>> rbf_interp_test 29-Jun-2012 14:01:37 RBF_INTERP_TEST: Test the RBF_INTERP library. RBF_INTERP_TEST01: RBF_WEIGHT computes weights for RBF interpolation. RBF_INTERP evaluates the RBF interpolant. Use the multiquadratic basis function PHI1(R). The product points: Row: 1 2 Col 1: 0.000000 0.000000 2: 0.500000 0.000000 3: 1.000000 0.000000 4: 1.500000 0.000000 5: 2.000000 0.000000 6: 0.000000 0.500000 7: 0.500000 0.500000 8: 1.000000 0.500000 9: 1.500000 0.500000 10: 2.000000 0.500000 11: 0.000000 1.000000 12: 0.500000 1.000000 13: 1.000000 1.000000 14: 1.500000 1.000000 15: 2.000000 1.000000 16: 0.000000 1.500000 17: 0.500000 1.500000 18: 1.000000 1.500000 19: 1.500000 1.500000 20: 2.000000 1.500000 21: 0.000000 2.000000 22: 0.500000 2.000000 23: 1.000000 2.000000 24: 1.500000 2.000000 25: 2.000000 2.000000 Scale factor R0 = 0.4 Function data: 1: 0 2: 0 3: 0 4: 0 5: 0 6: 0 7: 0.1947 8: 0.303265 9: 0.354275 10: 0.367879 11: 0 12: 0.303265 13: 0.367879 14: 0.334695 15: 0.270671 16: 0 17: 0.354275 18: 0.334695 19: 0.237148 20: 0.149361 21: 0 22: 0.367879 23: 0.270671 24: 0.149361 25: 0.0732626 Weight vector: 1: -0.501359 2: 0.300861 3: 0.23295 4: 0.369441 5: 0.645573 6: 0.300861 7: -0.155096 8: -0.367322 9: -0.407046 10: -1.30006 11: 0.23295 12: -0.367322 13: 0.182323 14: 0.203404 15: 0.514794 16: 0.369441 17: -0.407046 18: 0.203404 19: -0.12928 20: 0.00990249 21: 0.645573 22: -1.30006 23: 0.514794 24: 0.00990249 25: 0.0666187 L2 interpolation error averaged per interpolant node = 1.03012e-16 L2 approximation error averaged per 1000 samples = 0.0031887 RBF_INTERP_TEST02: RBF_WEIGHT computes weights for RBF interpolation. RBF_INTERP evaluates the RBF interpolant. Use the multiquadratic basis function PHI2(R). The product points: Row: 1 2 Col 1: 0.000000 0.000000 2: 0.500000 0.000000 3: 1.000000 0.000000 4: 1.500000 0.000000 5: 2.000000 0.000000 6: 0.000000 0.500000 7: 0.500000 0.500000 8: 1.000000 0.500000 9: 1.500000 0.500000 10: 2.000000 0.500000 11: 0.000000 1.000000 12: 0.500000 1.000000 13: 1.000000 1.000000 14: 1.500000 1.000000 15: 2.000000 1.000000 16: 0.000000 1.500000 17: 0.500000 1.500000 18: 1.000000 1.500000 19: 1.500000 1.500000 20: 2.000000 1.500000 21: 0.000000 2.000000 22: 0.500000 2.000000 23: 1.000000 2.000000 24: 1.500000 2.000000 25: 2.000000 2.000000 Scale factor R0 = 0.4 Function data: 1: 0 2: 0 3: 0 4: 0 5: 0 6: 0 7: 0.1947 8: 0.303265 9: 0.354275 10: 0.367879 11: 0 12: 0.303265 13: 0.367879 14: 0.334695 15: 0.270671 16: 0 17: 0.354275 18: 0.334695 19: 0.237148 20: 0.149361 21: 0 22: 0.367879 23: 0.270671 24: 0.149361 25: 0.0732626 Weight vector: 1: 0.00455693 2: -0.044454 3: -0.0711033 4: -0.0829322 5: -0.121156 6: -0.044454 7: 0.0524777 8: 0.0753675 9: 0.10031 10: 0.184681 11: -0.0711033 12: 0.0753675 13: 0.0199836 14: 0.00472747 15: -0.00972847 16: -0.0829322 17: 0.10031 18: 0.00472747 19: 0.00968919 20: -0.0108128 21: -0.121156 22: 0.184681 23: -0.00972847 24: -0.0108128 25: -0.0325387 L2 interpolation error averaged per interpolant node = 1.15367e-17 L2 approximation error averaged per 1000 samples = 0.00410702 RBF_INTERP_TEST03: RBF_WEIGHT computes weights for RBF interpolation. RBF_INTERP evaluates the RBF interpolant. Use the multiquadratic basis function PHI3(R). The product points: Row: 1 2 Col 1: 0.000000 0.000000 2: 0.500000 0.000000 3: 1.000000 0.000000 4: 1.500000 0.000000 5: 2.000000 0.000000 6: 0.000000 0.500000 7: 0.500000 0.500000 8: 1.000000 0.500000 9: 1.500000 0.500000 10: 2.000000 0.500000 11: 0.000000 1.000000 12: 0.500000 1.000000 13: 1.000000 1.000000 14: 1.500000 1.000000 15: 2.000000 1.000000 16: 0.000000 1.500000 17: 0.500000 1.500000 18: 1.000000 1.500000 19: 1.500000 1.500000 20: 2.000000 1.500000 21: 0.000000 2.000000 22: 0.500000 2.000000 23: 1.000000 2.000000 24: 1.500000 2.000000 25: 2.000000 2.000000 Scale factor R0 = 0.4 Function data: 1: 0 2: 0 3: 0 4: 0 5: 0 6: 0 7: 0.1947 8: 0.303265 9: 0.354275 10: 0.367879 11: 0 12: 0.303265 13: 0.367879 14: 0.334695 15: 0.270671 16: 0 17: 0.354275 18: 0.334695 19: 0.237148 20: 0.149361 21: 0 22: 0.367879 23: 0.270671 24: 0.149361 25: 0.0732626 Weight vector: 1: 0.433461 2: -0.204877 3: -0.0872454 4: -0.246184 5: -0.0676362 6: -0.204877 7: -0.0378926 8: 0.133989 9: 0.127043 10: 0.441877 11: -0.0872454 12: 0.133989 13: -0.0423124 14: -0.107041 15: -0.183447 16: -0.246184 17: 0.127043 18: -0.107041 19: -0.0378467 20: -0.107503 21: -0.0676362 22: 0.441877 23: -0.183447 24: -0.107503 25: 0.141281 L2 interpolation error averaged per interpolant node = 1.57084e-16 L2 approximation error averaged per 1000 samples = 0.0026366 RBF_INTERP_TEST04: RBF_WEIGHT computes weights for RBF interpolation. RBF_INTERP evaluates the RBF interpolant. Use the multiquadratic basis function PHI4(R). The product points: Row: 1 2 Col 1: 0.000000 0.000000 2: 0.500000 0.000000 3: 1.000000 0.000000 4: 1.500000 0.000000 5: 2.000000 0.000000 6: 0.000000 0.500000 7: 0.500000 0.500000 8: 1.000000 0.500000 9: 1.500000 0.500000 10: 2.000000 0.500000 11: 0.000000 1.000000 12: 0.500000 1.000000 13: 1.000000 1.000000 14: 1.500000 1.000000 15: 2.000000 1.000000 16: 0.000000 1.500000 17: 0.500000 1.500000 18: 1.000000 1.500000 19: 1.500000 1.500000 20: 2.000000 1.500000 21: 0.000000 2.000000 22: 0.500000 2.000000 23: 1.000000 2.000000 24: 1.500000 2.000000 25: 2.000000 2.000000 Scale factor R0 = 0.4 Function data: 1: 0 2: 0 3: 0 4: 0 5: 0 6: 0 7: 0.1947 8: 0.303265 9: 0.354275 10: 0.367879 11: 0 12: 0.303265 13: 0.367879 14: 0.334695 15: 0.270671 16: 0 17: 0.354275 18: 0.334695 19: 0.237148 20: 0.149361 21: 0 22: 0.367879 23: 0.270671 24: 0.149361 25: 0.0732626 Weight vector: 1: 0.0247652 2: -0.0461165 3: -0.0818427 4: -0.0622022 5: -0.17306 6: -0.0461165 7: 0.0838087 8: 0.173681 9: 0.128662 10: 0.37891 11: -0.0818427 12: 0.173681 13: 0.051454 14: 0.0737542 15: -0.0110253 16: -0.0622022 17: 0.128662 18: 0.0737542 19: 0.0630212 20: 0.076087 21: -0.17306 22: 0.37891 23: -0.0110253 24: 0.076087 25: -0.0125014 L2 interpolation error averaged per interpolant node = 7.81858e-18 L2 approximation error averaged per 1000 samples = 0.00413716 RBF_INTERP_TEST: Normal end of execution. 29-Jun-2012 14:01:38 >>