/usr/include/OTB-6.4/otbMachineLearningModelTraits.h is in libotb-dev 6.4.0+dfsg-1.
This file is owned by root:root, with mode 0o644.
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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 | /*
* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* 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
*
* 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 otbMachineLearningModelTraits_h
#define otbMachineLearningModelTraits_h
#include "itkVariableLengthVector.h"
#include "itkFixedArray.h"
#include "itkIsNumber.h"
#include "itkMetaProgrammingLibrary.h"
namespace otb
{
/**
* This is the struct defining the sample implementation for
* MachineLearningModel. It offers two type definitions: SampleType
* and ValueType.
*
* \tparam TInput : input sample type (can be either a scalar type or
* a VariableLengthVector
* \tparam isNumber either TrueType or FalseType for partial
* specialization
*/
template <typename TInput, typename isNumber> struct MLMSampleTraitsImpl;
/// \cond SPECIALIZATION_IMPLEMENTATION
// For Numbers
template <typename TInput> struct MLMSampleTraitsImpl<TInput, itk::mpl::TrueType> {
typedef TInput ValueType;
typedef itk::VariableLengthVector<TInput> SampleType;
};
// For Vectors
template <typename TInput> struct MLMSampleTraitsImpl<TInput, itk::mpl::FalseType> {
typedef typename TInput::ValueType ValueType;
typedef TInput SampleType;
};
/// \endcond
/**
* Simplified implementation of SampleTraits using MLMSampleTraitsImpl
*/
template <typename TInput> using MLMSampleTraits = MLMSampleTraitsImpl< TInput, typename itk::mpl::IsNumber<TInput>::Type >;
/**
* This is the struct defining the sample implementation for
* MachineLearningModel. It offers two type definitions: TargetType
* and ValueType.
*
* \tparam TInput : input sample type (can be either a scalar type or
* a VariableLengthVector or a FixedArray
* \tparam isNumber either TrueType or FalseType for partial
* specialization
*/
template <typename TInput, typename isNumber> struct MLMTargetTraitsImpl;
/// \cond SPECIALIZATION_IMPLEMENTATION
// For Numbers
template <typename TInput> struct MLMTargetTraitsImpl<TInput, itk::mpl::TrueType> {
typedef TInput ValueType;
typedef itk::FixedArray<TInput,1> SampleType;
};
// For Vectors
template <typename TInput> struct MLMTargetTraitsImpl<TInput, itk::mpl::FalseType> {
typedef typename TInput::ValueType ValueType;
typedef TInput SampleType;
};
/// \endcond
/**
* Simplified implementation of TargetTraits using MLMTargetTraitsImpl
*/
template <typename TInput> using MLMTargetTraits = MLMTargetTraitsImpl< TInput, typename itk::mpl::IsNumber<TInput>::Type >;
} // End namespace otb
#endif
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