Attributes of a fitted IDFModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.feature.IDFModel — 3 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.IDFModel, org.apache.spark.ml.util.Identifiable.
Summary
Functions
The JVM class these accessors belong to.
Returns the document frequency.
Returns the IDF vector.
Returns number of documents evaluated to compute idf.
Functions
@spec class() :: String.t()
The JVM class these accessors belong to.
@spec doc_freq(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Returns the document frequency.
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 idf(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Returns the IDF vector.
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 num_docs(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Returns number of documents evaluated to compute idf.
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.