Update args in SC documentation post-RT changes

release-packaging/HelpSource/Classes/FluidKDTree.schelp

@@ -14,41 +14,31 @@ METHOD:: new
 Make a new KDTree model for the given server.
 ARGUMENT:: server
 The server on which to make the model.
+ARGUMENT:: numNeighbours
+The number of neighbours to return.
+ARGUMENT:: lookupDataSet
+An optional link::Classes/FluidDataSet:: from which data points will be returned for realtime queries. This does not need to be the same DataSet that the tree was fitted against, but does need to have matching labels. Using this mechanism, we have a way to, e.g. associate labels with segments of playback buffers, without needing pass strings around the server. warning::This parameter can not be safely changed after the instance of FluidKDTree has been created::
 
 INSTANCEMETHODS::
 
 METHOD:: fit
 Build the tree by scanning the points of a LINK::Classes/FluidDataSet::
-
 ARGUMENT:: dataset
 The LINK::Classes/FluidDataSet:: of interest. This can either be a data set object itself, or the name of one.
-
 ARGUMENT:: action
 A function to run when indexing is complete.
 
-
 METHOD:: kNearest
 Returns the IDs of the CODE::k:: points nearest to the one passed.
-
 ARGUMENT:: buffer
 A LINK::Classes/Buffer:: containing a data point to match against. The number of frames in the buffer must match the dimensionality of the LINK::Classes/FluidDataSet:: the tree was fitted to.
-
-ARGUMENT:: k
-The number of neighbours to return.
-
-
 ARGUMENT:: action
 A function that will run when the query returns, whose argument is an array of point IDs from the tree's LINK::Classes/FluidDataSet::
 
 METHOD:: kNearestDist
 Get the distances of the K nearest neighbours to a point.
-
 ARGUMENT:: buffer
 A LINK::Classes/Buffer:: containing a data point to match against. The number of frames in the buffer must match the dimensionality of the LINK::Classes/FluidDataSet:: the tree was fitted to.
-
-ARGUMENT:: k
-The number of neighbours to search
-
 ARGUMENT:: action
 A function that will run when the query returns, whose argument is an array of distances.
 

release-packaging/HelpSource/Classes/FluidKMeans.schelp

@@ -11,9 +11,14 @@ https://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_lear
 CLASSMETHODS::
 
 METHOD:: new
-Construct a new K Means model on the passed server.
+Construct a new K Means model on the passed server. The parameters code::numClusters:: and code::maxIter:: can be modulated on an existing instance.
 ARGUMENT:: server
 If nil will use Server.default.
+ARGUMENT:: numClusters
+The number of clusters to classify data into.
+ARGUMENT:: maxIter
+The maximum number of iterations the algorithm will use whilst fitting.
+
 
 INSTANCEMETHODS::
 
@@ -23,10 +28,6 @@ METHOD:: fit
 Identify code::k:: clusters in a link::Classes/FluidDataSet::
 ARGUMENT:: dataset
 A link::Classes/FluidDataSet:: of data points.
-ARGUMENT:: k
-The number of clusters to identify in the data set.
-ARGUMENT:: maxIter
-Maximum number of iterations.
 ARGUMENT:: action
 A function to run when fitting is complete, taking as its argument an array with the number of data points for each cluster.
 
@@ -45,10 +46,6 @@ ARGUMENT:: dataset
 a link::Classes/FluidDataSet:: containing the data to fit and predict.
 ARGUMENT:: labelset
 a link::Classes/FluidLabelSet:: to retrieve the predicted clusters.
-ARGUMENT:: k
-The number of clusters.
-ARGUMENT:: maxIter
-Maximum number of iterations.
 ARGUMENT:: action
 A function to run when the server responds
 

release-packaging/HelpSource/Classes/FluidKNNClassifier.schelp

@@ -13,6 +13,10 @@ METHOD:: new
 Create a new KNNClassifier
 ARGUMENT:: server
 The server to make the model on
+ARGUMENT:: numNeighbours
+the number of neighours to consider
+ARGUMENT:: weight
+true / false: whether the neighbours should be weighted by distance
 
 INSTANCEMETHODS::
 
@@ -31,10 +35,6 @@ ARGUMENT:: dataset
 data to predict labels for
 ARGUMENT:: labelset
 place to write labels
-ARGUMENT:: k
-the number of neighours to consider
-ARGUMENT:: uniform
-true / false: whether the neighbours should be weighted by distance
 ARGUMENT:: action
 Run when done
 
@@ -42,10 +42,6 @@ METHOD:: predictPoint
 Given a fitted model, predict labels for a data point in a link::Classes/Buffer:: and return these to the caller
 ARGUMENT:: buffer
 A data point
-ARGUMENT:: k
-Number of neighbours to consider
-ARGUMENT:: uniform
-true / false: whether the neighbours should be weighted by distance (default) or uniformly
 ARGUMENT:: action
 Run when done, passes predicted label as argument
 

release-packaging/HelpSource/Classes/FluidKNNRegressor.schelp

@@ -14,6 +14,10 @@ METHOD:: new
 Create a new KNN regressor on the server
 ARGUMENT:: server
 The server to run this model on.
+ARGUMENT:: numNeighbours
+number of neigbours to consider in mapping, min 1
+ARGUMENT:: weight
+Whether to weight neighbours by distance when producing new point
 
 INSTANCEMETHODS::
 
@@ -33,10 +37,6 @@ ARGUMENT:: sourceDataset
 data to regress
 ARGUMENT:: targetDataset
 output data
-ARGUMENT:: k
-number of neigbours to consider in mapping, min 1
-ARGUMENT:: uniform
-Whether to weight neighbours by distance when producing new point
 ARGUMENT:: action
 Run when done
 
@@ -44,9 +44,6 @@ METHOD:: predictPoint
 Apply learned mapping to a data point in a link::Classes/Buffer::
 ARGUMENT:: buffer
 data point
-ARGUMENT:: k
-number of neigbours to consider in mapping, min 1
-ARGUMENT:: uniform
 Whether the neighbours should be weighted by distance (default) or uniformly
 ARGUMENT:: action
 Run when done

release-packaging/HelpSource/Classes/FluidPCA.schelp

@@ -15,6 +15,8 @@ METHOD:: new
 Make a new instance
 ARGUMENT:: server
 The server on which to run this model
+ARGUMENT:: numDimensions
+The number of dimensions to reduce to
 
 INSTANCEMETHODS::
 
@@ -24,8 +26,6 @@ METHOD:: fit
 Train this model on a link::Classes/FluidDataSet:: but don't transform the data
 ARGUMENT:: dataset
 A link::Classes/FluidDataSet:: to analyse
-ARGUMENT:: k
-The number of dimensions to reduce to
 ARGUMENT:: action
 Run when done
 
@@ -44,8 +44,6 @@ ARGUMENT:: sourceDataset
 Source data, or the dataset name
 ARGUMENT:: destDataset
 Destination data, or the dataset name
-ARGUMENT:: k
-The number of dimensions to reduce to
 ARGUMENT:: action
 Run when done