io.improbable.keanu.vertices.dbl.probabilistic

Class LogNormalVertex

java.lang.Object

io.improbable.keanu.vertices.Vertex<DoubleTensor>

io.improbable.keanu.vertices.dbl.DoubleVertex

io.improbable.keanu.vertices.dbl.probabilistic.LogNormalVertex

All Implemented Interfaces:

Variable<DoubleTensor,VertexState<DoubleTensor>>, HasShape, io.improbable.keanu.kotlin.DoubleOperators<DoubleVertex>, io.improbable.keanu.kotlin.NumberOperators<DoubleVertex>, Differentiable, ProbabilisticDouble, LogProbGraphSupplier, Observable<DoubleTensor>, Probabilistic<DoubleTensor>, Samplable<DoubleTensor>, SamplableWithManyScalars<DoubleTensor>, SamplableWithShape<DoubleTensor>

public class LogNormalVertex
extends DoubleVertex
implements Differentiable, ProbabilisticDouble, SamplableWithManyScalars<DoubleTensor>, LogProbGraphSupplier

Constructor Summary

Constructors

Constructor and Description
LogNormalVertex(double mu, double sigma)
LogNormalVertex(double mu, DoubleVertex sigma)
LogNormalVertex(DoubleVertex mu, double sigma)
LogNormalVertex(DoubleVertex mu, DoubleVertex sigma)
LogNormalVertex(long[] tensorShape, double mu, double sigma)
LogNormalVertex(long[] tensorShape, double mu, DoubleVertex sigma)
LogNormalVertex(long[] tensorShape, DoubleVertex mu, double sigma)
LogNormalVertex(long[] tensorShape, DoubleVertex mu, DoubleVertex sigma) One mu or s or both driving an arbitrarily shaped tensor of LogNormal https://en.wikipedia.org/wiki/Log-normal_distribution

Method Summary

Modifier and Type	Method and Description
java.util.Map<Vertex,DoubleTensor>	dLogProb(DoubleTensor value, java.util.Set<? extends Vertex> withRespectTo) The partial derivatives of the natural log prob.
DoubleVertex	getMu()
DoubleVertex	getSigma()
double	logProb(DoubleTensor value) This is the natural log of the probability at the supplied value.
LogProbGraph	logProbGraph()
DoubleTensor	sampleWithShape(long[] shape, KeanuRandom random)

Methods inherited from class io.improbable.keanu.vertices.dbl.DoubleVertex

abs, acos, asin, atan, atan2, ceil, concat, cos, div, div, divideBy, divideBy, equalTo, exp, floor, getValue, greaterThan, greaterThanOrEqualTo, lambda, lambda, lessThan, lessThanOrEqualTo, loadValue, log, logGamma, matrixDeterminant, matrixInverse, matrixMultiply, max, min, minus, minus, multiply, multiply, notEqualTo, observe, observe, permute, plus, plus, pow, pow, reshape, reverseDiv, reverseMinus, round, saveValue, setAndCascade, setAndCascade, setValue, setValue, setWithMask, setWithMask, sigmoid, sin, slice, sum, sum, take, tan, times, times, toGreaterThanMask, toGreaterThanMask, toGreaterThanOrEqualToMask, toGreaterThanOrEqualToMask, toInteger, toLessThanMask, toLessThanMask, toLessThanOrEqualToMask, toLessThanOrEqualToMask, transpose, unaryMinus

Methods inherited from class io.improbable.keanu.vertices.Vertex

addChild, addParent, addParents, equals, eval, getChildren, getConnectedGraph, getDegree, getId, getIndentation, getLabel, getObservedValue, getParents, getRank, getReference, getShape, getState, getValue, hashCode, hasValue, isDifferentiable, isObserved, isProbabilistic, lazyEval, observe, observeOwnValue, print, print, removeLabel, save, setAndCascade, setLabel, setLabel, setParents, setParents, setState, setValue, toString, unobserve

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface io.improbable.keanu.vertices.dbl.Differentiable

forwardModeAutoDifferentiation, reverseModeAutoDifferentiation, withRespectToSelf

Methods inherited from interface io.improbable.keanu.vertices.dbl.probabilistic.ProbabilisticDouble

dLogPdf, dLogPdf, dLogPdf, dLogPdf, dLogPdf, dLogPdf, logPdf, logPdf, logPdf

Methods inherited from interface io.improbable.keanu.vertices.Probabilistic

dLogProb, dLogProbAtValue, dLogProbAtValue, getValue, keepOnlyProbabilisticVertices, logProbAtValue

Methods inherited from interface io.improbable.keanu.vertices.Observable

getObservedValue, isObserved, observe, unobserve

Methods inherited from interface io.improbable.keanu.vertices.Samplable

sample, sample

Methods inherited from interface io.improbable.keanu.vertices.SamplableWithManyScalars

sample, sampleManyScalars, sampleManyScalars

Methods inherited from interface io.improbable.keanu.vertices.SamplableWithShape

sampleWithShape

Methods inherited from interface io.improbable.keanu.algorithms.variational.optimizer.HasShape

getShape

Constructor Detail

LogNormalVertex

public LogNormalVertex(long[] tensorShape,
                       DoubleVertex mu,
                       DoubleVertex sigma)

One mu or s or both driving an arbitrarily shaped tensor of LogNormal https://en.wikipedia.org/wiki/Log-normal_distribution

Parameters:

tensorShape - the desired shape of the vertex

mu - the mu (location) of the LogNormal with either the same tensor shape as specified for this vertex or mu scalar

sigma - the sigma of the Logistic with either the same shape as specified for this vertex or mu scalar

LogNormalVertex

public LogNormalVertex(long[] tensorShape,
                       DoubleVertex mu,
                       double sigma)

LogNormalVertex

public LogNormalVertex(long[] tensorShape,
                       double mu,
                       DoubleVertex sigma)

LogNormalVertex

public LogNormalVertex(long[] tensorShape,
                       double mu,
                       double sigma)

LogNormalVertex

public LogNormalVertex(DoubleVertex mu,
                       DoubleVertex sigma)

LogNormalVertex

public LogNormalVertex(double mu,
                       DoubleVertex sigma)

LogNormalVertex

public LogNormalVertex(DoubleVertex mu,
                       double sigma)

LogNormalVertex

public LogNormalVertex(double mu,
                       double sigma)

Method Detail

getMu

public DoubleVertex getMu()

getSigma

public DoubleVertex getSigma()

logProb

public double logProb(DoubleTensor value)

Description copied from interface: Probabilistic

This is the natural log of the probability at the supplied value. In the case of continuous vertices, this is actually the log of the density, which will differ from the probability by a constant.

Specified by:

logProb in interface Probabilistic<DoubleTensor>

Parameters:

value - The supplied value.

Returns:

The natural log of the probability function at the supplied value. For continuous variables this is called the PDF (probability density function). For discrete variables this is called the PMF (probability mass function).

logProbGraph

public LogProbGraph logProbGraph()

Specified by:

logProbGraph in interface LogProbGraphSupplier

dLogProb

public java.util.Map<Vertex,DoubleTensor> dLogProb(DoubleTensor value,
                                                   java.util.Set<? extends Vertex> withRespectTo)

Description copied from interface: Probabilistic

The partial derivatives of the natural log prob.

Specified by:

dLogProb in interface Probabilistic<DoubleTensor>

Parameters:

value - at a given value

withRespectTo - list of parents to differentiate with respect to

Returns:

the partial derivatives of the log of the probability function at the supplied value. For continuous variables this is called the PDF (probability density function). For discrete variables this is called the PMF (probability mass function).

sampleWithShape

public DoubleTensor sampleWithShape(long[] shape,
                                    KeanuRandom random)

Specified by:

sampleWithShape in interface SamplableWithShape<DoubleTensor>