Commit 8cafd175 authored by Rashad Kanavath's avatar Rashad Kanavath

ENH: apply spelling.patch from DebianGIS

This is a big one without much harm. It fixes incorrect spelling
and grammer when packaging OTB 5.0.0 and 5.2.0 for Debian.
Most of these changes are done by developers at DebianGIS.
For more info, I include the original patch header below.
Description: Fix some spelling errors to silence lintian
enabling  to concentrate on the real problem.
Author: Andreas Tille <tille@debian.org>
Author: Rashad Kanavath <rashad.kanavath@c-s.fr>
Author: Bas Couwenberg <sebastic@xs4all.nl>
Last-Update: 2015-12-22
parent 39c99797
......@@ -17,7 +17,7 @@ of Health. ITK is partially derived from VTK and VXL, hence some code is
copyrighted accordingly (see VTKCopyright.txt and VXLCopyright.txt).
The copyright of most of the files in the "Utilities" subdirectory is held by
third parties who allow to distribute this material under a license compatible
third parties who allow distributing this material under a license compatible
with the one used by ITK. Please read the content of the subdirectories for
specific details on those third-party licenses. You will also find details in
the README.txt file under the "Copyright" subdirectory.
......
......@@ -151,7 +151,7 @@ int main( int argc, char* argv[])
//
// Now we can define the mathematical expression to perform on the layers (b1, b2, b3, b4).
// The filter takes advantage of the parsing capabilities of the muParser library and
// allows to set the expression as on a digital calculator.
// allows setting the expression as on a digital calculator.
//
// The expression below returns 255 if the ratio $(NIR-RED)/(NIR+RED)$ is greater than 0.4 and 0 if not.
//
......
......@@ -51,7 +51,7 @@
// something accepting the syntax \code{foo()}. This can be
// implemented using classical C/C++ functions, but it is preferable
// to implement it using C++ functors. These are classical C++ classes
// which overload the \code{()} operator. This allows to use them with
// which overload the \code{()} operator. This allows using them with
// the same syntax as C/C++ functions.
//
// Since change detectors operate on neighborhoods, the functor
......
......@@ -32,8 +32,8 @@
// This example illustrates the class
// \doxygen{otb}{MultivariateAlterationChangeDetectorImageFilter},
// which implements the Multivariate Alteration Change Detector
// algorithm \cite{nielsen2007regularized}. This algorihtm allows to
// perform change detection from a pair multi-band images, including
// algorithm \cite{nielsen2007regularized}. This algorihtm allows
// performing change detection from a pair multi-band images, including
// images with different number of bands or modalities. Its output is
// a a multi-band image of change maps, each one being unccorrelated
// with the remaining. The number of bands of the output image is the
......
......@@ -135,8 +135,8 @@ int main(int itkNotUsed(argc), char * argv[])
//
// Other useful methods are:
// \begin{itemize}
// \item \code{SetNthElement()} and \code{GetNthElement()} allow to
// randomly access any element of the list.
// \item \code{SetNthElement()} and \code{GetNthElement()} allow
// randomly accessing any element of the list.
// \item \code{Front()} to access to the first element of the list.
// \item \code{Erase()} to remove an element.
// \end{itemize}
......
......@@ -40,7 +40,7 @@
// However, the \doxygen{itk}{Vector} is a fixed size array and it
// assumes that the number of channels of the image is known at
// compile time. Therefore, we prefer to use the
// \doxygen{otb}{VectorImage} class which allows to choose the number
// \doxygen{otb}{VectorImage} class which allows choosing the number
// of channels of the image at runtime. The pixels will be of type
// \doxygen{itk}{VariableLengthVector}.
//
......@@ -91,7 +91,7 @@ int main(int, char *[])
image->SetRegions(region);
// Software Guide : BeginLatex
// Since the pixel dimensionality is choosen at runtime, one has to
// Since the pixel dimensionality is chosen at runtime, one has to
// pass this parameter to the image before memory allocation.
// Software Guide : EndLatex
......
......@@ -25,7 +25,7 @@
// This example illustrates how a point set can be parameterized to manage a
// particular pixel type. It is quite common to associate vector values with
// points for producing geometric representations or storing
// multi-band informations. The following code shows
// multi-band information. The following code shows
// how vector values can be used as pixel type on the PointSet class. The
// \doxygen{itk}{Vector} class is used here as the pixel type. This class is
// appropriate for representing the relative position between two points. It
......
......@@ -26,7 +26,7 @@
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{otb}{ImageToPathListAlignFilter}.
// This filter allows to extract meaninful alignments. Alignments
// This filter allows extracting meaninful alignments. Alignments
// (that is edges and lines) are detected using the {\em Gestalt}
// approach proposed by Desolneux et al. \cite{desolneux}. In this
// context, an event is
......
......@@ -180,7 +180,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// The methods \code{SetLengthLine()} and \code{SetWidthLine()}
// allow to set the minimum length and the typical witdh of the
// allow setting the minimum length and the typical witdh of the
// lines which are to be detected.
//
// \index{otb::AssymetricFusionOfDetector!SetWidthLine()}
......
......@@ -185,7 +185,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// The methods \code{SetLengthLine()} and \code{SetWidthLine()}
// allow to set the minimum length and the typical witdh of the
// allow setting the minimum length and the typical witdh of the
// lines which are to be detected.
//
// \index{otb::LineCorrelationDetector!SetWidthLine()}
......
......@@ -194,7 +194,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// The methods \code{SetLengthLine()} and \code{SetWidthLine()}
// allow to set the minimum length and the typical witdh of the
// allow setting the minimum length and the typical witdh of the
// lines which are to be detected.
//
//
......
......@@ -191,7 +191,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// The methods \code{SetLengthLine()} and \code{SetWidthLine()}
// allow to set the minimum length and the typical witdh of the
// allow setting the minimum length and the typical witdh of the
// lines which are to be detected.
//
//
......
......@@ -184,7 +184,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// The methods \code{SetLengthLine()} and \code{SetWidthLine()}
// allow to set the minimum length and the typical witdh of the
// allow setting the minimum length and the typical witdh of the
// lines which are to be detected.
//
// \index{otb::LineRatioDetector!SetWidthLine()}
......
......@@ -24,7 +24,7 @@
// Software Guide : BeginLatex
//
// The \doxygen{otb}{VectorDataToMapFilter} allows to perform
// The \doxygen{otb}{VectorDataToMapFilter} allows performing
// rasterization of a given vector data as a binary mask. This example
// will demonstrate how to use this filter to perform rasterization of
// the SRTM water body masks available here:
......
......@@ -68,9 +68,9 @@ if(argc!=8)
// Software Guide : BeginLatex
//
// It allows to configure a directory containing DEM tiles (DTED or SRTM
// It allows configuring a directory containing DEM tiles (DTED or SRTM
// supported) using the \code{OpenDEMDirectory()} method. The \code{OpenGeoidFile()} method
// allows to input a geoid file as well. Last, a default height above ellipsoid
// allows inputting a geoid file as well. Last, a default height above ellipsoid
// can be set using the \code{SetDefaultHeightAboveEllipsoid()} method.
//
// Software Guide : EndLatex
......
......@@ -36,7 +36,7 @@
// OTB is using GDAL to support HDF. HDF4 and HDF5 supports in Gdal is
// not activated by default. You need to download the HDF run-time
// libraries and compile Gdal by adding the support of these formats. You can
// find more informations here : \url{http://trac.osgeo.org/gdal/wiki/HDF}
// find more information here : \url{http://trac.osgeo.org/gdal/wiki/HDF}
//
//
// The first step toward the use of these filters is to include the proper header files.
......@@ -87,9 +87,9 @@ int main(int itkNotUsed(argc), char * argv[])
// Software Guide : BeginLatex
//
// You can access to subdatasets' informations available in the HDF file using
// You can access to subdatasets' information available in the HDF file using
// the \code{GetSubDatasetInfo} method of \doxygen{otb}{GDALImageIO}.
// It allows to store HDF subdatasets names and descriptions in vector of string.
// It allows storing HDF subdatasets names and descriptions in vector of string.
// You can find below how to print the name and the decription
// of all the subdatasets.
//
......
......@@ -93,8 +93,8 @@ int main(int argc, char * argv[])
//
// The ExtractROI type is instantiated using
// the input and output pixel types. Using the pixel types as
// template parameters instead of the image types allows to
// restrict the use of this class to \doxygen{otb}{Image}s which
// template parameters instead of the image types allows
// restricting the use of this class to \doxygen{otb}{Image}s which
// are used with scalar pixel types. See section
// \ref{sec:ExtractROI} for the extraction of ROIs on
// \doxygen{otb}{VectorImage}s. A filter object is created with the
......
......@@ -30,7 +30,7 @@
// and MapFile products.
// Note that the \doxygen{otb}{KmzProductWriter} and the
// \doxygen{otb}{MapFileProductWriter} can only process inputs with a
// non empty geographical informations.
// non empty geographical information.
//
// The first step toward the use of these filters is to include the
// proper header files: the one for the rpc sensor estimation filter and
......@@ -113,7 +113,7 @@ int main(int argc, char* argv[])
// or its keywordlist is updated. In general, a dozen of GCPs are
// needed to estimate an accurate sensor model. The points are added
// via the method AddGCP(PointType2D, PointType3D). The outpput image
// obtained have the needed meta-data informations for the rest of the
// obtained have the needed meta-data information for the rest of the
// process.
//
// Software Guide : EndLatex
......
......@@ -178,7 +178,7 @@ int main(int itkNotUsed(argc), char* argv[])
// \subdoxygen{itk}{Statistics}{ListSample} as input and estimates the label of each
// input sample using the model. Finally, the
// \doxygen{otb}{ImageClassificationModel} inherits from the
// \doxygen{itk}{ImageToImageFilter} and allows to classify pixels in the
// \doxygen{itk}{ImageToImageFilter} and allows classifying pixels in the
// input image by predicting their labels using a model.
//
// Software Guide : EndLatex
......
......@@ -78,7 +78,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
// The filters \doxygen{otb}{HooverMatrixFilter} and \doxygen{otb}{HooverInstanceFilter}
// are designed to handle \doxygen{itk}{LabelMap} images, made with \doxygen{otb}{AttributesMapLabelObject}.
// This type of label object allows to store generic attributes. Each region can store
// This type of label object allows storing generic attributes. Each region can store
// a set of attributes: in this case, Hoover instances and metrics will be stored.
// Software Guide : EndLatex
......
......@@ -140,7 +140,7 @@ int main(int argc, char* argv[])
// not stored in a file nor a data base.
//
// Then, the processing is started by calling \code{Update()}. The actual
// serialization of the results is guaranteed to be completed when the ouput
// serialization of the results is guaranteed to be completed when the output
// geometries set object goes out of scope, or when \code{SyncToDisk} is
// called.
//
......
......@@ -302,7 +302,7 @@ int main(int argc, char *argv[])
// These {\em containers} will be given to the static function \texttt{Compute}
// from \doxygen{otb}{RadiometryCorrectionParametersToAtmosphericRadiativeTerms}
// class, which will call a 6S routine that will compute the needed
// radiometric informations and store them in a
// radiometric information and store them in a
// \doxygen{otb}{AtmosphericRadiativeTerms} class instance.
// For this,
// \doxygen{otb}{RadiometryCorrectionParametersToAtmosphericRadiativeTerms},
......@@ -461,7 +461,7 @@ int main(int argc, char *argv[])
// Software Guide : BeginLatex
//
// Once those parameters are loaded, they are used by the 6S library
// to compute the needed radiometric informations. The
// to compute the needed radiometric information. The
// RadiometryCorrectionParametersToAtmosphericRadiativeTerms class
// provides a static function to perform this step\footnote{Before version
// 4.2, it was done with the filter
......@@ -548,7 +548,7 @@ int main(int argc, char *argv[])
// Next (and last step) is the neighborhood correction.
// For this, the SurfaceAdjacencyEffectCorrectionSchemeFilter class is used.
// The previous surface reflectance inversion is performed under the assumption of a
// homogeneous ground environment. The following step allows to correct the adjacency
// homogeneous ground environment. The following step allows correcting the adjacency
// effect on the radiometry of pixels. The method is based on the decomposition of
// the observed signal as the summation of the own contribution of the target pixel and
// of the contributions of neighbored pixels moderated by their distance to the target pixel.
......
......@@ -294,7 +294,7 @@ int main( int argc, char *argv[] )
// The coloration of muscular tissue makes it easy to distinguish them from
// the surrounding anatomical structures. The optic vitrea on the other hand
// has a coloration that is not very homogeneous inside the eyeball and
// does not allow to generate a full segmentation based only on color.
// does not allow generating a full segmentation based only on color.
//
// Software Guide : EndLatex
......
......@@ -238,7 +238,7 @@ int main(int argc, char *argv[])
// \item PSI 0
// \end{itemize}
//
// More informations and data about leaf properties can be found at \emph{St\'{e}phane Jacquemoud} \href{http://teledetection.ipgp.jussieu.fr/opticleaf/}{OPTICLEAF} website.
// More information and data about leaf properties can be found at \emph{St\'{e}phane Jacquemoud} \href{http://teledetection.ipgp.jussieu.fr/opticleaf/}{OPTICLEAF} website.
// Software Guide : EndLatex
return EXIT_SUCCESS;
......
......@@ -91,7 +91,7 @@ int main(int argc, char * argv[])
// Software Guide : BeginLatex
//
// We need to pass the parameters to the filter for the extraction. This
// filter also allow to extract only a spatial subset of the image. However,
// filter also allow extracting only a spatial subset of the image. However,
// we will extract the whole channel in this case.
//
// To do that, we need to pass the desired region using the
......
......@@ -522,7 +522,7 @@ private:
#if 0
/** Related DataSource.
* Needed to acces OTB meta informations.
* Needed to acces OTB meta information.
*/
DataSourcePtr m_DataSource;
#endif
......
......@@ -46,13 +46,13 @@ namespace otb
* configured by this class and this will ensure consistency
* throughout the library.
*
* The class allows to configure a directory containing DEM tiles
* The class allows configuring a directory containing DEM tiles
* (DTED or SRTM supported) using the OpenDEMDirectory() method. The
* OpenGeoidFile() method allows to input a geoid file as well. Last,
* OpenGeoidFile() method allows inputting a geoid file as well. Last,
* a default height above ellipsoid can be set using the
* SetDefaultHeightAboveEllipsoid() method.
*
* The class allows to retrieve either height above ellipsoid or
* The class allows retrieving either height above ellipsoid or
* height above Mean Sea Level (MSL).
*
* Here is the complete description of both methods output depending
......@@ -169,11 +169,11 @@ protected:
void PrintSelf(std::ostream& os, itk::Indent indent) const;
// Ossim does not allow to retrieve the geoid file path
// Ossim does not allow retrieving the geoid file path
// We therefore must keep it on our side
std::string m_GeoidFile;
// Ossim does not allow to retrieve the default height above
// Ossim does not allow retrieving the default height above
// ellipsoid We therefore must keep it on our side
double m_DefaultHeightAboveEllipsoid;
......
......@@ -137,7 +137,7 @@ protected:
virtual void PrintSelf(std::ostream& os, itk::Indent indent) const;
private:
/** Geo informations are in this map */
/** Geo information are in this map */
KeywordlistMap m_Keywordlist;
// char m_Delimiter;
......
......@@ -140,7 +140,7 @@ int DEMConvertAdapter::Convert(std::string tempFilename, std::string output)
{
std::cerr << "std::exception thrown:" << std::endl;
std::cerr << e.what() << std::endl;
itkExceptionMacro("Error occurs writing the ouput image...");
itkExceptionMacro("Error occurs writing the output image...");
return EXIT_FAILURE;
}
}
......
......@@ -156,7 +156,7 @@ DEMHandler
ossimRefPtr<ossimGeoid> geoidPtr = new ossimGeoidEgm96(geoid);
if (geoidPtr->getErrorStatus() == ossimErrorCodes::OSSIM_OK)
{
// Ossim does not allow to retrieve the geoid file path
// Ossim does not allow retrieving the geoid file path
// We therefore must keep it on our side
m_GeoidFile = geoidFile;
otbMsgDevMacro(<< "Geoid successfully opened");
......@@ -244,7 +244,7 @@ void
DEMHandler
::SetDefaultHeightAboveEllipsoid(double h)
{
// Ossim does not allow to retrieve the default height above
// Ossim does not allow retrieving the default height above
// ellipsoid We therefore must keep it on our side
m_DefaultHeightAboveEllipsoid = h;
......@@ -257,7 +257,7 @@ double
DEMHandler
::GetDefaultHeightAboveEllipsoid() const
{
// Ossim does not allow to retrieve the default height above
// Ossim does not allow retrieving the default height above
// ellipsoid We therefore must keep it on our side
return m_DefaultHeightAboveEllipsoid;
}
......@@ -286,7 +286,7 @@ std::string DEMHandler::GetDEMDirectory(unsigned int idx) const
std::string DEMHandler::GetGeoidFile() const
{
// Ossim does not allow to retrieve the geoid file path
// Ossim does not allow retrieving the geoid file path
// We therefore must keep it on our side
return m_GeoidFile;
}
......
......@@ -73,9 +73,9 @@ private:
" are sorted by increasing correlation. If number of bands is "
" different, the change maps are sorted by decreasing correlation. \n"
" \n"
" The GetV1() and GetV2() methods allow to retrieve the linear "
" The GetV1() and GetV2() methods allow retrieving the linear "
" combinations used to generate the Mad change maps as a vnl_matrix of "
" double, and the GetRho() method allows to retrieve the correlation "
" double, and the GetRho() method allows retrieving the correlation "
" associated to each Mad change maps as a vnl_vector. \n"
" \n"
" This filter has been implemented from the Matlab code kindly made "
......
......@@ -71,7 +71,7 @@ private:
/** GROUP IO CLASSIFICATION */
AddParameter(ParameterType_Group,"io","Input and output images");
SetParameterDescription("io","This group of parameters allows to set input and output images for classification map regularization by Majority Voting.");
SetParameterDescription("io","This group of parameters allows setting input and output images for classification map regularization by Majority Voting.");
AddParameter(ParameterType_InputImage, "io.in", "Input classification image");
SetParameterDescription( "io.in", "The input labeled image to regularize.");
......@@ -82,7 +82,7 @@ private:
AddParameter(ParameterType_Group,"ip","Regularization parameters");
SetParameterDescription("ip","This group allows to set parameters for classification map regularization by Majority Voting.");
SetParameterDescription("ip","This group allows setting parameters for classification map regularization by Majority Voting.");
AddParameter(ParameterType_Int, "ip.radius", "Structuring element radius (in pixels)");
SetParameterDescription("ip.radius", "The radius of the ball shaped structuring element (expressed in pixels). By default, 'ip.radius = 1 pixel'.");
......
......@@ -76,10 +76,10 @@ private:
void DoInit()
{
SetName("ComputePolylineFeatureFromImage");
SetDescription("This application compute for each studied polyline, contained in the input VectorData, the choosen descriptors.");
SetDescription("This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors.");
SetDocName("Compute Polyline Feature From Image");
SetDocLongDescription("The first step in the classifier fusion based validation is to compute, for each studied polyline, the choosen descriptors. ");
SetDocLongDescription("The first step in the classifier fusion based validation is to compute, for each studied polyline, the chosen descriptors. ");
SetDocLimitations("Since it does not rely on streaming process, take care of the size of input image before launching application.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso(" ");
......
......@@ -406,7 +406,7 @@ private:
{
// An error has occurred in the optimization.
// Update the parameters
otbAppLogFATAL("ERROR: Exception Catched!" << std::endl);
otbAppLogFATAL("ERROR: Exception Caught!" << std::endl);
otbAppLogFATAL(<< err.GetDescription() << std::endl);
const unsigned int numberOfIterations = m_Optimizer->GetOptimizer()->get_num_evaluations();
otbAppLogFATAL("numberOfIterations : " << numberOfIterations << std::endl);
......
......@@ -95,7 +95,7 @@ private:
SetName("FusionOfClassifications");
SetDescription("Fuses several classifications maps of the same image on the basis of class labels.");
SetDocName("Fusion of Classifications");
SetDocLongDescription("This application allows to fuse several classification maps and produces a single more robust classification map. "
SetDocLongDescription("This application allows you to fuse several classification maps and produces a single more robust classification map. "
"Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels.\n "
"-MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected.\n "
"-DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated "
......@@ -346,7 +346,7 @@ private:
std::vector<std::string> confusionMatricesFilenameList = GetParameterStringList("method.dempstershafer.cmfl");
MassOfBeliefDefinitionMethod massOfBeliefDefMethod;
//setting default to supress warning
//setting default to suppress warning
massOfBeliefDefMethod = ConfusionMatrixToMassOfBeliefType::PRECISION;
switch (GetParameterInt("method.dempstershafer.mob"))
{
......
......@@ -81,7 +81,7 @@ private:
SetParameterDescription( "in", "The input image to classify.");
AddParameter(ParameterType_InputImage, "mask", "Input Mask");
SetParameterDescription( "mask", "The mask allows to restrict classification of the input image to the area where mask pixel values are greater than 0.");
SetParameterDescription( "mask", "The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0.");
MandatoryOff("mask");
AddParameter(ParameterType_InputFilename, "model", "Model file");
......
......@@ -141,7 +141,7 @@ private:
// TODO : use CSV input/output ?
AddParameter(ParameterType_InputImage, "mask", "Input Mask");
SetParameterDescription( "mask", "The mask allows to restrict "
SetParameterDescription( "mask", "The mask allow restricting "
"classification of the input image to the area where mask pixel values "
"are greater than 0.");
MandatoryOff("mask");
......
......@@ -115,8 +115,8 @@ void DoInit()
"Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by "
"the ComputeImagesStatistics application.\n The training vector data must contain polygons with a positive integer field "
"representing the class label. The name of this field can be set using the \"Class label field\" parameter. Training and validation "
"sample lists are built such that each class is equally represented in both lists. One parameter allows to control the ratio "
"between the number of samples in training and validation sets. Two parameters allow to manage the size of the training and "
"sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio "
"between the number of samples in training and validation sets. Two parameters allow managing the size of the training and "
"validation sets per class and per image.\n Several classifier parameters can be set depending on the chosen classifier. In the "
"validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. "
"In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels"
......@@ -128,7 +128,7 @@ void DoInit()
//Group IO
AddParameter(ParameterType_Group, "io", "Input and output data");
SetParameterDescription("io", "This group of parameters allows to set input and output data.");
SetParameterDescription("io", "This group of parameters allows setting input and output data.");
AddParameter(ParameterType_InputImageList, "io.il", "Input Image List");
SetParameterDescription("io.il", "A list of input images.");
AddParameter(ParameterType_InputVectorDataList, "io.vd", "Input Vector Data List");
......@@ -149,7 +149,7 @@ void DoInit()
//Group Sample list
AddParameter(ParameterType_Group, "sample", "Training and validation samples parameters");
SetParameterDescription("sample",
"This group of parameters allows to set training and validation sample lists parameters.");
"This group of parameters allows you to set training and validation sample lists parameters.");
AddParameter(ParameterType_Int, "sample.mt", "Maximum training sample size per class");
//MandatoryOff("mt");
......
......@@ -122,7 +122,7 @@ void DoInit()
//Group IO
AddParameter(ParameterType_Group, "io", "Input and output data");
SetParameterDescription("io", "This group of parameters allows to set input and output data.");
SetParameterDescription("io", "This group of parameters allows setting input and output data.");
AddParameter(ParameterType_InputImageList, "io.il", "Input Image List");
SetParameterDescription("io.il", "A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict.");
AddParameter(ParameterType_InputFilename, "io.csv", "Input CSV file");
......@@ -144,7 +144,7 @@ void DoInit()
//Group Sample list
AddParameter(ParameterType_Group, "sample", "Training and validation samples parameters");
SetParameterDescription("sample",
"This group of parameters allows to set training and validation sample lists parameters.");
"This group of parameters allows you to set training and validation sample lists parameters.");
AddParameter(ParameterType_Int, "sample.mt", "Maximum training predictors");
//MandatoryOff("mt");
......@@ -230,7 +230,7 @@ void ParseCSVPredictors(std::string path, ListSampleType* outputList)
}
if (words.size() < 2)
{
otbAppLogFATAL(<< "Can't parse CSV file : less than 2 columns or unknonw separator (knowns ones are tab, space, comma and semi-colon)");
otbAppLogFATAL(<< "Can't parse CSV file : less than 2 columns or invalid separator (valid separators are tab, space, comma and semi-colon)");
}
nbCols = words.size();
elem.SetSize(nbCols,false);
......
......@@ -29,7 +29,7 @@ namespace Wrapper
::InitBoostParams()
{
AddChoice("classifier.boost", "Boost classifier");
SetParameterDescription("classifier.boost", "This group of parameters allows to set Boost classifier parameters. "
SetParameterDescription("classifier.boost", "This group of parameters allows setting Boost classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/boosting.html}.");
//BoostType
AddParameter(ParameterType_Choice, "classifier.boost.t", "Boost Type");
......
......@@ -30,7 +30,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
{
AddChoice("classifier.dt", "Decision Tree classifier");
SetParameterDescription("classifier.dt",
"This group of parameters allows to set Decision Tree classifier parameters. "
"This group of parameters allows setting Decision Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/decision_trees.html}.");
//MaxDepth
AddParameter(ParameterType_Int, "classifier.dt.max", "Maximum depth of the tree");
......@@ -55,7 +55,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
//UseSurrogates : don't need to be exposed !
//AddParameter(ParameterType_Empty, "classifier.dt.sur", "Surrogate splits will be built");
//SetParameterDescription("classifier.dt.sur","These splits allow to work with missing data and compute variable importance correctly.");
//SetParameterDescription("classifier.dt.sur","These splits allow working with missing data and compute variable importance correctly.");
//MaxCategories
AddParameter(ParameterType_Int, "classifier.dt.cat",
......
......@@ -31,7 +31,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
AddChoice("classifier.gbt", "Gradient Boosted Tree classifier");
SetParameterDescription(
"classifier.gbt",
"This group of parameters allows to set Gradient Boosted Tree classifier parameters. "
"This group of parameters allows setting Gradient Boosted Tree classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/gradient_boosted_trees.html}.");
if (m_RegressionFlag)
......@@ -74,7 +74,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
//UseSurrogates : don't need to be exposed !
//AddParameter(ParameterType_Empty, "classifier.gbt.sur", "Surrogate splits will be built");
//SetParameterDescription("classifier.gbt.sur","These splits allow to work with missing data and compute variable importance correctly.");
//SetParameterDescription("classifier.gbt.sur","These splits allow working with missing data and compute variable importance correctly.");
}
......
......@@ -29,7 +29,7 @@ namespace Wrapper
::InitKNNParams()
{
AddChoice("classifier.knn", "KNN classifier");
SetParameterDescription("classifier.knn", "This group of parameters allows to set KNN classifier parameters. "
SetParameterDescription("classifier.knn", "This group of parameters allows setting KNN classifier parameters. "
"See complete documentation here \\url{http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html}.");
//K parameter
......
......@@ -29,7 +29,7 @@ namespace Wrapper
::InitLibSVMParams()
{
AddChoice("classifier.libsvm", "LibSVM classifier");
SetParameterDescription("classifier.libsvm", "This group of parameters allows to set SVM classifier parameters.");
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");
AddChoice("classifier.libsvm.k.rbf", "Gaussian radial basis function");
......