test_lsfit5.adb nx= 5, step= 5.00000000000000E-01 ny= 5, step= 5.00000000000000E-01 nz= 5, step= 5.00000000000000E-01 nt= 5, step= 5.00000000000000E-01 nu= 5, step= 5.00000000000000E-01 power 1 data test 5D a^ 0 b^ 0 c^ 0 d^ 0 e^ 0 a^ 1 b^ 0 c^ 0 d^ 0 e^ 0 a^ 0 b^ 1 c^ 0 d^ 0 e^ 0 a^ 0 b^ 0 c^ 1 d^ 0 e^ 0 a^ 0 b^ 0 c^ 0 d^ 1 e^ 0 a^ 0 b^ 0 c^ 0 d^ 0 e^ 1 C( 1)= 1.0000E+00 x^0 y^0 z^0 t^0 u^0 C( 2)= 2.0000E+00 x^1 y^0 z^0 t^0 u^0 C( 3)= 3.0000E+00 x^0 y^1 z^0 t^0 u^0 C( 4)= 4.0000E+00 x^0 y^0 z^1 t^0 u^0 C( 5)= 5.0000E+00 x^0 y^0 z^0 t^1 u^0 C( 6)= 6.0000E+00 x^0 y^0 z^0 t^0 u^1 power 1 data test 5D, maxerr= 0.00000000000000E+00 power 1 Bpoly test 5D a^ 0 b^ 0 c^ 0 d^ 0 e^ 0 a^ 1 b^ 0 c^ 0 d^ 0 e^ 0 a^ 0 b^ 1 c^ 0 d^ 0 e^ 0 a^ 0 b^ 0 c^ 1 d^ 0 e^ 0 a^ 0 b^ 0 c^ 0 d^ 1 e^ 0 a^ 0 b^ 0 c^ 0 d^ 0 e^ 1 simeq n= 6, k= 6, abs_pivot= 0.00000000000000E+00 C( 1)= 1.1735E+00 x^0 y^0 z^0 t^0 u^0 C( 2)= 2.0000E+00 x^1 y^0 z^0 t^0 u^0 C( 3)= 3.0000E+00 x^0 y^1 z^0 t^0 u^0 C( 4)= 4.0000E+00 x^0 y^0 z^1 t^0 u^0 C( 5)= 5.0000E+00 x^0 y^0 z^0 t^1 u^0 C( 6)= 0.0000E+00 x^0 y^0 z^0 t^0 u^1 expected= -9.000 fit= -5.826 error=-3.17350946090682E+00 expected= -6.000 fit= -5.826 error=-1.73509460906820E-01 expected= -3.000 fit= -5.826 error= 2.82649053909318E+00 expected= -6.500 fit= -3.326 error=-3.17350946090682E+00 expected= -3.500 fit= -3.326 error=-1.73509460906819E-01 expected= -0.500 fit= -3.326 error= 2.82649053909318E+00 expected= -4.000 fit= -0.826 error=-3.17350946090682E+00 expected= -1.000 fit= -0.826 error=-1.73509460906819E-01 expected= 2.000 fit= -0.826 error= 2.82649053909318E+00 expected= -7.000 fit= -3.826 error=-3.17350946090682E+00 expected= -4.000 fit= -3.826 error=-1.73509460906819E-01 expected= -1.000 fit= -3.826 error= 2.82649053909318E+00 expected= -4.500 fit= -1.326 error=-3.17350946090682E+00 expected= -1.500 fit= -1.326 error=-1.73509460906819E-01 expected= 1.500 fit= -1.326 error= 2.82649053909318E+00 expected= -2.000 fit= 1.174 error=-3.17350946090682E+00 expected= 1.000 fit= 1.174 error=-1.73509460906819E-01 expected= 4.000 fit= 1.174 error= 2.82649053909318E+00 expected= -5.000 fit= -1.826 error=-3.17350946090682E+00 expected= -2.000 fit= -1.826 error=-1.73509460906819E-01 expected= 1.000 fit= -1.826 error= 2.82649053909318E+00 expected= -2.500 fit= 0.674 error=-3.17350946090682E+00 expected= 0.500 fit= 0.674 error=-1.73509460906819E-01 expected= 3.500 fit= 0.674 error= 2.82649053909318E+00 expected= 0.000 fit= 3.174 error=-3.17350946090682E+00 expected= 3.000 fit= 3.174 error=-1.73509460906819E-01 expected= 6.000 fit= 3.174 error= 2.82649053909318E+00 expected= -7.500 fit= -4.326 error=-3.17350946090682E+00 expected= -4.500 fit= -4.326 error=-1.73509460906820E-01 expected= -1.500 fit= -4.326 error= 2.82649053909318E+00 expected= -5.000 fit= -1.826 error=-3.17350946090682E+00 expected= -2.000 fit= -1.826 error=-1.73509460906819E-01 expected= 1.000 fit= -1.826 error= 2.82649053909318E+00 expected= -2.500 fit= 0.674 error=-3.17350946090682E+00 expected= 0.500 fit= 0.674 error=-1.73509460906819E-01 expected= 3.500 fit= 0.674 error= 2.82649053909318E+00 expected= -5.500 fit= -2.326 error=-3.17350946090682E+00 expected= -2.500 fit= -2.326 error=-1.73509460906819E-01 expected= 0.500 fit= -2.326 error= 2.82649053909318E+00 expected= -3.000 fit= 0.174 error=-3.17350946090682E+00 expected= 0.000 fit= 0.174 error=-1.73509460906819E-01 expected= 3.000 fit= 0.174 error= 2.82649053909318E+00 expected= -0.500 fit= 2.674 error=-3.17350946090682E+00 expected= 2.500 fit= 2.674 error=-1.73509460906819E-01 expected= 5.500 fit= 2.674 error= 2.82649053909318E+00 expected= -3.500 fit= -0.326 error=-3.17350946090682E+00 expected= -0.500 fit= -0.326 error=-1.73509460906819E-01 expected= 2.500 fit= -0.326 error= 2.82649053909318E+00 expected= -1.000 fit= 2.174 error=-3.17350946090682E+00 expected= 2.000 fit= 2.174 error=-1.73509460906819E-01 expected= 5.000 fit= 2.174 error= 2.82649053909318E+00 expected= 1.500 fit= 4.674 error=-3.17350946090682E+00 expected= 4.500 fit= 4.674 error=-1.73509460906820E-01 expected= 7.500 fit= 4.674 error= 2.82649053909318E+00 expected= -6.000 fit= -2.826 error=-3.17350946090682E+00 expected= -3.000 fit= -2.826 error=-1.73509460906819E-01 expected= 0.000 fit= -2.826 error= 2.82649053909318E+00 expected= -3.500 fit= -0.326 error=-3.17350946090682E+00 expected= -0.500 fit= -0.326 error=-1.73509460906819E-01 expected= 2.500 fit= -0.326 error= 2.82649053909318E+00 expected= -1.000 fit= 2.174 error=-3.17350946090682E+00 expected= 2.000 fit= 2.174 error=-1.73509460906819E-01 expected= 5.000 fit= 2.174 error= 2.82649053909318E+00 expected= -4.000 fit= -0.826 error=-3.17350946090682E+00 expected= -1.000 fit= -0.826 error=-1.73509460906819E-01 expected= 2.000 fit= -0.826 error= 2.82649053909318E+00 expected= -1.500 fit= 1.674 error=-3.17350946090682E+00 expected= 1.500 fit= 1.674 error=-1.73509460906819E-01 expected= 4.500 fit= 1.674 error= 2.82649053909318E+00 expected= 1.000 fit= 4.174 error=-3.17350946090682E+00 expected= 4.000 fit= 4.174 error=-1.73509460906820E-01 expected= 7.000 fit= 4.174 error= 2.82649053909318E+00 expected= -2.000 fit= 1.174 error=-3.17350946090682E+00 expected= 1.000 fit= 1.174 error=-1.73509460906819E-01 expected= 4.000 fit= 1.174 error= 2.82649053909318E+00 expected= 0.500 fit= 3.674 error=-3.17350946090682E+00 expected= 3.500 fit= 3.674 error=-1.73509460906819E-01 expected= 6.500 fit= 3.674 error= 2.82649053909318E+00 expected= 3.000 fit= 6.174 error=-3.17350946090682E+00 expected= 6.000 fit= 6.174 error=-1.73509460906820E-01 expected= 9.000 fit= 6.174 error= 2.82649053909318E+00 expected= -8.000 fit= -4.826 error=-3.17350946090682E+00 expected= -5.000 fit= -4.826 error=-1.73509460906820E-01 expected= -2.000 fit= -4.826 error= 2.82649053909318E+00 expected= -5.500 fit= -2.326 error=-3.17350946090682E+00 expected= -2.500 fit= -2.326 error=-1.73509460906819E-01 expected= 0.500 fit= -2.326 error= 2.82649053909318E+00 expected= -3.000 fit= 0.174 error=-3.17350946090682E+00 expected= 0.000 fit= 0.174 error=-1.73509460906819E-01 expected= 3.000 fit= 0.174 error= 2.82649053909318E+00 expected= -6.000 fit= -2.826 error=-3.17350946090682E+00 expected= -3.000 fit= -2.826 error=-1.73509460906819E-01 expected= 0.000 fit= -2.826 error= 2.82649053909318E+00 expected= -3.500 fit= -0.326 error=-3.17350946090682E+00 expected= -0.500 fit= -0.326 error=-1.73509460906819E-01 expected= 2.500 fit= -0.326 error= 2.82649053909318E+00 expected= -1.000 fit= 2.174 error=-3.17350946090682E+00 expected= 2.000 fit= 2.174 error=-1.73509460906819E-01 expected= 5.000 fit= 2.174 error= 2.82649053909318E+00 expected= -4.000 fit= -0.826 error=-3.17350946090682E+00 expected= -1.000 fit= -0.826 error=-1.73509460906819E-01 expected= 2.000 fit= -0.826 error= 2.82649053909318E+00 expected= -1.500 fit= 1.674 error=-3.17350946090682E+00 expected= 1.500 fit= 1.674 error=-1.73509460906819E-01 expected= 4.500 fit= 1.674 error= 2.82649053909318E+00 expected= 1.000 fit= 4.174 error=-3.17350946090682E+00 expected= 4.000 fit= 4.174 error=-1.73509460906820E-01 expected= 7.000 fit= 4.174 error= 2.82649053909318E+00 expected= -6.500 fit= -3.326 error=-3.17350946090682E+00 expected= -3.500 fit= -3.326 error=-1.73509460906819E-01 expected= -0.500 fit= -3.326 error= 2.82649053909318E+00 expected= -4.000 fit= -0.826 error=-3.17350946090682E+00 expected= -1.000 fit= -0.826 error=-1.73509460906819E-01 expected= 2.000 fit= -0.826 error= 2.82649053909318E+00 expected= -1.500 fit= 1.674 error=-3.17350946090682E+00 expected= 1.500 fit= 1.674 error=-1.73509460906819E-01 expected= 4.500 fit= 1.674 error= 2.82649053909318E+00 expected= -4.500 fit= -1.326 error=-3.17350946090682E+00 expected= -1.500 fit= -1.326 error=-1.73509460906819E-01 expected= 1.500 fit= -1.326 error= 2.82649053909318E+00 expected= -2.000 fit= 1.174 error=-3.17350946090682E+00 expected= 1.000 fit= 1.174 error=-1.73509460906819E-01 expected= 4.000 fit= 1.174 error= 2.82649053909318E+00 expected= 0.500 fit= 3.674 error=-3.17350946090682E+00 expected= 3.500 fit= 3.674 error=-1.73509460906819E-01 expected= 6.500 fit= 3.674 error= 2.82649053909318E+00 expected= -2.500 fit= 0.674 error=-3.17350946090682E+00 expected= 0.500 fit= 0.674 error=-1.73509460906819E-01 expected= 3.500 fit= 0.674 error= 2.82649053909318E+00 expected= 0.000 fit= 3.174 error=-3.17350946090682E+00 expected= 3.000 fit= 3.174 error=-1.73509460906819E-01 expected= 6.000 fit= 3.174 error= 2.82649053909318E+00 expected= 2.500 fit= 5.674 error=-3.17350946090682E+00 expected= 5.500 fit= 5.674 error=-1.73509460906820E-01 expected= 8.500 fit= 5.674 error= 2.82649053909318E+00 expected= -5.000 fit= -1.826 error=-3.17350946090682E+00 expected= -2.000 fit= -1.826 error=-1.73509460906819E-01 expected= 1.000 fit= -1.826 error= 2.82649053909318E+00 expected= -2.500 fit= 0.674 error=-3.17350946090682E+00 expected= 0.500 fit= 0.674 error=-1.73509460906819E-01 expected= 3.500 fit= 0.674 error= 2.82649053909318E+00 expected= 0.000 fit= 3.174 error=-3.17350946090682E+00 expected= 3.000 fit= 3.174 error=-1.73509460906819E-01 expected= 6.000 fit= 3.174 error= 2.82649053909318E+00 expected= -3.000 fit= 0.174 error=-3.17350946090682E+00 expected= 0.000 fit= 0.174 error=-1.73509460906819E-01 expected= 3.000 fit= 0.174 error= 2.82649053909318E+00 expected= -0.500 fit= 2.674 error=-3.17350946090682E+00 expected= 2.500 fit= 2.674 error=-1.73509460906819E-01 expected= 5.500 fit= 2.674 error= 2.82649053909318E+00 expected= 2.000 fit= 5.174 error=-3.17350946090682E+00 expected= 5.000 fit= 5.174 error=-1.73509460906820E-01 expected= 8.000 fit= 5.174 error= 2.82649053909318E+00 expected= -1.000 fit= 2.174 error=-3.17350946090682E+00 expected= 2.000 fit= 2.174 error=-1.73509460906820E-01 expected= 5.000 fit= 2.174 error= 2.82649053909318E+00 expected= 1.500 fit= 4.674 error=-3.17350946090682E+00 expected= 4.500 fit= 4.674 error=-1.73509460906820E-01 expected= 7.500 fit= 4.674 error= 2.82649053909318E+00 expected= 4.000 fit= 7.174 error=-3.17350946090682E+00 expected= 7.000 fit= 7.174 error=-1.73509460906820E-01 expected= 10.000 fit= 7.174 error= 2.82649053909318E+00 expected= -7.000 fit= -3.826 error=-3.17350946090682E+00 expected= -4.000 fit= -3.826 error=-1.73509460906819E-01 expected= -1.000 fit= -3.826 error= 2.82649053909318E+00 expected= -4.500 fit= -1.326 error=-3.17350946090682E+00 expected= -1.500 fit= -1.326 error=-1.73509460906819E-01 expected= 1.500 fit= -1.326 error= 2.82649053909318E+00 expected= -2.000 fit= 1.174 error=-3.17350946090682E+00 expected= 1.000 fit= 1.174 error=-1.73509460906819E-01 expected= 4.000 fit= 1.174 error= 2.82649053909318E+00 expected= -5.000 fit= -1.826 error=-3.17350946090682E+00 expected= -2.000 fit= -1.826 error=-1.73509460906819E-01 expected= 1.000 fit= -1.826 error= 2.82649053909318E+00 expected= -2.500 fit= 0.674 error=-3.17350946090682E+00 expected= 0.500 fit= 0.674 error=-1.73509460906819E-01 expected= 3.500 fit= 0.674 error= 2.82649053909318E+00 expected= 0.000 fit= 3.174 error=-3.17350946090682E+00 expected= 3.000 fit= 3.174 error=-1.73509460906819E-01 expected= 6.000 fit= 3.174 error= 2.82649053909318E+00 expected= -3.000 fit= 0.174 error=-3.17350946090682E+00 expected= 0.000 fit= 0.174 error=-1.73509460906819E-01 expected= 3.000 fit= 0.174 error= 2.82649053909318E+00 expected= -0.500 fit= 2.674 error=-3.17350946090682E+00 expected= 2.500 fit= 2.674 error=-1.73509460906819E-01 expected= 5.500 fit= 2.674 error= 2.82649053909318E+00 expected= 2.000 fit= 5.174 error=-3.17350946090682E+00 expected= 5.000 fit= 5.174 error=-1.73509460906820E-01 expected= 8.000 fit= 5.174 error= 2.82649053909318E+00 expected= -5.500 fit= -2.326 error=-3.17350946090682E+00 expected= -2.500 fit= -2.326 error=-1.73509460906819E-01 expected= 0.500 fit= -2.326 error= 2.82649053909318E+00 expected= -3.000 fit= 0.174 error=-3.17350946090682E+00 expected= 0.000 fit= 0.174 error=-1.73509460906819E-01 expected= 3.000 fit= 0.174 error= 2.82649053909318E+00 expected= -0.500 fit= 2.674 error=-3.17350946090682E+00 expected= 2.500 fit= 2.674 error=-1.73509460906819E-01 expected= 5.500 fit= 2.674 error= 2.82649053909318E+00 expected= -3.500 fit= -0.326 error=-3.17350946090682E+00 expected= -0.500 fit= -0.326 error=-1.73509460906819E-01 expected= 2.500 fit= -0.326 error= 2.82649053909318E+00 expected= -1.000 fit= 2.174 error=-3.17350946090682E+00 expected= 2.000 fit= 2.174 error=-1.73509460906819E-01 expected= 5.000 fit= 2.174 error= 2.82649053909318E+00 expected= 1.500 fit= 4.674 error=-3.17350946090682E+00 expected= 4.500 fit= 4.674 error=-1.73509460906820E-01 expected= 7.500 fit= 4.674 error= 2.82649053909318E+00 expected= -1.500 fit= 1.674 error=-3.17350946090682E+00 expected= 1.500 fit= 1.674 error=-1.73509460906820E-01 expected= 4.500 fit= 1.674 error= 2.82649053909318E+00 expected= 1.000 fit= 4.174 error=-3.17350946090682E+00 expected= 4.000 fit= 4.174 error=-1.73509460906820E-01 expected= 7.000 fit= 4.174 error= 2.82649053909318E+00 expected= 3.500 fit= 6.674 error=-3.17350946090682E+00 expected= 6.500 fit= 6.674 error=-1.73509460906820E-01 expected= 9.500 fit= 6.674 error= 2.82649053909318E+00 expected= -4.000 fit= -0.826 error=-3.17350946090682E+00 expected= -1.000 fit= -0.826 error=-1.73509460906819E-01 expected= 2.000 fit= -0.826 error= 2.82649053909318E+00 expected= -1.500 fit= 1.674 error=-3.17350946090682E+00 expected= 1.500 fit= 1.674 error=-1.73509460906819E-01 expected= 4.500 fit= 1.674 error= 2.82649053909318E+00 expected= 1.000 fit= 4.174 error=-3.17350946090682E+00 expected= 4.000 fit= 4.174 error=-1.73509460906820E-01 expected= 7.000 fit= 4.174 error= 2.82649053909318E+00 expected= -2.000 fit= 1.174 error=-3.17350946090682E+00 expected= 1.000 fit= 1.174 error=-1.73509460906819E-01 expected= 4.000 fit= 1.174 error= 2.82649053909318E+00 expected= 0.500 fit= 3.674 error=-3.17350946090682E+00 expected= 3.500 fit= 3.674 error=-1.73509460906819E-01 expected= 6.500 fit= 3.674 error= 2.82649053909318E+00 expected= 3.000 fit= 6.174 error=-3.17350946090682E+00 expected= 6.000 fit= 6.174 error=-1.73509460906820E-01 expected= 9.000 fit= 6.174 error= 2.82649053909318E+00 expected= 0.000 fit= 3.174 error=-3.17350946090682E+00 expected= 3.000 fit= 3.174 error=-1.73509460906820E-01 expected= 6.000 fit= 3.174 error= 2.82649053909318E+00 expected= 2.500 fit= 5.674 error=-3.17350946090682E+00 expected= 5.500 fit= 5.674 error=-1.73509460906820E-01 expected= 8.500 fit= 5.674 error= 2.82649053909318E+00 expected= 5.000 fit= 8.174 error=-3.17350946090682E+00 expected= 8.000 fit= 8.174 error=-1.73509460906820E-01 expected= 11.000 fit= 8.174 error= 2.82649053909318E+00 power 1 Bpoly test 5D, maxerr= 3.17350946090682E+00 test_lsfit5.adb finished