/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="UsesEqualDefGibbsOp{T}"]/message_doc[@name="GibbsEvidence{TDist}(IList{TDist}, TDist, GibbsMarginal{TDist, T})"]/*'/>
/// <typeparam name="TDist">The type of the distribution over the variable.</typeparam>
public static double GibbsEvidence <TDist>(IList <TDist> Uses, TDist Def, GibbsMarginal <TDist, T> to_marginal)
where TDist : IDistribution <T>, Sampleable <T>, CanGetLogAverageOf <TDist>, SettableTo <TDist>, SettableToProduct <TDist>
{
if (Uses.Count == 1)
{
// the total evidence contribution of this variable should be Def.GetLogAverageOf(Uses[0]).
// but since this variable is sending a sample to Def and Use, and those factors will send their own evidence contribution,
// we need to cancel the contribution of those factors here.
return(Def.GetLogAverageOf(Uses[0]) - Def.GetLogProb(to_marginal.LastSample) - Uses[0].GetLogProb(to_marginal.LastSample));
}
else
{
//throw new InferRuntimeException("Gibbs Sampling does not support variables defined within a gate");
double z = 0.0;
TDist productBefore = (TDist)Def.Clone();
TDist product = (TDist)Def.Clone();
for (int i = 0; i < Uses.Count; i++)
{
if (i > 0)
{
product.SetToProduct(productBefore, Uses[i - 1]);
}
z += product.GetLogAverageOf(Uses[i]);
productBefore.SetTo(product);
}
// z is now log(sum_x Def(x)*prod_i Uses[i](x)), which is the desired total evidence.
// but we must also cancel the contribution of the parent and child factors that received a sample from us.
z -= Def.GetLogProb(to_marginal.LastSample);
for (int i = 0; i < Uses.Count; i++)
{
z -= Uses[i].GetLogProb(to_marginal.LastSample);
}
return(z);
}
}
/// <summary>Computations that depend on the observed value of firstCoin and numberOfIterations</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_firstCoin_numberOfIterations(int numberOfIterations)
{
if (this.Changed_firstCoin_numberOfIterations_isDone)
{
return;
}
bool[] secondCoin_uses_F;
Bernoulli[] secondCoin_uses_B;
// Create array for 'secondCoin_uses' Forwards messages.
secondCoin_uses_F = new bool[1];
// Create array for 'secondCoin_uses' Backwards messages.
secondCoin_uses_B = new Bernoulli[1];
secondCoin_uses_B[0] = Bernoulli.Uniform();
bool bothHeads_F = default(bool);
for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++)
{
// Message to 'secondCoin_marginal' from UsesEqualDef factor
this.secondCoin_marginal_F = UsesEqualDefGibbsOp <bool> .MarginalGibbs <Bernoulli>(secondCoin_uses_B, this.vBernoulli0, this.secondCoin_marginal_F);
// Message to 'secondCoin_uses' from UsesEqualDef factor
secondCoin_uses_F[0] = UsesEqualDefGibbsOp <bool> .UsesGibbs <Bernoulli>(this.secondCoin_marginal_F, 0, secondCoin_uses_F[0]);
// Message to 'bothHeads' from And factor
bothHeads_F = Factor.And(this.FirstCoin, secondCoin_uses_F[0]);
// Message to 'bothHeads_marginal' from ReplicateWithMarginal factor
this.bothHeads_marginal_F = ReplicateGibbsOp <bool> .MarginalGibbs <Bernoulli>(bothHeads_F, this.bothHeads_marginal_F);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
this.Changed_firstCoin_numberOfIterations_isDone = true;
}
/// <summary>Computations that depend on the observed value of numberOfIterationsDecreased</summary>
private void Changed_numberOfIterationsDecreased()
{
if (this.Changed_numberOfIterationsDecreased_isDone)
{
return;
}
this.firstCoin_marginal_F = new GibbsMarginal <Bernoulli, bool>(this.vBernoulli0, 100, 5, true, true, false);
this.secondCoin_marginal_F = new GibbsMarginal <Bernoulli, bool>(this.vBernoulli0, 100, 5, true, true, false);
this.bothHeads_marginal_F = new GibbsMarginal <Bernoulli, bool>(new Bernoulli(), 100, 5, true, true, false);
this.Changed_numberOfIterationsDecreased_isDone = true;
}
/// <summary>Computations that depend on the observed value of numberOfIterationsDecreased and must reset on changes to firstCoin</summary>
/// <param name="initialise">If true, reset messages that initialise loops</param>
private void Changed_numberOfIterationsDecreased_Init_firstCoin(bool initialise)
{
if (this.Changed_numberOfIterationsDecreased_Init_firstCoin_isDone && ((!initialise) || this.Changed_numberOfIterationsDecreased_Init_firstCoin_isInitialised))
{
return;
}
this.secondCoin_marginal_F = new GibbsMarginal <Bernoulli, bool>(this.vBernoulli0, 100, 5, true, true, false);
this.bothHeads_marginal_F = new GibbsMarginal <Bernoulli, bool>(new Bernoulli(), 100, 5, true, true, false);
this.Changed_numberOfIterationsDecreased_Init_firstCoin_isDone = true;
this.Changed_numberOfIterationsDecreased_Init_firstCoin_isInitialised = true;
}
[Stochastic] // must be labelled Stochastic to get correct schedule, even though it isn't Stochastic
public static GibbsMarginal <TDist, T> MarginalGibbs <TDist, T>(
T Use, [IgnoreDependency] TDist Def,
GibbsMarginal <TDist, T> to_marginal)
where TDist : IDistribution <T>, Sampleable <T>
{
GibbsMarginal <TDist, T> result = to_marginal;
TDist marginal = result.LastConditional;
marginal.Point = Use;
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
[Stochastic] // must be labelled Stochastic to get correct schedule, even though it isn't Stochastic
public static GibbsMarginal <TDist, TDomain> MarginalGibbs <TDist, TDomain>(
TDomain Def,
GibbsMarginal <TDist, TDomain> to_marginal)
where TDist : IDistribution <TDomain>, Sampleable <TDomain>
{
GibbsMarginal <TDist, TDomain> result = to_marginal;
TDist marginal = result.LastConditional;
marginal.Point = Def;
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
public static GibbsMarginal <TDist, T> MarginalGibbs <TDist>(
IList <TDist> Uses,
T Def,
GibbsMarginal <TDist, T> to_marginal) // must not be called 'result', because its value is used
where TDist : IDistribution <T>, SettableToProduct <TDist>, SettableToRatio <TDist>, SettableTo <TDist>, Sampleable <T>
{
GibbsMarginal <TDist, T> result = to_marginal;
TDist marginal = result.LastConditional;
marginal.Point = Def;
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
public static GibbsMarginal <TDist, T> MarginalGibbs <TDist, T>(
TDist Use,
[SkipIfUniform] TDist Def,
GibbsMarginal <TDist, T> to_marginal)
where TDist : IDistribution <T>, SettableToProduct <TDist>, Sampleable <T>
{
GibbsMarginal <TDist, T> result = to_marginal;
TDist marginal = result.LastConditional;
marginal.SetToProduct(Def, Use);
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
public static GibbsMarginal <TDist, T> MarginalGibbs <TDist, T>(
T Use,
[SkipIfUniform] TDist Def,
GibbsMarginal <TDist, T> to_marginal) // must not be called 'result', because its value is used
where TDist : IDistribution <T>, Sampleable <T>
{
GibbsMarginal <TDist, T> result = to_marginal;
TDist marginal = result.LastConditional;
marginal.Point = Use;
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
[Stochastic] // must be labelled Stochastic to get correct schedule, even though it isn't Stochastic
public static GibbsMarginal <TDist, T> MarginalGibbs <TDist>(
T[] Uses,
GibbsMarginal <TDist, T> to_marginal)
where TDist : IDistribution <T>, Sampleable <T>
{
GibbsMarginal <TDist, T> result = to_marginal;
TDist marginal = result.LastConditional;
if (Uses.Length != 1)
{
throw new ArgumentException("Uses.Length (" + Uses.Length + ") != 1");
}
marginal.Point = Uses[0];
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
public static GibbsMarginal <TDist, T> MarginalGibbs <TDist>(
IList <T> Uses,
[SkipIfUniform] TDist Def,
GibbsMarginal <TDist, T> to_marginal) // must not be called 'result', because its value is used
where TDist : IDistribution <T>, SettableToProduct <TDist>, SettableToRatio <TDist>, SettableTo <TDist>, Sampleable <T>
{
if (Uses.Count > 1)
{
throw new ArgumentException("Uses.Count > 1");
}
GibbsMarginal <TDist, T> result = to_marginal;
TDist marginal = result.LastConditional;
marginal.Point = Uses[0];
result.LastConditional = marginal;
// Allow a sample to be drawn from the last conditional, and add it to the sample
// list and conditional list
result.PostUpdate();
return(result);
}
/// <summary>Computations that depend on the observed value of bothHeads and numberOfIterations</summary>
/// <param name="numberOfIterations">The number of times to iterate each loop</param>
private void Changed_bothHeads_numberOfIterations(int numberOfIterations)
{
if (this.Changed_bothHeads_numberOfIterations_isDone)
{
return;
}
bool[] firstCoin_uses_F;
Bernoulli[] firstCoin_uses_B;
// Create array for 'firstCoin_uses' Forwards messages.
firstCoin_uses_F = new bool[1];
// Create array for 'firstCoin_uses' Backwards messages.
firstCoin_uses_B = new Bernoulli[1];
firstCoin_uses_B[0] = Bernoulli.Uniform();
bool[] secondCoin_uses_F;
// Create array for 'secondCoin_uses' Forwards messages.
secondCoin_uses_F = new bool[1];
for (int iteration = this.numberOfIterationsDone; iteration < numberOfIterations; iteration++)
{
// Message to 'secondCoin_marginal' from UsesEqualDef factor
this.secondCoin_marginal_F = UsesEqualDefGibbsOp <bool> .MarginalGibbs <Bernoulli>(this.secondCoin_uses_B, this.vBernoulli0, this.secondCoin_marginal_F);
// Message to 'secondCoin_uses' from UsesEqualDef factor
secondCoin_uses_F[0] = UsesEqualDefGibbsOp <bool> .UsesGibbs <Bernoulli>(this.secondCoin_marginal_F, 0, secondCoin_uses_F[0]);
// Message to 'firstCoin_uses' from And factor
firstCoin_uses_B[0] = BooleanAndOp.AAverageConditional(this.BothHeads, secondCoin_uses_F[0]);
// Message to 'firstCoin_marginal' from UsesEqualDef factor
this.firstCoin_marginal_F = UsesEqualDefGibbsOp <bool> .MarginalGibbs <Bernoulli>(firstCoin_uses_B, this.vBernoulli0, this.firstCoin_marginal_F);
// Message to 'firstCoin_uses' from UsesEqualDef factor
firstCoin_uses_F[0] = UsesEqualDefGibbsOp <bool> .UsesGibbs <Bernoulli>(this.firstCoin_marginal_F, 0, firstCoin_uses_F[0]);
// Message to 'secondCoin_uses' from And factor
this.secondCoin_uses_B[0] = BooleanAndOp.BAverageConditional(this.BothHeads, firstCoin_uses_F[0]);
this.OnProgressChanged(new ProgressChangedEventArgs(iteration));
}
this.Changed_bothHeads_numberOfIterations_isDone = true;
}
public static T UsesGibbs <TDist>([SkipIfUniform] GibbsMarginal <TDist, T> marginal, T def, int resultIndex, T result)
where TDist : IDistribution <T>, Sampleable <T>
{
if (def is bool[])
{
if (!Util.ValueEquals((bool[])(object)def, (bool[])(object)marginal.LastSample))
{
throw new Exception("gotcha");
}
}
else if (def is double[])
{
if (!Util.ValueEquals((double[])(object)def, (double[])(object)marginal.LastSample))
{
throw new Exception("gotcha");
}
}
else if (!def.Equals(marginal.LastSample))
{
throw new Exception("gotcha");
}
return(marginal.LastSample);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="UsesEqualDefGibbsOp{T}"]/message_doc[@name="DefGibbs{TDist}(GibbsMarginal{TDist, T}, T)"]/*'/>
/// <typeparam name="TDist">The type of the distribution over the variable.</typeparam>
public static T DefGibbs <TDist>([SkipIfUniform] GibbsMarginal <TDist, T> to_marginal, T result)
where TDist : IDistribution <T>, Sampleable <T>
{
return(to_marginal.LastSample);
}
/// <summary>
/// Gibbs evidence
/// </summary>
/// <returns></returns>
public static double GibbsEvidence <TDist, T>(TDist Use, TDist Def, GibbsMarginal <TDist, T> marginal)
where TDist : IDistribution <T>, Sampleable <T>, CanGetLogAverageOf <TDist>
{
return(Def.GetLogAverageOf(Use) - Def.GetLogProb(marginal.LastSample) - Use.GetLogProb(marginal.LastSample));
}
/// <summary>
/// Gibbs sample message to 'Uses'
/// </summary>
/// <typeparam name="TDist">Gibbs marginal type</typeparam>
/// <typeparam name="T">Domain type</typeparam>
/// <param name="marginal">The Gibbs marginal</param>
/// <param name="def"></param>
/// <param name="result">Result</param>
/// <returns></returns>
/// <remarks><para>
/// The outgoing message is the current Gibbs sample.
/// </para></remarks>
public static T UseGibbs <TDist, T>([SkipIfUniform] GibbsMarginal <TDist, T> marginal, TDist def, T result)
where TDist : IDistribution <T>, Sampleable <T>
{
// This method must depend on Def, even though Def isn't used, in order to get the right triggers
return(marginal.LastSample);
}
public static T UsesGibbs <TDist>([SkipIfUniform] GibbsMarginal <TDist, T> marginal, TDist def, int resultIndex, T result)
where TDist : IDistribution <T>, Sampleable <T>
{
return(marginal.LastSample);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ReplicateGibbsOp{T}"]/message_doc[@name="UsesGibbs{TDist}(GibbsMarginal{TDist, T}, TDist, int, T)"]/*'/>
/// <typeparam name="TDist">The type of the distribution over the replicated variable.</typeparam>
public static T UsesGibbs <TDist>([SkipIfUniform] GibbsMarginal <TDist, T> to_marginal, [IgnoreDependency] TDist def, [IgnoreDependency] int resultIndex, T result)
where TDist : IDistribution <T>, Sampleable <T>
{
// This method must depend on Def, even though Def isn't used, in order to get the right triggers
return(to_marginal.LastSample);
}