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
>>
>>
>>
>>
>>
>

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