| public Infer ( MathNet.Numerics.LinearAlgebra.Double.SparseMatrix input ) : Vector | ||
| input | MathNet.Numerics.LinearAlgebra.Double.SparseMatrix | |
| return | Vector | |
public void ErrorWhenLearningAfterInferenceMode()
{
var node = new SpatialNode2DGaussian();
var mat = new SparseMatrix(4, 4, 4.0);
var learnAfterInferFails = false;
var learnAfterTBInferFails = false;
node.Learn(mat);
node.Infer(mat);
try
{
node.Learn(mat);
}
catch (HtmRuleException e)
{
learnAfterInferFails = true;
Debug.WriteLine(e.Message);
}
node.TimeInfer(mat);
try
{
node.Learn(mat);
}
catch (HtmRuleException e)
{
learnAfterTBInferFails = true;
Debug.WriteLine(e.Message);
}
Assert.IsTrue(learnAfterInferFails);
Assert.IsTrue(learnAfterTBInferFails);
}
public void InferCorrectlyOnIdenticalMatricesWithMaxDistance0()
{
var node = new SpatialNode2DGaussian();
var ones = new SparseMatrix(4, 5, 1.0);
var twos = new SparseMatrix(4, 5, 2.0);
var expectedLower = Math.Exp(-(ones.RowCount * ones.ColumnCount) / (2 * node.SquaredSigma)) - 0.0001;
var expectedUpper = Math.Exp(-(ones.RowCount * ones.ColumnCount) / (2 * node.SquaredSigma)) + 0.0001;
node.Learn(ones);
node.Learn(twos);
var output = node.Infer(ones);
Assert.IsTrue(output[0] == 1.0 && output[1] > expectedLower && output[1] < expectedUpper);
}
public void InferEmptyVectorIfNoCoincidencesLearned()
{
var node = new SpatialNode2DGaussian(1.0);
var output = node.Infer(new SparseMatrix(5, 5, 2.0));
Assert.AreEqual(new SparseVector(1), output);
output = node.TimeInfer(new SparseMatrix(5, 5, 2.0));
Assert.AreEqual(new SparseVector(1), output);
}
