Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/117#discussion_r10860087
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
---
@@ -0,0 +1,179 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.linalg
+
+import java.lang.{Iterable => JavaIterable}
+
+import scala.annotation.varargs
+import scala.collection.JavaConverters._
+
+import breeze.linalg.{Vector => BV, DenseVector => BDV, SparseVector =>
BSV}
+
+/**
+ * Represents a numeric vector, whose index type is Int and value type is
Double.
+ */
+trait Vector extends Serializable {
+
+ /**
+ * Size of the vector.
+ */
+ def size: Int
+
+ /**
+ * Converts the instance to a double array.
+ */
+ def toArray: Array[Double]
+
+ override def equals(other: Any): Boolean = {
+ other match {
+ case v: Vector =>
+ this.toArray.view.equals(v.toArray.view)
+ case _ => false
+ }
+ }
+
+ override def hashCode(): Int = toArray.view.hashCode()
+
+ /**
+ * Converts the instance to a breeze vector.
+ */
+ private[mllib] def toBreeze: BV[Double]
+}
+
+/**
+ * Represents a vector with random access to its elements.
+ *
+ */
+trait RandomAccessVector extends Vector {
+ // empty
+}
+
+/**
+ * Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
+ */
+object Vectors {
+
+ /**
+ * Creates a dense vector.
+ */
+ @varargs
+ def dense(values: Double*): Vector = new DenseVector(values.toArray)
+
+ // A dummy implicit is used to avoid signature collision with the one
generated by @varargs.
+ /**
+ * Creates a dense vector from a double array.
+ */
+ def dense(values: Array[Double])(implicit d: DummyImplicit): Vector =
new DenseVector(values)
--- End diff --
This will be problematic to call from Java. The right workaround is this:
```
def dense(values: Array[Double]) { ... }
def dense(firstValue: Double, otherValues: Double*) { ... }
```
I believe it should work in Java too but you should try it (add Java unit
tests for this)
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