PerceptronClassifier

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

   Definition Classes

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5. val burnInIterations: Int

   

6. def classOf(d: Datum[L, F]): L

   

   Returns the argmax for this datum

   Returns the argmax for this datum

   Definition Classes

   PerceptronClassifier → Classifier

7. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

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   @throws( ... )

8. def displayWeights(pw: PrintWriter): Unit

   

9. val epochs: Int

   

10. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

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11. def equals(arg0: Any): Boolean

    

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12. def finalize(): Unit

    

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    protected[java.lang]

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13. final def getClass(): Class[_]

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. val marginRatio: Double

    

17. final def ne(arg0: AnyRef): Boolean

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def saveTo(w: Writer): Unit

    

    Saves to writer.

    Saves to writer. Does NOT close the writer

    Definition Classes

    PerceptronClassifier → Classifier

21. def saveTo(fileName: String): Unit

    

    Saves the current model to a file

    Saves the current model to a file

    Definition Classes

    Classifier

22. def scoresOf(d: Datum[L, F]): Counter[L]

    

    Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

    Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

    Definition Classes

    PerceptronClassifier → Classifier

23. final def synchronized[T0](arg0: ⇒ T0): T0

    

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24. def toString(): String

    

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25. def train(dataset: Dataset[L, F], indices: Array[Int]): Unit

    

    Trains a classifier, using only the datums specified in indices indices is useful for bagging

    Trains a classifier, using only the datums specified in indices indices is useful for bagging

    Definition Classes

    PerceptronClassifier → Classifier

26. def train(dataset: Dataset[L, F], spans: Option[Iterable[(Int, Int)]] = None): Unit

    

    Trains the classifier on the given dataset spans is useful during cross validation

    Trains the classifier on the given dataset spans is useful during cross validation

    Definition Classes

    Classifier

27. final def wait(): Unit

    

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28. final def wait(arg0: Long, arg1: Int): Unit

    

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29. final def wait(arg0: Long): Unit

    

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