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TfidfTransformer.Fit

Learn the document frequencies from a count matrix.

public TfidfTransformer Fit(CsrMatrix counts)

Parameters — counts is the count matrix to learn from: one row per document, one column per term.

Returns — TfidfTransformer, the same instance, so a call can be chained.

Exceptions — ArgumentNullException when counts is null.

Example — fit on the training counts, weight a later matrix with them.

using Lodestar.Abstractions;
using Lodestar.Text.Vectorization;

var cv = new CountVectorizer();
CsrMatrix training = cv.FitTransform(["the cat eats", "the dog eats"]);

var tfidf = new TfidfTransformer();
tfidf.Fit(training);

CsrMatrix later = tfidf.Transform(cv.Transform(["the cat"]));
int columns = later.ColumnCount;  // => 4

Remarks — what is learned is one IDF value per column, so a matrix transformed afterwards must have the same number of columns and the same column meanings. That is why the count vectorizer above is reused rather than refitted: a new one would sort a different vocabulary into the same column indices and the weights would be applied to the wrong terms, silently.

After fitting, Idf holds those values in column order.

Applies to — net10.0, netstandard2.0.

See also — TfidfTransformer.Transform, TfidfTransformer.FitTransform.