C# Bernoulli.GetLogAverageOf方法代码示例

		/// <summary>
		/// Evidence message for EP
		/// </summary>
		/// <param name="isPositive">Incoming message from 'isPositive'.</param>
		/// <param name="x">Incoming message from 'x'.</param>
		/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
		/// <remarks><para>
		/// The formula for the result is <c>log(sum_(isPositive,x) p(isPositive,x) factor(isPositive,x))</c>.
		/// </para></remarks>
		public static double LogAverageFactor(Bernoulli isPositive, Gaussian x)
		{
			if (isPositive.IsPointMass && x.Precision == 0) {
				double tau = x.MeanTimesPrecision;
				if (isPositive.Point && tau < 0) {
					// int I(x>0) exp(tau*x) dx = -1/tau
					return -Math.Log(-tau);
				}
				if (!isPositive.Point && tau > 0) {
					// int I(x<0) exp(tau*x) dx = 1/tau
					return -Math.Log(tau);
				}
			}
			Bernoulli to_isPositive = IsPositiveAverageConditional(x);
			return isPositive.GetLogAverageOf(to_isPositive);
		}

		/// <summary>
		/// Evidence message for EP
		/// </summary>
		/// <param name="isPositive">Incoming message from 'isPositive'.</param>
		/// <param name="x">Incoming message from 'x'.</param>
		/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
		/// <remarks><para>
		/// The formula for the result is <c>log(sum_(isPositive,x) p(isPositive,x) factor(isPositive,x))</c>.
		/// </para></remarks>
		public static double LogAverageFactor(Bernoulli isPositive, Gaussian x)
		{
			Bernoulli to_isPositive = IsPositiveAverageConditional(x);
			return isPositive.GetLogAverageOf(to_isPositive);
#if false
			// Z = p(b=T) p(x > 0) + p(b=F) p(x <= 0)
			//   = p(b=F) + (p(b=T) - p(b=F)) p(x > 0)
			if (x.IsPointMass) {
				return Factor.IsPositive(x.Point) ? isPositive.GetLogProbTrue() : isPositive.GetLogProbFalse();
			} else if(x.IsUniform()) {
				return Bernoulli.LogProbEqual(isPositive.LogOdds,0.0);
			} else {
				// m/sqrt(v) = (m/v)/sqrt(1/v)
				double z = x.MeanTimesPrecision / Math.Sqrt(x.Precision);
				if (isPositive.IsPointMass) {
					return isPositive.Point ? MMath.NormalCdfLn(z) : MMath.NormalCdfLn(-z);
				} else {
					return MMath.LogSumExp(isPositive.GetLogProbTrue() + MMath.NormalCdfLn(z), isPositive.GetLogProbFalse() + MMath.NormalCdfLn(-z));
				}
			}
#endif
		}

		/// <summary>
		/// Evidence message for EP
		/// </summary>
		/// <param name="sample">Incoming message from 'sample'.</param>
		/// <param name="to_sample">Outgoing message to 'sample'.</param>
		/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
		/// <remarks><para>
		/// The formula for the result is <c>log(sum_(sample) p(sample) factor(sample,probTrue))</c>.
		/// </para></remarks>
		public static double LogAverageFactor(Bernoulli sample, [Fresh] Bernoulli to_sample)
		{
			return sample.GetLogAverageOf(to_sample);
		}

		/// <summary>
		/// Evidence message for EP
		/// </summary>
		/// <param name="areEqual">Constant value for 'areEqual'.</param>
		/// <param name="A">Incoming message from 'a'.</param>
		/// <param name="B">Constant value for 'b'.</param>
		/// <param name="to_A">Outgoing message to 'a'.</param>
		/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
		/// <remarks><para>
		/// The formula for the result is <c>log(sum_(a) p(a) factor(areEqual,a,b))</c>.
		/// </para></remarks>
		public static double LogAverageFactor(bool areEqual, Bernoulli A, bool B, [Fresh] Bernoulli to_A)
		{
			//Bernoulli toA = AAverageConditional(areEqual, B);
			return A.GetLogAverageOf(to_A);
		}

 public static double LogEvidenceRatio(
     Bernoulli label, Bernoulli to_label, Vector point, GaussianGamma shapeParamsX, GaussianGamma shapeParamsY)
 {
     return LogAverageFactor(label, point, shapeParamsX, shapeParamsY) - label.GetLogAverageOf(to_label);
 }