Merge branch 'FluidRobustScale' of https://bitbucket.org/flucoma/flucoma-supercollider into FluidRobustScale

nix
Gerard 5 years ago
commit c8c9a8ed4d

@ -36,7 +36,7 @@ Source data, or the DataSet name
ARGUMENT:: destDataSet ARGUMENT:: destDataSet
Destination data, or the DataSet name Destination data, or the DataSet name
ARGUMENT:: action ARGUMENT:: action
Run when done. The variance is passed as an argument, aka the fidelity of the new representation: a value near 1.0 means a higher fidelity to the original. Run when done. The fraction of accounted variance is passed as an argument, aka the fidelity of the new representation: a value near 1.0 means a higher fidelity to the original.
METHOD:: fitTransform METHOD:: fitTransform
link::Classes/FluidPCA#fit:: and link::Classes/FluidPCA#transform:: in a single pass link::Classes/FluidPCA#fit:: and link::Classes/FluidPCA#transform:: in a single pass
@ -45,7 +45,7 @@ Source data, or the DataSet name
ARGUMENT:: destDataSet ARGUMENT:: destDataSet
Destination data, or the DataSet name Destination data, or the DataSet name
ARGUMENT:: action ARGUMENT:: action
Run when done. The variance is passed as an argument, aka the fidelity of the new representation: a value near 1.0 means a higher fidelity to the original. Run when done. The fraction of accounted variance is passed as an argument, aka the fidelity of the new representation: a value near 1.0 means a higher fidelity to the original.
METHOD:: transformPoint METHOD:: transformPoint
Given a trained model, transform the data point in a link::Classes/Buffer:: and write to an output Given a trained model, transform the data point in a link::Classes/Buffer:: and write to an output

@ -25,8 +25,6 @@ ARGUMENT:: iterations
The number of iterations that the algorithm will go through to optimise the new representation The number of iterations that the algorithm will go through to optimise the new representation
ARGUMENT:: learnRate ARGUMENT:: learnRate
The learning rate of the algorithm, aka how much of the error it uses to estimate the next iteration. The learning rate of the algorithm, aka how much of the error it uses to estimate the next iteration.
ARGUMENT:: batchSize
The training batch size.
INSTANCEMETHODS:: INSTANCEMETHODS::

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