[SPARK-29121][ML][MLLIB] Support for dot product operation on Vector(s)

### What changes were proposed in this pull request?

Support for dot product with:
- `ml.linalg.Vector`
- `ml.linalg.Vectors`
- `mllib.linalg.Vector`
- `mllib.linalg.Vectors`

### Why are the changes needed?

Dot product is useful for feature engineering and scoring.  BLAS routines are already there, just a wrapper is needed.

### Does this PR introduce any user-facing change?

No user facing changes, just some new functionality.

### How was this patch tested?

Tests were written and added to the appropriate `VectorSuites` classes.  They can be quickly run with:

```
sbt "mllib-local/testOnly org.apache.spark.ml.linalg.VectorsSuite"
sbt "mllib/testOnly org.apache.spark.mllib.linalg.VectorsSuite"
```

Closes #25818 from phpisciuneri/SPARK-29121.

Authored-by: Patrick Pisciuneri <phpisciuneri@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>

mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala

@@ -178,6 +178,14 @@ sealed trait Vector extends Serializable {
    */
   @Since("2.0.0")
   def argmax: Int
+
+  /**
+   * Calculate the dot product of this vector with another.
+   *
+   * If `size` does not match an [[IllegalArgumentException]] is thrown.
+   */
+  @Since("3.0.0")
+  def dot(v: Vector): Double = BLAS.dot(this, v)
 }
 
 /**

mllib-local/src/test/scala/org/apache/spark/ml/linalg/VectorsSuite.scala

@@ -380,4 +380,27 @@ class VectorsSuite extends SparkMLFunSuite {
       Vectors.sparse(-1, Array((1, 2.0)))
     }
   }
+
+  test("dot product only supports vectors of same size") {
+    val vSize4 = Vectors.dense(arr)
+    val vSize1 = Vectors.zeros(1)
+    intercept[IllegalArgumentException]{ vSize1.dot(vSize4) }
+  }
+
+  test("dense vector dot product") {
+    val dv = Vectors.dense(arr)
+    assert(dv.dot(dv) === 0.26)
+  }
+
+  test("sparse vector dot product") {
+    val sv = Vectors.sparse(n, indices, values)
+    assert(sv.dot(sv) === 0.26)
+  }
+
+  test("mixed sparse and dense vector dot product") {
+    val sv = Vectors.sparse(n, indices, values)
+    val dv = Vectors.dense(arr)
+    assert(sv.dot(dv) === 0.26)
+    assert(dv.dot(sv) === 0.26)
+  }
 }

mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

@@ -204,6 +204,14 @@ sealed trait Vector extends Serializable {
    */
   @Since("2.0.0")
   def asML: newlinalg.Vector
+
+  /**
+   * Calculate the dot product of this vector with another.
+   *
+   * If `size` does not match an [[IllegalArgumentException]] is thrown.
+   */
+  @Since("3.0.0")
+  def dot(v: Vector): Double = BLAS.dot(this, v)
 }
 
 /**

mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala

@@ -510,4 +510,27 @@ class VectorsSuite extends SparkFunSuite with Logging {
       Vectors.sparse(-1, Array((1, 2.0)))
     }
   }
+
+  test("dot product only supports vectors of same size") {
+    val vSize4 = Vectors.dense(arr)
+    val vSize1 = Vectors.zeros(1)
+    intercept[IllegalArgumentException]{ vSize1.dot(vSize4) }
+  }
+
+  test("dense vector dot product") {
+    val dv = Vectors.dense(arr)
+    assert(dv.dot(dv) === 0.26)
+  }
+
+  test("sparse vector dot product") {
+    val sv = Vectors.sparse(n, indices, values)
+    assert(sv.dot(sv) === 0.26)
+  }
+
+  test("mixed sparse and dense vector dot product") {
+    val sv = Vectors.sparse(n, indices, values)
+    val dv = Vectors.dense(arr)
+    assert(sv.dot(dv) === 0.26)
+    assert(dv.dot(sv) === 0.26)
+  }
 }