Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package linalg
    Definition Classes
    ml
  • DenseMatrix
  • DenseVector
  • Matrices
  • Matrix
  • SQLDataTypes
  • SparseMatrix
  • SparseVector
  • Vector
  • Vectors

object Vectors

Factory methods for org.apache.spark.ml.linalg.Vector. We don't use the name Vector because Scala imports scala.collection.immutable.Vector by default.

Annotations
@Since("2.0.0")
Source
Vectors.scala
Linear Supertypes
AnyRef, Any
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Value Members

  1. def dense(values: Array[Double]): Vector

    Creates a dense vector from a double array.

    Creates a dense vector from a double array.

    Annotations
    @Since("2.0.0")
  2. def dense(firstValue: Double, otherValues: Double*): Vector

    Creates a dense vector from its values.

    Creates a dense vector from its values.

    Annotations
    @varargs() @Since("2.0.0")
  3. def norm(vector: Vector, p: Double): Double

    Returns the p-norm of this vector.

    Returns the p-norm of this vector.

    vector

    input vector.

    p

    norm.

    returns

    norm in Lp space.

    Annotations
    @Since("2.0.0")
  4. def sparse(size: Int, elements: Iterable[(Integer, Double)]): Vector

    Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.

    Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.

    size

    vector size.

    elements

    vector elements in (index, value) pairs.

    Annotations
    @Since("2.0.0")
  5. def sparse(size: Int, elements: Seq[(Int, Double)]): Vector

    Creates a sparse vector using unordered (index, value) pairs.

    Creates a sparse vector using unordered (index, value) pairs.

    size

    vector size.

    elements

    vector elements in (index, value) pairs.

    Annotations
    @Since("2.0.0")
  6. def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector

    Creates a sparse vector providing its index array and value array.

    Creates a sparse vector providing its index array and value array.

    size

    vector size.

    indices

    index array, must be strictly increasing.

    values

    value array, must have the same length as indices.

    Annotations
    @Since("2.0.0")
  7. def sqdist(v1: Vector, v2: Vector): Double

    Returns the squared distance between two Vectors.

    Returns the squared distance between two Vectors.

    v1

    first Vector.

    v2

    second Vector.

    returns

    squared distance between two Vectors.

    Annotations
    @Since("2.0.0")
  8. def zeros(size: Int): Vector

    Creates a vector of all zeros.

    Creates a vector of all zeros.

    size

    vector size

    returns

    a zero vector

    Annotations
    @Since("2.0.0")