I guess the setup of the model and usage of the vector got to me.
Setup takes position 1 , 2 , 3 - like this in the build example - "1:0.0
2:0.0 3:0.0"
I thought I need to follow the same numbering while creating vector too.
thanks a bunch
On Thu, Aug 4, 2016 at 12:39 AM, Sean Owen <so...@cloudera.com> wrote:
> 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)
> >
> >
>
