internal void ProbabilisticIndexMap()
{
//TODO: change the path for cross platform using
double[,] dataIn = MatlabReader.ReadMatrix(new double[10, 6400 * 3], @"c:\temp\pim\chand.txt", ' ');
Vector[,] pixData = new Vector[10, 6400];
for (int i = 0; i < pixData.GetLength(0); i++)
{
int ct = 0;
for (int j = 0; j < pixData.GetLength(1); j++)
{
pixData[i, j] = Vector.FromArray(dataIn[i, ct++], dataIn[i, ct++], dataIn[i, ct++]);
}
}
Range images = new Range(pixData.GetLength(0));
Range pixels = new Range(pixData.GetLength(1));
VariableArray2D <Vector> pixelData = Variable.Constant(pixData, images, pixels);
// For each image we have a palette of L multivariate Gaussians
Range L = new Range(2);
VariableArray2D <Vector> means = Variable.Array <Vector>(images, L).Named("means");
means[images, L] = Variable.VectorGaussianFromMeanAndPrecision(
Vector.FromArray(0.5, 0.5, 0.5),
PositiveDefiniteMatrix.Identity(3)).ForEach(images, L);
VariableArray2D <PositiveDefiniteMatrix> precs = Variable.Array <PositiveDefiniteMatrix>(images, L).Named("precs");
precs[images, L] = Variable.WishartFromShapeAndScale(1.0, PositiveDefiniteMatrix.Identity(3)).ForEach(images, L);
// Across all pixels we have a
VariableArray <Vector> pi = Variable.Array <Vector>(pixels);
pi[pixels] = Variable.Dirichlet(L, new double[] { 1.1, 1.0 }).ForEach(pixels);
// For each pixel of each image we have a discrete indicator
VariableArray2D <int> ind = Variable.Array <int>(images, pixels).Named("ind");
ind[images, pixels] = Variable.Discrete(pi[pixels]).ForEach(images);
using (Variable.ForEach(pixels))
{
using (Variable.ForEach(images))
{
using (Variable.Switch(ind[images, pixels]))
{
pixelData[images, pixels] = Variable.VectorGaussianFromMeanAndPrecision(means[images, ind[images, pixels]],
precs[images, ind[images, pixels]]);
}
}
}
InferenceEngine ie = new InferenceEngine(new VariationalMessagePassing());
ie.ShowProgress = true;
ie.NumberOfIterations = 5;
Console.WriteLine("Dist over L: " + ie.Infer(pi));
}
internal void ProbabilisticIndexMapNoGate()
{
//TODO: change path for cross platform using
double[,] pixData = MatlabReader.ReadMatrix(new double[10, 6400], @"c:\temp\pim\chand2.txt", ' ');
Range images = new Range(pixData.GetLength(0));
Range pixels = new Range(pixData.GetLength(1));
VariableArray2D <double> pixelData = Variable.Constant(pixData, images, pixels);
//pixelData.QuoteInMSL = false;
// For each image we have a palette of L multivariate Gaussians
VariableArray <double> means = Variable.Array <double>(images).Named("means");
means[images] = Variable.GaussianFromMeanAndPrecision(0.5, 1).ForEach(images);
VariableArray <double> precs = Variable.Array <double>(images).Named("precs");
precs[images] = Variable.GammaFromShapeAndScale(1.0, 1.0).ForEach(images);
// Across all pixels we have a
VariableArray <Vector> pi = Variable.Array <Vector>(pixels).Named("pi");
Dirichlet[] dinit = new Dirichlet[pixels.SizeAsInt];
for (int i = 0; i < dinit.Length; i++)
{
double d = Rand.Double();
dinit[i] = new Dirichlet(1.0 + d / 10, 1.0 - d / 10);
}
pi[pixels] = Variable.Dirichlet(new double[] { 1.0 }).ForEach(pixels);
// For each pixel of each image we have a discrete indicator
VariableArray2D <int> ind = Variable.Array <int>(images, pixels).Named("ind");
ind[images, pixels] = Variable.Discrete(pi[pixels]).ForEach(images);
using (Variable.ForEach(pixels))
{
using (Variable.ForEach(images))
{
pixelData[images, pixels] = Variable.GaussianFromMeanAndPrecision(means[images], //10);
precs[images]);
Variable.ConstrainEqualRandom(ind[images, pixels], Discrete.Uniform(1));
}
}
InferenceEngine ie = new InferenceEngine(new VariationalMessagePassing());
ie.ModelName = "PIM_NoGate";
ie.NumberOfIterations = 8;
ie.ShowTimings = true;
DistributionArray <Dirichlet> piDist = ie.Infer <DistributionArray <Dirichlet> >(pi);
//Console.WriteLine("Dist over pi: " + ie.Infer(pi));
//TODO: change path for cross platform using
WriteMatrix(piDist.ToArray(), @"C:\temp\pim\results.txt");
}
public void FactorAnalysisMslTest()
{
InferenceEngine engine = new InferenceEngine(new VariationalMessagePassing());
engine.Compiler.DeclarationProvider = RoslynDeclarationProvider.Instance;
double[,] dataIn = MatlabReader.ReadMatrix(new double[400, 64],
Path.Combine(
#if NETCORE
Path.GetDirectoryName(typeof(VmpMslTests).Assembly.Location), // work dir is not the one with Microsoft.ML.Probabilistic.Tests.dll on netcore and neither is .Location on netfull
#endif
"Data", "pca.txt"),
' ');
int C = 8;
engine.ShowProgress = true;
engine.ShowTimings = true;
engine.NumberOfIterations = 20;
engine.Compiler.DeclarationProvider = RoslynDeclarationProvider.Instance;
var ca = engine.Compiler.Compile(FactorAnalysisModel, C, dataIn,
BioTests.RandomGaussianArray(C, dataIn.GetLength(1)),
BioTests.RandomGaussianArray(dataIn.GetLength(0), C));
ca.Execute(engine.NumberOfIterations);
DistributionArray2D <Gaussian> wMarginal = ca.Marginal <DistributionArray2D <Gaussian> >("W");
DistributionArray2D <Gaussian> xMarginal = ca.Marginal <DistributionArray2D <Gaussian> >("x");
//WriteMatrix(wMarginal.ToArray<Gaussian[,]>(), @"..\..\faresultsW.txt");
//WriteMatrix(xMarginal.ToArray<Gaussian[,]>(), @"..\..\faresultsX.txt");
// Reconstruct
DistributionArray2D <Gaussian> productMarginal = MatrixMultiplyOp.MatrixMultiplyAverageLogarithm(xMarginal, wMarginal, null);
double error = 0;
for (int i = 0; i < productMarginal.GetLength(0); i++)
{
for (int j = 0; j < productMarginal.GetLength(1); j++)
{
//Assert.True(productMarginal[i,j].GetLogProb(dataIn[i,j]) > -130);
error += System.Math.Abs(productMarginal[i, j].GetMean() - dataIn[i, j]);
}
}
error /= productMarginal.Count;
// error = 0.121231278712027
Console.WriteLine("error = {0}", error);
Assert.True(error < 0.15); // C=2: 0.15
}
