunction is a subclass of MembershipFunctionBase
 * that models class membership (or likelihood) using a multivariate
 * Gaussian function. The mean and covariance structure of the
 * Gaussian are established using the methods SetMean() and
 * SetCovariance(). The mean is a vector-type that is the same
 * vector-type as the measurement vector but guaranteed to have a real
 * element type. For instance, if the measurement type is an
 * Vector<int,3>, then the mean is Vector<double,3>. If the
 * measurement type is a VariableLengthVector<float>, then the mean is
 * VariableLengthVector<double>. In contrast to this behavior, the
 * covariance is always a VariableSizeMatrix<double>.
 *
 * If the covariance is singular or nearly singular, the membership function
 * behaves somewhat like an impulse located at the mean. In this case,
 * we specify the covariance to be a diagonal matrix with large values
 * along the diagonal. This membership function, therefore,
 * will return small but differentiable values everywher and increase
 * sharply near the mean.
 *
 * \ingroup ITKStatistics
 */

template< typename TMeasurementVector >
class GaussianMembershipFunction:
  public MembershipFunctionBase< TMeasurementVector >
{
public:
  /** Standard class typedefs */
  typedef GaussianMembershipFunction                   Self;
  typedef MembershipFunctionBase< TMeasurementVector > Superclass;
  typedef SmartPointer< Self >                         Pointer;
  typedef SmartPointer< const Self >                   ConstPointer;

  /** Standard macros */
  itkTypeMacro(GaussianMembershipFunction, MembershipFunction);
  itkNewMacro(Self);

  /** SmartPointer class for superclass */
  typedef typename Superclass::Pointer MembershipFunctionPointer;

  /** Typedef alias for the measurement vectors */
  typedef TMeasurementVector MeasurementVectorType;

  /** Length of each measurement vector */
  typedef typename Superclass::MeasurementVectorSizeType MeasurementVectorSizeType;

  /** Type of the mean vector. RealType on a vector-type is the same
   * vector-type but with a real element type.  */
  typedef typename itk::NumericTraits< MeasurementVectorType >::RealType MeasurementVectorRealType;
  typedef MeasurementVectorRealType MeanVectorType;

  /** Type of the covariance matrix */
  typedef VariableSizeMatrix< double > CovarianceMatrixType;

  /** Set the mean of the Gaussian distribution. Mean is a vector type
   * similar to the measurement type but with a real element type. */
  void SetMean(const MeanVectorType & mean);

  /** Get the mean of the Gaussian distribution. Mean is a vector type
   * similar to the measurement type but with a real element type. */
  itkGetConstReferenceMacro(Mean, MeanVectorType);

  /** Set the covariance matrix. Covariance matrix is a
   * VariableSizeMatrix of doubles. The inverse of the covariance
   * matrix and the normlization term for the multivariate Gaussian
   * are calculate whenever the covaraince matrix is changed. */
  void SetCovariance(const CovarianceMatrixType & cov);

  /* Get the covariance matrix. Covariance matrix is a
  VariableSizeMatrix of doubles. */
  itkGetConstReferenceMacro(Covariance, CovarianceMatrixType);

  /* Get the inverse covariance matrix. Covariance matrix is a
  VariableSizeMatrix of doubles. */
  itkGetConstReferenceMacro(InverseCovariance, CovarianceMatrixType);

  /** Evaluate the probability density of a measurement vector. */
  double Evaluate(const MeasurementVectorType & measurement) const ITK_OVERRIDE;

  /** Method to clone a membership function, i.e. create a new instance of
   * the same type of membership function and configure its ivars to
   * match. */
  virtual typename LightObject::Pointer InternalClone() const ITK_OVERRIDE;

protected:
  GaussianMembershipFunction();
  virtual ~GaussianMembershipFunction(void) {}
  void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;

private:
  GaussianMembershipFunction(const Self &) ITK_DELETE_FUNCTION;
  void operator=(const Self &) ITK_DELETE_FUNCTION;

  MeanVectorType       m_Mean;            // mean
  CovarianceMatrixType m_Covariance;      // covariance matrix

  // inverse covariance matrix. automatically calculated
  // when covariace matirx is set.
  CovarianceMatrixType m_InverseCovariance;

  // pre_factor (normalization term). automatically calculated
  // when covariace matirx is set.
  double m_PreFactor;

  /** Boolean to cache whether the covarinace is singular or nearly singular */
  bool m_CovarianceNonsingular;
};
} // end of namespace Statistics
} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkGaussianMembershipFunction.hxx"
#endif

#endif
