Attributes of a fitted PCAModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.feature.PCAModel — 2 of them — so tab completion is that
allowlist. The server refuses anything else with CONNECT_ML.ATTRIBUTE_NOT_ALLOWED; these
refuse a model of the wrong class before it gets that far, and name the module that would
have taken it.
Allowed by the server but not here: toString is Identifiable's, and tells you less than
the model's own uid. Latu.ML.attribute/2 and Latu.ML.attribute/3 will send any of these
names; it is what comes back that has nowhere to go.
The allowlist is inherited rather than per class: this one is the union of
org.apache.spark.ml.feature.PCAModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
The JVM class these accessors belong to.
Returns a vector of proportions of variance explained by each principal component.
Returns a principal components Matrix. Each column is one principal component.
Functions
@spec class() :: String.t()
The JVM class these accessors belong to.
@spec explained_variance(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Returns a vector of proportions of variance explained by each principal component.
An action: it reaches the server. A Vector or Matrix comes back as an Nx.Tensor, or a
Latu.ML.SparseVector where densifying would be this package's decision rather than yours.
@spec pc(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Returns a principal components Matrix. Each column is one principal component.
An action: it reaches the server. A Vector or Matrix comes back as an Nx.Tensor, or a
Latu.ML.SparseVector where densifying would be this package's decision rather than yours.