C# VectorGaussian.SetMeanAndVariance方法代码示例

		public static VectorGaussian RotateAverageLogarithm([SkipIfUniform] Gaussian x, [SkipIfUniform] Gaussian y, [Proper] WrappedGaussian angle, VectorGaussian result)
		{
			// for x ~ N(m,v):
			// E[cos(x)] = cos(m)*exp(-v/2)
			// E[sin(x)] = sin(m)*exp(-v/2)
			if (angle.Period != 2*Math.PI) throw new ArgumentException("angle.Period ("+angle.Period+") != 2*PI ("+2*Math.PI+")");
			double angleMean, angleVar;
			angle.Gaussian.GetMeanAndVariance(out angleMean, out angleVar);
			double expHalfVar = Math.Exp(-0.5*angleVar);
			double mCos = Math.Cos(angleMean)*expHalfVar;
			double mSin = Math.Sin(angleMean)*expHalfVar;
			double mCos2 = mCos*mCos;
			double mSin2 = mSin*mSin;
			//  E[cos(x)^2] = 0.5 E[1+cos(2x)] = 0.5 (1 + cos(2m) exp(-2v))
			//  E[sin(x)^2] = E[1 - cos(x)^2] = 0.5 (1 - cos(2m) exp(-2v))
			double expVar = expHalfVar*expHalfVar;
			// cos2m = cos(2m)*exp(-v)
			double cos2m = 2*mCos2 - expVar;
			double mCosSqr = 0.5*(1 + cos2m*expVar);
			double mSinSqr = 1 - mCosSqr;
			double mSinCos = mSin*mCos*expVar;
			if (result.Dimension != 2) throw new ArgumentException("result.Dimension ("+result.Dimension+") != 2");
			double mx, vx, my, vy;
			x.GetMeanAndVariance(out mx, out vx);
			y.GetMeanAndVariance(out my, out vy);
			Vector mean = Vector.Zero(2);
			mean[0] = mCos*mx - mSin*my;
			mean[1] = mSin*mx + mCos*my;
			double mx2 = mx*mx + vx;
			double my2 = my*my + vy;
			double mxy = mx*my;
			PositiveDefiniteMatrix variance = new PositiveDefiniteMatrix(2, 2);
			variance[0, 0] = mx2*mCosSqr - 2*mxy*mSinCos + my2*mSinSqr - mean[0]*mean[0];
			variance[0, 1] = (mx2 - my2)*mSinCos + mxy*(mCosSqr - mSinSqr) - mean[0]*mean[1];
			variance[1, 0] = variance[0, 1];
			variance[1, 1] = mx2*mSinSqr + 2*mxy*mSinCos + my2*mCosSqr - mean[1]*mean[1];
			result.SetMeanAndVariance(mean, variance);
			return result;
		}

		/// <summary>
		/// EP message to 'product'
		/// </summary>
		/// <param name="A">Constant value for 'a'.</param>
		/// <param name="BMean">Buffer 'BMean'.</param>
		/// <param name="BVariance">Buffer 'BVariance'.</param>
		/// <param name="result">Modified to contain the outgoing message</param>
		/// <returns><paramref name="result"/></returns>
		/// <remarks><para>
		/// The outgoing message is the factor viewed as a function of 'product' conditioned on the given values.
		/// </para></remarks>
		public static VectorGaussian ProductAverageConditional(Matrix A, [Fresh] Vector BMean, [Fresh] PositiveDefiniteMatrix BVariance, VectorGaussian result)
		{
			// P.mean = A*B.mean
			// P.var = A*B.var*A'
			// if A is invertible, then
			// P.prec = inv(A)'*inv(B.var)*inv(A)
			// P.precTimesMean = inv(A)'*B.precTimesMean
			Vector rmean = A * BMean;
			PositiveDefiniteMatrix rvariance = new PositiveDefiniteMatrix(result.Dimension, result.Dimension);
			Matrix temp = (A*BVariance).Transpose();
			rvariance.SetToProduct(A, temp);
			result.SetMeanAndVariance(rmean, rvariance);
			return result;
		}

		/// <summary>
		/// EP message to 'subvector'
		/// </summary>
		/// <param name="SourceMean">Buffer 'SourceMean'.</param>
		/// <param name="SourceVariance">Buffer 'SourceVariance'.</param>
		/// <param name="startIndex">Constant value for 'startIndex'.</param>
		/// <param name="result">Modified to contain the outgoing message</param>
		/// <returns><paramref name="result"/></returns>
		/// <remarks><para>
		/// The outgoing message is the factor viewed as a function of 'subvector' conditioned on the given values.
		/// </para></remarks>
		public static VectorGaussian SubvectorAverageConditional([Fresh] Vector SourceMean, [Fresh] PositiveDefiniteMatrix SourceVariance, int startIndex, VectorGaussian result)
		{
			PositiveDefiniteMatrix subVariance = new PositiveDefiniteMatrix(result.Dimension, result.Dimension);
			subVariance.SetToSubmatrix(SourceVariance, startIndex, startIndex);
			Vector subMean = Vector.Zero(result.Dimension);
			subMean.SetToSubvector(SourceMean, startIndex);
			result.SetMeanAndVariance(subMean, subVariance);
			return result;
		}