private void CreateExemplaryMatrix()
{
//_matrix = new SparseDoubleMatrix(6, 20);
_matrix = new SparseDoubleMatrix(1024, 1024);
_matrix.SetRow(0);
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(0, 1.0));
_matrix.FinishRow();
_matrix.SetRow(1);
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(1, 2.0));
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(2, 3.0));
_matrix.FinishRow();
_matrix.SetRow(2);
_matrix.FinishRow();
_matrix.SetRow(3);
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(0, 4.0));
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(4, 5.0));
_matrix.FinishRow();
_matrix.SetRow(4);
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(4, 6.0));
_matrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(3, 7.0));
_matrix.FinishRow();
}
private SparseDoubleMatrix CreateDerivedMatrix(Dictionary <long, bool> exactlyOneStates, Dictionary <long, bool> exactlyZeroStates)
{
//Derived matrix is 0-based. Row i is equivalent to the probability distribution of state i (this is not the case for the Markov Chain).
var derivedMatrix = new SparseDoubleMatrix(MarkovChain.States, MarkovChain.Transitions + MarkovChain.States); //Transitions+States is a upper limit
var enumerator = MarkovChain.GetEnumerator();
for (var sourceState = 0; sourceState < MarkovChain.States; sourceState++)
{
enumerator.SelectSourceState(sourceState);
derivedMatrix.SetRow(sourceState);
if (exactlyOneStates.ContainsKey(sourceState) || exactlyZeroStates.ContainsKey(sourceState))
{
// only add a self reference entry
derivedMatrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(sourceState, 1.0));
}
else
{
// if state is neither exactlyOneStates nor exactlyZeroStates, it is a toCalculateState
var selfReferenceAdded = false;
while (enumerator.MoveNextTransition())
{
var columnValueEntry = enumerator.CurrentTransition;
var targetState = columnValueEntry.Column;
if (targetState == sourceState)
{
//this implements the removal of the identity matrix
derivedMatrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(sourceState, columnValueEntry.Value - 1.0));
selfReferenceAdded = true;
}
else
{
derivedMatrix.AddColumnValueToCurrentRow(columnValueEntry);
}
}
if (!selfReferenceAdded)
{
//this implements the removal of the identity matrix (if not already done)
derivedMatrix.AddColumnValueToCurrentRow(new SparseDoubleMatrix.ColumnValue(sourceState, -1.0));
}
}
derivedMatrix.FinishRow();
}
return(derivedMatrix);
}