/// <summary>Update the buffer <c>Buffers</c>.</summary>
/// <param name="Buffers">Buffer <c>Buffers</c>.</param>
/// <param name="list">Incoming message from <c>list</c>. Must be a proper distribution. If any element is uniform, the result will be uniform.</param>
/// <param name="IndexOfMaximumDouble">Incoming message from <c>indexOfMaximumDouble</c>.</param>
/// <returns>New value of buffer <c>Buffers</c>.</returns>
/// <remarks>
/// <para />
/// </remarks>
/// <exception cref="ImproperMessageException">
/// <paramref name="list" /> is not a proper distribution.</exception>
/// <typeparam name="GaussianList">The type of an incoming message from <c>list</c>.</typeparam>
public static IndexOfMaximumBuffer[] Buffers <GaussianList>(IndexOfMaximumBuffer[] Buffers, [SkipIfUniform] GaussianList list, Discrete IndexOfMaximumDouble)
where GaussianList : IList <Gaussian>
{
for (int i = 0; i < list.Count; i++)
{
Buffers[i].to_list = new DistributionStructArray <Gaussian, double>(list.Select(o => Gaussian.Uniform()).ToArray());
Buffers[i].to_list[i] = Buffers[i].MessagesToMax.Aggregate((p, q) => p * q);
Buffers[i] = IndexOfMaximumOp.Buffer(Buffers[i], list, i);
}
return(Buffers);
}
/// <summary>EP message to <c>indexOfMaximumDouble</c>.</summary>
/// <param name="list">Incoming message from <c>list</c>.</param>
/// <param name="Buffers">Buffer <c>Buffers</c>.</param>
/// <returns>The outgoing EP message to the <c>indexOfMaximumDouble</c> argument.</returns>
/// <remarks>
/// <para>The outgoing message is a distribution matching the moments of <c>indexOfMaximumDouble</c> as the random arguments are varied. The formula is <c>proj[p(indexOfMaximumDouble) sum_(list) p(list) factor(indexOfMaximumDouble,list)]/p(indexOfMaximumDouble)</c>.</para>
/// </remarks>
/// <typeparam name="GaussianList">The type of an incoming message from <c>list</c>.</typeparam>
public static Discrete IndexOfMaximumDoubleAverageConditional <GaussianList>(GaussianList list, IndexOfMaximumBuffer[] Buffers)
where GaussianList : DistributionStructArray <Gaussian, double>
{
// var results = list.Select(o => list.Select(p => Gaussian.Uniform()).ToList()).ToArray();
// TODO: if IndexOfMaximumDouble is uniform we will never call this routine so buffers will not get set, so messages to IndexOfMaximumDouble will be incorrect
var evidences = new double[list.Count];
for (int i = 0; i < list.Count; i++)
{
//var res = new DistributionStructArray<Gaussian, double>(list.Select(o => Gaussian.Uniform()).ToArray());
//res[i] = Buffer[i].MessagesToMax.Aggregate((p, q) => p * q);
evidences[i] = IndexOfMaximumOp.LogAverageFactor(Buffers[i], list, i);
}
return(new Discrete(MMath.Softmax(evidences)));
}
public static double LogAverageFactor <GaussianList>([SkipIfUniform] GaussianList list, GaussianList to_list, IndexOfMaximumBuffer[] Buffers, Discrete IndexOfMaximumDouble)
where GaussianList : DistributionStructArray <Gaussian, double>
{
var evidences = new double[list.Count];
var tempBuffer = new IndexOfMaximumBuffer();
for (int i = 0; i < list.Count; i++)
{
tempBuffer.to_list = new DistributionStructArray <Gaussian, double>(list.Select(o => Gaussian.Uniform()).ToArray());
tempBuffer.to_list[i] = Buffers[i].MessagesToMax.Aggregate((p, q) => p * q);
tempBuffer.MessagesToMax = new DistributionStructArray <Gaussian, double>(Buffers[i].MessagesToMax.Select(o => (Gaussian)o.Clone()).ToArray());
evidences[i] = IndexOfMaximumOp.LogAverageFactor(tempBuffer, list, i) + IndexOfMaximumDouble.GetLogProb(i);;
}
return(MMath.LogSumExp(evidences));
}
/// <summary>Initialize the buffer <c>Buffers</c>.</summary>
/// <param name="list">Incoming message from <c>list</c>.</param>
/// <param name="IndexOfMaximumDouble">Incoming message from <c>indexOfMaximumDouble</c>.</param>
/// <returns>Initial value of buffer <c>Buffers</c>.</returns>
/// <remarks>
/// <para />
/// </remarks>
/// <typeparam name="GaussianList">The type of an incoming message from <c>list</c>.</typeparam>
public static IndexOfMaximumBuffer[] BuffersInit <GaussianList>([IgnoreDependency] GaussianList list, [IgnoreDependency] Discrete IndexOfMaximumDouble)
where GaussianList : IList <Gaussian>
{
return(list.Select(o => IndexOfMaximumOp.BufferInit(list)).ToArray());
}
