scala-smfsb
API
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scala-smfsb/smfsb

smfsb

package smfsb

Object containing basic types used throughout the library.

Attributes

Members list

Type members

Classlikes

object Abc

Functions for parameter inference using ABC (and ABC-SMC) methods

Functions for parameter inference using ABC (and ABC-SMC) methods

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Abc.type
trait CsvRow[T]

Type class for vectors that can be rendered to a CSV string (and a Breeze DenseVector[Double]), extended by State

Type class for vectors that can be rendered to a CSV string (and a Breeze DenseVector[Double]), extended by State

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
trait State[S]
implicit class CsvRowSyntax[T](value: T)

Syntax for CsvRow

Syntax for CsvRow

Attributes

Supertypes
class Object
trait Matchable
class Any
trait DataSet[D]

Data set type class, for ABC methods

Data set type class, for ABC methods

Attributes

Supertypes
class Object
trait Matchable
class Any
case class MarkedSpn[S](species: List[String], m: S, pre: DenseMatrix[Int], post: DenseMatrix[Int], h: (S, Time) => HazardVec)(implicit evidence$1: State[S]) extends Spn[S]

Case class for SPNs that include an initial marking

Case class for SPNs that include an initial marking

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
trait Spn[S]
class Object
trait Matchable
class Any
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object Mcmc

Functions for constucting generic Metropolis-Hastings MCMC algorithms, and associated utilities. Can be used in conjunction with an unbiased estimate of marginal model likelihood for constructing pseudo-marginal MCMC algorithms, such as PMMH pMCMC.

Functions for constucting generic Metropolis-Hastings MCMC algorithms, and associated utilities. Can be used in conjunction with an unbiased estimate of marginal model likelihood for constructing pseudo-marginal MCMC algorithms, such as PMMH pMCMC.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Mcmc.type
object Mll

Functions associated with particle filtering of Markov process models against time series data and the computation of marginal model likelihoods.

Functions associated with particle filtering of Markov process models against time series data and the computation of marginal model likelihoods.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Mll.type
case class PMatrix[T](x: Int, y: Int, r: Int, c: Int, data: ParVector[T])

Comonadic pointed 2d image/matrix type (parallel implementation), used by the spatial simulation functions.

Comonadic pointed 2d image/matrix type (parallel implementation), used by the spatial simulation functions.

Attributes

Companion
object
Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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case object PMatrix

Attributes

Companion
class
Supertypes
trait Singleton
trait Product
trait Mirror
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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Self type
PMatrix.type
case class PVector[T](cur: Int, vec: ParVector[T])

Comonadic pointed vector type (parallel implementation), used by the spatial simulation functions.

Comonadic pointed vector type (parallel implementation), used by the spatial simulation functions.

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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object Sim

Functions for simulating data associated with a Markov process given an appropriate transition kernel.

Functions for simulating data associated with a Markov process given an appropriate transition kernel.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Sim.type
object Spatial

All functions and utilities relating to spatial simulation

All functions and utilities relating to spatial simulation

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Spatial.type
sealed trait Spn[S]

Main trait for stochastic Petri nets (SPNs)

Main trait for stochastic Petri nets (SPNs)

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
class MarkedSpn[S]
class UnmarkedSpn[S]
object SpnModels

Some example SPN models, each of which can be instantiated with either discrete or continous states.

Some example SPN models, each of which can be instantiated with either discrete or continous states.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
SpnModels.type
trait State[S] extends CsvRow[S]

State type class, with implementations for Breeze DenseVector Ints and Doubles

State type class, with implementations for Breeze DenseVector Ints and Doubles

Attributes

Supertypes
trait CsvRow[S]
class Object
trait Matchable
class Any
object Step

Functions which accept a Spn and return a function for simulating from the transition kernel of that model

Functions which accept a Spn and return a function for simulating from the transition kernel of that model

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Step.type
trait Thinnable[F[_]]

A type class for things which can be "thinned", with an implementation for Stream. Useful for MCMC algorithms.

A type class for things which can be "thinned", with an implementation for Stream. Useful for MCMC algorithms.

Attributes

Supertypes
class Object
trait Matchable
class Any
implicit class ThinnableSyntax[T, F[T]](value: F[T])

Provision of the .thin syntax for Thinnable things

Provision of the .thin syntax for Thinnable things

Attributes

Supertypes
class Object
trait Matchable
class Any
case class UnmarkedSpn[S](species: List[String], pre: DenseMatrix[Int], post: DenseMatrix[Int], h: (S, Time) => HazardVec)(implicit evidence$1: State[S]) extends Spn[S]

Case class for SPNs without an initial marking

Case class for SPNs without an initial marking

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
trait Spn[S]
class Object
trait Matchable
class Any
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Types

type DoubleState = DenseVector[Double]

Alias for a Breeze DenseVector[Double]

Alias for a Breeze DenseVector[Double]

Attributes

type HazardVec = DenseVector[Double]

Type for a SPN hazard vector

Type for a SPN hazard vector

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type IntState = DenseVector[Int]

Alias for a Breeze DenseVector[Int]

Alias for a Breeze DenseVector[Int]

Attributes

type LogLik = Double

Type representing log-likelihoods - just an alias for Double. All likelihoods in this library are on a log scale. There should be no raw likelihoods passed into or out of any user-facing function.

Type representing log-likelihoods - just an alias for Double. All likelihoods in this library are on a log scale. There should be no raw likelihoods passed into or out of any user-facing function.

Attributes

type Time = Double

Type representing time, but just an alias for Double

Type representing time, but just an alias for Double

Attributes

type Ts[S] = List[(Time, S)]

The main time series class, for representing output from simulation functions, and for observed time course data

The main time series class, for representing output from simulation functions, and for observed time course data

Attributes

Implicits

Implicits

final implicit def CsvRowSyntax[T](value: T): CsvRowSyntax[T]

Syntax for CsvRow

Syntax for CsvRow

Attributes

final implicit def ThinnableSyntax[T, F[T]](value: F[T]): ThinnableSyntax[T, F]

Provision of the .thin syntax for Thinnable things

Provision of the .thin syntax for Thinnable things

Attributes

implicit val dvdState: State[DoubleState]

Evidence that DoubleState belongs to the State type class

Evidence that DoubleState belongs to the State type class

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implicit val dviState: State[IntState]

Evidence that IntState belongs to the State type class

Evidence that IntState belongs to the State type class

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implicit val streamThinnable: Thinnable[LazyList]

A Thinnable instance for Stream

A Thinnable instance for Stream

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implicit val tsdsDs: DataSet[Ts[DoubleState]]

Evidence that Ts[DoubleState] is a DataSet

Evidence that Ts[DoubleState] is a DataSet

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implicit val tsisDs: DataSet[Ts[IntState]]

Evidence that Ts[IntState] is a DataSet

Evidence that Ts[IntState] is a DataSet

Attributes

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