Sparse Vector vs Dense Vector

问题:

How to create SparseVector and dense Vector representations

if the DenseVector is:

denseV = np.array([0., 3., 0., 4.])

What will be the Sparse Vector representation ?

回答1:

Unless I have thoroughly misunderstood your doubt, the MLlib data type documentation illustrates this quite clearly:

import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;

// Create a dense vector (1.0, 0.0, 3.0).
Vector dv = Vectors.dense(1.0, 0.0, 3.0);
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.
Vector sv = Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0});

Where the second argument of Vectors.sparse is an array of the indices, and the third argument is the array of the actual values in those indices.

回答2:

Sparse vectors are when you have a lot of values in the vector as zero. While a dense vector is when most of the values in the vector are non zero.

If you have to create a sparse vector from the dense vector you specified, use the following syntax:

Vector sparseVector = Vectors.sparse(4, new int[] {1, 3}, new double[] {3.0, 4.0});