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From Theodore Vasiloudis <theodoros.vasilou...@gmail.com>
Subject Re: SVM Multiclass classification
Date Fri, 14 Oct 2016 07:53:05 GMT
Hello Kursat,

As noted in the documentation, the SVM implementation is for binary
classification only for the time being.

Regards,
Theodore

-- 
Sent from a mobile device. May contain autocorrect errors.

On Oct 13, 2016 8:53 PM, "Kürşat Kurt" <kursat@kursatkurt.com> wrote:

> Hi;
>
>
>
> I am trying to classify documents.
>
> When i try to predict (same of training set) there is only 1 and -1
> predictions.
>
> Accuracy is 0%.
>
>
>
>
>
> Can you help me please?
>
>
>
> *val* env = ExecutionEnvironment.getExecutionEnvironment
>
>     *val* training = Seq(
>
>       *new* LabeledVector(1.0, *new* SparseVector(10, Array(0, 2, 3),
> Array(1.0, 1.0, 1.0))),
>
>       *new* LabeledVector(1.0, *new* SparseVector(10, Array(0, 1, 5, 9),
> Array(1.0, 1.0, 1.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(0, 2), Array(
> 0.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(0), Array(0.0
> ))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0, 2), Array(
> 0.0, 1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0), Array(0.0
> ))),
>
>       *new* LabeledVector(1.0, *new* SparseVector(10, Array(0, 3), Array(
> 1.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(0, 2, 3),
> Array(0.0, 1.0, 1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0, 7, 9),
> Array(0.0, 1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(2,3,4), Array(
> 0.0,1.0,1.0))),
>
>       *new* LabeledVector(2.0, *new* SparseVector(10, Array(0, 3), Array(
> 1.0, 1.0))),
>
>       *new* LabeledVector(0.0, *new* SparseVector(10, Array(2, 3,9),
> Array(1.0, 0.0, 1.0)))
>
>
>
>     );
>
>     *val* trainingDS = env.fromCollection(training)
>
>     *val* testingDS = env.fromCollection(training)
>
>     *val* svm = *new* SVM().setBlocks(env.getParallelism)
>
>     svm.fit(trainingDS)
>
>     *val* predictions = *svm*.evaluate(testingDS.map(x => (x.vector, x.
> label)))
>
>     predictions.print();
>
>
>
> Sample output:
>
>
>
> (1.0,1.0)
>
> (1.0,1.0)
>
> (0.0,1.0)
>
> (0.0,-1.0)
>
> (2.0,1.0)
>
> (2.0,-1.0)
>
> (1.0,1.0)
>
> (0.0,1.0)
>
> (2.0,1.0)
>
> (2.0,1.0)
>
> (2.0,1.0)
>
> (0.0,1.0)
>

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