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177 lines
6.5 KiB
XML
177 lines
6.5 KiB
XML
<root>
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<key>TrainRegression-libsvm</key>
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<exec>otbcli_TrainRegression</exec>
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<longname>TrainRegression (libsvm)</longname>
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<group>Learning</group>
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<description>Train a classifier from multiple images to perform regression.</description>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
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<key>io.il</key>
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<name>Input Image List</name>
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<description>A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict.</description>
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<datatype />
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
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<key>io.csv</key>
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<name>Input CSV file</name>
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<description>Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given</description>
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<isFolder />
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<optional>True</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
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<key>io.imstat</key>
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<name>Input XML image statistics file</name>
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<description>Input XML file containing the mean and the standard deviation of the input images.</description>
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<isFolder />
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<optional>True</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
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<key>io.out</key>
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<name>Output regression model</name>
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<description>Output file containing the model estimated (.txt format).</description>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
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<key>io.mse</key>
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<name>Mean Square Error</name>
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<description>Mean square error computed with the validation predictors</description>
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<minValue />
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<maxValue />
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<default>0.0</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
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<key>sample.mt</key>
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<name>Maximum training predictors</name>
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<description>Maximum number of training predictors (default = 1000) (no limit = -1).</description>
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<minValue />
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<maxValue />
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<default>1000</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
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<key>sample.mv</key>
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<name>Maximum validation predictors</name>
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<description>Maximum number of validation predictors (default = 1000) (no limit = -1).</description>
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<minValue />
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<maxValue />
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<default>1000</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
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<key>sample.vtr</key>
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<name>Training and validation sample ratio</name>
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<description>Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).</description>
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<minValue />
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<maxValue />
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<default>0.5</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
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<key>classifier</key>
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<name>Classifier to use for the training</name>
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<description>Choice of the classifier to use for the training.</description>
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<options>
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<choices>
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<choice>libsvm</choice>
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</choices>
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</options>
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<default>0</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
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<key>classifier.libsvm.k</key>
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<name>SVM Kernel Type</name>
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<description>SVM Kernel Type.</description>
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<options>
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<choices>
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<choice>linear</choice>
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<choice>rbf</choice>
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<choice>poly</choice>
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<choice>sigmoid</choice>
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</choices>
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</options>
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<default>0</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
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<key>classifier.libsvm.m</key>
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<name>SVM Model Type</name>
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<description>Type of SVM formulation.</description>
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<options>
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<choices>
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<choice>epssvr</choice>
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<choice>nusvr</choice>
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</choices>
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</options>
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<default>0</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
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<key>classifier.libsvm.c</key>
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<name>Cost parameter C</name>
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<description>SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.</description>
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<minValue />
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<maxValue />
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<default>1</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
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<key>classifier.libsvm.opt</key>
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<name>Parameters optimization</name>
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<description>SVM parameters optimization flag.</description>
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<default>True</default>
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<optional>True</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
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<key>classifier.libsvm.prob</key>
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<name>Probability estimation</name>
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<description>Probability estimation flag.</description>
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<default>True</default>
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<optional>True</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
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<key>classifier.libsvm.eps</key>
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<name>Epsilon</name>
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<description />
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<minValue />
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<maxValue />
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<default>0.001</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
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<key>classifier.libsvm.nu</key>
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<name>Nu</name>
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<description />
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<minValue />
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<maxValue />
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<default>0.5</default>
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<optional>False</optional>
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</parameter>
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<parameter>
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<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
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<key>rand</key>
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<name>set user defined seed</name>
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<description>Set specific seed. with integer value.</description>
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<minValue />
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<maxValue />
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<default>0</default>
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<optional>True</optional>
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</parameter>
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</root>
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