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otb
Commits
a43ca34c
Commit
a43ca34c
authored
Apr 02, 2015
by
Guillaume Pasero
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DOC: doxygen warnings
parent
c71b76e4
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14
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14 changed files
with
75 additions
and
68 deletions
+75
-68
Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h
Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h
+1
-1
Modules/Adapters/GdalAdapters/include/otbGeometriesToGeometriesFilter.h
...rs/GdalAdapters/include/otbGeometriesToGeometriesFilter.h
+4
-5
Modules/Core/LabelMap/include/otbBandsStatisticsAttributesLabelMapFilter.h
...lMap/include/otbBandsStatisticsAttributesLabelMapFilter.h
+1
-1
Modules/Core/Transform/include/otbInverseLogPolarTransform.h
Modules/Core/Transform/include/otbInverseLogPolarTransform.h
+1
-1
Modules/Core/Transform/include/otbLogPolarTransform.h
Modules/Core/Transform/include/otbLogPolarTransform.h
+1
-1
Modules/Core/VectorDataBase/include/otbDataNode.h
Modules/Core/VectorDataBase/include/otbDataNode.h
+1
-1
Modules/Feature/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h
...ure/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h
+1
-1
Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h
Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h
+1
-1
Modules/Filtering/MathParserX/include/otbBandMathXImageFilter.h
...s/Filtering/MathParserX/include/otbBandMathXImageFilter.h
+1
-1
Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
...earning/Supervised/include/otbBoostMachineLearningModel.h
+1
-1
Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
.../Supervised/include/otbDecisionTreeMachineLearningModel.h
+1
-1
Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
...rvised/include/otbKNearestNeighborsMachineLearningModel.h
+2
-2
Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
...Supervised/include/otbNeuralNetworkMachineLearningModel.h
+2
-2
Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
...Supervised/include/otbRandomForestsMachineLearningModel.h
+57
-49
No files found.
Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h
View file @
a43ca34c
...
...
@@ -31,7 +31,7 @@ class Layer;
class
GeometriesSet
;
}
// otb namespace
/**\defgroup GeometriesFilters
/**\defgroup GeometriesFilters
Filters of geometries sets
* \ingroup gGeometry Filters
* Filters of geometries sets.
*/
...
...
Modules/Adapters/GdalAdapters/include/otbGeometriesToGeometriesFilter.h
View file @
a43ca34c
...
...
@@ -180,7 +180,6 @@ struct FieldCopyTransformation
}
/**
* In-place transformation: does nothing.
* \param[in] inoutFeature \c Feature to change.
* \throw Nothing
*/
void
fieldsTransform
(
ogr
::
Feature
const
&
itkNotUsed
(
inoutFeature
))
const
...
...
@@ -189,8 +188,8 @@ struct FieldCopyTransformation
}
/**
* By-Copy transformation: copies all fields.
* \param[in] inFeature input \c Feature
* \param[in,out] outFeature output \c Feature
* \param
[in] inFeature input \c Feature
* \param
[in,out] outFeature output \c Feature
*
* \throw itk::ExceptionObject if the fields cannot be copied.
*/
...
...
@@ -199,8 +198,8 @@ struct FieldCopyTransformation
/**
* Defines the fields in the destination layer.
* The default action is to copy all fieds from one layer to another.
* \param[in] source source \c Layer
* \param[in,out] dest destination \c Layer
* \param
[in] source source \c Layer
* \param
[in,out] dest destination \c Layer
* \throw itk::ExceptionObject in case the operation can't succeed.
*/
void
DefineFields
(
ogr
::
Layer
const
&
source
,
ogr
::
Layer
&
dest
)
const
;
...
...
Modules/Core/LabelMap/include/otbBandsStatisticsAttributesLabelMapFilter.h
View file @
a43ca34c
...
...
@@ -121,7 +121,7 @@ private:
* StatisticsAttributesLabelMapFilter on each channel independently
*
* The feature name is constructed as:
* 'STATS' + '::' + 'Band' +
#BandIndex + '::' + StatisticN
ame
* 'STATS' + '::' + 'Band' +
band_index + '::' + statistic_n
ame
*
* The ReducedAttributesSet flag allows to tell the internal
* statistics filter to compute only the main attributes (mean, variance, skewness and kurtosis).
...
...
Modules/Core/Transform/include/otbInverseLogPolarTransform.h
View file @
a43ca34c
...
...
@@ -88,7 +88,7 @@ public:
virtual
ParametersType
&
GetParameters
(
void
)
const
;
/**
* Set the Fixed Parameters
* \param
The F
ixed parameters of the transform.
* \param
param The f
ixed parameters of the transform.
*/
virtual
void
SetFixedParameters
(
const
ParametersType
&
param
)
{
this
->
m_FixedParameters
=
param
;
}
...
...
Modules/Core/Transform/include/otbLogPolarTransform.h
View file @
a43ca34c
...
...
@@ -90,7 +90,7 @@ public:
/**
* Set the Fixed Parameters
* \param
The F
ixed parameters of the transform.
* \param
param The f
ixed parameters of the transform.
*/
virtual
void
SetFixedParameters
(
const
ParametersType
&
param
)
{
this
->
m_FixedParameters
=
param
;
}
...
...
Modules/Core/VectorDataBase/include/otbDataNode.h
View file @
a43ca34c
...
...
@@ -254,7 +254,7 @@ public:
/**
* Copy the field list from a DataNode
* \param datanode where to get the keywordlist to copy.
* \param data
Node data
node where to get the keywordlist to copy.
*/
void
CopyFieldList
(
const
DataNode
*
dataNode
);
...
...
Modules/Feature/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h
View file @
a43ca34c
...
...
@@ -106,7 +106,7 @@ public:
*
* Orientation is expressed in degree in the range [0, 360] with a precision of 10 degrees.
*
* \example
FeatureExtraction
/SIFTExample.cxx
* \example
Patented
/SIFTExample.cxx
*
*
* \ingroup OTBDescriptors
...
...
Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h
View file @
a43ca34c
...
...
@@ -41,7 +41,7 @@ namespace otb
*
* \sa ImageToSIFTKeyPointSetFilter
*
* \example
FeatureExtraction
/SIFTFastExample.cxx
* \example
Patented
/SIFTFastExample.cxx
*
* \ingroup OTBDescriptors
*/
...
...
Modules/Filtering/MathParserX/include/otbBandMathXImageFilter.h
View file @
a43ca34c
...
...
@@ -40,7 +40,7 @@ namespace otb
*
* This filter is based on the mathematical parser library muParserX.
* The built in functions and operators list is available at:
*
\url{http:*articles.beltoforion.de/article.php?a=muparserx}
.
*
http://articles.beltoforion.de/article.php?a=muparserx
.
*
* In order to use this filter, at least one input image is to be
* set. An associated variable name can be specified or not by using
...
...
Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
View file @
a43ca34c
...
...
@@ -79,7 +79,7 @@ public:
/** Setters/Getters to the threshold WeightTrimRate.
* A threshold between 0 and 1 used to save computational time.
* Samples with summary weight \
leq 1 - WeightTrimRate
do not participate in the next iteration of training.
* Samples with summary weight \
f$ w \leq 1 - WeightTrimRate \f$
do not participate in the next iteration of training.
* Set this parameter to 0 to turn off this functionality.
* Default is 0.95
* \see http://docs.opencv.org/modules/ml/doc/boosting.html#cvboostparams-cvboostparams
...
...
Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
View file @
a43ca34c
...
...
@@ -90,7 +90,7 @@ public:
itkGetMacro
(
UseSurrogates
,
bool
);
itkSetMacro
(
UseSurrogates
,
bool
);
/** Cluster possible values of a categorical variable into
K \leq max_categories
clusters to find
/** Cluster possible values of a categorical variable into
\f$ K \leq MaxCategories \f$
clusters to find
* a suboptimal split. If a discrete variable, on which the training procedure tries to make a split,
* takes more than max_categories values, the precise best subset estimation may take a very long time
* because the algorithm is exponential. Instead, many decision trees engines (including ML) try to find
...
...
Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
View file @
a43ca34c
...
...
@@ -55,14 +55,14 @@ public:
/** Setters/Getters to the number of neighbors to use
* Default is 32
*
see @
http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*
\see
http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*/
itkGetMacro
(
K
,
int
);
itkSetMacro
(
K
,
int
);
/** Setters/Getters to IsRegression flag
* Default is False
*
see @
http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*
\see
http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*/
itkGetMacro
(
IsRegression
,
bool
);
itkSetMacro
(
IsRegression
,
bool
);
...
...
Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
View file @
a43ca34c
...
...
@@ -117,14 +117,14 @@ public:
itkGetMacro
(
BackPropMomentScale
,
double
);
itkSetMacro
(
BackPropMomentScale
,
double
);
/** Initial value \
Delta_0 of update-values \Delta_{ij}
in RPROP method.
/** Initial value \
f$ \Delta_0 \f$ of update-values \f$ \Delta_{ij} \f$
in RPROP method.
* Default is 0.1
* \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
*/
itkGetMacro
(
RegPropDW0
,
double
);
itkSetMacro
(
RegPropDW0
,
double
);
/** Update-values lower limit \
Delta_{min}
in RPROP method.
/** Update-values lower limit \
f$ \Delta_{min} \f$
in RPROP method.
* It must be positive. Default is FLT_EPSILON
* \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
*/
...
...
Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
View file @
a43ca34c
...
...
@@ -83,55 +83,21 @@ public:
//@}
//Setters of RT parameters (documentation get from opencv doxygen 2.4)
/* the depth of the tree. A low value will likely underfit and conversely a
* high value will likely overfit. The optimal value can be obtained using cross
* validation or other suitable methods. */
itkGetMacro
(
MaxDepth
,
int
);
itkSetMacro
(
MaxDepth
,
int
);
/* minimum samples required at a leaf node for it to be split. A reasonable
* value is a small percentage of the total data e.g. 1%. */
itkGetMacro
(
MinSampleCount
,
int
);
itkSetMacro
(
MinSampleCount
,
int
);
/* Termination criteria for regression trees. If all absolute differences
* between an estimated value in a node and values of train samples in this node
* are less than this parameter then the node will not be split */
itkGetMacro
(
RegressionAccuracy
,
double
);
itkSetMacro
(
RegressionAccuracy
,
double
);
itkGetMacro
(
ComputeSurrogateSplit
,
bool
);
itkSetMacro
(
ComputeSurrogateSplit
,
bool
);
/* Cluster possible values of a categorical variable into K \leq
* max_categories clusters to find a suboptimal split. If a discrete variable,
* on which the training procedure tries to make a split, takes more than
* max_categories values, the precise best subset estimation may take a very
* long time because the algorithm is exponential. Instead, many decision
* trees engines (including ML) try to find sub-optimal split in this case by
* clustering all the samples into max categories clusters that is some
* categories are merged together. The clustering is applied only in n>2-class
* classification problems for categorical variables with N > max_categories
* possible values. In case of regression and 2-class classification the
* optimal split can be found efficiently without employing clustering, thus
* the parameter is not used in these cases.
*/
itkGetMacro
(
MaxNumberOfCategories
,
int
);
itkSetMacro
(
MaxNumberOfCategories
,
int
);
/* The array of a priori class probabilities, sorted by the class label
* value. The parameter can be used to tune the decision tree preferences toward
* a certain class. For example, if you want to detect some rare anomaly
* occurrence, the training base will likely contain much more normal cases than
* anomalies, so a very good classification performance will be achieved just by
* considering every case as normal. To avoid this, the priors can be specified,
* where the anomaly probability is artificially increased (up to 0.5 or even
* greater), so the weight of the misclassified anomalies becomes much bigger,
* and the tree is adjusted properly. You can also think about this parameter as
* weights of prediction categories which determine relative weights that you
* give to misclassification. That is, if the weight of the first category is 1
* and the weight of the second category is 10, then each mistake in predicting
* the second category is equivalent to making 10 mistakes in predicting the
first category. */
std
::
vector
<
float
>
GetPriors
()
const
{
return
m_Priors
;
...
...
@@ -141,29 +107,22 @@ public:
{
m_Priors
=
priors
;
}
/* If true then variable importance will be calculated and then it can be
retrieved by CvRTrees::get_var_importance(). */
itkGetMacro
(
CalculateVariableImportance
,
bool
);
itkSetMacro
(
CalculateVariableImportance
,
bool
);
/* The size of the randomly selected subset of features at each tree node and
* that are used to find the best split(s). If you set it to 0 then the size will
be set to the square root of the total number of features. */
itkGetMacro
(
MaxNumberOfVariables
,
int
);
itkSetMacro
(
MaxNumberOfVariables
,
int
);
/* The maximum number of trees in the forest (surprise, surprise). Typically
* the more trees you have the better the accuracy. However, the improvement in
* accuracy generally diminishes and asymptotes pass a certain number of
* trees. Also to keep in mind, the number of tree increases the prediction time
linearly. */
itkGetMacro
(
MaxNumberOfTrees
,
int
);
itkSetMacro
(
MaxNumberOfTrees
,
int
);
/* Sufficient accuracy (OOB error) */
itkGetMacro
(
ForestAccuracy
,
float
);
itkSetMacro
(
ForestAccuracy
,
float
);
/* The type of the termination criteria */
itkGetMacro
(
TerminationCriteria
,
int
);
itkSetMacro
(
TerminationCriteria
,
int
);
/* Perform regression instead of classification */
itkGetMacro
(
RegressionMode
,
bool
);
itkSetMacro
(
RegressionMode
,
bool
);
...
...
@@ -193,17 +152,66 @@ private:
void
operator
=
(
const
Self
&
);
//purposely not implemented
CvRTrees
*
m_RFModel
;
/** The depth of the tree. A low value will likely underfit and conversely a
* high value will likely overfit. The optimal value can be obtained using cross
* validation or other suitable methods. */
int
m_MaxDepth
;
/** minimum samples required at a leaf node for it to be split. A reasonable
* value is a small percentage of the total data e.g. 1%. */
int
m_MinSampleCount
;
/** Termination criteria for regression trees. If all absolute differences
* between an estimated value in a node and values of train samples in this node
* are less than this parameter then the node will not be split */
float
m_RegressionAccuracy
;
bool
m_ComputeSurrogateSplit
;
/** Cluster possible values of a categorical variable into
* \f$ K \leq MaxCategories \f$
* clusters to find a suboptimal split. If a discrete variable,
* on which the training procedure tries to make a split, takes more than
* max_categories values, the precise best subset estimation may take a very
* long time because the algorithm is exponential. Instead, many decision
* trees engines (including ML) try to find sub-optimal split in this case by
* clustering all the samples into max categories clusters that is some
* categories are merged together. The clustering is applied only in n>2-class
* classification problems for categorical variables with N > max_categories
* possible values. In case of regression and 2-class classification the
* optimal split can be found efficiently without employing clustering, thus
* the parameter is not used in these cases.
*/
int
m_MaxNumberOfCategories
;
/** The array of a priori class probabilities, sorted by the class label
* value. The parameter can be used to tune the decision tree preferences toward
* a certain class. For example, if you want to detect some rare anomaly
* occurrence, the training base will likely contain much more normal cases than
* anomalies, so a very good classification performance will be achieved just by
* considering every case as normal. To avoid this, the priors can be specified,
* where the anomaly probability is artificially increased (up to 0.5 or even
* greater), so the weight of the misclassified anomalies becomes much bigger,
* and the tree is adjusted properly. You can also think about this parameter as
* weights of prediction categories which determine relative weights that you
* give to misclassification. That is, if the weight of the first category is 1
* and the weight of the second category is 10, then each mistake in predicting
* the second category is equivalent to making 10 mistakes in predicting the
* first category. */
std
::
vector
<
float
>
m_Priors
;
/** If true then variable importance will be calculated and then it can be
* retrieved by CvRTrees::get_var_importance(). */
bool
m_CalculateVariableImportance
;
/** The size of the randomly selected subset of features at each tree node and
* that are used to find the best split(s). If you set it to 0 then the size will
* be set to the square root of the total number of features. */
int
m_MaxNumberOfVariables
;
/** The maximum number of trees in the forest (surprise, surprise). Typically
* the more trees you have the better the accuracy. However, the improvement in
* accuracy generally diminishes and asymptotes pass a certain number of
* trees. Also to keep in mind, the number of tree increases the prediction time
*linearly. */
int
m_MaxNumberOfTrees
;
/** Sufficient accuracy (OOB error) */
float
m_ForestAccuracy
;
/** The type of the termination criteria */
int
m_TerminationCriteria
;
/** Perform regression instead of classification */
bool
m_RegressionMode
;
};
}
// end namespace otb
...
...
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