C# Beta类代码示例

    static void Main() 
    { 
        Alpha objA = new Alpha(4); 
        Beta objВ = new Beta (9); 
        // Продемонстрировать сначала контравариантность. 
        // Объявить делегат SomeOp<Alpha> и задать для него метод IsEven. 
        SomeOp<Alpha> checklt = IsEven;

        // Объявить делегат SomeOp<Beta>. 
        SomeOp<Beta> checklt2;
        //А теперь- присвоить делегат SomeOp<Alpha> делегату SomeOp<Beta>. 
        // *** Это допустимо только благодаря контравариантности. *** 
        checklt2 = checklt;
        // Вызвать метод через делегат. 
        Console.WriteLine(checklt2(objВ));
        // Далее, продемонстрировать контравариантность. 
        // Объявить сначала два делегата типа AnotherOp. 
        // Здесь возвращаемым типом является класс Beta, 
        //а параметром типа — класс Alpha. 
        // Обратите внимание на то, что для делегата modifylt 
        // задается метод Changelt. 
        AnotherOp<Beta, Alpha> modifylt = Changelt;
        // Здесь возвращаемым типом является класс Alpha, 
        //а параметром типа — тот же класс Alpha. 
        AnotherOp<Alpha, Alpha> modifyIt2;
        // А теперь присвоить делегат modifylt делегату modifyIt2. 
        // *** Это допустимо только благодаря ковариантности. *** 
        modifyIt2 = modifylt;
        // Вызвать метод и вывести результаты на экран. 
        objA = modifyIt2(objA);
        Console.WriteLine(objA.Val);
    }

 static void Main(){
     
     Stopwatch timer = new Stopwatch();
     timer.Start();
     
     
     Alpha oAlpha = new Alpha();
     Beta oBeta = new Beta();
     
     // create the threads
     Thread aThread = new Thread(new ThreadStart(oAlpha.Counter));
     Thread bThread = new Thread(new ThreadStart(oBeta.Counter));
     
     
     // start the threads
     aThread.Start();
     bThread.Start();
     
     // spin until both threads have finished
     while(aThread.IsAlive && bThread.IsAlive);
     
     timer.Stop();
     
     TimeSpan ts = timer.Elapsed;
     
     Console.WriteLine("Execution Time: " + ts);
     
     
     
     
 }

		// Sample data from a DINA/NIDA model and then use Infer.NET to recover the parameters.
		public void Run()
		{
			InferenceEngine engine = new InferenceEngine();
			if (!(engine.Algorithm is ExpectationPropagation))
			{
				Console.WriteLine("This example only runs with Expectation Propagation");
				return;
			}

			bool useDina = true;
			Beta slipPrior = new Beta(1, 10);
			Beta guessPrior = new Beta(1, 10);
			Rand.Restart(0);
			int nStudents = 100;
			int nQuestions = 20;
			int nSkills = 3;
			int[][] skillsRequired = new int[nQuestions][];
			for (int q = 0; q < nQuestions; q++) {
				// each question requires a random set of skills
				int[] skills = Rand.Perm(nSkills);
				int n = Rand.Int(nSkills)+1;
				skillsRequired[q] = Util.ArrayInit(n, i => skills[i]);
				Console.WriteLine("skillsRequired[{0}] = {1}", q, Util.CollectionToString(skillsRequired[q]));
			}
			double[] pSkill, slip, guess;
			bool[][] hasSkill;
			VariableArray<double> slipVar, guessVar, pSkillVar;
			VariableArray<VariableArray<bool>,bool[][]> hasSkillVar;
			if (useDina) {
				bool[][] responses = DinaSample(nStudents, nSkills, skillsRequired, slipPrior, guessPrior, out pSkill, out slip, out guess, out hasSkill);
				DinaModel(responses, nSkills, skillsRequired, slipPrior, guessPrior, out pSkillVar, out slipVar, out guessVar, out hasSkillVar);
			} else {
				bool[][] responses = NidaSample(nStudents, nSkills, skillsRequired, slipPrior, guessPrior, out pSkill, out slip, out guess, out hasSkill);
				NidaModel(responses, nSkills, skillsRequired, slipPrior, guessPrior, out pSkillVar, out slipVar, out guessVar, out hasSkillVar);
			}

			engine.NumberOfIterations = 10;
			Bernoulli[][] hasSkillPost = engine.Infer<Bernoulli[][]>(hasSkillVar);
			int numErrors = 0;
			for (int i = 0; i < nStudents; i++) {
				for (int s = 0; s < nSkills; s++) {
					if (hasSkill[i][s] != (hasSkillPost[i][s].LogOdds > 0)) numErrors++;
				}
			}
			Console.WriteLine("{0:0}% of skills recovered correctly", 100.0 - 100.0*numErrors/(nStudents*nSkills));
			Beta[] pSkillPost = engine.Infer<Beta[]>(pSkillVar);
			Beta[] slipPost = engine.Infer<Beta[]>(slipVar);
			Beta[] guessPost = engine.Infer<Beta[]>(guessVar);
			for (int s = 0; s < nSkills; s++) {
				Console.WriteLine("pSkill[{0}] = {1} (sampled from {2})", s, pSkillPost[s], pSkill[s].ToString("g4"));				
			}
			for (int i = 0; i < Math.Min(3,slipPost.Length); i++)	{
				Console.WriteLine("slip[{0}] = {1} (sampled from {2})", i, slipPost[i], slip[i].ToString("g4"));			 
			}
			for (int i = 0; i < Math.Min(3,guessPost.Length); i++) {
				Console.WriteLine("guess[{0}] = {1} (sampled from {2})", i, guessPost[i], guess[i].ToString("g4"));
			}
		}

		/// <summary>
		/// Evidence message for EP
		/// </summary>
		/// <param name="sample">Constant value for 'sample'.</param>
		/// <param name="p">Incoming message from 'p'.</param>
		/// <param name="trialCount">Incoming message from 'trialCount'.</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_(p,trialCount) p(p,trialCount) factor(sample,trialCount,p))</c>.
		/// </para></remarks>
		public static double LogAverageFactor(int sample, Beta p, Discrete trialCount)
		{
			double logZ = Double.NegativeInfinity;
			for (int n = 0; n < trialCount.Dimension; n++)
			{
				logZ = MMath.LogSumExp(logZ, trialCount.GetLogProb(n) + LogAverageFactor(sample, p, n));
			}
			return logZ;
		}

		public void Infer(bool[] treated, bool[] placebo)
		{
			// Set the observed values
			numberPlacebo.ObservedValue = placebo.Length;
			numberTreated.ObservedValue = treated.Length;
			placeboGroupOutcomes.ObservedValue = placebo;
			treatedGroupOutcomes.ObservedValue = treated;

			// Infer the hidden values
			posteriorTreatmentIsEffective = engine.Infer<Bernoulli>(isEffective);
			posteriorProbIfPlacebo = engine.Infer<Beta>(probIfPlacebo);
			posteriorProbIfTreated = engine.Infer<Beta>(probIfTreated);
		}

		private static void drawBetaDistribution(Rectangle rect, Beta dist)
		{
			GradientStopCollection gsc = new GradientStopCollection();
			int numStops = 21;
			double mean = dist.GetMean();
			double meanDensity = Math.Exp(dist.GetLogProb(mean));
			double inc = 1.0 / (numStops-1);
			double curr = 0.0;
			double maxLogProb = Double.MinValue;
			double minLogProb = -5.0;
			for (int i=0; i < numStops; i++)
			{
				double logProb = dist.GetLogProb(curr);
				if (logProb > maxLogProb) maxLogProb = logProb;
				curr += inc;
			}
			if (maxLogProb <= minLogProb)
				maxLogProb = minLogProb + 1.0;
			double diff = maxLogProb - minLogProb;
			double mult =  1.0 / (maxLogProb - minLogProb);
			curr = 0.0;
			double blueLeft = 0; double blueRight = 0;
			double redLeft = 255; double redRight = 255;
			double greenLeft = 255; double greenRight = 255;

			for (int i=0; i < numStops; i++)
			{
				double red, green, blue;
				double logProb = dist.GetLogProb(curr);
				if (logProb < minLogProb) logProb = minLogProb;
				double level = mult * (logProb - minLogProb);
				red = level * (mean * redRight + (1.0 - mean) * redLeft);
				green = level * (mean * greenRight + (1.0 - mean) * greenLeft);
				blue =level * (mean * blueRight + (1.0 - mean) * blueLeft);
				byte redb = red < 0.0 ? (byte)0 : red > 255.0 ? (byte)255 : (byte)red;
				byte greenb = green < 0.0 ? (byte)0 : green > 255.0 ? (byte)255 : (byte)green;
				byte blueb = blue < 0.0 ? (byte)0 : blue > 255.0 ? (byte)255 : (byte)blue;
				gsc.Add(new GradientStop { Color =new Color() { A = 255, R = redb, G = greenb, B = blueb } , Offset = curr});
    			curr += inc;
			}
			LinearGradientBrush brush = rect.Fill as LinearGradientBrush;
			brush.GradientStops = gsc;
		}

		// Sample data from the DINA model
		public static bool[][] DinaSample(int nStudents, int nSkills, int[][] skillsRequired, Beta slipPrior, Beta guessPrior, 
			out double[] pSkillOut, out double[] slip, out double[] guess, out bool[][] hasSkill)
		{
			int nQuestions = skillsRequired.Length;
			double[] pSkill = Util.ArrayInit(nSkills, q => Rand.Double());
			slip = Util.ArrayInit(nQuestions, q => slipPrior.Sample());
			guess = Util.ArrayInit(nQuestions, q => guessPrior.Sample());
			hasSkill = Util.ArrayInit(nStudents, t => Util.ArrayInit(nSkills, s => Rand.Double() < pSkill[s]));
			bool[][] responses = new bool[nStudents][];
			for (int t = 0; t < nStudents; t++) {
				responses[t] = new bool[nQuestions];
				for (int q = 0; q < nQuestions; q++) {
					bool hasAllSkills = Factor.AllTrue(Factor.Subarray(hasSkill[t], skillsRequired[q]));
					if (hasAllSkills) responses[t][q] = (Rand.Double() > slip[q]);
					else responses[t][q] = (Rand.Double() < guess[q]);
				}
			}
			pSkillOut = pSkill;
			return responses;
		}

 public void ValidateMaximum()
 {
     var n = new Beta(1.0, 1.0);
     Assert.AreEqual(1.0, n.Maximum);
 }

 public void ValidateToString()
 {
     var n = new Beta(1.0, 2.0);
     Assert.AreEqual("Beta(A = 1, B = 2)", n.ToString());
 }

		public static double LogEvidenceRatio(Bernoulli sample, Beta probTrue)
		{
			return 0.0;
		}

		/// <summary>
		/// Evidence message for EP
		/// </summary>
		/// <param name="sample">Constant value for 'sample'.</param>
		/// <param name="probTrue">Incoming message from 'probTrue'.</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_(probTrue) p(probTrue) factor(sample,probTrue))</c>.
		/// </para></remarks>
		public static double LogAverageFactor(bool sample, Beta probTrue)
		{
			Bernoulli to_sample = SampleAverageConditional(probTrue);
			return to_sample.GetLogProb(sample);
		}

		public static double LogEvidenceRatio(Beta sample, double mean, double variance) { return 0.0; }

		/// <summary>
		/// VMP message to 'mean'
		/// </summary>
		/// <param name="mean">Incoming message from 'mean'. Must be a proper distribution.  If uniform, the result will be uniform. Must be a proper distribution.  If uniform, the result will be uniform.</param>
		/// <param name="totalCount">Incoming message from 'totalCount'. Must be a proper distribution.  If uniform, the result will be uniform.</param>
		/// <param name="prob">Incoming message from 'prob'. Must be a proper distribution.  If uniform, the result will be uniform.</param>
		/// <param name="to_mean">Previous outgoing message to 'Mean'.</param>
		/// <returns>The outgoing VMP message to the 'mean' argument</returns>
		/// <remarks><para>
		/// The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except 'mean'.
		/// The formula is <c>exp(sum_(totalCount,prob) p(totalCount,prob) log(factor(prob,mean,totalCount)))</c>.
		/// </para></remarks>
		/// <exception cref="ImproperMessageException"><paramref name="mean"/> is not a proper distribution</exception>
		/// <exception cref="ImproperMessageException"><paramref name="totalCount"/> is not a proper distribution</exception>
		/// <exception cref="ImproperMessageException"><paramref name="prob"/> is not a proper distribution</exception>
		public static Beta MeanAverageLogarithm([Proper] Beta mean, [Proper] Gamma totalCount, [SkipIfUniform] Beta prob, Beta to_mean)
		{
			// Calculate gradient using method for DirichletOp
			double ELogP, ELogOneMinusP;
			prob.GetMeanLogs(out ELogP, out ELogOneMinusP);
			Vector gradS = DirichletOp.CalculateGradientForMean(
				 Vector.FromArray(new double[] { mean.TrueCount, mean.FalseCount }),
				 totalCount,
				 Vector.FromArray(new double[] { ELogP, ELogOneMinusP }));
			// Project onto a Beta distribution 
			Matrix A = new Matrix(2, 2);
			double c = MMath.Trigamma(mean.TotalCount);
			A[0, 0] = MMath.Trigamma(mean.TrueCount) - c;
			A[1, 0] = A[0, 1] = -c;
			A[1, 1] = MMath.Trigamma(mean.FalseCount) - c;
			Vector theta = GammaFromShapeAndRateOp.twoByTwoInverse(A)*gradS;
			Beta approximateFactor = new Beta(theta[0] + 1, theta[1] + 1);
			if (damping == 0.0)
				return approximateFactor;
			else
				return (approximateFactor^(1-damping)) * (to_mean ^ damping);
		}

 public void ValidateMedianThrowsNotSupportedException()
 {
     var n = new Beta(0.0, 1.0);
     Assert.Throws<NotSupportedException>(() => { var m = n.Median; });
 }

 public void CanSample()
 {
     var n = new Beta(2.0, 3.0);
     n.Sample();
 }