Reputation: 1325
I have created a table that has a pair of IDs and coordinate fro each of them so that I can calculate pairwise cosine similarity between them.
The number of dimension for the coords are currently 128, but it can vary. But the number dimensions for a pair of ID are always same in the same table.
coord1
and coord2
are repeated field (array) with floating point values.
Is there a way to calculate cosine similarity between them?
My expected output would have three columns, id1
, id2
and cosine_similarity
.
Upvotes: 3
Views: 1938
Reputation: 173046
Below is for BigQuery Standard SQL
#standardSQL
SELECT id1, id2, (
SELECT
SUM(value1 * value2)/
SQRT(SUM(value1 * value1))/
SQRT(SUM(value2 * value2))
FROM UNNEST(coord1) value1 WITH OFFSET pos1
JOIN UNNEST(coord2) value2 WITH OFFSET pos2
ON pos1 = pos2
) cosine_similarity
FROM `project.dataset.table`
below is dummy example for you to play with
#standardSQL
WITH `project.dataset.table` AS (
SELECT 1 id1, [1.0, 2.0, 3.0, 4.0] coord1, 2 id2, [1.0, 2.0, 3.0, 4.0] coord2 UNION ALL
SELECT 3, [2.0, 0.0, 1.0, 1.0, 0, 2.0, 1.0, 1.0], 4, [2.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]
)
SELECT id1, id2, (
SELECT
SUM(value1 * value2)/
SQRT(SUM(value1 * value1))/
SQRT(SUM(value2 * value2))
FROM UNNEST(coord1) value1 WITH OFFSET pos1
JOIN UNNEST(coord2) value2 WITH OFFSET pos2
ON pos1 = pos2
) cosine_similarity
FROM `project.dataset.table`
with result
Row id1 id2 cosine_similarity
1 1 2 1.0
2 3 4 0.8215838362577491
Upvotes: 8