Packages

  • package root
    Definition Classes
    root
  • package com
    Definition Classes
    root
  • package nvidia
    Definition Classes
    com
  • package mdx

    this is the base transform for mdx analytics SDK, the transform comprise of

    this is the base transform for mdx analytics SDK, the transform comprise of


    core : mdx streaming core classes
    xapp : comprise of sample applications

    Definition Classes
    nvidia
  • package core

    this is the core transform for mdx SDK, contains transform and classes for the core mdx functionality

    this is the core transform for mdx SDK, contains transform and classes for the core mdx functionality

    Definition Classes
    mdx
  • package util
    Definition Classes
    core
  • package kafka
    Definition Classes
    util
  • package kml

    primarily comprise of KMLWriter classes, these utility classed are used to validate calibration, malformed trajectories etc.

    primarily comprise of KMLWriter classes, these utility classed are used to validate calibration, malformed trajectories etc.

    Definition Classes
    util
  • package osm
    Definition Classes
    util
  • package playback

    primarily comprise of playback classes, these utility classed are used to simulate perception layer, send Frames or Messages from input file.

    primarily comprise of playback classes, these utility classed are used to simulate perception layer, send Frames or Messages from input file.

    Definition Classes
    util
  • CRP
  • Config
  • DistanceUtil
  • Encryptor
  • Implicits
  • MapMatchingUtil
  • SchemaUtil
  • Util

object CRP

from wikipedia definition

In probability theory, the Chinese restaurant process is a discrete-time stochastic process, analogous to seating customers at tables in a Chinese restaurant. Imagine a Chinese restaurant with an infinite number of circular tables, each with infinite capacity. Customer 1 is seated at an unoccupied table with probability 1. At time n + 1, a new customer chooses uniformly at random to sit at one of the following n + 1 places: directly to the left of one of the n customers already sitting at an occupied table, or at a new, unoccupied table.

At time n, the value of the process is a partition of the set of n customers, where the tables are the blocks of the partition. Mathematicians are interested in the probability distribution of this random partition.

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  1. type FloatVector = Array[Float]
  2. type IdList = MutableList[Int]
  3. class Model extends Serializable

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  6. def cluster(vecs: Array[FloatVector], pnew: Double = 0.9): Model

    Given a list of normalized vectors return the CRP cluster model.

    Given a list of normalized vectors return the CRP cluster model. default pnew=0.9, threshold for creation of new cluster if given a new vector is not close enough to any of the existing cluster.

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