Class/Object

org.apache.spark.mllib.feature

HashingTF

Related Docs: object HashingTF | package feature

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class HashingTF extends Serializable

Maps a sequence of terms to their term frequencies using the hashing trick.

Annotations: @Since( "1.1.0" )
Source: HashingTF.scala

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Instance Constructors

new HashingTF()

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@Since( "1.1.0" )
new HashingTF(numFeatures: Int)

numFeatures
number of features (default: 2²⁰)

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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

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def hashCode(): Int

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def indexOf(term: Any): Int

Returns the index of the input term.
Returns the index of the input term.

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@Since( "1.1.0" )
final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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val numFeatures: Int

number of features (default: 2²⁰)
def setBinary(value: Boolean): HashingTF.this.type

If true, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: false)
If true, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: false)

Annotations
@Since( "2.0.0" )
def setHashAlgorithm(value: String): HashingTF.this.type

Set the hash algorithm used when mapping term to integer.
Set the hash algorithm used when mapping term to integer. (default: murmur3)

Annotations
@Since( "2.0.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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def transform[D <: Iterable[_]](dataset: JavaRDD[D]): JavaRDD[Vector]

Transforms the input document to term frequency vectors (Java version).
Transforms the input document to term frequency vectors (Java version).

Annotations
@Since( "1.1.0" )
def transform[D <: Iterable[_]](dataset: RDD[D]): RDD[Vector]

Transforms the input document to term frequency vectors.
Transforms the input document to term frequency vectors.

Annotations
@Since( "1.1.0" )
def transform(document: Iterable[_]): Vector

Transforms the input document into a sparse term frequency vector (Java version).
Transforms the input document into a sparse term frequency vector (Java version).

Annotations
@Since( "1.1.0" )
def transform(document: Iterable[_]): Vector

Transforms the input document into a sparse term frequency vector.
Transforms the input document into a sparse term frequency vector.

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@Since( "1.1.0" )
final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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