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The matrix times a dense vector, in one pass over the stored cells.

public double[] Multiply(ReadOnlySpan<double> vector)

Parameters — vector is the dense vector to multiply by, and must hold exactly ColumnCount values.

Returns — double[] of length RowCount, each entry the dot product of that row with vector.

Exceptions — ArgumentException when vector is not ColumnCount long.

Example — multiplying by all ones totals each row, which is that document's term count.

using Lodestar.Text.Vectorization;

string[] docs = ["the cat eats", "the dog eats", "the cat and the dog"];
CsrMatrix counts = new CountVectorizer().FitTransform(docs);

double[] totals = counts.Multiply([1, 1, 1, 1, 1]);

double first = totals[0];   // => 3
double third = totals[2];   // => 5

Remarks — the cost is NonZeroCount, not RowCount × ColumnCount, which is the whole reason to keep the matrix sparse. The third document totals 5 rather than 4 because the appears twice in it and the count is a count.

This is the operation behind scoring a corpus against a linear model: the weights are the vector, and each row's dot product is that document's score.

Applies to — net10.0, netstandard2.0.

See also — CsrMatrix, CsrMatrix.ToDense.