C# Gaussian.GetLogAverageOf方法代码示例

		// logw1 = N(mx;m1,vx+v1) phi((mx1 - m2)/sqrt(vx1+v2))
		// a1 = N(mx1;m2,vx1+v2)/phi
		internal static void ComputeStats(Gaussian max, Gaussian a, Gaussian b, out double logz,
			out double logw1, out double a1, out double vx1, out double mx1,
			out double logw2, out double a2, out double vx2, out double mx2)
		{
			double m1, v1, m2, v2;
			a.GetMeanAndVariance(out m1, out v1);
			b.GetMeanAndVariance(out m2, out v2);
			if (max.IsPointMass) {
				vx1 = 0.0;
				mx1 = max.Point;
				vx2 = 0.0;
				mx2 = max.Point;
				if (b.IsPointMass) {
					if (b.Point > max.Point) throw new AllZeroException();
					else if (b.Point == max.Point) {
						// the factor reduces to the constraint (max.Point > a)
						logw1 = Double.NegativeInfinity;
						logw2 = MMath.NormalCdfLn((max.Point - m1)/Math.Sqrt(v1));
						logz = logw2;
						a1 = 0;
						a2 = Math.Exp(Gaussian.GetLogProb(max.Point, m1, v1) - logw2);
						return;
					} else {
						// b.Point < max.Point
						// the factor reduces to the constraint (a == max.Point)
						throw new NotImplementedException();
					}
				} else if (a.IsPointMass) throw new NotImplementedException();
			} else {
				if (a.IsPointMass) {
					vx1 = 0.0;
					mx1 = a.Point;
				} else {
					vx1 = 1.0 / (max.Precision + a.Precision);
					mx1 = vx1 * (max.MeanTimesPrecision + a.MeanTimesPrecision);
				}
				if (b.IsPointMass) {
					vx2 = 0.0;
					mx2 = b.Point;
				} else {
					vx2 = 1.0 / (max.Precision + b.Precision);
					mx2 = vx2 * (max.MeanTimesPrecision + b.MeanTimesPrecision);
				}
			}
			logw1 = max.GetLogAverageOf(a);
			double logPhi1 = MMath.NormalCdfLn((mx1 - m2) / Math.Sqrt(vx1 + v2));
			logw1 += logPhi1;

			logw2 = max.GetLogAverageOf(b);
			double logPhi2 = MMath.NormalCdfLn((mx2 - m1) / Math.Sqrt(vx2 + v1));
			logw2 += logPhi2;

			logz = MMath.LogSumExp(logw1, logw2);

			double logN1 = Gaussian.GetLogProb(mx1, m2, vx1 + v2);
			a1 = Math.Exp(logN1 - logPhi1);
			double logN2 = Gaussian.GetLogProb(mx2, m1, vx2 + v1);
			a2 = Math.Exp(logN2 - logPhi2);
		}

		public static double LogEvidenceRatio([SkipIfUniform] Gaussian Product, [SkipIfUniform] Gaussian A, [SkipIfUniform] Gaussian B, Gaussian to_A, Gaussian to_product)
		{
			return LogAverageFactor(Product, A, B, to_A) - to_product.GetLogAverageOf(Product);
		}

		public static double LogEvidenceRatio(Gaussian sample, Gaussian mean, Gamma variance, Gaussian to_sample)
		{
			return LogAverageFactor(sample, mean, variance) - to_sample.GetLogAverageOf(sample);
		}