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Julien Cabieces
otb
Commits
aa97ab1b
Commit
aa97ab1b
authored
13 years ago
by
Julien Malik
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ENH: use choice parameter type for choosing kernel type
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dd4932dc
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Applications/Classification/otbTrainSVMImagesClassifier.cxx
+14
-10
14 additions, 10 deletions
Applications/Classification/otbTrainSVMImagesClassifier.cxx
with
14 additions
and
10 deletions
Applications/Classification/otbTrainSVMImagesClassifier.cxx
+
14
−
10
View file @
aa97ab1b
...
...
@@ -144,25 +144,29 @@ private:
AddParameter
(
ParameterType_InputVectorDataList
,
"vd"
,
"Vector Data of sample used to train the estimator"
);
AddParameter
(
ParameterType_Filename
,
"dem"
,
"A DEM repository"
);
MandatoryOff
(
"dem"
);
AddParameter
(
ParameterType_Filename
,
"imstat"
,
"XML file containing mean and standard deviation of input images
.
"
);
AddParameter
(
ParameterType_Filename
,
"imstat"
,
"XML file containing mean and standard deviation of input images"
);
MandatoryOff
(
"imstat"
);
AddParameter
(
ParameterType_Filename
,
"out"
,
"Output SVM model
.
"
);
AddParameter
(
ParameterType_Float
,
"m"
,
"Margin for SVM learning
.
"
);
AddParameter
(
ParameterType_Filename
,
"out"
,
"Output SVM model"
);
AddParameter
(
ParameterType_Float
,
"m"
,
"Margin for SVM learning"
);
MandatoryOff
(
"m"
);
AddParameter
(
ParameterType_Int
,
"b"
,
"Balance and grow the training set
.
"
);
AddParameter
(
ParameterType_Int
,
"b"
,
"Balance and grow the training set"
);
MandatoryOff
(
"b"
);
AddParameter
(
ParameterType_
Int
,
"k"
,
"
Type of kernel use to estimate SVM model : 0 = LINEAR (default), 1 = RBF, 2 = POLY, 3 = SIGMOID.
"
);
AddParameter
(
ParameterType_
Choice
,
"k"
,
"
SVM Kernel Type
"
);
MandatoryOff
(
"k"
);
SetParameterInt
(
"k"
,
0
);
AddParameter
(
ParameterType_Int
,
"mt"
,
"Maximum size of the training sample (default = -1)."
);
AddChoice
(
"k.linear"
,
"Linear"
);
AddChoice
(
"k.rbf"
,
"Neareast Neighbor"
);
AddChoice
(
"k.poly"
,
"Polynomial"
);
AddChoice
(
"k.sigmoid"
,
"Sigmoid"
);
SetParameterString
(
"k"
,
"linear"
);
AddParameter
(
ParameterType_Int
,
"mt"
,
"Maximum size of the training sample (default = -1)"
);
MandatoryOff
(
"mt"
);
SetParameterInt
(
"mt"
,
-
1
);
AddParameter
(
ParameterType_Int
,
"mv"
,
"Maximum size of the validation sample (default = -1)
.
"
);
AddParameter
(
ParameterType_Int
,
"mv"
,
"Maximum size of the validation sample (default = -1)"
);
MandatoryOff
(
"mv"
);
SetParameterInt
(
"mv"
,
-
1
);
AddParameter
(
ParameterType_Float
,
"vtr"
,
"Ratio between training and validation sample (0.0 = all training, 1.0 = all validation) default = 0.5
.
"
);
"Ratio between training and validation sample (0.0 = all training, 1.0 = all validation) default = 0.5"
);
MandatoryOff
(
"vtr"
);
SetParameterFloat
(
"vtr"
,
0.5
);
AddParameter
(
ParameterType_Empty
,
"opt"
,
"Use SVM parameters optimization"
);
...
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