<root>
  <key>TrainRegression-knn</key>
  <exec>otbcli_TrainRegression</exec>
  <longname>TrainRegression (knn)</longname>
  <group>Learning</group>
  <description>Train a classifier from multiple images to perform regression.</description>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
    <key>io.il</key>
    <name>Input Image List</name>
    <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>
    <datatype />
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
    <key>io.csv</key>
    <name>Input CSV file</name>
    <description>Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given</description>
    <isFolder />
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
    <key>io.imstat</key>
    <name>Input XML image statistics file</name>
    <description>Input XML file containing the mean and the standard deviation of the input images.</description>
    <isFolder />
    <optional>True</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
    <key>io.out</key>
    <name>Output regression model</name>
    <description>Output file containing the model estimated (.txt format).</description>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
    <key>io.mse</key>
    <name>Mean Square Error</name>
    <description>Mean square error computed with the validation predictors</description>
    <minValue />
    <maxValue />
    <default>0.0</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>sample.mt</key>
    <name>Maximum training predictors</name>
    <description>Maximum number of training predictors (default = 1000) (no limit = -1).</description>
    <minValue />
    <maxValue />
    <default>1000</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>sample.mv</key>
    <name>Maximum validation predictors</name>
    <description>Maximum number of validation predictors (default = 1000) (no limit = -1).</description>
    <minValue />
    <maxValue />
    <default>1000</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
    <key>sample.vtr</key>
    <name>Training and validation sample ratio</name>
    <description>Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).</description>
    <minValue />
    <maxValue />
    <default>0.5</default>
    <optional>False</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>knn</choice>
      </choices>
    </options>
    <default>0</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
    <key>classifier.knn.k</key>
    <name>Number of Neighbors</name>
    <description>The number of neighbors to use.</description>
    <minValue />
    <maxValue />
    <default>32</default>
    <optional>False</optional>
  </parameter>
  <parameter>
    <parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
    <key>classifier.knn.rule</key>
    <name>Decision rule</name>
    <description>Decision rule for regression output</description>
    <options>
      <choices>
        <choice>mean</choice>
        <choice>median</choice>
      </choices>
    </options>
    <default>0</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>
