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
}
public int LoadData(int r)
{
Rand.Restart(r);
var xTrainList = new List <Vector>();
var xTestList = new List <Vector>();
var yTrainList = new List <bool>();
var yTestList = new List <bool>();
var ydata = BioTests.ReadCSV(baseDir + "y.csv");
var xdata = BioTests.ReadCSV(baseDir + "x.csv");
int N = ydata.GetLength(0);
int K = xdata.GetLength(1);
for (int i = 0; i < N; i++)
{
var x = Vector.Zero(K);
for (int j = 0; j < K; j++)
{
x[j] = xdata[i, j];
}
if (Rand.Double() < propTest)
{
yTestList.Add(ydata[i, 0] == 1.0);
xTestList.Add(x);
}
else
{
yTrainList.Add(ydata[i, 0] == 1.0);
xTrainList.Add(x);
}
}
xtrain = xTrainList.ToArray();
xtest = xTestList.ToArray();
ytrain = yTrainList.ToArray();
ytest = yTestList.ToArray();
return(0);
}
