# LinearVectorClassifier

#### class LinearVectorClassifier[L <: DiscreteVar, F <: VectorVar] extends LinearMulticlassClassifier with VectorClassifier[L, F]

A VectorClassifier in which the score for each class is a dot-product between the observed feature vector and a vector of parameters. Examples include NaiveBayes, MultivariateLogisticRegression, LinearSVM, and many others. Counter-examples include KNearestNeighbor.

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1. LinearVectorClassifier
2. VectorClassifier
3. Classifier
4. LinearMulticlassClassifier
5. OptimizablePredictor
6. Parameters
7. MulticlassClassifier
8. Classifier
9. Predictor
10. AnyRef
11. Any
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### Value Members

1. #### final def !=(arg0: AnyRef): Boolean

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2. #### final def !=(arg0: Any): Boolean

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3. #### final def ##(): Int

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4. #### final def ==(arg0: AnyRef): Boolean

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5. #### final def ==(arg0: Any): Boolean

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6. #### def Weights(t4: ⇒ Tensor4): Weights4

Definition Classes
Parameters
7. #### def Weights(t3: ⇒ Tensor3): Weights3

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Parameters
8. #### def Weights(t2: ⇒ Tensor2): Weights2

Definition Classes
Parameters
9. #### def Weights(t1: ⇒ Tensor1): Weights1

Definition Classes
Parameters
10. #### def accumulateObjectiveGradient(accumulator: WeightsMapAccumulator, features: la.Tensor1, gradient: la.Tensor1, weight: Double): Unit

Put gradient of objective with respect to parameters into the accumulator.

Put gradient of objective with respect to parameters into the accumulator. The contract states we cannot mutate the "input" argument inside this method.

accumulator

weight

Definition Classes
LinearMulticlassClassifierOptimizablePredictor
11. #### def accuracy(labels: Iterable[L with LabeledDiscreteVar]): Double

Definition Classes
Classifier
12. #### def asDotTemplate[T <: LabeledMutableDiscreteVar](l2f: (T) ⇒ TensorVar)(implicit ml: Manifest[T]): DotTemplateWithStatistics2[T, TensorVar] { ... /* 2 definitions in type refinement */ }

Definition Classes
LinearMulticlassClassifier
13. #### final def asInstanceOf[T0]: T0

Definition Classes
Any
14. #### def asTemplate[Value <: DiscreteValue, T <: LabeledMutableDiscreteVar, F <: variable.Var { type Value = cc.factorie.la.Tensor1 }](l2f: (T) ⇒ F)(implicit ml: Manifest[T], mf: Manifest[F]): ClassifierTemplate[la.Tensor1, Value, T, F]

Definition Classes
MulticlassClassifier
15. #### def bestLabelIndex(v: L): Int

Definition Classes
LinearVectorClassifierClassifier
16. #### def classification(v: L): Classification[L]

Definition Classes
LinearVectorClassifierClassifier
17. #### def classification(input: la.Tensor1): MulticlassClassification

Definition Classes
MulticlassClassifierClassifier
18. #### def classifications(labels: Iterable[L]): Seq[Classification[L]]

Definition Classes
Classifier
19. #### def classify(labels: Iterable[L with MutableDiscreteVar]): Seq[Classification[L]]

Definition Classes
Classifier
20. #### def classify[L2 <: L with MutableDiscreteVar](v: L2): Classification[L]

Definition Classes
Classifier
21. #### def clone(): AnyRef

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protected[java.lang]
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@throws( ... )
22. #### final def eq(arg0: AnyRef): Boolean

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23. #### def equals(arg0: Any): Boolean

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24. #### val featureSize: Int

Definition Classes
LinearMulticlassClassifier
25. #### def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
26. #### final def getClass(): Class[_]

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27. #### def hashCode(): Int

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28. #### final def isInstanceOf[T0]: Boolean

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29. #### val labelSize: Int

Definition Classes
LinearMulticlassClassifier

31. #### final def ne(arg0: AnyRef): Boolean

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32. #### final def notify(): Unit

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33. #### final def notifyAll(): Unit

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34. #### val parameters: WeightsSet

Definition Classes
Parameters
35. #### def predict(features: la.Tensor1): la.Tensor1

Definition Classes
LinearMulticlassClassifierPredictor
36. #### final def synchronized[T0](arg0: ⇒ T0): T0

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37. #### def toString(): String

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38. #### final def wait(): Unit

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@throws( ... )
39. #### final def wait(arg0: Long, arg1: Int): Unit

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40. #### final def wait(arg0: Long): Unit

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41. #### val weights: Weights2

Definition Classes
LinearMulticlassClassifier