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/*
* 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.ml.linalg
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.sql.catalyst.JavaTypeInference
import org.apache.spark.sql.types._
class VectorUDTSuite extends SparkFunSuite {
test("preloaded VectorUDT") {
val dv1 = Vectors.dense(Array.empty[Double])
val dv2 = Vectors.dense(1.0, 2.0)
val sv1 = Vectors.sparse(2, Array.empty, Array.empty)
val sv2 = Vectors.sparse(2, Array(1), Array(2.0))
for (v <- Seq(dv1, dv2, sv1, sv2)) {
val udt = UDTRegistration.getUDTFor(v.getClass.getName).get.newInstance()
.asInstanceOf[VectorUDT]
assert(v === udt.deserialize(udt.serialize(v)))
assert(udt.typeName == "vector")
assert(udt.simpleString == "vector")
}
}
test("JavaTypeInference with VectorUDT") {
val (dataType, _) = JavaTypeInference.inferDataType(classOf[LabeledPoint])
assert(dataType.asInstanceOf[StructType].fields.map(_.dataType)
=== Seq(new VectorUDT, DoubleType))
}
}
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