Warning about not enouch training point when using PQ with high subquantizer

    /// 13. TestSubquantizersValidation

    /// Description

    ///     Test that the PQ’s subquantizers value is validated with dimensions correctly.

    ///     The invalid argument exception should be thrown when the vector index is created

    ///     with invalid subquantizers which are not a divisor of the dimensions or zero.

    /// Steps

    ///     1. Copy database words_db.

    ///     2. Create a vector index named "words_index" in _default.words collection.

    ///         - expression: "vector"

    ///         - dimensions: 300

    ///         - centroids: 8

    ///         - PQ(subquantizers: 2, bits: 8)

    ///     3. Check that the index is created without an error returned.

    ///     4. Delete the "words_index".

    ///     5. Repeat steps 2 to 4 by changing the subquantizers to

    ///       3, 4, 5, 6, 10, 12, 15, 20, 25, 30, 50, 60, 75, 100, 150, and 300.

    ///     6. Repeat step 2 to 4 by changing the subquantizers to 0 and 7.

    ///     7. Check that an invalid argument exception is thrown.

    func testSubquantizersValidation() throws {

        let collection = try db.collection(name: "words")!

        var config = VectorIndexConfiguration(expression: "vector", dimensions: 300, centroids: 8)

        config.encoding = .productQuantizer(subquantizers: 2, bits: 8)

        try collection.createIndex(withName: "words_index", config: config)

        let names = try collection.indexes()

        XCTAssert(names.contains("words_index"))

        // Step 5: Use valid subquantizer values

        for numberOfSubq in  [3, 4, 5, 6, 10, 12, 15, 20, 25, 30, 50, 60, 75, 100, 150, 300] {

            try collection.deleteIndex(forName: "words_index")

            config.encoding = .productQuantizer(subquantizers: UInt32(numberOfSubq), bits: 8)

            try collection.createIndex(withName: "words_index", config: config)

            // Query:

            let sql = "select meta().id, word from _default.words where vector_match(words_index, $vector, 20)"

            let parameters = Parameters()

            parameters.setValue(dinnerVector, forName: "vector")

            let q = try self.db.createQuery(sql)

            q.parameters = parameters

            let explain = try q.explain() as NSString

            XCTAssertNotEqual(explain.range(of: "SCAN kv_.words:vector:words_index").location, NSNotFound)

            let rs = try q.execute()

            XCTAssertEqual(rs.allResults().count, 20)

            XCTAssert(checkIndexWasTrained())

        }

        // Step 7: Check if exception thrown for wrong subquantizers:

        for numberOfSubq in [0, 7] {

            try collection.deleteIndex(forName: "words_index")

            config.encoding = .productQuantizer(subquantizers: UInt32(numberOfSubq), bits: 8)

            expectExcepion(exception: .invalidArgumentException) {

                try! collection.createIndex(withName: "words_index", config: config)

            }

        }

    }

WARNING clustering 300 points to 256 centroids: please provide at least 9984 training points