Infer.NET user guide : Controlling how inference is performed
Using a precompiled inference algorithm
Internally Infer.NET compiles a model into a C# class for performing inference on that model. By default, the source of the generated class is placed at: (Debug/Release)\bin\GeneratedSource\[ModelName].cs. and has a standard structure. This page describes how this compiled inference algorithm can be directly included in a C# project. This might be useful for one of the following reasons:
To remove the dependence on the compiler. For standalone applications that make heavy use of a specific inference algorithm it might be desired to remove the dependence on the compiler.
To use Infer.NET from Silverlight. Security restrictions in Silverlight do not allow dynamic compilation, so you must precompile your model before including it in a Silverlight project.
To run multi-threaded inference. Including the compiled algorithm directly allows different instances of the compiled algorithm to be used in each thread separately without recompilation.
Speed-up. Inclusion of the compiled algorithm avoids compilation on first time usage.
To manually edit and alter the created output code. For advanced usage of Infer.Net it might be desired to alter the generated code. This mainly applies when specific features are not available in Infer.Net and you want to use the compiler to get a starting point for your own implementation of inference algorithms. This is only recommended if you know what you are doing!
Here is a simple example of how to use pre-compiled code, based on the Learning a Gaussian tutorial.
To ensure that the output code has a convenient interface, it is useful to follow a few guidelines:
- Use ‘observed’ variables rather than ‘constant’ variables for any values which are fixed for a given call to the inference algorithm, but which may change between invocations (see Creating Variables).
- Use the inline .Named(“name”) method for all variables you want to Infer or set as a given. Sensible choice of naming ensures a higher level of readability of the compiled output code.
// Initial Data double dataSet = new double; for (int i = 0; i < dataSet.Length; i++) dataSet[i] = Rand.Normal(0, 1); // Observed variables for data and data count Variable<int> dataCount = Variable.Observed(dataSet.Length).Named("dataCount"); Range N = new Range(dataCount); VariableArray<double> data = Variable.Observed<double>(dataSet, N).Named("data"); // Observations are assumed to be sampled from a Gaussian with unknown parameters Variable<double> mean = Variable.GaussianFromMeanAndVariance(0, 100).Named("mean"); Variable<double> precision = Variable.GammaFromShapeAndScale(1, 1).Named("precision"); data[N] = Variable.GaussianFromMeanAndPrecision(mean, precision).ForEach(N); // Create an inference engine for VMP InferenceEngine engine = new InferenceEngine(new VariationalMessagePassing()); // Retrieve the posterior distributions Console.WriteLine("mean=" + engine.Infer(mean));Console.WriteLine("prec=" + engine.Infer(precision));
Run this example, take the generated output code (it will be named Model_VMP.cs unless you set the ModelName property on the engine) and add it to your project. This instance contains the compiled model class Models.Model_VMP.
There are two ways to call the compiled inference algorithm from your code. The easiest way to use this class is through its
IGeneratedAlgorithm interface as described in Controlling how inference is performed. Alternatively you can use the strongly-typed model-specific properties and methods as documented below.
Calling the compiled model class directly
The 4 steps are illustrated in the example code.
// Run-time data double dataSet = new double; for (int i = 0; i < dataSet.Length; i++) dataSet[i] = Rand.Normal(0, 1); // 1) Create an instance of the class Model_VMP model = new Model_VMP(); // 2) Set the value of any observed variables e.g. data, priors model.data = dataSet; model.dataCount = dataSet.Length; // 3) Call the Execute() method, or Reset() followed by Update() model.Execute(20); // 4) Use the XXXMarginal() methods to retrieve posterior marginals // for different variables. Gaussian inferredMean = model.MeanMarginal(); Gamma inferredPrec = model.PrecisionMarginal(); // Print out the results Console.WriteLine("mean=" + inferredMean); Console.WriteLine("prec=" + inferredPrec);