/// <summary>
/// EP message to 'array'.
/// </summary>
/// <param name="allTrue">Incoming message from 'allTrue'. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="array">Incoming message from 'array'.</param>
/// <param name="result">Modified to contain the outgoing message.</param>
/// <returns><paramref name="result"/></returns>
/// <remarks><para>
/// The outgoing message is a distribution matching the moments of 'array' as the random arguments are varied.
/// The formula is <c>proj[p(array) sum_(allTrue) p(allTrue) factor(allTrue,array)]/p(array)</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="allTrue"/> is not a proper distribution</exception>
public static BernoulliList ArrayAverageConditional <BernoulliList>([SkipIfUniform] Bernoulli allTrue, IList <Bernoulli> array, BernoulliList result)
where BernoulliList : IList <Bernoulli>
{
if (result.Count == 0)
{
}
else if (result.Count == 1)
{
result[0] = allTrue;
}
else if (result.Count == 2)
{
result[0] = BooleanAndOp.AAverageConditional(allTrue, array[1]);
result[1] = BooleanAndOp.BAverageConditional(allTrue, array[0]);
}
else // result.Count >= 3
{
double notallTruePrevious = Double.NegativeInfinity;
double[] notallTrueNext = new double[result.Count];
notallTrueNext[notallTrueNext.Length - 1] = Double.NegativeInfinity;
for (int i = notallTrueNext.Length - 2; i >= 0; i--)
{
notallTrueNext[i] = Bernoulli.Or(-array[i + 1].LogOdds, notallTrueNext[i + 1]);
}
for (int i = 0; i < result.Count; i++)
{
double notallTrueExcept = Bernoulli.Or(notallTruePrevious, notallTrueNext[i]);
result[i] = Bernoulli.FromLogOdds(-Bernoulli.Gate(-allTrue.LogOdds, notallTrueExcept));
notallTruePrevious = Bernoulli.Or(notallTruePrevious, -array[i].LogOdds);
}
}
return(result);
}
/// <summary>EP message to <c>array</c>.</summary>
/// <param name="allTrue">Incoming message from <c>allTrue</c>. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="array">Constant value for <c>array</c>.</param>
/// <param name="result">Modified to contain the outgoing message.</param>
/// <returns>
/// <paramref name="result" />
/// </returns>
/// <remarks>
/// <para>The outgoing message is a distribution matching the moments of <c>array</c> as the random arguments are varied. The formula is <c>proj[p(array) sum_(allTrue) p(allTrue) factor(allTrue,array)]/p(array)</c>.</para>
/// </remarks>
/// <exception cref="ImproperMessageException">
/// <paramref name="allTrue" /> is not a proper distribution.</exception>
/// <typeparam name="BernoulliList">The type of the resulting array.</typeparam>
public static BernoulliList ArrayAverageConditional <BernoulliList>([SkipIfUniform] Bernoulli allTrue, IList <bool> array, BernoulliList result)
where BernoulliList : IList <Bernoulli>
{
if (result.Count == 0)
{
}
else if (result.Count == 1)
{
result[0] = allTrue;
}
else if (result.Count == 2)
{
result[0] = BooleanAndOp.AAverageConditional(allTrue, array[1]);
result[1] = BooleanAndOp.BAverageConditional(allTrue, array[0]);
}
else
{
// result.Count >= 3
int trueCount = 0;
int firstFalseIndex = -1;
for (int i = 0; i < array.Count; i++)
{
if (array[i])
{
trueCount++;
}
else if (firstFalseIndex < 0)
{
firstFalseIndex = i;
}
}
if (trueCount == array.Count)
{
for (int i = 0; i < result.Count; i++)
{
result[i] = BooleanAndOp.AAverageConditional(allTrue, true);
}
}
else
{
for (int i = 0; i < result.Count; i++)
{
result[i] = BooleanAndOp.AAverageConditional(allTrue, false);
}
if (trueCount == result.Count - 1)
{
result[firstFalseIndex] = BooleanAndOp.AAverageConditional(allTrue, true);
}
}
}
return(result);
}