public float GetSlope(SectionPathLossCalcParam param, short[] altitudes, int nIndex)
{
int fNumber = (int) (param.MergeEdgeMaxDis / (4f * param.CalcResolution));
if (fNumber < 1)
{
fNumber = 1;
}
fNumber = Math.Min(this.Trim(fNumber, 5, 10), nIndex);
float[] a = new float[fNumber * 2];
float[] y = new float[fNumber];
int num2 = nIndex;
for (int i = 0; i < fNumber; i++)
{
a[i] = num2 * param.CalcResolution;
y[i] = altitudes[num2 - 1];
num2--;
}
float[] numArray3 = new float[2];
float[] numArray4 = new float[3];
LinearRegression regression = new LinearRegression();
MatrixOperation operation = new MatrixOperation();
regression.mvfitgn(a, y, numArray3, fNumber, 2, false);
return numArray3[1];
}
private ReturnValue Recursion(float[] valueMatrix, float[] actualLossMatrix, float[] coeMatrix, int validDataNumber, int coeNumNeedAdjust, bool isAdjustClutter)
{
ReturnValue value2 = ReturnValue.PROPADJ_SUCCESS;
if (coeNumNeedAdjust == 0)
{
return ReturnValue.PROPCOR_NOSUCESS;
}
if (validDataNumber < 10)
{
return ReturnValue.PROPCOR_LACKDATAERROR;
}
float[] numArray = new float[validDataNumber];
LinearRegression regression = new LinearRegression();
MatrixOperation operation = new MatrixOperation();
int num = 0;
try
{
num = regression.mvfitgn(valueMatrix, actualLossMatrix, coeMatrix, validDataNumber, coeNumNeedAdjust, isAdjustClutter);
}
catch (Exception exception)
{
WriteLog.Logger.Error(exception.StackTrace);
}
if (num != 0)
{
return ReturnValue.PROPCOR_NOTFIT;
}
return value2;
}
