You mean "new int[] {0,1,2}" because vectors are 0-indexed.
On Wed, Aug 3, 2016 at 11:52 AM, Tony Lane <tonylane....@gmail.com> wrote:
> Hi Sean,
>
> I did not understand,
> I created a KMeansModel with 3 dimensions and then I am calling predict
> method on this model with a 3 dimension vector ?
> I am not sre what is wrong in this approach. i am missing a point ?
>
> Tony
>
> On Wed, Aug 3, 2016 at 11:22 PM, Sean Owen <so...@cloudera.com> wrote:
>>
>> You declare that the vector has 3 dimensions, but then refer to its
>> 4th dimension (at index 3). That is the error.
>>
>> On Wed, Aug 3, 2016 at 10:43 AM, Tony Lane <tonylane....@gmail.com> wrote:
>> > I am using the following vector definition in java
>> >
>> > Vectors.sparse(3, new int[] { 1, 2, 3 }, new double[] { 1.1, 1.1, 1.1
>> > }))
>> >
>> > However when I run the predict method on this vector it leads to
>> >
>> > Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: 3
>> > at org.apache.spark.mllib.linalg.BLAS$.dot(BLAS.scala:143)
>> > at org.apache.spark.mllib.linalg.BLAS$.dot(BLAS.scala:115)
>> > at
>> >
>> > org.apache.spark.mllib.util.MLUtils$.fastSquaredDistance(MLUtils.scala:298)
>> > at
>> >
>> > org.apache.spark.mllib.clustering.KMeans$.fastSquaredDistance(KMeans.scala:606)
>> > at
>> >
>> > org.apache.spark.mllib.clustering.KMeans$$anonfun$findClosest$1.apply(KMeans.scala:580)
>> > at
>> >
>> > org.apache.spark.mllib.clustering.KMeans$$anonfun$findClosest$1.apply(KMeans.scala:574)
>> > at scala.collection.mutable.ArraySeq.foreach(ArraySeq.scala:74)
>> > at
>> > org.apache.spark.mllib.clustering.KMeans$.findClosest(KMeans.scala:574)
>> > at
>> >
>> > org.apache.spark.mllib.clustering.KMeansModel.predict(KMeansModel.scala:59)
>> > at org.apache.spark.ml.clustering.KMeansModel.predict(KMeans.scala:130)
>
>
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