learning.rate: learning rate for stochastic variational inference
In gongx030/tcm: TCM visualizes trajectories and cell populations from single cell data
Description
learning rate for stochastic variational inference
Usage
1
learning.rate(t, control)
Arguments
t
current iteration step
decay.rate
down-weights for early iterations (default: 5)
forgetting.rate
controls how quickly old information is forgotten (default: 0.75)
gongx030/tcm documentation built on June 4, 2019, 7:26 p.m.
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Description
learning rate for stochastic variational inference
Usage
1 | learning.rate(t, control)
|
Arguments
t |
current iteration step |
decay.rate |
down-weights for early iterations (default: 5) |
forgetting.rate |
controls how quickly old information is forgotten (default: 0.75) |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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