removee FluidKNN

release-packaging/Classes/FluidKNN.sc

@@ -1,23 +0,0 @@
-FluidKNN : FluidManipulationClient {
-
-  *new {|server|
-		var uid = UniqueID.next;
-		^super.new(server,uid)!?{|inst|inst.init(uid);inst}
-	}
-
-	init {|uid|
-		id = uid;
-	}
-
-  fit{|dataset, action|
-     this.prSendMsg(\fit,[dataset.asString],action);
-  }
-
-  classifyPoint{ |buffer, labelset, k, action|
-      this.prSendMsg(\classify,[buffer.asUGenInput, labelset.asString, k],action,[string(FluidMessageResponse,_,_)]);
-  }
-
-  regressPoint { |buffer,dataset, k, action|
-      this.prSendMsg(\regress,[buffer.asUGenInput, dataset.asString,k],action,[number(FluidMessageResponse,_,_)]);
-  }
-}

release-packaging/HelpSource/Classes/FluidKNN.schelp

@@ -1,68 +0,0 @@
-TITLE:: FluidKNN
-summary:: K Nearest Neighbours machine learning
-categories:: FluidManipulation
-related:: Classes/FluidKDTree
-
-DESCRIPTION::
-Simple machine learning tasks (classification and regression) using K Nearest Neighbours.
-
-See
-https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-classification
-
-https://scikit-learn.org/stable/modules/neighbors.html#nearest-neighbors-regression
-
-
-CLASSMETHODS::
-
-INSTANCEMETHODS::
-
-METHOD:: fit
-'Train' the KNN on a source link::Classes/FluidDataSet::
-
-ARGUMENT:: dataset
-source link::Classes/FluidDataSet::
-
-ARGUMENT:: action
-A function to run when fitting is complete
-
-METHOD:: regressPoint
-Map a point between a source link::Classes/FluidDataSet:: used when link::Classes/FluidKNN#index::ing this KNN, and a target data set passed as an argument.
-
-For this to work, the target data set must have labels in common with the source data set. The code::k:: nearest neighbours to the supplied data point are retrrived from the source tree, and then a mapping is obtained through the average of the equivalently labelled points in the target data set.
-
-WARNING:: For now the target data set can only be 1D::
-
-ARGUMENT:: buffer
-The data point to map, in a link::Classes/Buffer::
-
-ARGUMENT:: dataset
-The target link::Classes/FluidDataSet::
-
-ARGUMENT:: k
-The number of neighbours to use in the estimation
-
-ARGUMENT:: action
-A function to run when the server responds, taking the value of the regressed point as its argument
-
-METHOD:: classifyPoint
-Classify a point, using categories from the supplied label set, which maps labels from the source data set to category IDs. This works by getting the labels of the code::k:: nearest points to the passed data point from the source data set, and looking up their IDs in the passed label set. The most frequently ocurring ID is designated as the class for the point.
-
-ARGUMENT:: buffer
-The data point to classify, in a link::Classes/Buffer::
-
-ARGUMENT:: labelset
-A link::Classes/FluidLabelSet:: of categories mapped to labels in source data set
-
-ARGUMENT:: k
-The number of neighbours to use in the classificaton
-
-ARGUMENT:: action
-A function to run when the server responds, taking the assigned label as its argument
-
-
-
-EXAMPLES::
-
-code::
-(some example code)
-::