Infer from which of 2 distributions the data was drawn

• 28. října 2011 10:52

   

   
  

  0

  After working thought the new 101 document, I am trying to implement some basic models. I ran into a problem trying to implement a quadratic discriminant classifier:

  I have 2 VectorGaussian distributions (representing 2 classes of data) with parameters that I have learned from training data and want to infer the posterior probability that a data point was drawn from the first of the 2 distributions.

  I am trying to do this using a Variable.If/IfNot pair. If my boolean variable is true the data was drawn from the first distribution / belongs to class 1.

  This is the code I was hoping to model this with:

   

  class PredictionModel
      {
          public InferenceEngine inferenceEngine;
          protected VariableArray<bool> isClass1;
          protected VectorGaussian class1Dist;
          protected VectorGaussian class2Dist;
          protected Variable<int> nSamples; // number of data samples
          protected VariableArray<Vector> data; // input data
          protected VariableArray<Bernoulli> predictions; // predictions
          protected Variable<double> isClass1Prior;
  
          public PredictionModel(int nDims)
          {
              nSamples = Variable.New<int>().Named("nSamples");
              Range sampleRange = new Range(nSamples);
              isClass1 = Variable.Array<bool>(sampleRange).Named("isClass1");
              isClass1Prior = Variable.Beta(1, 1).Named("isClass1Prior");
              class1Dist = new VectorGaussian(nDims);
              class2Dist = new VectorGaussian(nDims);
              data = Variable.Array<Vector>(sampleRange).Named("Data");
              using (Variable.ForEach(sampleRange))
              {
                  isClass1[sampleRange] = Variable.Bernoulli(isClass1Prior);
                  using (Variable.If(isClass1[sampleRange]))
                      data[sampleRange] = Variable.Random<Vector>(class1Dist);
                  using (Variable.IfNot(isClass1[sampleRange]))
                      data[sampleRange] = Variable.Random<Vector>(class2Dist);                
              }
  
              if (inferenceEngine == null)
              {
                  inferenceEngine = new InferenceEngine(new VariationalMessagePassing());
              }
          }
  
          // Set model parameters
          public void SetModelData(ModelData class1Para, ModelData class2Para)
          {
              Vector class1Mean = class1Para.mean.GetMean();
              PositiveDefiniteMatrix class1Precision = class1Para.precision.GetMean();
              class1Dist.SetMeanAndPrecision(class1Mean, class1Precision);
              
              Vector class2Mean = class2Para.mean.GetMean();
              PositiveDefiniteMatrix class2Precision = class2Para.precision.GetMean();
              class2Dist.SetMeanAndPrecision(class2Mean, class2Precision);
          }
  
          // Infer class of input data
          public void InferClasses(Vector[] inputData)
          {
              nSamples.ObservedValue = inputData.Length;
              Range sampleRange = new Range(nSamples);
              data.ObservedValue = inputData;
              inferenceEngine.ShowFactorGraph = true;
              predictions = Variable.Array<Bernoulli>(sampleRange).Named("Predictions");
              predictions = inferenceEngine.Infer<VariableArray<Bernoulli>>(isClass1);            
          }
      }
  

   

  
  The Main() is this:

   

  // Create an instance of the prediction model
  PredictionModel predModel = new PredictionModel(nDims);
  
  // Set the model parameters
  predModel.SetModelData(class1Posteriors, class2Posteriors);
  
  // Collect data for Inference
  double[] feature1 = DataReader.readFromCSV(".\\data\\feature1.csv");
  double[] feature2 = DataReader.readFromCSV(".\\data\\feature2.csv");
  double[] feature3 = DataReader.readFromCSV(".\\data\\feature3.csv");
  int nSamples = feature1.Length;
              
  Vector[] inputData = new Vector[nSamples];
  for (int i = 0; i < nSamples; i++)
  {
      inputData[i] = Vector.FromArray(feature2[i], feature3[i], feature1[i]);
  }
  
  // Infer classes given the input data
  predModel.InferClasses(inputData);
  

   

  
  Running this I get the following error:

  "An unhandled exception of type 'System.ArgumentException' occurred in Infer.Runtime.dll
  
  Additional information: Cannot convert distribution type DistributionStructArray<Bernoulli,bool> to type VariableArray<Bernoulli>"

   

  Would you please tell me what I am doing wrong here?

  Many thanks!

  
  

  • Upravený M.Arnold 28. října 2011 10:55
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