Try this:
val df = spark.createDataFrame(Seq(Vectors.dense(Array(10, 590, 190,
700))).map(Tuple1.apply)).toDF("features")
On Sun, 28 Aug 2016 at 11:06 yaroslav <yar...@gmail.com> wrote:
> Hi,
>
> We use such kind of logic for training our model
>
> val model = new LogisticRegressionWithLBFGS()
> .setNumClasses(3)
> .run(train)
>
> Next, during spark streaming, we load model and apply incoming data to this
> model to get specific class, for example:
>
> model.predict(Vectors.dense(10, 590, 190, 700))
>
> How we could achieve the same logic for OneVsRest classification:
>
> val classifier = new LogisticRegression()
> .setMaxIter(10)
> .setTol(1E-6)
> .setFitIntercept(true)
>
> val ovr = new OneVsRest().setClassifier(classifier)
> val model = ovr.fit(train)
>
> How call "predict" for this model with vector Vectors.dense(10, 590, 190,
> 700) and get class ?
>
> We try play with this:
>
> val df = spark.createDataFrame(Array((10, 590, 190, 700)))
> val pr_class = model.transform(df)
>
> but get error.
>
> Thank you.
>
>
>
>
>
>
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