ds.dbm.top2LatentDims: Two-dimensional representation of latent features
In stefan-m-lenz/dsBoltzmannMachinesClient: Client for dsBoltzmannMachines
Description
Usage
Arguments
Value
Description
Calculates the mean-field approximation of the activation of the top hidden nodes in
the DBM. If there are more than two nodes in the top layer, the dimensionality is further
reduced using a principal component analysis (PCA).
Usage
1
ds.dbm.top2LatentDims(datasources, dbm = "dbm", data = "D")
Arguments
datasources
A list of Opal object(s) as a handle to the server-side session
dbm
The name of the DBM model on the server-side. Defaults to "dbm".
data
The name of the variable that holds the data on the server-side.
Defaults to "D".
Value
A matrix with two columns for each of the sites,
containing the two-dimensional representation for
each of the samples in the rows. The samples are shuffled at random.
stefan-m-lenz/dsBoltzmannMachinesClient documentation built on May 2, 2021, 10:46 a.m.
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Description Usage Arguments Value
Description
Calculates the mean-field approximation of the activation of the top hidden nodes in the DBM. If there are more than two nodes in the top layer, the dimensionality is further reduced using a principal component analysis (PCA).
Usage
1 | ds.dbm.top2LatentDims(datasources, dbm = "dbm", data = "D")
|
Arguments
datasources |
A list of Opal object(s) as a handle to the server-side session |
dbm |
The name of the DBM model on the server-side. Defaults to |
data |
The name of the variable that holds the data on the server-side.
Defaults to |
Value
A matrix with two columns for each of the sites, containing the two-dimensional representation for each of the samples in the rows. The samples are shuffled at random.
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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