Hi, No problem I'm going to create a JIRA.
Regards Thomas 2016-10-17 21:34 GMT+02:00 Theodore Vasiloudis < theodoros.vasilou...@gmail.com>: > That is my bad, I must have been testing against a private branch when > writing the guide, the SVM as it stands only has a predict operation for > Vector not LabeledVector. > > IMHO I would like to have a predict operator for LabeledVector for all > predictors (that would just call the existing Vector prediction > internally), but IIRC we decided to go with an Evaluate operator instead as > written in the evaluation PR <https://github.com/apache/flink/pull/1849>. > > I'll make a PR to fix the guide, any chance you can create a JIRA for this? > > Regards, > Theodore > > On Mon, Oct 17, 2016 at 6:22 PM, Thomas FOURNIER < > thomasfournier...@gmail.com> wrote: > >> Hi, >> >> Executing the following code (see QuickStart): >> >> val env = ExecutionEnvironment.getExecutionEnvironment >> val survival = env.readCsvFile[(String, String, String, >> String)]("src/main/resources/haberman.data", ",") >> >> >> val survivalLV = survival >> .map { tuple => >> val list = tuple.productIterator.toList >> val numList = list.map(_.asInstanceOf[String].toDouble) >> LabeledVector(numList(3), DenseVector(numList.take(3).toArray)) >> } >> >> >> >> val astroTrain = MLUtils.readLibSVM(env, "src/main/resources/svmguide1") >> val astroTest = MLUtils.readLibSVM(env, "src/main/resources/svmguide1.t") >> >> >> val svm = SVM() >> .setBlocks(env.getParallelism) >> .setIterations(100) >> .setRegularization(0.001) >> .setStepsize(0.1) >> .setSeed(42) >> >> svm.fit(astroTrain) >> svm.predict(astroTest) >> >> >> I encounter the following error: >> >> Exception in thread "main" java.lang.RuntimeException: There is no >> PredictOperation defined for org.apache.flink.ml.classification.SVM which >> takes a DataSet[org.apache.flink.ml.common.LabeledVector] as input. >> >> Any idea ? >> >> Thanks >> >> Thomas >> >> >> >> >> >
