Commit 2d0c7c01 authored by Antoine Regimbeau's avatar Antoine Regimbeau

Got the file from doc sprint, in order to work properly on the app

parent de9c2717
......@@ -37,9 +37,23 @@ namespace Wrapper
SetParameterDescription("classifier.libsvm", "This group of parameters allows setting SVM classifier parameters.");
AddParameter(ParameterType_Choice, "classifier.libsvm.k", "SVM Kernel Type");
AddChoice("classifier.libsvm.k.linear", "Linear");
"Linear Kernel, no mapping is done, this is the fastest option.");
AddChoice("classifier.libsvm.k.rbf", "Gaussian radial basis function");
"This kernel is a good choice in most of the case. It is "
"an exponential function of the euclidian distance between "
"the vectors.");
AddChoice("classifier.libsvm.k.poly", "Polynomial");
"Polynomial Kernel, the mapping is a polynomial function.");
AddChoice("classifier.libsvm.k.sigmoid", "Sigmoid");
"The kernel is a hyperbolic tangente function of the vectors.");
SetParameterString("classifier.libsvm.k", "linear", false);
SetParameterDescription("classifier.libsvm.k", "SVM Kernel Type.");
AddParameter(ParameterType_Choice, "classifier.libsvm.m", "SVM Model Type");
......@@ -47,21 +61,44 @@ namespace Wrapper
if (this->m_RegressionFlag)
AddChoice("classifier.libsvm.m.epssvr", "Epsilon Support Vector Regression");
"The distance between feature vectors from the training set and the "
"fitting hyper-plane must be less than Epsilon. For outliers the penalty "
"multiplier C is used ");
AddChoice("classifier.libsvm.m.nusvr", "Nu Support Vector Regression");
SetParameterString("classifier.libsvm.m", "epssvr", false);
"Same as the epsilon regression except that this time the bounded "
"parameter nu is used instead of epsilon");
AddChoice("classifier.libsvm.m.csvc", "C support vector classification");
"This formulation allows imperfect separation of classes. The penalty "
"is set through the cost parameter C.");
AddChoice("classifier.libsvm.m.nusvc", "Nu support vector classification");
"This formulation allows imperfect separation of classes. The penalty "
"is set through the cost parameter Nu. As compared to C, Nu is harder "
"to optimize, and may not be as fast.");
AddChoice("classifier.libsvm.m.oneclass", "Distribution estimation (One Class SVM)");
"All the training data are from the same class, SVM builds a boundary "
"that separates the class from the rest of the feature space.");
SetParameterString("classifier.libsvm.m", "csvc", false);
AddParameter(ParameterType_Float, "classifier.libsvm.c", "Cost parameter C");
SetParameterFloat("classifier.libsvm.c",1.0, false);
"SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.");
"SVM models have a cost parameter C (1 by default) to control the "
"trade-off between training errors and forcing rigid margins.");
// It seems that it miss a nu parameter for the nu-SVM use.
AddParameter(ParameterType_Empty, "classifier.libsvm.opt", "Parameters optimization");
SetParameterDescription("classifier.libsvm.opt", "SVM parameters optimization flag.");
......@@ -73,8 +110,13 @@ namespace Wrapper
AddParameter(ParameterType_Float, "classifier.libsvm.eps", "Epsilon");
SetParameterFloat("classifier.libsvm.eps",1e-3, false);
"Parameter for the epsilon regression mode.");
AddParameter(ParameterType_Float, "", "Nu");
SetParameterFloat("",0.5, false);
"Cost parameter Nu, in the range 0..1, the larger the value, "
"the smoother the decision.");
......@@ -163,4 +205,4 @@ namespace Wrapper
} //end namespace wrapper
} //end namespace otb
\ No newline at end of file
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