/// <summary>Computations that depend on the observed value of wPrior and xIndices and xValueCount</summary>
public void Changed_wPrior_xIndices_xValueCount()
{
if (this.Changed_wPrior_xIndices_xValueCount_iterationsDone == 1)
{
return;
}
this.wSparse_F = SubarrayOp <double> .ItemsAverageConditional <Gaussian, DistributionStructArray <Gaussian, double> >(this.WPrior, this.XIndices, this.wSparse_F);
this.Changed_wPrior_xIndices_xValueCount_iterationsDone = 1;
this.Changed_xValueCount_xValues_wPrior_xIndices_iterationsDone = 0;
this.Changed_y_biasPrior_xValueCount_xValues_wPrior_xIndices_iterationsDone = 0;
}
/// <summary>
/// Computes the inner product of the feature weights and values.
/// </summary>
/// <param name="weights">The feature weights.</param>
/// <param name="nonZeroValues">The sparse feature values.</param>
/// <param name="nonZeroIndices">The sparse feature indices.</param>
/// <returns>The contribution of the features.</returns>
private static Gaussian ComputeFeatureContribution(IList <Gaussian> weights, IList <double> nonZeroValues, IList <int> nonZeroIndices)
{
Debug.Assert(nonZeroValues.Count == nonZeroIndices.Count, "The number of values must be equal to the number of indices.");
int count = nonZeroValues.Count;
var nonZeroWeights = new Gaussian[count];
SubarrayOp <double> .ItemsAverageConditional(weights, nonZeroIndices, nonZeroWeights);
var products = new List <Gaussian>(count);
for (int i = 0; i < count; ++i)
{
products.Add(GaussianProductOp.ProductAverageConditional(nonZeroWeights[i], nonZeroValues[i]));
}
return(FastSumOp.SumAverageConditional(products));
}