/// <summary>
/// Evidence message for VMP
/// </summary>
/// <param name="sample">Constant value for 'sample'.</param>
/// <param name="probsTrue">Incoming message from 'probsTrue'. Must be a proper distribution. If any element is uniform, the result will be uniform.</param>
/// <param name="MeanLogOneMinus">Buffer 'MeanLogOneMinus'.</param>
/// <returns>Average of the factor's log-value across the given argument distributions</returns>
/// <remarks><para>
/// The formula for the result is <c>sum_(probsTrue) p(probsTrue) log(factor(sample,probsTrue))</c>.
/// Adding up these values across all factors and variables gives the log-evidence estimate for VMP.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="probsTrue"/> is not a proper distribution</exception>
public static double AverageLogFactor(IList <bool> sample, [Proper] SparseBetaList probsTrue, SparseVector MeanLogOneMinus)
{
//var MeanLogOneMinus = probsTrue.GetMeanLogOneMinus();
double ev = MeanLogOneMinus.Sum();
int count = sample.Count;
// assume 'sample' is very sparse
bool[] sampleAsArray = (bool[])sample;
for (int i = 0; i < count; i++)
{
if (!sampleAsArray[i])
{
continue;
}
ev -= MeanLogOneMinus[i];
ev += SparseBetaList.ComputeMeanLog(probsTrue.TrueCounts[i], probsTrue.FalseCounts[i]);
}
return(ev);
}
