Latu.ML.Feature.StandardScalerModel (latu_ml v0.2.0)

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Attributes of a fitted StandardScalerModel.

Every accessor here is one name on the server's allowlist for org.apache.spark.ml.feature.StandardScalerModel — 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.StandardScalerModel, org.apache.spark.ml.util.Identifiable.

Summary

Functions

class()

The JVM class these accessors belong to.

mean(holder)

Mean of the StandardScalerModel.

std(holder)

Standard deviation of the StandardScalerModel.

Functions

class()

@spec class() :: String.t()

The JVM class these accessors belong to.

mean(holder)

@spec mean(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}

Mean of the StandardScalerModel.

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.

std(holder)

@spec std(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}

Standard deviation of the StandardScalerModel.

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.