1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
|
/*=========================================================================
*
* 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 itkMaximumRatioDecisionRule_h
#define itkMaximumRatioDecisionRule_h
#include <vector>
#include "vnl/vnl_matrix.h"
#include "itkNumericTraits.h"
#include "itkDecisionRule.h"
#include "ITKStatisticsExport.h"
namespace itk
{
namespace Statistics
{
/** \class MaximumRatioDecisionRule
* \brief A decision rule that operates as a frequentist's
* approximation to Bayes rule.
*
* MaximumRatioDecisionRule returns the class label using a Bayesian
* style decision rule. The discriminant scores are evaluated in the
* context of class priors. If the discriminant scores are actual
* conditional probabilites (likelihoods) and the class priors are
* actual a priori class probabilities, then this decision rule operates
* as Bayes rule, returning the class \f$i\f$ if
* \f$p(x|i) p(i) > p(x|j) p(j)\f$ for all class \f$j\f$. The
* discriminant scores and priors are not required to be true
* probabilities.
*
* This class is named the MaximumRatioDecisionRule as it can be
* implemented as returning the class \f$i\f$ if
* \f$\frac{p(x|i)}{p(x|j)} > \frac{p(j)}{p(i)}\f$ for all class
* \f$j\f$.
*
* A priori values need to be set before calling the Evaluate
* method. If they are not set, a uniform prior is assumed.
*
* \sa MaximumDecisionRule, MinimumDecisionRule
* \ingroup ITKStatistics
*/
class ITKStatistics_EXPORT MaximumRatioDecisionRule : public DecisionRule
{
public:
/** Standard class typedefs */
typedef MaximumRatioDecisionRule Self;
typedef DecisionRule Superclass;
typedef SmartPointer< Self > Pointer;
/** Run-time type information (and related methods) */
itkTypeMacro(MaximumRatioDecisionRule, DecisionRule);
/** Standard New() method support */
itkNewMacro(Self);
/** Types for discriminant values and vectors. */
typedef Superclass::MembershipValueType MembershipValueType;
typedef Superclass::MembershipVectorType MembershipVectorType;
/** Types for class identifiers. */
typedef Superclass::ClassIdentifierType ClassIdentifierType;
/** Types for priors and values */
typedef MembershipValueType PriorProbabilityValueType;
typedef std::vector< PriorProbabilityValueType > PriorProbabilityVectorType;
typedef PriorProbabilityVectorType::size_type PriorProbabilityVectorSizeType;
/**
* Evaluate the decision rule \f$p(x|i) p(i) > p(x|j) p(j)\f$. Prior
* probabilities need to be set before calling Evaluate() using the
* SetPriorProbabilities() method (otherwise a uniform prior is
* assumed). Parameter to Evaluate() is the discriminant score in
* the form of a likelihood \f$p(x|i)\f$.
*/
virtual ClassIdentifierType Evaluate(const MembershipVectorType & discriminantScores) const ITK_OVERRIDE;
/** Set the prior probabilities used in evaluating
* \f$p(x|i) p(i) > p(x|j) p(j)\f$. The likelihoods are set using
* the Evaluate() method. SetPriorProbabilities needs to be called before
* Evaluate(). If not set, assumes a uniform prior. */
void SetPriorProbabilities(const PriorProbabilityVectorType& p);
/** Get the prior probabilities. */
itkGetConstReferenceMacro(PriorProbabilities, PriorProbabilityVectorType);
protected:
MaximumRatioDecisionRule();
virtual ~MaximumRatioDecisionRule() ITK_OVERRIDE {}
virtual void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
private:
ITK_DISALLOW_COPY_AND_ASSIGN(MaximumRatioDecisionRule);
PriorProbabilityVectorType m_PriorProbabilities;
}; // end of class
} // end of Statistics namespace
} // end of ITK namespace
#endif
|
