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<root>
<key>TrainVectorClassifier-ann</key>
<exec>otbcli_TrainVectorClassifier</exec>
<longname>TrainVectorClassifier (ann)</longname>
<group>Learning</group>
<description>Train a classifier based on labeled geometries and a list of features to consider.</description>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputVectorData">ParameterVector</parameter_type>
<key>io.vd</key>
<name>Input Vector Data</name>
<description>Input geometries used for training (note : all geometries from the layer will be used)</description>
<shapetype />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>io.stats</key>
<name>Input XML image statistics file</name>
<description>XML file containing mean and variance of each feature.</description>
<isFolder />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.confmatout</key>
<name>Output confusion matrix</name>
<description>Output file containing the confusion matrix (.csv format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.out</key>
<name>Output model</name>
<description>Output file containing the model estimated (.txt format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_StringList">ParameterString</parameter_type>
<key>feat</key>
<name>Field names for training features.</name>
<description>List of field names in the input vector data to be used as features for training.</description>
<options />
<default />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_String">ParameterString</parameter_type>
<key>cfield</key>
<name>Field containing the class id for supervision</name>
<description>Field containing the class id for supervision. Only geometries with this field available will be taken into account.</description>
<default>class</default>
<multiline />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>layer</key>
<name>Layer Index</name>
<description>Index of the layer to use in the input vector file.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputVectorData">ParameterVector</parameter_type>
<key>valid.vd</key>
<name>Validation Vector Data</name>
<description>Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used)</description>
<shapetype />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>valid.layer</key>
<name>Layer Index</name>
<description>Index of the layer to use in the validation vector file.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier</key>
<name>Classifier to use for the training</name>
<description>Choice of the classifier to use for the training.</description>
<options>
<choices>
<choice>ann</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.ann.t</key>
<name>Train Method Type</name>
<description>Type of training method for the multilayer perceptron (MLP) neural network.</description>
<options>
<choices>
<choice>reg</choice>
<choice>back</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_StringList">ParameterString</parameter_type>
<key>classifier.ann.sizes</key>
<name>Number of neurons in each intermediate layer</name>
<description>The number of neurons in each intermediate layer (excluding input and output layers).</description>
<default />
<multiline />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.ann.f</key>
<name>Neuron activation function type</name>
<description>Neuron activation function.</description>
<options>
<choices>
<choice>ident</choice>
<choice>sig</choice>
<choice>gau</choice>
</choices>
</options>
<default>1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.a</key>
<name>Alpha parameter of the activation function</name>
<description>Alpha parameter of the activation function (used only with sigmoid and gaussian functions).</description>
<minValue />
<maxValue />
<default>1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.b</key>
<name>Beta parameter of the activation function</name>
<description>Beta parameter of the activation function (used only with sigmoid and gaussian functions).</description>
<minValue />
<maxValue />
<default>1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.bpdw</key>
<name>Strength of the weight gradient term in the BACKPROP method</name>
<description>Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1.</description>
<minValue />
<maxValue />
<default>0.1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.bpms</key>
<name>Strength of the momentum term (the difference between weights on the 2 previous iterations)</name>
<description>Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.</description>
<minValue />
<maxValue />
<default>0.1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.rdw</key>
<name>Initial value Delta_0 of update-values Delta_{ij} in RPROP method</name>
<description>Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1).</description>
<minValue />
<maxValue />
<default>0.1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.rdwm</key>
<name>Update-values lower limit Delta_{min} in RPROP method</name>
<description>Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7).</description>
<minValue />
<maxValue />
<default>1e-07</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.ann.term</key>
<name>Termination criteria</name>
<description>Termination criteria.</description>
<options>
<choices>
<choice>iter</choice>
<choice>eps</choice>
<choice>all</choice>
</choices>
</options>
<default>2</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.ann.eps</key>
<name>Epsilon value used in the Termination criteria</name>
<description>Epsilon value used in the Termination criteria.</description>
<minValue />
<maxValue />
<default>0.01</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>classifier.ann.iter</key>
<name>Maximum number of iterations used in the Termination criteria</name>
<description>Maximum number of iterations used in the Termination criteria.</description>
<minValue />
<maxValue />
<default>1000</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>rand</key>
<name>set user defined seed</name>
<description>Set specific seed. with integer value.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
</root>
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