Thu Sep 13 10:33:19 2018 LOG_NORMAL_TRUNCATED_AB_TEST Python version: 3.6.5 Test the LOG_NORMAL_TRUNCATED_AB library. LOG_NORMAL_CDF_TEST Python version: 3.6.5 LOG_NORMAL_CDF evaluates the Log Normal CDF LOG_NORMAL_CDF_INV inverts the Log Normal CDF. LOG_NORMAL_PDF evaluates the Log Normal PDF PDF parameter MU = 1 PDF parameter SIGMA = 0.5 X PDF CDF CDF_INV 1.84269 0.320041 0.218418 1.84269 6.38999 0.0289652 0.956318 6.38999 4.37591 0.115871 0.829509 4.37591 2.93772 0.268345 0.561695 2.93772 2.44255 0.319271 0.415307 2.44255 1.2806 0.20066 0.0661187 1.2806 1.96322 0.328846 0.257578 1.96322 1.472 0.255411 0.109957 1.472 1.15726 0.160368 0.043829 1.15726 3.22582 0.233263 0.633966 3.22582 LOG_NORMAL_CDF_TEST Normal end of execution. LOG_NORMAL_SAMPLE_TEST Python version: 3.6.5 LOG_NORMAL_MEAN computes the Log Normal mean LOG_NORMAL_SAMPLE samples the Log Normal distribution LOG_NORMAL_VARIANCE computes the Log Normal variance. PDF parameter MU = 1 PDF parameter SIGMA = 0.5 PDF mean = 3.08022 PDF variance = 2.69476 Sample size = 1000 Sample mean = 3.08493 Sample variance = 2.63391 Sample maximum = 11.3816 Sample minimum = 0.63615 LOG_NORMAL_SAMPLE_TEST Normal end of execution. LOG_NORMAL_TRUNCATED_AB_CDF_TEST Python version: 3.6.5 LOG_NORMAL_TRUNCATED_AB_CDF evaluates the Log Normal Truncated AB CDF LOG_NORMAL_TRUNCATED_AB_CDF_INV inverts the Log Normal Truncated AB CDF. LOG_NORMAL_TRUNCATED_AB_PDF evaluates the Log Normal Truncated AB PDF PDF parameter MU = 0.5 PDF parameter SIGMA = 3 PDF parameter A = 1.64872 PDF parameter B = 665.142 X PDF CDF CDF_INV 3.64375 0.0738451 0.218418 3.64375 278.982 0.000231325 0.956318 278.982 71.9026 0.00175554 0.829509 71.9026 14.8427 0.0143555 0.561695 14.8427 7.82823 0.0311052 0.415307 7.82823 2.09078 0.132853 0.0661187 2.09078 4.21879 0.0628864 0.257578 4.21879 2.44921 0.112782 0.109957 2.44921 1.92971 0.144196 0.043829 1.92971 21.1661 0.00916677 0.633966 21.1661 LOG_NORMAL_TRUNCATED_AB_CDF_TEST Normal end of execution. LOG_NORMAL_TRUNCATED_AB_SAMPLE_TEST Python version: 3.6.5 LOG_NORMAL_TRUNCATED_AB_MEAN computes the Log Normal Truncated AB mean LOG_NORMAL_TRUNCATED_AB_SAMPLE samples the Log Normal Truncated AB distribution LOG_NORMAL_TRUNCATED_AB_VARIANCE computes the Log Normal Truncated AB variance. PDF parameter MU = 0.5 PDF parameter SIGMA = 3 PDF parameter A = 1.64872 PDF parameter B = 665.142 PDF mean = 48.9182 PDF variance = 9451.08 Sample size = 1000 Sample mean = 49.0829 Sample variance = 9773.64 Sample maximum = 629.881 Sample minimum = 1.65963 LOG_NORMAL_TRUNCATED_AB_SAMPLE_TEST Normal end of execution. NORMAL_01_CDF_TEST Python version: 3.6.5 NORMAL_01_CDF evaluates the Normal 01 CDF NORMAL_01_CDF_INV inverts the Normal 01 CDF. NORMAL_01_PDF evaluates the Normal 01 PDF X PDF CDF CDF_INV 1.67904 0.0974392 0.953428 1.67904 -0.56606 0.339884 0.285677 -0.56606 1.21293 0.191179 0.887423 1.21293 1.26938 0.178244 0.897847 1.26938 -1.66609 0.0995733 0.0478481 -1.66609 -2.24246 0.0322815 0.0124657 -2.24246 0.0396749 0.398628 0.515824 0.0396749 0.673068 0.318081 0.749548 0.673068 -0.275127 0.384125 0.391609 -0.275127 2.164 0.0383732 0.984768 2.164 NORMAL_01_CDF_TEST Normal end of execution. NORMAL_01_CDF_VALUES_TEST: Python version: 3.6.5 NORMAL_01_CDF_VALUES stores values of the unit normal CDF. X NORMAL_01_CDF(X) 0.000000 0.5000000000000000 0.100000 0.5398278372770290 0.200000 0.5792597094391030 0.300000 0.6179114221889526 0.400000 0.6554217416103242 0.500000 0.6914624612740131 0.600000 0.7257468822499270 0.700000 0.7580363477769270 0.800000 0.7881446014166033 0.900000 0.8159398746532405 1.000000 0.8413447460685429 1.500000 0.9331927987311419 2.000000 0.9772498680518208 2.500000 0.9937903346742240 3.000000 0.9986501019683699 3.500000 0.9997673709209645 4.000000 0.9999683287581669 NORMAL_01_CDF_VALUES_TEST: Normal end of execution. NORMAL_01_SAMPLE_TEST Python version: 3.6.5 NORMAL_01_MEAN computes the Normal 01 mean NORMAL_01_SAMPLE samples the Normal 01 distribution NORMAL_01_VARIANCE returns the Normal 01 variance. PDF mean = 0 PDF variance = 1 Sample size = 1000 Sample mean = 0.00581875 Sample variance = 0.998375 Sample maximum = 3.32858 Sample minimum = -3.02975 NORMAL_01_SAMPLE_TEST Normal end of execution. NORMAL_CDF_TEST Python version: 3.6.5 NORMAL_CDF evaluates the Normal CDF NORMAL_CDF_INV inverts the Normal CDF. NORMAL_PDF evaluates the Normal PDF PDF parameter A = 100 PDF parameter B = 15 X PDF CDF CDF_INV 125.186 0.00649595 0.953428 125.186 91.5091 0.0226589 0.285677 91.5091 118.194 0.0127453 0.887423 118.194 119.041 0.0118829 0.897847 119.041 75.0087 0.00663822 0.0478481 75.0087 66.363 0.0021521 0.0124657 66.363 100.595 0.0265752 0.515824 100.595 110.096 0.0212054 0.749548 110.096 95.8731 0.0256084 0.391609 95.8731 132.46 0.00255821 0.984768 132.46 NORMAL_CDF_TEST Normal end of execution. NORMAL_SAMPLE_TEST Python version: 3.6.5 NORMAL_MEAN computes the Normal mean NORMAL_SAMPLE samples the Normal distribution NORMAL_VARIANCE returns the Normal variance. PDF parameter A = 100 PDF parameter B = 15 PDF mean = 100 PDF variance = 225 Sample size = 1000 Sample mean = 100.087 Sample variance = 224.634 Sample maximum = 149.929 Sample minimum = 54.5537 NORMAL_SAMPLE_TEST Normal end of execution. R8_UNIFORM_01_TEST Python version: 3.6.5 R8_UNIFORM_01 produces a sequence of random values. Using random seed 123456789 SEED R8_UNIFORM_01(SEED) 469049721 0.218418 2053676357 0.956318 1781357515 0.829509 1206231778 0.561695 891865166 0.415307 141988902 0.066119 553144097 0.257578 236130416 0.109957 94122056 0.043829 1361431000 0.633966 Verify that the sequence can be restarted. Set the seed back to its original value, and see that we generate the same sequence. SEED R8_UNIFORM_01(SEED) 469049721 0.218418 2053676357 0.956318 1781357515 0.829509 1206231778 0.561695 891865166 0.415307 141988902 0.066119 553144097 0.257578 236130416 0.109957 94122056 0.043829 1361431000 0.633966 R8_UNIFORM_01_TEST Normal end of execution. R8_UNIFORM_AB_TEST Python version: 3.6.5 R8_UNIFORM_AB returns random values in a given range: [ A, B ] For this problem: A = 10.000000 B = 20.000000 12.184183 19.563176 18.295092 15.616954 14.153071 10.661187 12.575778 11.099568 10.438290 16.339657 R8_UNIFORM_AB_TEST Normal end of execution R8POLY_PRINT_TEST Python version: 3.6.5 R8POLY_PRINT prints an R8POLY. The R8POLY: p(x) = 9 * x^5 + 0.78 * x^4 + 56 * x^2 - 3.4 * x + 12 R8POLY_PRINT_TEST: Normal end of execution. R8POLY_VALUE_HORNER_TEST Python version: 3.6.5 R8POLY_VALUE_HORNER evaluates a polynomial at a point using Horners method. The polynomial coefficients: p(x) = 1 * x^4 - 10 * x^3 + 35 * x^2 - 50 * x + 24 I X P(X) 0 0.0000 24 1 0.3333 10.8642 2 0.6667 3.45679 3 1.0000 0 4 1.3333 -0.987654 5 1.6667 -0.691358 6 2.0000 0 7 2.3333 0.493827 8 2.6667 0.493827 9 3.0000 0 10 3.3333 -0.691358 11 3.6667 -0.987654 12 4.0000 0 13 4.3333 3.45679 14 4.6667 10.8642 15 5.0000 24 R8POLY_VALUE_HORNER_TEST: Normal end of execution. R8VEC_MAX_TEST Python version: 3.6.5 R8VEC_MAX computes the maximum entry in an R8VEC. Input vector: 0: -5.63163 1: 9.12635 2: 6.59018 3: 1.23391 4: -1.69386 5: -8.67763 6: -4.84844 7: -7.80086 8: -9.12342 9: 2.67931 Max = 9.12635 R8VEC_MAX_TEST: Normal end of execution. R8VEC_MEAN_TEST Python version: 3.6.5 R8VEC_MEAN computes the mean of an R8VEC. Input vector: 0: -2.81582 1: 4.56318 2: 3.29509 3: 0.616954 4: -0.846929 5: -4.33881 6: -2.42422 7: -3.90043 8: -4.56171 9: 1.33966 Value = -0.907304 R8VEC_MEAN_TEST: Normal end of execution. R8VEC_MIN_TEST Python version: 3.6.5 R8VEC_MIN computes the minimum entry in an R8VEC. Input vector: 0: -5.63163 1: 9.12635 2: 6.59018 3: 1.23391 4: -1.69386 5: -8.67763 6: -4.84844 7: -7.80086 8: -9.12342 9: 2.67931 Min = -9.12342 R8VEC_MIN_TEST: Normal end of execution. R8VEC_PRINT_TEST Python version: 3.6.5 R8VEC_PRINT prints an R8VEC. Here is an R8VEC: 0: 123.456 1: 5e-06 2: -1e+06 3: 3.14159 R8VEC_PRINT_TEST: Normal end of execution. R8VEC_UNIFORM_AB_TEST Python version: 3.6.5 R8VEC_UNIFORM_AB computes a random R8VEC. -1 <= X <= 5 Initial seed is 123456789 Random R8VEC: 0: 0.31051 1: 4.73791 2: 3.97706 3: 2.37017 4: 1.49184 5: -0.603288 6: 0.545467 7: -0.340259 8: -0.737026 9: 2.80379 R8VEC_UNIFORM_AB_TEST: Normal end of execution. R8VEC_VARIANCE_TEST Python version: 3.6.5 R8VEC_VARIANCE computes the variance of an R8VEC. Input vector: 0: -2.81582 1: 4.56318 2: 3.29509 3: 0.616954 4: -0.846929 5: -4.33881 6: -2.42422 7: -3.90043 8: -4.56171 9: 1.33966 Value = 10.5549 R8VEC_VARIANCE_TEST: Normal end of execution. LOG_NORMAL_TRUNCATED_AB_TEST: Normal end of execution. Thu Sep 13 10:33:19 2018