Tue Oct 19 11:58:48 2021 log_normal_truncated_ab_test Python version: 3.6.9 Test log_normal_truncated_ab(). log_normal_cdf_test Python version: 3.6.9 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 5.64353 0.0486263 0.927995 5.64353 6.14122 0.0344123 0.948454 6.14122 3.74714 0.173278 0.739559 3.74714 1.06812 0.130448 0.0308668 1.06812 2.92945 0.269334 0.559472 2.92945 0.981689 0.102088 0.020827 0.981689 3.155 0.241914 0.617139 3.155 4.40621 0.113561 0.832985 4.40621 2.52007 0.313003 0.439821 2.52007 4.41342 0.113018 0.833802 4.41342 log_normal_cdf_test Normal end of execution. log_normal_sample_test Python version: 3.6.9 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 = 3.06341 Sample variance = 2.47562 Sample maximum = 13.5851 Sample minimum = 0.552195 log_normal_sample_test Normal end of execution. log_normal_truncated_ab_cdf_test Python version: 3.6.9 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 28.5965 0.00619872 0.689832 28.5965 2.4227 0.114069 0.10695 2.4227 2.61793 0.105178 0.12833 2.61793 117.315 0.000864637 0.885138 117.315 2.24248 0.123604 0.0855551 2.24248 321.642 0.000184802 0.965135 321.642 3.49859 0.0771779 0.20746 3.49859 44.3325 0.00344263 0.762141 44.3325 14.4608 0.0148281 0.556124 14.4608 24.203 0.0077098 0.659482 24.203 log_normal_truncated_ab_cdf_test Normal end of execution. log_normal_truncated_ab_sample_test Python version: 3.6.9 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 = 49.3335 Sample variance = 9535.42 Sample maximum = 662.761 Sample minimum = 1.65068 log_normal_truncated_ab_sample_test Normal end of execution. normal_01_cdf_test Python version: 3.6.9 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 -0.9278 0.25941 0.176756 -0.9278 1.07521 0.223806 0.858859 1.07521 0.556902 0.341636 0.711203 0.556902 -0.288373 0.382695 0.386531 -0.288373 0.95156 0.253683 0.82934 0.95156 -0.824787 0.283917 0.204746 -0.824787 0.929702 0.258952 0.823737 0.929702 -0.203373 0.390777 0.419422 -0.203373 1.2075 0.19244 0.886381 1.2075 0.149886 0.394486 0.559573 0.149886 normal_01_cdf_test Normal end of execution. normal_01_cdf_values_test: Python version: 3.6.9 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: 3.6.9 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 = -0.0128385 Sample variance = 1.03369 Sample maximum = 2.94113 Sample minimum = -3.05274 normal_01_sample_test Normal end of execution. normal_cdf_test Python version: 3.6.9 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 90.9688 0.0221872 0.273561 90.9688 85.6684 0.0168497 0.169677 85.6684 90.6235 0.021876 0.265952 90.6235 81.6041 0.0125378 0.110025 81.6041 100.471 0.026583 0.512532 100.471 102.324 0.0262788 0.561564 102.324 117.295 0.0136815 0.875548 117.295 111.83 0.019487 0.784855 111.83 117.688 0.0132699 0.880842 117.688 94.9137 0.0251103 0.367273 94.9137 normal_cdf_test Normal end of execution. normal_sample_test Python version: 3.6.9 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 = 100.681 Sample variance = 197.462 Sample maximum = 144.29 Sample minimum = 48.4613 normal_sample_test Normal end of execution. r8poly_print_test Python version: 3.6.9 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: 3.6.9 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. log_normal_truncated_ab_test: Normal end of execution. Tue Oct 19 11:58:48 2021