Moved ml function description into separate docs file

docs/FunctionDescriptions.md

@@ -35,38 +35,5 @@ Index of the main documentation of the implemented functions, their purpose and
   - [soilMoisture_plateaus](docs/funcs/SoilMoisture.md#soilmoisture_plateaus)
   - [soilMoisture_byFrost](docs/funcs/SoilMoisture.md#soilmoisture_byfrost)
   - [soilMoisture_byPrecipitation](#soilmoisture_byprecipitation)
-- [Machine Learning](#machine-learning)
-  - [machinelearning](#machinelearning)
-
-
-
-
-## Machine Learning
-
-### machinelearning
-
-```
-machinelearning(references, window_values, window_flags, path)
-```
-
-| parameter | data type  | default value  | description |
-| --------- | ---------- | -------------- | ----------- |
-| references    | string or list of strings        |           | the fieldnames of the data series that should be used as reference variables |
-| window_values    | integer        |           | Window size that is used to derive the gradients of both the field- and reference-series inside the moving window|
-| window_flags   | integer        |          | Window size that is used to count the surrounding automatic flags that have been set before |
-| path    | string        |           | Path to the respective model object, i.e. its name and the respective value of the grouping variable. e.g. "models/model_0.2.pkl" |
-
-
-This Function uses pre-trained machine-learning model objects for flagging. 
-This requires training a model by use of the [training script](../ressources/machine_learning/train_machine_learning.py) provided. 
-For flagging, inputs to the model are the data of the variable of interest, 
-data of reference variables and the automatic flags that were assigned by other 
-tests inside SaQC. Therefore, this function should be defined last in the config-file, i.e. it should be the last test that is executed.
-Internally, context information for each point is gathered in form of moving 
-windows. The size of the moving windows for counting of the surrounding 
-automatic flags and for calculation of gradients in the data is specified by 
-the user during model training. For the model to work, the parameters 
-'references', 'window_values' and 'window_flags' have to be set to the same 
-values as during training. For a more detailed description of the modeling 
-aproach see the [training script](../ressources/machine_learning/train_machine_learning.py).
-
+- [Machine Learning](docs/funcs/MachineLearning.md)
+  - [machinelearning](docs/funcs/MachineLearning.md#machinelearning)

docs/funcs/MachineLearning.md

+# Machine Learning
+
+A collection of interfaces to pre-trained machine learning models.
+
+## Index
+- [machinelearning](#machinelearning)
+
+
+## machinelearning
+
+```
+machinelearning(references, window_values, window_flags, path)
+```
+
+| parameter     | data type                 | default value | description                                                                                                                       |
+|---------------|---------------------------|---------------|-----------------------------------------------------------------------------------------------------------------------------------|
+| references    | string or list of strings |               | the field names of the data series that should be used as reference variables                                                      |
+| window_values | integer                   |               | Window size that is used to derive the gradients of both the field- and reference-series inside the moving window                 |
+| window_flags  | integer                   |               | Window size that is used to count the surrounding automatic flags that have been set before                                       |
+| path          | string                    |               | Path to the respective model object, i.e. its name and the respective value of the grouping variable. e.g. "models/model_0.2.pkl" |
+
+
+This Function uses pre-trained machine-learning model objects for flagging. 
+This requires training a model by use of the [training script](../ressources/machine_learning/train_machine_learning.py) provided. 
+For flagging, inputs to the model are the data of the variable of interest, 
+data of reference variables and the automatic flags that were assigned by other 
+tests inside SaQC. Therefore, this function should be executed after all other tests.
+Internally, context information for each point is gathered in form of moving 
+windows. The size of the moving windows for counting of the surrounding 
+automatic flags and for calculation of gradients in the data is specified by 
+the user during model training. For the model to work, the parameters 
+'references', 'window_values' and 'window_flags' have to be set to the same 
+values as during training. For a more detailed description of the modeling 
+approach see the [training script](ressources/machine_learning/train_machine_learning.py).
+