51 #define roundl(x) (long double)((long long)((x == 0) ? 0.0 : ( (x) + ( ((x) > 0) ? 0.5 : -0.5) ))) 57 static const double eu = 0.577215664901532860606;
62 {-3.00, 6.80E-4, 6.85E-4, 6.90E-4, 6.95E-4, 7.00E-4, 7.05E-4, 7.10E-4, 7.15E-4, 7.21E-4, 7.26E-4, 7.31E-4},
63 {-2.50, 9.73E-3, 9.80E-3, 9.87E-3, 9.93E-3, 1.00E-2, 1.01E-2, 1.01E-2, 1.02E-2, 1.03E-2, 1.03E-2, 1.04E-2},
64 {-2.00, 4.44E-2, 4.47E-2, 4.50E-2, 4.53E-2, 4.55E-2, 4.58E-2, 4.61E-2, 4.64E-2, 4.67E-2, 4.70E-2, 4.73E-2},
65 {-1.50, 1.02E-1, 1.02E-1, 1.03E-1, 1.03E-1, 1.04E-1, 1.04E-1, 1.05E-1, 1.06E-1, 1.06E-1, 1.07E-1, 1.07E-1},
66 {-1.00, 1.53E-1, 1.54E-1, 1.55E-1, 1.55E-1, 1.56E-1, 1.57E-1, 1.58E-1, 1.59E-1, 1.59E-1, 1.60E-1, 1.61E-1},
67 {-0.50, 1.79E-1, 1.80E-1, 1.81E-1, 1.82E-1, 1.83E-1, 1.83E-1, 1.84E-1, 1.85E-1, 1.86E-1, 1.87E-1, 1.88E-1},
68 { 0.00, 1.81E-1, 1.81E-1, 1.82E-1, 1.83E-1, 1.84E-1, 1.84E-1, 1.85E-1, 1.86E-1, 1.87E-1, 1.88E-1, 1.88E-1},
69 { 0.50, 1.67E-1, 1.68E-1, 1.68E-1, 1.69E-1, 1.69E-1, 1.70E-1, 1.71E-1, 1.71E-1, 1.72E-1, 1.73E-1, 1.73E-1},
70 { 1.00, 1.47E-1, 1.47E-1, 1.48E-1, 1.48E-1, 1.49E-1, 1.49E-1, 1.49E-1, 1.50E-1, 1.50E-1, 1.51E-1, 1.51E-1},
71 { 1.50, 1.25E-1, 1.26E-1, 1.26E-1, 1.26E-1, 1.27E-1, 1.27E-1, 1.27E-1, 1.28E-1, 1.28E-1, 1.29E-1, 1.29E-1},
72 { 2.00, 1.06E-1, 1.06E-1, 1.06E-1, 1.07E-1, 1.07E-1, 1.07E-1, 1.07E-1, 1.08E-1, 1.08E-1, 1.08E-1, 1.08E-1},
73 { 2.50, 8.91E-2, 8.93E-2, 8.94E-2, 8.96E-2, 8.97E-2, 8.99E-2, 9.00E-2, 9.02E-2, 9.03E-2, 9.04E-2, 9.06E-2},
74 { 3.00, 7.50E-2, 7.51E-2, 7.52E-2, 7.53E-2, 7.53E-2, 7.54E-2, 7.55E-2, 7.56E-2, 7.57E-2, 7.58E-2, 7.59E-2},
75 { 3.50, 6.34E-2, 6.34E-2, 6.34E-2, 6.35E-2, 6.35E-2, 6.36E-2, 6.36E-2, 6.36E-2, 6.37E-2, 6.37E-2, 6.37E-2},
76 { 4.00, 5.38E-2, 5.38E-2, 5.38E-2, 5.38E-2, 5.38E-2, 5.38E-2, 5.38E-2, 5.39E-2, 5.39E-2, 5.39E-2, 5.39E-2},
77 { 4.50, 4.60E-2, 4.60E-2, 4.60E-2, 4.59E-2, 4.59E-2, 4.59E-2, 4.59E-2, 4.59E-2, 4.58E-2, 4.58E-2, 4.58E-2},
78 { 5.00, 3.96E-2, 3.95E-2, 3.95E-2, 3.95E-2, 3.94E-2, 3.94E-2, 3.93E-2, 3.93E-2, 3.93E-2, 3.92E-2, 3.92E-2},
79 { 5.50, 3.43E-2, 3.42E-2, 3.42E-2, 3.41E-2, 3.41E-2, 3.40E-2, 3.40E-2, 3.39E-2, 3.39E-2, 3.38E-2, 3.38E-2},
80 { 6.00, 2.99E-2, 2.98E-2, 2.97E-2, 2.97E-2, 2.96E-2, 2.96E-2, 2.95E-2, 2.95E-2, 2.94E-2, 2.93E-2, 2.93E-2},
81 { 6.50, 2.62E-2, 2.61E-2, 2.61E-2, 2.60E-2, 2.59E-2, 2.59E-2, 2.58E-2, 2.57E-2, 2.57E-2, 2.56E-2, 2.55E-2},
82 { 7.00, 2.31E-2, 2.30E-2, 2.30E-2, 2.29E-2, 2.28E-2, 2.28E-2, 2.27E-2, 2.26E-2, 2.26E-2, 2.25E-2, 2.24E-2},
83 { 7.50, 2.05E-2, 2.04E-2, 2.04E-2, 2.03E-2, 2.02E-2, 2.01E-2, 2.01E-2, 2.00E-2, 1.99E-2, 1.99E-2, 1.98E-2},
84 { 8.00, 1.83E-2, 1.82E-2, 1.81E-2, 1.81E-2, 1.80E-2, 1.79E-2, 1.78E-2, 1.78E-2, 1.77E-2, 1.76E-2, 1.75E-2},
85 { 8.50, 1.64E-2, 1.63E-2, 1.62E-2, 1.62E-2, 1.61E-2, 1.60E-2, 1.59E-2, 1.59E-2, 1.58E-2, 1.57E-2, 1.56E-2},
86 { 9.00, 1.47E-2, 1.47E-2, 1.46E-2, 1.45E-2, 1.45E-2, 1.44E-2, 1.43E-2, 1.42E-2, 1.42E-2, 1.41E-2, 1.40E-2},
87 { 9.50, 1.33E-2, 1.33E-2, 1.32E-2, 1.31E-2, 1.30E-2, 1.30E-2, 1.29E-2, 1.28E-2, 1.27E-2, 1.27E-2, 1.26E-2},
88 {10.00, 1.21E-2, 1.20E-2, 1.20E-2, 1.19E-2, 1.18E-2, 1.17E-2, 1.17E-2, 1.16E-2, 1.15E-2, 1.14E-2, 1.14E-2},
89 {11.00, 1.01E-2, 1.00E-2, 9.94E-3, 9.87E-3, 9.80E-3, 9.73E-3, 9.66E-3, 9.59E-3, 9.51E-3, 9.44E-3, 9.37E-3},
90 {12.00, 8.51E-3, 8.44E-3, 8.37E-3, 8.31E-3, 8.24E-3, 8.17E-3, 8.10E-3, 8.03E-3, 7.96E-3, 7.89E-3, 7.82E-3},
91 {13.00, 7.26E-3, 7.20E-3, 7.14E-3, 7.07E-3, 7.00E-3, 6.94E-3, 6.87E-3, 6.81E-3, 6.74E-3, 6.67E-3, 6.61E-3},
92 {14.00, 6.27E-3, 6.20E-3, 6.14E-3, 6.08E-3, 6.02E-3, 5.95E-3, 5.89E-3, 5.83E-3, 5.76E-3, 5.70E-3, 5.64E-3},
93 {15.00, 5.46E-3, 5.40E-3, 5.34E-3, 5.28E-3, 5.22E-3, 5.16E-3, 5.10E-3, 5.04E-3, 4.97E-3, 4.91E-3, 4.85E-3},
94 {16.00, 4.79E-3, 4.73E-3, 4.67E-3, 4.62E-3, 4.56E-3, 4.50E-3, 4.44E-3, 4.39E-3, 4.33E-3, 4.27E-3, 4.21E-3},
95 {17.00, 4.23E-3, 4.18E-3, 4.12E-3, 4.07E-3, 4.01E-3, 3.96E-3, 3.90E-3, 3.85E-3, 3.79E-3, 3.73E-3, 3.68E-3},
96 {18.00, 3.77E-3, 3.72E-3, 3.66E-3, 3.61E-3, 3.56E-3, 3.50E-3, 3.45E-3, 3.40E-3, 3.34E-3, 3.29E-3, 3.24E-3},
97 {19.00, 3.37E-3, 3.32E-3, 3.27E-3, 3.22E-3, 3.17E-3, 3.12E-3, 3.07E-3, 3.02E-3, 2.97E-3, 2.91E-3, 2.86E-3},
98 {20.00, 3.04E-3, 2.99E-3, 2.94E-3, 2.89E-3, 2.84E-3, 2.79E-3, 2.74E-3, 2.69E-3, 2.64E-3, 2.60E-3, 2.55E-3},
99 {22.00, 2.49E-3, 2.45E-3, 2.40E-3, 2.36E-3, 2.31E-3, 2.27E-3, 2.22E-3, 2.18E-3, 2.13E-3, 2.09E-3, 2.04E-3},
100 {24.00, 2.08E-3, 2.04E-3, 2.00E-3, 1.96E-3, 1.92E-3, 1.88E-3, 1.83E-3, 1.79E-3, 1.75E-3, 1.71E-3, 1.66E-3},
101 {26.00, 1.77E-3, 1.73E-3, 1.69E-3, 1.65E-3, 1.61E-3, 1.57E-3, 1.53E-3, 1.49E-3, 1.45E-3, 1.41E-3, 1.37E-3},
102 {28.00, 1.51E-3, 1.48E-3, 1.44E-3, 1.41E-3, 1.37E-3, 1.33E-3, 1.30E-3, 1.26E-3, 1.22E-3, 1.18E-3, 1.15E-3},
103 {30.00, 1.31E-3, 1.28E-3, 1.24E-3, 1.21E-3, 1.18E-3, 1.14E-3, 1.11E-3, 1.07E-3, 1.04E-3, 1.00E-3, 9.68E-4},
104 {32.00, 1.15E-3, 1.12E-3, 1.08E-3, 1.05E-3, 1.02E-3, 9.87E-4, 9.54E-4, 9.22E-4, 8.89E-4, 8.56E-4, 8.24E-4},
105 {34.00, 1.01E-3, 9.81E-4, 9.51E-4, 9.21E-4, 8.90E-4, 8.60E-4, 8.29E-4, 7.98E-4, 7.68E-4, 7.37E-4, 7.06E-4},
106 {36.00, 8.98E-4, 8.70E-4, 8.41E-4, 8.12E-4, 7.83E-4, 7.54E-4, 7.25E-4, 6.96E-4, 6.67E-4, 6.38E-4, 6.09E-4}};
111 {-3.00, 7.01E-4, 7.18E-4, 7.34E-4, 7.52E-4, 7.69E-4, 7.87E-4, 8.06E-4, 8.25E-4, 8.44E-4, 8.64E-4, 8.84E-4},
112 {-2.50, 1.00E-2, 1.02E-2, 1.05E-2, 1.07E-2, 1.09E-2, 1.12E-2, 1.14E-2, 1.16E-2, 1.19E-2, 1.21E-2, 1.24E-2},
113 {-2.00, 4.58E-2, 4.67E-2, 4.76E-2, 4.85E-2, 4.94E-2, 5.04E-2, 5.13E-2, 5.23E-2, 5.33E-2, 5.44E-2, 5.54E-2},
114 {-1.50, 1.05E-1, 1.06E-1, 1.08E-1, 1.10E-1, 1.12E-1, 1.14E-1, 1.16E-1, 1.18E-1, 1.20E-1, 1.22E-1, 1.24E-1},
115 {-1.00, 1.58E-1, 1.60E-1, 1.62E-1, 1.65E-1, 1.67E-1, 1.70E-1, 1.73E-1, 1.75E-1, 1.78E-1, 1.81E-1, 1.83E-1},
116 {-0.50, 1.85E-1, 1.87E-1, 1.89E-1, 1.92E-1, 1.95E-1, 1.97E-1, 2.00E-1, 2.02E-1, 2.05E-1, 2.08E-1, 2.10E-1},
117 { 0.00, 1.86E-1, 1.88E-1, 1.90E-1, 1.92E-1, 1.95E-1, 1.97E-1, 1.99E-1, 2.01E-1, 2.03E-1, 2.06E-1, 2.08E-1},
118 { 0.50, 1.72E-1, 1.74E-1, 1.75E-1, 1.77E-1, 1.78E-1, 1.80E-1, 1.82E-1, 1.83E-1, 1.85E-1, 1.87E-1, 1.88E-1},
119 { 1.00, 1.51E-1, 1.52E-1, 1.53E-1, 1.54E-1, 1.55E-1, 1.57E-1, 1.58E-1, 1.59E-1, 1.60E-1, 1.61E-1, 1.62E-1},
120 { 1.50, 1.29E-1, 1.30E-1, 1.31E-1, 1.31E-1, 1.32E-1, 1.33E-1, 1.33E-1, 1.34E-1, 1.34E-1, 1.35E-1, 1.36E-1},
121 { 2.00, 1.09E-1, 1.10E-1, 1.10E-1, 1.10E-1, 1.11E-1, 1.11E-1, 1.11E-1, 1.11E-1, 1.12E-1, 1.12E-1, 1.12E-1},
122 { 2.50, 9.18E-2, 9.19E-2, 9.20E-2, 9.21E-2, 9.22E-2, 9.22E-2, 9.23E-2, 9.23E-2, 9.23E-2, 9.24E-2, 9.24E-2},
123 { 3.00, 7.73E-2, 7.72E-2, 7.71E-2, 7.70E-2, 7.69E-2, 7.68E-2, 7.67E-2, 7.66E-2, 7.64E-2, 7.62E-2, 7.61E-2},
124 { 3.50, 6.53E-2, 6.51E-2, 6.49E-2, 6.47E-2, 6.45E-2, 6.42E-2, 6.40E-2, 6.37E-2, 6.34E-2, 6.32E-2, 6.29E-2},
125 { 4.00, 5.54E-2, 5.52E-2, 5.49E-2, 5.46E-2, 5.43E-2, 5.40E-2, 5.36E-2, 5.33E-2, 5.29E-2, 5.26E-2, 5.22E-2},
126 { 4.50, 4.74E-2, 4.71E-2, 4.67E-2, 4.64E-2, 4.60E-2, 4.56E-2, 4.53E-2, 4.49E-2, 4.45E-2, 4.40E-2, 4.36E-2},
127 { 5.00, 4.08E-2, 4.04E-2, 4.00E-2, 3.96E-2, 3.92E-2, 3.88E-2, 3.84E-2, 3.80E-2, 3.76E-2, 3.71E-2, 3.67E-2},
128 { 5.50, 3.53E-2, 3.49E-2, 3.45E-2, 3.41E-2, 3.37E-2, 3.33E-2, 3.28E-2, 3.24E-2, 3.19E-2, 3.15E-2, 3.10E-2},
129 { 6.00, 3.08E-2, 3.04E-2, 3.00E-2, 2.95E-2, 2.91E-2, 2.87E-2, 2.82E-2, 2.78E-2, 2.73E-2, 2.69E-2, 2.64E-2},
130 { 6.50, 2.70E-2, 2.66E-2, 2.62E-2, 2.57E-2, 2.53E-2, 2.49E-2, 2.44E-2, 2.40E-2, 2.35E-2, 2.31E-2, 2.26E-2},
131 { 7.00, 2.38E-2, 2.34E-2, 2.30E-2, 2.26E-2, 2.21E-2, 2.17E-2, 2.13E-2, 2.08E-2, 2.04E-2, 1.99E-2, 1.94E-2},
132 { 7.50, 2.11E-2, 2.07E-2, 2.03E-2, 1.99E-2, 1.95E-2, 1.90E-2, 1.86E-2, 1.82E-2, 1.77E-2, 1.73E-2, 1.68E-2},
133 { 8.00, 1.88E-2, 1.84E-2, 1.80E-2, 1.76E-2, 1.72E-2, 1.68E-2, 1.64E-2, 1.59E-2, 1.55E-2, 1.51E-2, 1.46E-2},
134 { 8.50, 1.69E-2, 1.65E-2, 1.61E-2, 1.57E-2, 1.53E-2, 1.49E-2, 1.45E-2, 1.40E-2, 1.36E-2, 1.32E-2, 1.27E-2},
135 { 9.00, 1.52E-2, 1.48E-2, 1.44E-2, 1.40E-2, 1.36E-2, 1.32E-2, 1.28E-2, 1.24E-2, 1.20E-2, 1.16E-2, 1.12E-2},
136 { 9.50, 1.37E-2, 1.34E-2, 1.30E-2, 1.26E-2, 1.22E-2, 1.18E-2, 1.14E-2, 1.10E-2, 1.06E-2, 1.02E-2, 9.81E-3},
137 {10.00, 1.25E-2, 1.21E-2, 1.17E-2, 1.14E-2, 1.10E-2, 1.06E-2, 1.02E-2, 9.84E-3, 9.45E-3, 9.05E-3, 8.65E-3},
138 {11.00, 1.04E-2, 1.00E-2, 9.70E-3, 9.35E-3, 8.99E-3, 8.63E-3, 8.27E-3, 7.91E-3, 7.54E-3, 7.17E-3, 6.79E-3},
139 {12.00, 8.77E-3, 8.44E-3, 8.11E-3, 7.78E-3, 7.45E-3, 7.11E-3, 6.77E-3, 6.43E-3, 6.08E-3, 5.73E-3, 5.38E-3},
140 {13.00, 7.49E-3, 7.18E-3, 6.87E-3, 6.56E-3, 6.24E-3, 5.92E-3, 5.60E-3, 5.28E-3, 4.95E-3, 4.63E-3, 4.30E-3},
141 {14.00, 6.46E-3, 6.17E-3, 5.87E-3, 5.58E-3, 5.28E-3, 4.98E-3, 4.68E-3, 4.38E-3, 4.07E-3, 3.76E-3, 3.45E-3},
142 {15.00, 5.62E-3, 5.35E-3, 5.07E-3, 4.79E-3, 4.51E-3, 4.22E-3, 3.94E-3, 3.65E-3, 3.36E-3, 3.07E-3, 2.78E-3},
143 {16.00, 4.93E-3, 4.67E-3, 4.41E-3, 4.14E-3, 3.88E-3, 3.61E-3, 3.34E-3, 3.07E-3, 2.80E-3, 2.52E-3, 2.24E-3},
144 {17.00, 4.36E-3, 4.11E-3, 3.86E-3, 3.61E-3, 3.36E-3, 3.10E-3, 2.85E-3, 2.59E-3, 2.33E-3, 2.07E-3, 1.81E-3},
145 {18.00, 3.88E-3, 3.65E-3, 3.41E-3, 3.17E-3, 2.93E-3, 2.69E-3, 2.44E-3, 2.20E-3, 1.95E-3, 1.71E-3, 1.46E-3},
146 {19.00, 3.48E-3, 3.25E-3, 3.02E-3, 2.79E-3, 2.57E-3, 2.34E-3, 2.10E-3, 1.87E-3, 1.64E-3, 1.41E-3, 1.17E-3},
147 {20.00, 3.13E-3, 2.91E-3, 2.70E-3, 2.48E-3, 2.26E-3, 2.04E-3, 1.82E-3, 1.60E-3, 1.38E-3, 1.16E-3, 9.32E-4},
148 {22.00, 2.57E-3, 2.37E-3, 2.17E-3, 1.98E-3, 1.78E-3, 1.58E-3, 1.37E-3, 1.17E-3, 9.71E-4, 7.69E-4, 5.66E-4},
149 {24.00, 1.97E-3, 1.80E-3, 1.63E-3, 1.47E-3, 1.30E-3, 1.13E-3, 9.69E-4, 8.04E-4, 6.39E-4, 4.75E-4, 3.11E-4},
150 {26.00, 1.14E-3, 1.04E-3, 9.34E-4, 8.32E-4, 7.31E-4, 6.31E-4, 5.34E-4, 4.38E-4, 3.44E-4, 2.52E-4, 1.61E-4},
151 {28.00, 6.50E-4, 5.85E-4, 5.22E-4, 4.61E-4, 4.02E-4, 3.45E-4, 2.89E-4, 2.35E-4, 1.84E-4, 1.34E-4, 8.60E-5},
152 {30.00, 3.95E-4, 3.53E-4, 3.12E-4, 2.73E-4, 2.36E-4, 2.00E-4, 1.66E-4, 1.34E-4, 1.03E-4, 7.46E-5, 4.76E-5}};
157 {-3.50, 0, 0, 0, 0, 0, 0, 1.01E-5, 1.06E-5, 1.10E-5, 1.14E-5, 1.19E-5},
158 {-3.00, 7.23E-4, 7.50E-4, 7.78E-4, 8.07E-4, 8.37E-4, 8.68E-4, 9.00E-4, 9.34E-4, 9.69E-4, 1.01E-3, 1.04E-3},
159 {-2.50, 1.03E-2, 1.07E-2, 1.10E-2, 1.14E-2, 1.18E-2, 1.22E-2, 1.26E-2, 1.30E-2, 1.35E-2, 1.39E-2, 1.44E-2},
160 {-2.00, 4.72E-2, 4.86E-2, 5.00E-2, 5.15E-2, 5.31E-2, 5.47E-2, 5.63E-2, 5.80E-2, 5.98E-2, 6.15E-2, 6.34E-2},
161 {-1.50, 1.08E-1, 1.11E-1, 1.14E-1, 1.17E-1, 1.20E-1, 1.23E-1, 1.26E-1, 1.29E-1, 1.33E-1, 1.36E-1, 1.40E-1},
162 {-1.00, 1.62E-1, 1.66E-1, 1.70E-1, 1.74E-1, 1.78E-1, 1.82E-1, 1.86E-1, 1.90E-1, 1.94E-1, 1.99E-1, 2.03E-1},
163 {-0.50, 1.90E-1, 1.94E-1, 1.98E-1, 2.01E-1, 2.05E-1, 2.09E-1, 2.13E-1, 2.17E-1, 2.21E-1, 2.25E-1, 2.30E-1},
164 { 0.00, 1.92E-1, 1.95E-1, 1.98E-1, 2.01E-1, 2.04E-1, 2.07E-1, 2.10E-1, 2.14E-1, 2.17E-1, 2.20E-1, 2.23E-1},
165 { 0.50, 1.77E-1, 1.79E-1, 1.82E-1, 1.84E-1, 1.86E-1, 1.88E-1, 1.90E-1, 1.92E-1, 1.95E-1, 1.97E-1, 1.99E-1},
166 { 1.00, 1.56E-1, 1.57E-1, 1.58E-1, 1.60E-1, 1.61E-1, 1.62E-1, 1.64E-1, 1.65E-1, 1.66E-1, 1.67E-1, 1.68E-1},
167 { 1.50, 1.33E-1, 1.34E-1, 1.35E-1, 1.35E-1, 1.36E-1, 1.36E-1, 1.37E-1, 1.37E-1, 1.38E-1, 1.38E-1, 1.39E-1},
168 { 2.00, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1, 1.13E-1},
169 { 2.50, 9.46E-2, 9.44E-2, 9.42E-2, 9.40E-2, 9.37E-2, 9.33E-2, 9.30E-2, 9.26E-2, 9.21E-2, 9.16E-2, 9.11E-2},
170 { 3.00, 7.96E-2, 7.92E-2, 7.87E-2, 7.82E-2, 7.77E-2, 7.71E-2, 7.65E-2, 7.58E-2, 7.51E-2, 7.44E-2, 7.36E-2},
171 { 3.50, 6.73E-2, 6.67E-2, 6.60E-2, 6.53E-2, 6.46E-2, 6.39E-2, 6.31E-2, 6.23E-2, 6.14E-2, 6.06E-2, 5.96E-2},
172 { 4.00, 5.71E-2, 5.64E-2, 5.57E-2, 5.49E-2, 5.41E-2, 5.32E-2, 5.23E-2, 5.14E-2, 5.05E-2, 4.95E-2, 4.85E-2},
173 { 4.50, 4.88E-2, 4.80E-2, 4.72E-2, 4.64E-2, 4.55E-2, 4.46E-2, 4.37E-2, 4.27E-2, 4.17E-2, 4.07E-2, 3.97E-2},
174 { 5.00, 4.20E-2, 4.12E-2, 4.03E-2, 3.95E-2, 3.86E-2, 3.76E-2, 3.67E-2, 3.57E-2, 3.47E-2, 3.36E-2, 3.26E-2},
175 { 5.50, 3.64E-2, 3.55E-2, 3.47E-2, 3.38E-2, 3.29E-2, 3.19E-2, 3.10E-2, 3.00E-2, 2.90E-2, 2.80E-2, 2.69E-2},
176 { 6.00, 3.17E-2, 3.09E-2, 3.00E-2, 2.91E-2, 2.82E-2, 2.73E-2, 2.63E-2, 2.53E-2, 2.44E-2, 2.33E-2, 2.23E-2},
177 { 6.50, 2.78E-2, 2.70E-2, 2.61E-2, 2.52E-2, 2.43E-2, 2.34E-2, 2.25E-2, 2.15E-2, 2.06E-2, 1.96E-2, 1.86E-2},
178 { 7.00, 2.45E-2, 2.37E-2, 2.29E-2, 2.20E-2, 2.11E-2, 2.02E-2, 1.93E-2, 1.84E-2, 1.74E-2, 1.65E-2, 1.55E-2},
179 { 7.50, 2.18E-2, 2.09E-2, 2.01E-2, 1.93E-2, 1.84E-2, 1.76E-2, 1.67E-2, 1.58E-2, 1.49E-2, 1.39E-2, 1.30E-2},
180 { 8.00, 1.94E-2, 1.86E-2, 1.78E-2, 1.70E-2, 1.62E-2, 1.53E-2, 1.45E-2, 1.36E-2, 1.27E-2, 1.18E-2, 1.09E-2},
181 { 8.50, 1.74E-2, 1.66E-2, 1.58E-2, 1.50E-2, 1.42E-2, 1.34E-2, 1.26E-2, 1.18E-2, 1.09E-2, 1.00E-2, 9.17E-3},
182 { 9.00, 1.57E-2, 1.49E-2, 1.41E-2, 1.34E-2, 1.26E-2, 1.18E-2, 1.10E-2, 1.02E-2, 9.39E-3, 8.56E-3, 7.72E-3},
183 { 9.50, 1.42E-2, 1.34E-2, 1.27E-2, 1.19E-2, 1.12E-2, 1.04E-2, 9.66E-3, 8.88E-3, 8.09E-3, 7.30E-3, 6.49E-3},
184 {10.00, 1.28E-2, 1.21E-2, 1.14E-2, 1.07E-2, 9.99E-3, 9.25E-3, 8.51E-3, 7.75E-3, 6.99E-3, 6.23E-3, 5.45E-3},
185 {11.00, 1.07E-2, 1.00E-2, 9.37E-3, 8.70E-3, 8.02E-3, 7.34E-3, 6.64E-3, 5.95E-3, 5.24E-3, 4.53E-3, 3.81E-3},
186 {12.00, 9.01E-3, 8.39E-3, 7.77E-3, 7.14E-3, 6.50E-3, 5.87E-3, 5.22E-3, 4.58E-3, 3.93E-3, 3.27E-3, 2.61E-3},
187 {13.00, 7.35E-3, 6.79E-3, 6.23E-3, 5.68E-3, 5.12E-3, 4.55E-3, 3.99E-3, 3.43E-3, 2.86E-3, 2.29E-3, 1.73E-3},
188 {14.00, 5.54E-3, 5.08E-3, 4.63E-3, 4.18E-3, 3.73E-3, 3.28E-3, 2.84E-3, 2.40E-3, 1.97E-3, 1.54E-3, 1.11E-3},
189 {15.00, 3.97E-3, 3.61E-3, 3.27E-3, 2.92E-3, 2.59E-3, 2.26E-3, 1.93E-3, 1.62E-3, 1.31E-3, 1.00E-3, 7.05E-4},
190 {16.00, 2.81E-3, 2.54E-3, 2.28E-3, 2.02E-3, 1.78E-3, 1.54E-3, 1.30E-3, 1.08E-3, 8.59E-4, 6.49E-4, 4.48E-4},
191 {17.00, 2.00E-3, 1.80E-3, 1.60E-3, 1.41E-3, 1.23E-3, 1.05E-3, 8.82E-4, 7.21E-4, 5.68E-4, 4.23E-4, 2.86E-4},
192 {18.00, 1.44E-3, 1.29E-3, 1.14E-3, 9.93E-4, 8.56E-4, 7.26E-4, 6.03E-4, 4.87E-4, 3.79E-4, 2.77E-4, 1.83E-4},
193 {19.00, 1.05E-3, 9.32E-4, 8.17E-4, 7.08E-4, 6.05E-4, 5.08E-4, 4.17E-4, 3.33E-4, 2.55E-4, 1.83E-4, 1.18E-4},
194 {20.00, 7.77E-4, 6.83E-4, 5.94E-4, 5.10E-4, 4.32E-4, 3.59E-4, 2.91E-4, 2.29E-4, 1.72E-4, 1.21E-4, 7.58E-5},
195 {22.00, 4.31E-4, 3.73E-4, 3.20E-4, 2.70E-4, 2.24E-4, 1.82E-4, 1.44E-4, 1.10E-4, 7.96E-5, 5.34E-5, 3.11E-5},
196 {24.00, 2.40E-4, 2.05E-4, 1.73E-4, 1.43E-4, 1.16E-4, 9.22E-5, 7.08E-5, 5.32E-5, 3.63E-5, 2.30E-5, 1.23E-5},
197 {26.00, 1.30E-4, 1.09E-4, 9.02E-5, 7.34E-5, 5.84E-5, 4.51E-5, 3.37E-5, 2.39E-5, 1.58E-5, 0, 0}};
202 {-3.50, 0, 0, 0, 0, 1.00E-5, 1.06E-5, 1.11E-5, 1.18E-5, 1.24E-5, 1.31E-5, 1.38E-5},
203 {-3.00, 7.45E-4, 7.82E-4, 8.21E-4, 8.62E-4, 9.05E-4, 9.50E-4, 9.98E-4, 1.05E-3, 1.10E-3, 1.15E-3, 1.21E-3},
204 {-2.50, 1.07E-2, 1.11E-2, 1.16E-2, 1.21E-2, 1.27E-2, 1.33E-2, 1.38E-2, 1.45E-2, 1.51E-2, 1.58E-2, 1.65E-2},
205 {-2.00, 4.86E-2, 5.05E-2, 5.25E-2, 5.46E-2, 5.67E-2, 5.90E-2, 6.13E-2, 6.37E-2, 6.62E-2, 6.88E-2, 7.15E-2},
206 {-1.50, 1.11E-1, 1.15E-1, 1.19E-1, 1.23E-1, 1.27E-1, 1.32E-1, 1.36E-1, 1.41E-1, 1.45E-1, 1.50E-1, 1.55E-1},
207 {-1.00, 1.67E-1, 1.72E-1, 1.77E-1, 1.82E-1, 1.88E-1, 1.93E-1, 1.99E-1, 2.04E-1, 2.10E-1, 2.16E-1, 2.22E-1},
208 {-0.50, 1.96E-1, 2.01E-1, 2.06E-1, 2.10E-1, 2.15E-1, 2.20E-1, 2.26E-1, 2.31E-1, 2.36E-1, 2.42E-1, 2.47E-1},
209 { 0.00, 1.98E-1, 2.01E-1, 2.05E-1, 2.09E-1, 2.13E-1, 2.17E-1, 2.21E-1, 2.25E-1, 2.28E-1, 2.32E-1, 2.37E-1},
210 { 0.50, 1.83E-1, 1.85E-1, 1.88E-1, 1.90E-1, 1.93E-1, 1.95E-1, 1.98E-1, 2.00E-1, 2.02E-1, 2.05E-1, 2.07E-1},
211 { 1.00, 1.60E-1, 1.62E-1, 1.63E-1, 1.65E-1, 1.66E-1, 1.67E-1, 1.68E-1, 1.69E-1, 1.70E-1, 1.71E-1, 1.72E-1},
212 { 1.50, 1.37E-1, 1.38E-1, 1.38E-1, 1.39E-1, 1.39E-1, 1.39E-1, 1.39E-1, 1.39E-1, 1.39E-1, 1.39E-1, 1.39E-1},
213 { 2.00, 1.16E-1, 1.16E-1, 1.16E-1, 1.15E-1, 1.15E-1, 1.14E-1, 1.14E-1, 1.13E-1, 1.13E-1, 1.12E-1, 1.11E-1},
214 { 2.50, 9.75E-2, 9.69E-2, 9.62E-2, 9.54E-2, 9.45E-2, 9.36E-2, 9.26E-2, 9.16E-2, 9.04E-2, 8.92E-2, 8.79E-2},
215 { 3.00, 8.21E-2, 8.11E-2, 8.01E-2, 7.90E-2, 7.79E-2, 7.66E-2, 7.54E-2, 7.40E-2, 7.26E-2, 7.10E-2, 6.94E-2},
216 { 3.50, 6.93E-2, 6.82E-2, 6.70E-2, 6.57E-2, 6.43E-2, 6.29E-2, 6.15E-2, 5.99E-2, 5.83E-2, 5.67E-2, 5.49E-2},
217 { 4.00, 5.89E-2, 5.76E-2, 5.63E-2, 5.49E-2, 5.34E-2, 5.19E-2, 5.04E-2, 4.88E-2, 4.71E-2, 4.53E-2, 4.35E-2},
218 { 4.50, 5.03E-2, 4.90E-2, 4.76E-2, 4.61E-2, 4.46E-2, 4.31E-2, 4.15E-2, 3.99E-2, 3.82E-2, 3.64E-2, 3.46E-2},
219 { 5.00, 4.33E-2, 4.19E-2, 4.05E-2, 3.90E-2, 3.75E-2, 3.60E-2, 3.44E-2, 3.27E-2, 3.11E-2, 2.93E-2, 2.75E-2},
220 { 5.50, 3.75E-2, 3.61E-2, 3.47E-2, 3.32E-2, 3.17E-2, 3.02E-2, 2.86E-2, 2.70E-2, 2.54E-2, 2.37E-2, 2.20E-2},
221 { 6.00, 3.27E-2, 3.13E-2, 2.99E-2, 2.85E-2, 2.70E-2, 2.55E-2, 2.40E-2, 2.24E-2, 2.08E-2, 1.92E-2, 1.75E-2},
222 { 6.50, 2.86E-2, 2.73E-2, 2.59E-2, 2.45E-2, 2.31E-2, 2.17E-2, 2.02E-2, 1.87E-2, 1.71E-2, 1.55E-2, 1.39E-2},
223 { 7.00, 2.53E-2, 2.40E-2, 2.26E-2, 2.13E-2, 1.99E-2, 1.85E-2, 1.70E-2, 1.56E-2, 1.41E-2, 1.26E-2, 1.11E-2},
224 { 7.50, 2.24E-2, 2.11E-2, 1.98E-2, 1.85E-2, 1.72E-2, 1.58E-2, 1.45E-2, 1.31E-2, 1.16E-2, 1.02E-2, 8.73E-3},
225 { 8.00, 1.98E-2, 1.86E-2, 1.74E-2, 1.61E-2, 1.48E-2, 1.35E-2, 1.22E-2, 1.09E-2, 9.57E-3, 8.21E-3, 6.83E-3},
226 { 8.50, 1.74E-2, 1.62E-2, 1.51E-2, 1.39E-2, 1.27E-2, 1.15E-2, 1.03E-2, 9.04E-3, 7.80E-3, 6.55E-3, 5.29E-3},
227 { 9.00, 1.50E-2, 1.39E-2, 1.28E-2, 1.18E-2, 1.07E-2, 9.57E-3, 8.47E-3, 7.37E-3, 6.27E-3, 5.16E-3, 4.06E-3},
228 { 9.50, 1.27E-2, 1.17E-2, 1.07E-2, 9.77E-3, 8.80E-3, 7.84E-3, 6.88E-3, 5.92E-3, 4.97E-3, 4.03E-3, 3.09E-3},
229 {10.00, 1.05E-2, 9.69E-3, 8.84E-3, 7.99E-3, 7.16E-3, 6.33E-3, 5.51E-3, 4.70E-3, 3.90E-3, 3.11E-3, 2.33E-3},
230 {11.00, 7.12E-3, 6.48E-3, 5.85E-3, 5.23E-3, 4.62E-3, 4.03E-3, 3.45E-3, 2.89E-3, 2.35E-3, 1.83E-3, 1.32E-3},
231 {12.00, 4.77E-3, 4.30E-3, 3.84E-3, 3.39E-3, 2.96E-3, 2.54E-3, 2.15E-3, 1.77E-3, 1.41E-3, 1.07E-3, 7.45E-4},
232 {13.00, 3.21E-3, 2.86E-3, 2.53E-3, 2.21E-3, 1.90E-3, 1.61E-3, 1.34E-3, 1.08E-3, 8.42E-4, 6.21E-4, 4.18E-4},
233 {14.00, 2.18E-3, 1.92E-3, 1.68E-3, 1.45E-3, 1.23E-3, 1.03E-3, 8.38E-4, 6.65E-4, 5.06E-4, 3.62E-4, 2.34E-4},
234 {15.00, 1.49E-3, 1.30E-3, 1.12E-3, 9.53E-4, 7.99E-4, 6.57E-4, 5.27E-4, 4.09E-4, 3.04E-4, 2.11E-4, 1.30E-4},
235 {16.00, 1.01E-3, 8.76E-4, 7.47E-4, 6.28E-4, 5.19E-4, 4.20E-4, 3.31E-4, 2.51E-4, 1.81E-4, 1.21E-4, 7.12E-5},
236 {17.00, 6.88E-4, 5.88E-4, 4.96E-4, 4.12E-4, 3.35E-4, 2.67E-4, 2.06E-4, 1.53E-4, 1.07E-4, 6.91E-5, 3.84E-5},
237 {18.00, 4.60E-4, 3.89E-4, 3.24E-4, 2.66E-4, 2.13E-4, 1.67E-4, 1.26E-4, 9.16E-5, 6.25E-5, 3.87E-5, 2.03E-5},
238 {19.00, 3.01E-4, 2.52E-4, 2.07E-4, 1.68E-4, 1.33E-4, 1.02E-4, 7.62E-5, 5.41E-5, 3.59E-5, 2.15E-5, 1.07E-5},
239 {20.00, 1.94E-4, 1.61E-4, 1.31E-4, 1.05E-4, 8.18E-5, 6.21E-5, 4.55E-5, 3.17E-5, 2.06E-5, 1.20E-5, 0},
240 {22.00, 7.97E-5, 6.47E-5, 5.16E-5, 4.03E-5, 3.07E-5, 2.27E-5, 1.61E-5, 1.08E-5, 0, 0, 0},
241 {24.00, 3.26E-5, 2.59E-5, 2.01E-5, 1.53E-5, 1.12E-5, 0, 0, 0, 0, 0, 0},
242 {26.00, 1.33E-5, 1.04E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
247 {-3.50, 1.07E-5, 1.26E-5, 1.47E-5, 1.73E-5, 2.03E-5, 2.37E-5, 2.78E-5, 3.26E-5, 3.82E-5, 4.48E-5, 5.24E-5},
248 {-3.00, 1.00E-3, 1.16E-3, 1.33E-3, 1.53E-3, 1.75E-3, 2.02E-3, 2.32E-3, 2.66E-3, 3.05E-3, 3.50E-3, 4.02E-3},
249 {-2.50, 1.44E-2, 1.62E-2, 1.83E-2, 2.06E-2, 2.32E-2, 2.61E-2, 2.93E-2, 3.30E-2, 3.71E-2, 4.17E-2, 4.68E-2},
250 {-2.00, 6.56E-2, 7.25E-2, 8.00E-2, 8.83E-2, 9.74E-2, 1.07E-1, 1.18E-1, 1.30E-1, 1.43E-1, 1.57E-1, 1.73E-1},
251 {-1.50, 1.50E-1, 1.62E-1, 1.76E-1, 1.90E-1, 2.05E-1, 2.21E-1, 2.38E-1, 2.57E-1, 2.76E-1, 2.97E-1, 3.19E-1},
252 {-1.00, 2.26E-1, 2.40E-1, 2.54E-1, 2.69E-1, 2.84E-1, 3.00E-1, 3.16E-1, 3.32E-1, 3.49E-1, 3.66E-1, 3.83E-1},
253 {-0.50, 2.65E-1, 2.75E-1, 2.85E-1, 2.96E-1, 3.05E-1, 3.15E-1, 3.24E-1, 3.32E-1, 3.40E-1, 3.47E-1, 3.52E-1},
254 { 0.00, 2.66E-1, 2.71E-1, 2.76E-1, 2.79E-1, 2.82E-1, 2.84E-1, 2.84E-1, 2.84E-1, 2.82E-1, 2.78E-1, 2.73E-1},
255 { 0.50, 2.44E-1, 2.43E-1, 2.42E-1, 2.39E-1, 2.36E-1, 2.31E-1, 2.25E-1, 2.18E-1, 2.09E-1, 1.99E-1, 1.88E-1},
256 { 1.00, 2.07E-1, 2.03E-1, 1.97E-1, 1.91E-1, 1.83E-1, 1.75E-1, 1.65E-1, 1.55E-1, 1.44E-1, 1.31E-1, 1.18E-1},
257 { 1.50, 1.66E-1, 1.59E-1, 1.51E-1, 1.43E-1, 1.34E-1, 1.24E-1, 1.14E-1, 1.03E-1, 9.21E-2, 8.05E-2, 6.86E-2},
258 { 2.00, 1.26E-1, 1.18E-1, 1.10E-1, 1.01E-1, 9.25E-2, 8.35E-2, 7.43E-2, 6.51E-2, 5.58E-2, 4.66E-2, 3.75E-2},
259 { 2.50, 9.11E-2, 8.37E-2, 7.62E-2, 6.87E-2, 6.11E-2, 5.36E-2, 4.63E-2, 3.91E-2, 3.22E-2, 2.56E-2, 1.94E-2},
260 { 3.00, 6.35E-2, 5.71E-2, 5.09E-2, 4.48E-2, 3.88E-2, 3.31E-2, 2.77E-2, 2.26E-2, 1.78E-2, 1.35E-2, 9.60E-3},
261 { 3.50, 4.28E-2, 3.77E-2, 3.29E-2, 2.82E-2, 2.39E-2, 1.98E-2, 1.60E-2, 1.26E-2, 9.52E-3, 6.84E-3, 4.55E-3},
262 { 4.00, 2.79E-2, 2.41E-2, 2.06E-2, 1.72E-2, 1.42E-2, 1.14E-2, 8.95E-3, 6.78E-3, 4.91E-3, 3.35E-3, 2.08E-3},
263 { 4.50, 1.77E-2, 1.50E-2, 1.25E-2, 1.02E-2, 8.21E-3, 6.42E-3, 4.87E-3, 3.55E-3, 2.46E-3, 1.59E-3, 9.22E-4},
264 { 5.00, 1.10E-2, 9.10E-3, 7.42E-3, 5.93E-3, 4.63E-3, 3.51E-3, 2.58E-3, 1.81E-3, 1.20E-3, 7.34E-4, 3.96E-4},
265 { 5.50, 6.65E-3, 5.39E-3, 4.30E-3, 3.35E-3, 2.55E-3, 1.88E-3, 1.33E-3, 9.02E-4, 5.72E-4, 3.30E-4, 1.66E-4},
266 { 6.00, 3.93E-3, 3.13E-3, 2.44E-3, 1.85E-3, 1.37E-3, 9.83E-4, 6.75E-4, 4.39E-4, 2.66E-4, 1.45E-4, 6.73E-5},
267 { 6.50, 2.28E-3, 1.78E-3, 1.35E-3, 1.01E-3, 7.25E-4, 5.04E-4, 3.34E-4, 2.09E-4, 1.21E-4, 6.24E-5, 2.68E-5},
268 { 7.00, 1.30E-3, 9.91E-4, 7.38E-4, 5.35E-4, 3.76E-4, 2.53E-4, 1.63E-4, 9.79E-5, 5.41E-5, 2.64E-5, 1.05E-5},
269 { 7.50, 7.27E-4, 5.43E-4, 3.96E-4, 2.80E-4, 1.91E-4, 1.25E-4, 7.76E-5, 4.49E-5, 2.36E-5, 1.08E-5, 0},
270 { 8.00, 4.00E-4, 2.92E-4, 2.08E-4, 1.44E-4, 9.57E-5, 6.08E-5, 3.64E-5, 2.02E-5, 1.02E-5, 0, 0},
271 { 8.50, 2.17E-4, 1.55E-4, 1.08E-4, 7.29E-5, 4.72E-5, 2.91E-5, 1.69E-5, 0, 0, 0, 0},
272 { 9.00, 1.16E-4, 8.12E-5, 5.53E-5, 3.63E-5, 2.29E-5, 1.37E-5, 0, 0, 0, 0, 0},
273 { 9.50, 6.08E-5, 4.19E-5, 2.79E-5, 1.79E-5, 1.09E-5, 0, 0, 0, 0, 0, 0},
274 {10.00, 3.17E-5, 2.13E-5, 1.39E-5, 0, 0, 0, 0, 0, 0, 0, 0}};
279 {-3.50, 1.44E-5, 1.83E-5, 2.32E-5, 2.95E-5, 3.75E-5, 4.76E-5, 6.04E-5, 7.69E-5, 9.72E-5, 1.23E-4, 1.56E-4},
280 {-3.00, 1.36E-3, 1.67E-3, 2.04E-3, 2.50E-3, 3.07E-3, 3.76E-3, 4.60E-3, 5.62E-3, 6.87E-3, 8.39E-3, 1.02E-2},
281 {-2.50, 1.94E-2, 2.30E-2, 2.72E-2, 3.22E-2, 3.81E-2, 4.49E-2, 5.29E-2, 6.23E-2, 7.33E-2, 8.61E-2, 1.01E-1},
282 {-2.00, 8.86E-2, 1.01E-1, 1.16E-1, 1.32E-1, 1.50E-1, 1.71E-1, 1.94E-1, 2.19E-1, 2.47E-1, 2.79E-1, 3.13E-1},
283 {-1.50, 2.02E-1, 2.23E-1, 2.46E-1, 2.70E-1, 2.96E-1, 3.23E-1, 3.52E-1, 3.82E-1, 4.13E-1, 4.44E-1, 4.76E-1},
284 {-1.00, 3.03E-1, 3.23E-1, 3.42E-1, 3.62E-1, 3.81E-1, 3.99E-1, 4.15E-1, 4.30E-1, 4.42E-1, 4.52E-1, 4.57E-1},
285 {-0.50, 3.44E-1, 3.54E-1, 3.62E-1, 3.68E-1, 3.71E-1, 3.72E-1, 3.70E-1, 3.64E-1, 3.54E-1, 3.40E-1, 3.22E-1},
286 { 0.00, 3.23E-1, 3.20E-1, 3.15E-1, 3.09E-1, 2.97E-1, 2.85E-1, 2.69E-1, 2.51E-1, 2.30E-1, 2.07E-1, 1.81E-1},
287 { 0.50, 2.60E-1, 2.49E-1, 2.36E-1, 2.21E-1, 2.05E-1, 1.87E-1, 1.68E-1, 1.48E-1, 1.27E-1, 1.06E-1, 8.54E-2},
288 { 1.00, 1.86E-1, 1.72E-1, 1.57E-1, 1.41E-1, 1.25E-1, 1.09E-1, 9.28E-2, 7.71E-2, 6.20E-2, 4.79E-2, 3.50E-2},
289 { 1.50, 1.21E-1, 1.08E-1, 9.44E-2, 8.15E-2, 6.91E-2, 5.73E-2, 4.63E-2, 3.62E-2, 2.72E-2, 1.93E-2, 1.28E-2},
290 { 2.00, 7.20E-2, 6.19E-2, 5.22E-2, 4.33E-2, 3.51E-2, 2.79E-2, 2.11E-2, 1.55E-2, 1.09E-2, 7.11E-3, 4.22E-3},
291 { 2.50, 3.99E-2, 3.31E-2, 2.69E-2, 2.13E-2, 1.65E-2, 1.24E-2, 8.97E-3, 6.19E-3, 4.02E-3, 2.41E-3, 1.28E-3},
292 { 3.00, 2.07E-2, 1.66E-2, 1.30E-2, 9.87E-3, 7.30E-3, 5.21E-3, 3.56E-3, 2.31E-3, 1.39E-3, 7.61E-4, 3.60E-4},
293 { 3.50, 1.02E-2, 7.84E-3, 5.89E-3, 4.31E-3, 3.04E-3, 2.06E-3, 1.33E-3, 8.09E-4, 4.52E-4, 2.26E-4, 9.47E-5},
294 { 4.00, 4.74E-3, 3.52E-3, 2.55E-3, 1.78E-3, 1.20E-3, 7.76E-4, 4.73E-4, 2.69E-4, 1.39E-4, 6.32E-5, 2.34E-5},
295 { 4.50, 2.10E-3, 1.51E-3, 1.05E-3, 7.05E-4, 4.54E-4, 2.78E-4, 1.60E-4, 8.52E-5, 4.08E-5, 1.68E-5, 0},
296 { 5.00, 8.98E-4, 6.21E-4, 4.15E-4, 2.67E-4, 1.64E-4, 9.55E-5, 5.19E-5, 2.58E-5, 1.14E-5, 0, 0},
297 { 5.50, 3.68E-4, 2.45E-4, 1.58E-4, 9.71E-5, 5.70E-5, 3.15E-5, 1.61E-5, 0, 0, 0, 0},
298 { 6.00, 1.45E-4, 9.32E-5, 5.76E-5, 3.41E-5, 1.91E-5, 1.00E-5, 0, 0, 0, 0, 0},
299 { 6.50, 5.53E-5, 3.43E-5, 2.04E-5, 1.15E-5, 0, 0, 0, 0, 0, 0, 0},
300 { 7.00, 2.04E-5, 1.22E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
305 {-3.50, 1.94E-5, 2.64E-5, 3.59E-5, 4.87E-5, 6.61E-5, 8.96E-5, 1.21E-4, 1.64E-4, 2.22E-4, 3.00E-4, 4.05E-4},
306 {-3.00, 1.83E-3, 2.37E-3, 3.06E-3, 3.94E-3, 5.08E-3, 6.53E-3, 8.39E-3, 1.08E-2, 1.38E-2, 1.76E-2, 2.25E-2},
307 {-2.50, 2.62E-2, 3.22E-2, 3.95E-2, 4.84E-2, 5.91E-2, 7.20E-2, 8.76E-2, 1.06E-1, 1.28E-1, 1.55E-1, 1.86E-1},
308 {-2.00, 1.19E-1, 1.39E-1, 1.63E-1, 1.89E-1, 2.18E-1, 2.51E-1, 2.88E-1, 3.29E-1, 3.74E-1, 4.23E-1, 4.75E-1},
309 {-1.50, 2.69E-1, 2.99E-1, 3.31E-1, 3.64E-1, 3.97E-1, 4.31E-1, 4.65E-1, 4.97E-1, 5.26E-1, 5.52E-1, 5.73E-1},
310 {-1.00, 3.85E-1, 4.07E-1, 4.26E-1, 4.43E-1, 4.56E-1, 4.66E-1, 4.69E-1, 4.67E-1, 4.58E-1, 4.42E-1, 4.17E-1},
311 {-0.50, 4.01E-1, 4.02E-1, 4.00E-1, 3.92E-1, 3.80E-1, 3.63E-1, 3.42E-1, 3.15E-1, 2.84E-1, 2.49E-1, 2.11E-1},
312 { 0.00, 3.29E-1, 3.13E-1, 2.94E-1, 2.73E-1, 2.49E-1, 2.22E-1, 1.94E-1, 1.65E-1, 1.36E-1, 1.08E-1, 8.10E-2},
313 { 0.50, 2.23E-1, 2.02E-1, 1.80E-1, 1.57E-1, 1.34E-1, 1.12E-1, 9.10E-2, 7.13E-2, 5.35E-2, 3.80E-2, 2.50E-2},
314 { 1.00, 1.29E-1, 1.11E-1, 9.38E-2, 7.73E-2, 6.21E-2, 4.84E-2, 3.64E-2, 2.61E-2, 1.78E-2, 1.12E-2, 6.42E-3},
315 { 1.50, 6.60E-2, 5.39E-2, 4.30E-2, 3.34E-2, 2.51E-2, 1.83E-2, 1.27E-2, 8.37E-3, 5.15E-3, 2.88E-3, 1.42E-3},
316 { 2.00, 3.01E-2, 2.33E-2, 1.76E-2, 1.29E-2, 9.10E-3, 6.15E-3, 3.95E-3, 2.38E-3, 1.32E-3, 6.54E-4, 2.74E-4},
317 { 2.50, 1.24E-2, 9.15E-3, 6.53E-3, 4.50E-3, 2.98E-3, 1.88E-3, 1.11E-3, 6.12E-4, 3.05E-4, 1.33E-4, 4.73E-5},
318 { 3.00, 4.71E-3, 3.29E-3, 2.22E-3, 1.44E-3, 8.94E-4, 5.24E-4, 2.87E-4, 1.44E-4, 6.44E-5, 2.46E-5, 0},
319 { 3.50, 1.65E-3, 1.09E-3, 6.99E-4, 4.28E-4, 2.48E-4, 1.35E-4, 6.81E-5, 3.11E-5, 1.55E-5, 0, 0},
320 { 4.00, 5.38E-4, 3.39E-4, 2.05E-4, 1.18E-4, 6.41E-5, 3.24E-5, 1.51E-5, 0, 0, 0, 0},
321 { 4.50, 1.65E-4, 9.86E-5, 5.64E-5, 3.05E-5, 1.55E-5, 0, 0, 0, 0, 0, 0},
322 { 5.00, 4.75E-5, 2.70E-5, 1.46E-5, 0, 0, 0, 0, 0, 0, 0, 0},
323 { 5.50, 1.30E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
328 {-4.00, 0, 0, 0, 0, 0, 1.32E-5, 3.04E-5, 6.90E-5, 1.55E-4, 3.44E-4, 7.55E-4},
329 {-3.75, 1.38E-5, 2.98E-5, 6.38E-5, 1.35E-4, 2.83E-4, 5.85E-4, 1.20E-3, 2.41E-3, 4.78E-3, 9.35E-3, 1.80E-2},
330 {-3.50, 3.81E-4, 7.44E-4, 1.44E-3, 2.73E-3, 5.12E-3, 9.45E-3, 1.71E-2, 3.05E-2, 5.33E-2, 9.11E-2, 1.52E-1},
331 {-3.25, 4.78E-3, 8.44E-3, 1.47E-2, 2.50E-2, 4.19E-2, 6.88E-2, 1.10E-1, 1.73E-1, 2.64E-1, 3.90E-1, 5.57E-1},
332 {-3.00, 3.19E-2, 5.08E-2, 7.95E-2, 1.22E-1, 1.82E-1, 2.64E-1, 3.73E-1, 5.11E-1, 6.75E-1, 8.56E-1, 1.03E+0},
333 {-2.75, 1.27E-1, 1.83E-1, 2.56E-1, 3.51E-1, 4.66E-1, 6.00E-1, 7.44E-1, 8.86E-1, 1.01E+0, 1.08E+0, 1.09E+0},
334 {-2.50, 3.28E-1, 4.26E-1, 5.38E-1, 6.58E-1, 7.77E-1, 8.81E-1, 9.56E-1, 9.85E-1, 9.56E-1, 8.63E-1, 7.14E-1},
335 {-2.25, 5.89E-1, 6.91E-1, 7.84E-1, 8.57E-1, 8.97E-1, 8.95E-1, 8.46E-1, 7.51E-1, 6.18E-1, 4.65E-1, 3.12E-1},
336 {-2.00, 7.75E-1, 8.22E-1, 8.37E-1, 8.15E-1, 7.56E-1, 6.63E-1, 5.44E-1, 4.14E-1, 2.88E-1, 1.78E-1, 9.59E-2},
337 {-1.75, 7.79E-1, 7.45E-1, 6.81E-1, 5.91E-1, 4.85E-1, 3.73E-1, 2.65E-1, 1.72E-1, 1.01E-1, 5.10E-2, 2.17E-2},
338 {-1.50, 6.16E-1, 5.32E-1, 4.36E-1, 3.38E-1, 2.45E-1, 1.64E-1, 1.01E-1, 5.60E-2, 2.73E-2, 1.13E-2, 3.74E-3},
339 {-1.25, 3.95E-1, 3.07E-1, 2.26E-1, 1.56E-1, 9.96E-2, 5.85E-2, 3.11E-2, 1.46E-2, 5.90E-3, 1.97E-3, 5.05E-4},
340 {-1.00, 2.09E-1, 1.47E-1, 9.68E-2, 5.94E-2, 3.35E-2, 1.72E-2, 7.85E-3, 3.12E-3, 1.05E-3, 2.80E-4, 5.49E-5},
341 {-0.75, 9.33E-2, 5.91E-2, 3.49E-2, 1.91E-2, 9.47E-3, 4.23E-3, 1.66E-3, 5.59E-4, 1.54E-4, 3.29E-5, 0},
342 {-0.50, 3.56E-2, 2.04E-2, 1.08E-2, 5.22E-3, 2.29E-3, 8.90E-4, 3.00E-4, 8.49E-5, 1.93E-5, 0, 0},
343 {-0.25, 1.18E-2, 6.07E-3, 2.88E-3, 1.24E-3, 4.78E-4, 1.62E-4, 4.67E-5, 1.12E-5, 0, 0, 0},
344 { 0.00, 3.41E-3, 1.59E-3, 6.74E-4, 2.58E-4, 8.75E-5, 2.57E-5, 0, 0, 0, 0, 0},
345 { 0.25, 8.74E-4, 3.67E-4, 1.40E-4, 4.74E-5, 1.42E-5, 0, 0, 0, 0, 0, 0},
346 { 0.50, 2.00E-4, 7.57E-5, 2.58E-5, 0, 0, 0, 0, 0, 0, 0, 0},
347 { 0.75, 4.11E-5, 1.41E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
352 {-4.40, 0, 0, 0, 0, 0, 0, 0, 0, 1.11E-5, 3.90E-5, 1.33E-4},
353 {-4.20, 0, 0, 0, 0, 0, 2.17E-5, 6.93E-5, 2.15E-4, 6.46E-4, 1.88E-3, 5.28E-3},
354 {-4.00, 0, 1.07E-5, 3.24E-5, 9.54E-5, 2.73E-4, 7.60E-4, 2.04E-3, 5.31E-3, 1.33E-2, 3.18E-2, 7.28E-2},
355 {-3.80, 1.11E-4, 2.99E-4, 7.80E-4, 1.97E-3, 4.82E-3, 1.14E-2, 2.57E-2, 5.57E-2, 1.15E-1, 2.24E-1, 4.12E-1},
356 {-3.60, 1.77E-3, 4.11E-3, 9.26E-3, 2.01E-2, 4.18E-2, 8.32E-2, 1.58E-1, 2.83E-1, 4.78E-1, 7.51E-1, 1.09E+0},
357 {-3.40, 1.54E-2, 3.10E-2, 6.01E-2, 1.12E-1, 1.97E-1, 3.31E-1, 5.23E-1, 7.74E-1, 1.06E+0, 1.33E+0, 1.50E+0},
358 {-3.20, 7.96E-2, 1.39E-1, 2.33E-1, 3.69E-1, 5.54E-1, 7.81E-1, 1.02E+0, 1.24E+0, 1.37E+0, 1.35E+0, 1.17E+0},
359 {-3.00, 2.63E-1, 3.99E-1, 5.74E-1, 7.78E-1, 9.89E-1, 1.17E+0, 1.27E+0, 1.25E+0, 1.10E+0, 8.46E-1, 5.51E-1},
360 {-2.80, 5.86E-1, 7.71E-1, 9.54E-1, 1.10E+0, 1.18E+0, 1.17E+0, 1.04E+0, 8.35E-1, 5.83E-1, 3.46E-1, 1.68E-1},
361 {-2.60, 9.21E-1, 1.05E+0, 1.12E+0, 1.10E+0, 9.97E-1, 8.19E-1, 6.02E-1, 3.88E-1, 2.14E-1, 9.71E-2, 3.44E-2},
362 {-2.40, 1.06E+0, 1.04E+0, 9.55E-1, 8.02E-1, 6.12E-1, 4.18E-1, 2.52E-1, 1.30E-1, 5.63E-2, 1.94E-2, 4.95E-3},
363 {-2.20, 9.18E-1, 7.85E-1, 6.16E-1, 4.40E-1, 2.83E-1, 1.61E-1, 7.89E-2, 3.27E-2, 1.10E-2, 2.84E-3, 5.18E-4},
364 {-2.00, 6.17E-1, 4.57E-1, 3.08E-1, 1.87E-1, 1.01E-1, 4.75E-2, 1.90E-2, 6.29E-3, 1.64E-3, 3.16E-4, 4.05E-5},
365 {-1.80, 3.28E-1, 2.10E-1, 1.22E-1, 6.30E-2, 2.85E-2, 1.11E-2, 3.62E-3, 9.50E-4, 1.91E-4, 2.78E-5, 0},
366 {-1.60, 1.41E-1, 7.84E-2, 3.89E-2, 1.71E-2, 6.49E-3, 2.09E-3, 5.52E-4, 1.15E-4, 1.77E-5, 0, 0},
367 {-1.40, 4.99E-2, 2.40E-2, 1.02E-2, 3.80E-3, 1.21E-3, 3.22E-4, 6.88E-5, 1.13E-5, 0, 0, 0},
368 {-1.20, 1.47E-2, 6.10E-3, 2.23E-3, 7.05E-4, 1.88E-4, 4.12E-5, 0, 0, 0, 0, 0},
369 {-1.00, 3.64E-3, 1.31E-3, 4.11E-4, 1.10E-4, 2.46E-5, 0, 0, 0, 0, 0, 0},
370 {-0.80, 7.71E-4, 2.40E-4, 6.46E-5, 1.47E-5, 0, 0, 0, 0, 0, 0, 0},
371 {-0.60, 1.41E-4, 3.80E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0},
372 {-0.40, 2.24E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
377 {-4.60, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.85E-5, 1.79E-4},
378 {-4.40, 0, 0, 0, 0, 0, 1.18E-5, 5.14E-5, 2.12E-4, 8.32E-4, 3.08E-3, 1.07E-2},
379 {-4.20, 0, 0, 1.41E-5, 5.57E-5, 2.10E-4, 7.50E-4, 2.54E-3, 8.11E-3, 2.42E-2, 6.69E-2, 1.70E-1},
380 {-4.00, 5.27E-5, 1.84E-4, 6.15E-4, 1.95E-3, 5.83E-3, 1.64E-2, 4.32E-2, 1.06E-1, 2.37E-1, 4.82E-1, 8.79E-1},
381 {-3.80, 1.43E-3, 4.07E-3, 1.10E-2, 2.78E-2, 6.61E-2, 1.46E-1, 2.96E-1, 5.49E-1, 9.16E-1, 1.35E+0, 1.73E+0},
382 {-3.60, 1.79E-2, 4.17E-2, 9.09E-2, 1.85E-1, 3.46E-1, 5.96E-1, 9.30E-1, 1.30E+0, 1.59E+0, 1.68E+0, 1.47E+0},
383 {-3.40, 1.16E-1, 2.20E-1, 3.88E-1, 6.29E-1, 9.31E-1, 1.24E+0, 1.48E+0, 1.54E+0, 1.38E+0, 1.02E+0, 5.99E-1},
384 {-3.20, 4.21E-1, 6.50E-1, 9.23E-1, 1.19E+0, 1.39E+0, 1.44E+0, 1.30E+0, 1.01E+0, 6.46E-1, 3.31E-1, 1.28E-1},
385 {-3.00, 9.12E-1, 1.15E+0, 1.31E+0, 1.35E+0, 1.24E+0, 9.87E-1, 6.74E-1, 3.84E-1, 1.76E-1, 6.16E-2, 1.53E-2},
386 {-2.80, 1.25E+0, 1.28E+0, 1.18E+0, 9.66E-1, 6.92E-1, 4.25E-1, 2.18E-1, 9.11E-2, 2.95E-2, 6.96E-3, 1.09E-3},
387 {-2.60, 1.13E+0, 9.45E-1, 7.01E-1, 4.56E-1, 2.55E-1, 1.20E-1, 4.64E-2, 1.41E-2, 3.19E-3, 5.02E-4, 4.87E-5},
388 {-2.40, 7.06E-1, 4.80E-1, 2.87E-1, 1.48E-1, 6.46E-2, 2.33E-2, 6.71E-3, 1.47E-3, 2.33E-4, 2.41E-5, 0},
389 {-2.20, 3.13E-1, 1.73E-1, 8.33E-2, 3.41E-2, 1.16E-2, 3.19E-3, 6.84E-4, 1.08E-4, 1.18E-5, 0, 0},
390 {-2.00, 1.02E-1, 4.59E-2, 1.77E-2, 5.74E-3, 1.52E-3, 3.19E-4, 5.06E-5, 0, 0, 0, 0},
391 {-1.80, 2.48E-2, 9.10E-3, 2.82E-3, 7.24E-4, 1.49E-4, 2.37E-5, 0, 0, 0, 0, 0},
392 {-1.60, 4.64E-3, 1.38E-3, 3.45E-4, 6.99E-5, 1.11E-5, 0, 0, 0, 0, 0, 0},
393 {-1.40, 6.77E-4, 1.64E-4, 3.29E-5, 0, 0, 0, 0, 0, 0, 0, 0},
394 {-1.20, 7.84E-5, 1.55E-5, 0, 0, 0, 0, 0, 0, 0, 0, 0}};
408 return vavilovKappaValues[ikappa];
422 return vavilovNLambda[ikappa];
438 static double myRound (
double x,
double y,
double& xmantissa,
int digits) {
443 double power =
std::pow (10.0, exponent);
444 double mantissa = y/power;
445 double dpower =
std::pow (10.0, digits-1);
446 mantissa =
roundl (mantissa*dpower)/dpower;
447 if (mantissa >= 10) {
453 mantissa =
roundl (xmantissa*dpower)/dpower;
454 return mantissa*power;
458 return myRound (x, y, xmantissa, digits);
462 static std::string
format (
double x,
double y,
int digits,
int width) {
467 double power =
std::pow (10.0, exponent);
468 double mantissa = y/power;
469 double dpower =
std::pow (10.0, digits-1);
470 mantissa =
roundl (mantissa*dpower)/dpower;
471 if (mantissa >= 10) {
475 mantissa =
roundl (x/power*dpower)/dpower;
477 std::stringstream out;
478 out << std::setw(width-4) << std::fixed << std::setprecision(digits-1) << mantissa;
479 out <<
"e" << std::showpos << std::setw(3)<< std::internal << std::setfill(
'0') << exponent;
485 double maxabsdiff, maxdiffmantissa, agreefraction, agreediffmantissa;
486 GetPdfTestParams (v, maxabsdiff, maxdiffmantissa, agreefraction, agreediffmantissa);
487 return VavilovTest::PdfTest (v, os, maxabsdiff, maxdiffmantissa, agreefraction, agreediffmantissa);
491 double maxabsdiff,
double maxdiffmantissa,
492 double agreefraction,
double agreediffmantissa) {
494 std::ios::fmtflags defaultflags = os.flags();
495 int defaultwidth = os.width();
496 int defaultprecision = os.precision();
500 os <<
"\n\nTesting Pdf\n\n";
502 for (
int ikappa = 0; ikappa <
GetSBNKappa(); ++ikappa) {
504 os <<
"\n\nkappa = " << kappa <<
"\n\n";
506 double maxreldiff = 0;
507 double maxmandiff = 0;
512 for (
int ilambda = 0; ilambda <
GetSBNLambda (ikappa); ++ilambda) {
514 os << std::setw(5) << std::fixed << std::setprecision(2) << lambda;
516 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
519 double pdf = v.
Pdf(x);
522 myRound (pdf-val, val, diffmantissa, 3);
524 if (val > 0 && pdf > 0) {
525 double absdiff =
fabs(pdf-val);
526 if (absdiff > maxabsdiff &&
fabs(diffmantissa) > maxdiffmantissa) {
530 if (
fabs(diffmantissa) > 0.006) {
531 if (absdiff > maxabsdiff) maxabsdiff = absdiff;
532 if (absdiff/val > maxreldiff) maxreldiff = absdiff/val;
533 if (
fabs(diffmantissa) > maxmandiff) maxmandiff =
fabs(diffmantissa);
535 if (
fabs(diffmantissa) > agreediffmantissa) ++disagree;
else ++agree;
541 else if (
fabs(diffmantissa) > 0.006)
542 os <<
format (pdf-val, val, 3, 10);
548 os.flags (defaultflags);
549 if (agree < disagree*agreefraction) {
554 os <<
"Max abs diff: " << maxabsdiff <<
", max rel diff: " << maxreldiff
555 <<
", max diff mantissa: " << std::fixed << std::setprecision(2) << maxmandiff
556 <<
", agree/disagree=" << agree <<
"/" << disagree
557 <<
", pass=" << pass << std::endl;
559 os <<
"\n\nNumber of failed tests: " << nfail << std::endl;
561 os.flags (defaultflags);
562 os.width(defaultwidth);
563 os.precision(defaultprecision);
569 double& mean,
double& variance,
570 double& skewness,
double& kurtosis) {
575 double dt = t/nsteps;
579 for (
int i = 0; i < nsteps; ++i) {
580 double x = (i+0.5)*dt + t0;
581 double pdf = v.
Pdf(x);
590 for (
int i = 0; i < nsteps; ++i) {
591 double x = (i+0.5)*dt + t0;
592 double pdf = v.
Pdf(x);
593 sumx2 +=
pow(x-mean, 2)*pdf;
594 sumx3 +=
pow(x-mean, 3)*pdf;
595 sumx4 +=
pow(x-mean, 4)*pdf;
597 variance = sumx2/
sum;
598 skewness = sumx3/sum*
pow (variance, -1.5);
599 kurtosis = sumx4/sum/(variance*variance)-3;
603 std::ios::fmtflags defaultflags = os.flags();
604 int defaultwidth = os.width();
605 int defaultprecision = os.precision();
607 for (
int ikappa = 0; ikappa <
GetSBNKappa(); ++ikappa) {
609 os <<
"\n\nkappa = " << kappa <<
"\n\n";
611 for (
int ilambda = 0; ilambda <
GetSBNLambda (ikappa); ++ilambda) {
613 os << std::setw(5) << std::fixed << std::setprecision(2) << lambda;
615 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
618 double pdf = v.
Pdf(x);
620 os << std::setw(digits+7) << std::scientific << std::setprecision(digits-1) << pdf;
622 os << std::setw(digits+7) <<
" ";
627 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
630 os << std::setw(digits+7) << std::fixed << std::setprecision(digits-1) << v.
GetLambdaMin();
634 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
637 os << std::setw(digits+7) << std::fixed << std::setprecision(digits-1) << v.
GetLambdaMax();
641 double integral[11], calcmean[11], calcvariance[11], calcskewness[11], calckurtosis[11];
642 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
645 moments (v, integral[ibeta], calcmean[ibeta], calcvariance[ibeta], calcskewness[ibeta], calckurtosis[ibeta]);
646 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << integral[ibeta];
649 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
650 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << calcmean[ibeta];
653 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
655 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << v.
Mean (kappa, beta2);
658 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
659 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << calcvariance[ibeta];
662 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
664 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << v.
Variance (kappa, beta2);
667 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
668 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << calcskewness[ibeta];
671 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
673 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << v.
Skewness (kappa, beta2);
676 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
677 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << calckurtosis[ibeta];
680 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
682 os << std::setw(digits+7) << std::fixed << std::setprecision(digits+1) << v.
Kurtosis (kappa, beta2);
687 os.flags (defaultflags);
688 os.width(defaultwidth);
689 os.precision(defaultprecision);
693 double maxabsdiff, maxcdfdiff;
699 std::ios::fmtflags defaultflags = os.flags();
700 int defaultwidth = os.width();
701 int defaultprecision = os.precision();
705 os <<
"\n\nTesting Cdf and Cdf_c\n\n";
707 for (
int ikappa = 0; ikappa <
GetSBNKappa(); ++ikappa) {
709 os <<
"\n\nkappa = " << kappa <<
"\n\n";
711 double absdiffmax = 0;
712 double reldiffmax = 0;
713 double cdfdiffmax = 0;
717 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
721 for (
int ilambda = 0; ilambda <
GetSBNLambda (ikappa); ++ilambda) {
723 os << std::setw(5) << std::fixed << std::setprecision(2) << lambda1;
724 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
728 if (lambda1 > lambda0) {
730 double dlambda = (lambda1 - lambda0)/n;
731 for (
int i = 0; i <
n; ++i) {
732 double lambda = lambda0 + (i+0.5)*dlambda;
733 cdf_calc[ibeta] += v.
Pdf(lambda)*dlambda;
737 double cdf = v.
Cdf(lambda1);
738 double cdf_c = v.
Cdf_c(lambda1);
739 double val = cdf_calc[ibeta];
741 if (
fabs(cdf-val) > absdiffmax) absdiffmax =
fabs(cdf-val);
742 if (
fabs(cdf+cdf_c-1) > cdfdiffmax) cdfdiffmax = cdf+cdf_c-1;
743 if (val > 0 &&
fabs(cdf/val-1) > reldiffmax) reldiffmax =
fabs(cdf/val-1);
748 os << std::scientific << std::setw(10) << std::setprecision(2) << cdf-val;
752 os.flags (defaultflags);
753 if (absdiffmax > maxabsdiff) pass =
false;
754 if (cdfdiffmax > maxcdfdiff) pass =
false;
756 os <<
"Max abs diff: " << absdiffmax <<
", max rel diff: " << reldiffmax
757 <<
", max diff cdf+cdf_c-1: " << cdfdiffmax
758 <<
", pass=" << pass << std::endl;
760 os <<
"\n\nNumber of failed tests: " << nfail << std::endl;
762 os.flags (defaultflags);
763 os.width(defaultwidth);
764 os.precision(defaultprecision);
778 std::ios::fmtflags defaultflags = os.flags();
779 int defaultwidth = os.width();
780 int defaultprecision = os.precision();
784 static const double qvalues[45] = {0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,
785 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09,
786 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,
787 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99,
788 0.991, 0.992, 0.993, 0.994, 0.995, 0.996, 0.997, 0.998, 0.999};
790 os <<
"\n\nTesting Quantile\n\n";
792 for (
int ikappa = 0; ikappa <
GetSBNKappa(); ++ikappa) {
794 os <<
"\n\nkappa = " << kappa <<
"\n\n";
796 double absdiffmax = 0;
799 for (
int iq = 0;
iq < 45; ++
iq) {
800 double qval = qvalues[
iq];
801 os << std::setw(5) << std::fixed << std::setprecision(3) << qval;
802 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
806 double cdfval = v.
Cdf(lambda);
807 if (qval > qmin && (1-qval) > qmin) {
808 if (
fabs(cdfval-qval) > absdiffmax) absdiffmax =
fabs(cdfval-qval);
809 os <<
" " << std::fixed << std::setw(7) << std::setprecision(4) << cdfval-qval <<
" ";
812 os <<
" (" << std::fixed << std::setw(7) << std::setprecision(4) << cdfval-qval <<
")";
817 os.flags (defaultflags);
818 if (absdiffmax > maxabsdiff) pass =
false;
820 os <<
"Max abs diff: " << absdiffmax
821 <<
", pass=" << pass << std::endl;
824 os <<
"\n\nTesting Quantile_c\n\n";
826 for (
int ikappa = 0; ikappa <
GetSBNKappa(); ++ikappa) {
828 os <<
"\n\nkappa = " << kappa <<
"\n\n";
830 double absdiffmax = 0;
833 for (
int iq = 0;
iq < 45; ++
iq) {
834 double qval = qvalues[
iq];
835 os << std::setw(5) << std::fixed << std::setprecision(3) << qval;
836 for (
int ibeta = 0; ibeta <
GetSBNBeta2(); ++ibeta) {
840 double cdf_c_val = v.
Cdf_c(lambda_c);
841 if (qval > qmin && (1-qval) > qmin) {
842 if (
fabs(cdf_c_val-qval) > absdiffmax) absdiffmax =
fabs(cdf_c_val-qval);
844 os <<
" " << std::fixed << std::setw(7) << std::setprecision(4) << cdf_c_val-qval <<
" ";
847 os <<
" (" << std::fixed << std::setw(7) << std::setprecision(4) << cdf_c_val-qval <<
")";
852 os.flags (defaultflags);
853 if (absdiffmax > maxabsdiff) pass =
false;
855 os <<
"Max abs diff: " << absdiffmax
856 <<
", pass=" << pass << std::endl;
858 os <<
"\n\nNumber of failed tests: " << nfail << std::endl;
860 os.flags (defaultflags);
861 os.width(defaultwidth);
862 os.precision(defaultprecision);
868 if (dynamic_cast <const VavilovFast *>(&v)) {
870 maxdiffmantissa = 0.1;
872 agreediffmantissa = 0.9;
876 maxdiffmantissa = 0.03;
878 agreediffmantissa = 0.015;
883 if (dynamic_cast <const VavilovFast *>(&v)) {
894 if (dynamic_cast <const VavilovFast *>(&v)) {
Base class describing a Vavilov distribution.
static long int sum(long int i)
static void GetCdfTestParams(const Vavilov &v, double &maxabsdiff, double &maxcdfdiff)
Namespace for new ROOT classes and functions.
static double vavilovPdfValues7[20][12]
static int PdfTest(Vavilov &v, std::ostream &os, double maxabsdiff, double maxdiffmantissa, double agreefraction, double agreediffmantissa)
Test the pdf values against the tables of Seltzer and Berger.
static int vavilovNLambda[10]
static double vavilovPdfValues6[19][12]
virtual double Variance() const
Return the theoretical variance .
static int GetSBNKappa()
Return the number of values in the tables of Seltzer and Berger.
static double(*[10] vavilovPdfValues)[12]
static double vavilovPdfValues8[21][12]
virtual double Cdf_c(double x) const =0
Evaluate the Vavilov complementary cumulative probability density function.
static double GetSBLambda(int ikappa, int ilambda)
Return the value in the tables of Seltzer and Berger.
static double vavilovKappaValues[10]
static double vavilovPdfValues1[42][12]
static std::string format(double x, double y, int digits, int width)
virtual double Pdf(double x) const =0
Evaluate the Vavilov probability density function.
static double vavilovPdfValues4[28][12]
virtual double GetLambdaMin() const =0
Return the minimum value of for which is nonzero in the current approximation.
static void PrintPdfTable(Vavilov &v, std::ostream &os, int digits=3)
Print a table of the pdf values to stream os.
static double myRound(double x, double y, double &xmantissa, int digits)
double cdf(double *x, double *p)
static double vavilovPdfValues2[41][12]
double pow(double, double)
static void GetPdfTestParams(const Vavilov &v, double &maxabsdiff, double &maxdiffmantissa, double &agreefraction, double &agreediffmantissa)
static double GetSBVavilovPdf(int ikappa, int ibeta2, int ilambda)
Return the value of in the tables of Seltzer and Berger.
virtual double Kurtosis() const
Return the theoretical kurtosis .
static int GetSBNBeta2()
Return the number of values in the tables of Seltzer and Berger.
virtual double Cdf(double x) const =0
Evaluate the Vavilov cumulative probability density function.
virtual double Quantile(double z) const =0
Evaluate the inverse of the Vavilov cumulative probability density function.
static double vavilovPdfValues3[41][12]
static void GetQuantileTestParams(const Vavilov &v, double &maxabsdiff)
virtual double Skewness() const
Return the theoretical skewness .
static double vavilovPdfValues0[45][12]
VecExpr< UnaryOp< Fabs< T >, VecExpr< A, T, D >, T >, T, D > fabs(const VecExpr< A, T, D > &rhs)
static int QuantileTest(Vavilov &v, std::ostream &os, double maxabsdiff)
Test the quantile values against the cdf Returns 0 if the test is passed.
static void moments(ROOT::Math::Vavilov &v, double &integral, double &mean, double &variance, double &skewness, double &kurtosis)
static int CdfTest(Vavilov &v, std::ostream &os, double maxabsdiff, double maxcdfdiff)
Test the cdf values against the integral of the pdf Returns 0 if the test is passed.
virtual double Quantile_c(double z) const =0
Evaluate the inverse of the complementary Vavilov cumulative probability density function.
Namespace for new Math classes and functions.
static double GetSBKappa(int ikappa)
Return the value for ikappa in the tables of Seltzer and Berger.
static double vavilovPdfValues9[21][12]
virtual double GetLambdaMax() const =0
Return the maximum value of for which is nonzero in the current approximation.
static int GetSBNLambda(int ikappa)
Return the number of values in the tables of Seltzer and Berger.
static double GetSBBeta2(int ibeta2)
Return the value in the tables of Seltzer and Berger.
virtual void SetKappaBeta2(double kappa, double beta2)=0
Change and and recalculate coefficients if necessary.
virtual double Mean() const
Return the theoretical mean , where is Euler's constant.
static double vavilovPdfValues5[22][12]