/// <summary>
/// Generates a resource consumption pattern matrix
/// </summary>
/// <param name="ip">The current InputParameters object</param>
private RectangularMatrix GenResConsPat(InputParameters ip)
{
bool throwAway;
int numThrows = 0;
RectangularMatrix outputMatrix;
do
{
throwAway = false;
outputMatrix = new RectangularMatrix(ip.RCP, ip.CO);
// Flowchart 5.1(a): Generate vector X
RowVector X = GenRandNumbers.GenStdNormalVec(ip.CO);
// The following code is used in both 5.1(b) and 5.1(c):
RowVector[] Y = new RowVector[ip.RCP - 1];
RowVector[] Z = new RowVector[Y.Length];
for (int i = 0; i < Y.Length; ++i)
{
Y[i] = GenRandNumbers.GenStdNormalVec(ip.CO);
}
// Flowchart 5.1(b): Generate (DISP1 - 1) vectors Y
// Then create Z vectors based on X and Y
double COR1 =
GenRandNumbers.GenUniformDbl(ip.COR1LB, ip.COR1UB);
double sqrtConstant1 = Math.Sqrt(1 - COR1 * COR1);
for (int i = 0; i < ip.DISP1 - 1; ++i)
{
Z[i] = (COR1 * X) + (sqrtConstant1 * Y[i]);
}
// Flowchart 5.1(c): Generate (RCP - DISP1) vectors Y
// Then create the remaining Z vectors based on X and Y
double COR2 =
GenRandNumbers.GenUniformDbl(ip.COR2LB, ip.COR2UB);
double sqrtConstant2 = Math.Sqrt(1 - COR2 * COR2);
for (int i = ip.DISP1 - 1; i < Z.Length; ++i)
{
Z[i] = (COR2 * X) + (sqrtConstant2 * Y[i]);
}
// Flowchart 5.1(d):
// Take the absolute values of X and the Z's and
// scale both by 10.0.
X = X.Map(x => 10.0 * Math.Abs(x));
for (int i = 0; i < Z.Length; ++i)
{
Z[i] = Z[i].Map(z => 10.0 * Math.Abs(z));
}
// Round X and the Z's to integers
X = X.Map(x => Math.Ceiling(x));
for (int i = 0; i < Z.Length; ++i)
{
Z[i] = Z[i].Map(z => Math.Ceiling(z));
}
// Flowchart 5.1(e):
// Now punch out values in the Z's at random to make
// the matrix sparse
for (int i = 0; i < Z.Length; ++i)
{
Z[i] = Z[i].Map(x => ((GenRandNumbers.GenUniformDbl() < D) ? x : 0.0));
}
// Flowchart 5.1(f):
// Copy X into first row of outputMatrix.
outputMatrix.CopyRowInto(X, 0);
// Copy the Z's into the remaining rows of outputMatrix.
for (int i = 0; i < Z.Length; ++i)
{
outputMatrix.CopyRowInto(Z[i], i + 1);
}
// Ensure that the first row has no zeros
// There is a very small probability of getting a zero with
// the Ceiling function, but given that there are a finite
// number of double-precision floating point numbers, it
// is not impossible to get a 0.0.
double[] firstRow = outputMatrix.Row(0).ToArray();
if (Array.Exists(firstRow, x => x == 0.0))
{
throwAway = true;
break;
}
// Ensure that each *row* has at least one non-zero entry
for (int i = 0; i < outputMatrix.RowCount; ++i)
{
double[] nextRow = outputMatrix.Row(i).ToArray();
if (Array.TrueForAll(nextRow, x => x == 0.0))
{
throwAway = true;
break;
}
}
// Ensure that each *column* has at least one non-zero entry
// Technically, this check is redundant, as the first row, X,
// is not supposed to have any zero entries. But just to be
// on the safe side...
for (int j = 0; j < outputMatrix.ColumnCount; ++j)
{
double[] nextCol = outputMatrix.Column(j).ToArray();
if (Array.TrueForAll(nextCol, x => x == 0.0))
{
string s = "There is a column with all zeros. " +
"That should not happen since the first row is " +
"supposed to have no zeros.";
throw new ApplicationException(s);
}
}
if (throwAway)
{
++numThrows;
}
} while (throwAway);
Console.WriteLine("RES_CONS_PAT: {0} Throw aways\n", numThrows);
return(outputMatrix);
}