On Mon, Jan 12, 2015 at 8:14 PM, Meethu Mathew wrote:
> Hi,
>
> This is the function defined in PythonMLLibAPI.scala
> def findPredict(
> data: JavaRDD[Vector],
> wt: Object,
> mu: Array[Object],
> si: Array[Object]): RDD[Array[Double]] = {
> }
>
> So the parameter mu sho
Hi,
This is the function defined in PythonMLLibAPI.scala
def findPredict(
data: JavaRDD[Vector],
wt: Object,
mu: Array[Object],
si: Array[Object]): RDD[Array[Double]] = {
}
So the parameter mu should be converted to Array[object].
mu = (Vectors.dense([0.8786, -0.7855])
On Sun, Jan 11, 2015 at 10:21 PM, Meethu Mathew
wrote:
> Hi,
>
> This is the code I am running.
>
> mu = (Vectors.dense([0.8786, -0.7855]),Vectors.dense([-0.1863, 0.7799]))
>
> membershipMatrix = callMLlibFunc("findPredict", rdd.map(_convert_to_vector),
> mu)
What's the Java API looks like? all t
Hi,
This is the code I am running.
mu = (Vectors.dense([0.8786, -0.7855]),Vectors.dense([-0.1863, 0.7799]))
membershipMatrix = callMLlibFunc("findPredict",
rdd.map(_convert_to_vector), mu)
Regards,
Meethu
On Monday 12 January 2015 11:46 AM, Davies Liu wrote:
Could you post a piece of code h
Could you post a piece of code here?
On Sun, Jan 11, 2015 at 9:28 PM, Meethu Mathew wrote:
> Hi,
> Thanks Davies .
>
> I added a new class GaussianMixtureModel in clustering.py and the method
> predict in it and trying to pass numpy array from this method.I converted it
> to DenseVector and its s
Hi,
Thanks Davies .
I added a new class GaussianMixtureModel in clustering.py and the method
predict in it and trying to pass numpy array from this method.I
converted it to DenseVector and its solved now.
Similarly I tried passing a List of more than one dimension to the
function _py2java ,
Hey Meethu,
The Java API accepts only Vector, so you should convert the numpy array into
pyspark.mllib.linalg.DenseVector.
BTW, which class are you using? the KMeansModel.predict() accept numpy.array,
it will do the conversion for you.
Davies
On Fri, Jan 9, 2015 at 4:45 AM, Meethu Mathew wrote
