Thu Sep 13 15:56:54 2018 INITIALIZE: The RNGLIB package has been initialized. RANLIB_TEST Python version: 3.6.5 Test the RANLIB library. RANLIB_TEST_GENBET GENBET generates Beta deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: A = 6.4999 B = 7.66391 Sample data range: 0.0590955 0.83501 Sample mean, variance: 0.461185 0.0169405 Distribution mean, variance 0.458909 0.0163753 GENBET_TEST Normal end of execution. GENCHI_TEST GENCHI generates Chi-square deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: DF = 6.4999 Sample data range: 0.346677 25.8286 Sample mean, variance: 6.53591 14.4705 Distribution mean, variance 6.4999 12.9998 GENCHI_TEST Normal end of execution. GENEXP_TEST GENEXP generates exponential deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: MU = 6.30545 Sample data range: 0.000758901 39.5328 Sample mean, variance: 6.2853 40.1683 Distribution mean, variance 6.30545 39.7587 GENEXP_TEST Normal end of execution. RANLIB_TEST_GENF Test GENF, which generates F deviates. INITIALIZE: The RNGLIB package has been initialized. N = 10000 Parameters: DFN = 7.2777 DFD = 8.70217 Sample data range: 0.0298811 29.6318 Sample mean, variance: 1.31494 1.46549 Distribution mean, variance 1.29841 1.37742 GENF_TEST Normal end of execution. GENGAM_TEST GENGAM generates Gamma deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: A = 6.4999 R = 7.66391 Sample data range: 0.22456 3.05807 Sample mean, variance: 1.17507 0.177328 Distribution mean, variance 1.17908 0.1814 GENGAM_TEST Normal end of execution. GENMN_TEST GENMN generates multivariate normal deviates. Warning! - No test code has been provided. GENMN_TEST: Normal end of execution. GENMUL_TEST Python version: 3.6.5 GENMUL generates a multinomial random deviate. INITIALIZE: The RNGLIB package has been initialized. Try 10 successive trials: 28 15 5 37 15 35 7 7 33 18 39 16 6 22 17 31 14 5 30 20 44 9 6 32 9 36 13 3 34 14 39 11 4 31 15 40 7 4 25 24 37 14 3 31 15 30 12 9 30 19 GENMUL_TEST: Normal end of execution. GENNCH_TEST GENNCH generates noncentral Chi-square deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: DF = 6.8888 XNONC = 1.48087 Sample data range: 0.819308 27.3249 Sample mean, variance: 8.31481 19.201 Distribution mean, variance 8.36967 19.7011 GENNCH_TEST Normal end of execution. GENNF_TEST GENNF generates noncentral F deviates. INITIALIZE: The RNGLIB package has been initialized. N = 10000 Parameters: DFN = 7.2777 DFD = 8.70217 XNONC = 0.822309 Sample data range: 0.0522895 20.3529 Sample mean, variance: 1.46807 1.68193 Distribution mean, variance 1.44512 1.69784 GENNF_TEST Normal end of execution. GENNOR_TEST: GENNOR generates normal deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: MU = 2.22199 SD = 3.02663 Sample data range: -7.29457 13.0079 Sample mean, variance: 2.23376 9.19249 Distribution mean, variance 2.22199 9.16047 GENNOR_TEST Normal end of execution. GENPRM_TEST Python version: 3.6.5 GENPRM generates a random permutation. INITIALIZE: The RNGLIB package has been initialized. Array: 0 1 2 3 4 5 6 7 8 9 Permuted: 5 0 4 2 3 8 9 6 7 1 GENPRM_TEST: Normal end of execution. GENUNF_TEST: GENUNF generates uniform deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: A = 6.4999 B = 14.1638 Sample data range: 6.50123 14.1577 Sample mean, variance: 10.3083 4.88394 Distribution mean, variance 10.3319 4.89462 GENUNF_TEST Normal end of execution. IGNBIN_TEST IGNBIN generates binomial deviates. INITIALIZE: The RNGLIB package has been initialized. N = 10000 Parameters: NN = 12 PP = 0.740434 Sample data range: 3 12 Sample mean, variance: 8.8692 2.26232 Distribution mean, variance 8.88521 2.3063 IGNBIN_TEST Normal end of execution. IGNNBN_TEST IGNNBN generates negative binomial deviates. INITIALIZE: The RNGLIB package has been initialized. N = 10000 Parameters: NN = 13 PP = 0.740434 Sample data range: 0 17 Sample mean, variance: 4.564 6.24793 Distribution mean, variance 4.55727 6.15486 IGNNBN_TEST Normal end of execution. IGNPOI_TEST IGNPOI generates Poisson deviates. INITIALIZE: The RNGLIB package has been initialized. N = 1000 Parameters: MU = 12.4164 Sample data range: 1 25 Sample mean, variance: 12.344 12.298 Distribution mean, variance 12.4164 12.4164 IGNPOI_TEST Normal end of execution. IGNUIN_TEST Python version: 3.6.5 IGNUIN generates uniformly distributed integers in a range. INITIALIZE: The RNGLIB package has been initialized. N = 10 Parameters: LOW = 222 HIGH = 972 847 570 689 674 332 758 361 252 626 794 IGNUIN_TEST Normal end of execution. LENNOB_TEST Python version: 3.6.5 LENNOB returns the length of string to the last nonblank. LEN LENNOB ---------S--------- 8 8 "Hi, Bob!" 23 18 " How are you? " 4 0 " " LENNOB_TEST Normal end of execution. LOW_LEVEL_TEST Python version: 3.6.5 Test the lower level random number generators. Five of the 32 generators will be tested. We generate 100000 numbers, reset the block and do it again. No disagreements should occur. INITIALIZE: The RNGLIB package has been initialized. Testing generator 1 Testing generator 5 Testing generator 10 Testing generator 20 Testing generator 32 Number of disagreements found was 0 LOW_LEVEL_TEST Normal end of execution. PHRTSD_TEST Python version: 3.6.5 PHRTSD converts a phrase into two numeric seeds. Phrase: "A1" Seeds: 297715297 395612060 Phrase: "shazam" Seeds: 810103054 104790073 Phrase: "Happy birthday" Seeds: 948353268 801147965 PHRTSD_TEST: Normal end of execution. PRCOMP_TEST PRCOMP prints and compares covariance information. Warning! - No test code has been provided. PRCOMP_TEST: Normal end of execution. R4_EXP_TEST Python version: 3.6.5 R4_EXP returns the exponential of a real number. X R4_EXP(X) -80 0 -70 0 -60 8.75651e-27 -50 1.92875e-22 -40 4.24835e-18 -30 9.35762e-14 -20 2.06115e-09 -10 4.53999e-05 0 1 10 22026.5 20 4.85165e+08 30 1.06865e+13 40 2.35385e+17 50 5.18471e+21 60 1.14201e+26 70 1e+30 80 1e+30 R4_EXP_TEST Normal end of execution. R4_EXPONENTIAL_TEST Python version: 3.6.5 R4_EXPONENTIAL samples the exponential distribution. 0.082351 0.084188 0.145816 0.373512 1.300662 0.223208 0.197499 0.463073 0.189966 0.883375 0.127458 1.004645 0.393576 0.656724 0.199304 0.049759 0.050218 0.416410 0.300146 0.244824 R4_EXPONENTIAL_TEST Normal end of execution. R4VEC_COVARIANCE_TEST: R4VEC_COVARIANCE computes the covariance of two R4VECs. Vector V1: 1 0 Vector V2: 0.123928 0 Covariance(V1,V2) = 0.061964 Vector V2: 0.859645 0.496316 Covariance(V1,V2) = 0.181664 Vector V2: 0.489533 0.847895 Covariance(V1,V2) = -0.179181 Vector V2: 2.80782e-17 0.458552 Covariance(V1,V2) = -0.229276 Vector V2: -0.116828 0.202352 Covariance(V1,V2) = -0.15959 Vector V2: -0.115449 0.0666544 Covariance(V1,V2) = -0.0910516 Vector V2: -0.745887 9.13448e-17 Covariance(V1,V2) = -0.372944 Vector V2: -0.263007 -0.151847 Covariance(V1,V2) = -0.0555798 Vector V2: -0.177817 -0.307988 Covariance(V1,V2) = 0.0650855 Vector V2: -7.47519e-17 -0.40693 Covariance(V1,V2) = 0.203465 Vector V2: 0.397443 -0.688391 Covariance(V1,V2) = 0.542917 Vector V2: 0.60534 -0.349493 Covariance(V1,V2) = 0.477417 R4VEC_COVARIANCE_TEST Normal end of execution. R8_EXPONENTIAL_TEST Python version: 3.6.5 R8_EXPONENTIAL samples the exponential distribution. 0.026952 0.139951 0.649361 0.543810 0.475531 0.333831 0.484229 0.698627 0.432778 1.211338 0.078197 0.558433 0.143692 0.304750 0.538502 0.906956 0.563085 1.392085 0.198820 0.062818 R8_EXPONENTIAL_TEST Normal end of execution. R8VEC_COVARIANCE_TEST: R8VEC_COVARIANCE computes the covariance of two R8VECs. Vector V1: 1 0 Vector V2: 0.503727 0 Covariance(V1,V2) = 0.251863 Vector V2: 0.159295 0.0919688 Covariance(V1,V2) = 0.0336629 Vector V2: 0.374955 0.64944 Covariance(V1,V2) = -0.137243 Vector V2: 5.03422e-17 0.822151 Covariance(V1,V2) = -0.411075 Vector V2: -0.314001 0.543866 Covariance(V1,V2) = -0.428934 Vector V2: -0.553521 0.319575 Covariance(V1,V2) = -0.436548 Vector V2: -0.601027 7.36045e-17 Covariance(V1,V2) = -0.300513 Vector V2: -0.359188 -0.207377 Covariance(V1,V2) = -0.0759053 Vector V2: -0.157901 -0.273493 Covariance(V1,V2) = 0.0577959 Vector V2: -2.56729e-18 -0.0139757 Covariance(V1,V2) = 0.00698783 Vector V2: 0.383241 -0.663794 Covariance(V1,V2) = 0.523518 Vector V2: 0.783093 -0.452119 Covariance(V1,V2) = 0.617606 R8VEC_COVARIANCE_TEST Normal end of execution. SETCOV_TEST Python version: 3.6.5 SETCOV aets a covariance matrix. Number of variables P = 3 Common correlation = 0.25 Variances: 0.5 0.2 0.9 Covariance matrix: 0.500000 0.079057 0.167705 0.079057 0.200000 0.106066 0.167705 0.106066 0.900000 SETCOV_TEST Normal end of execution. SEXPO_TEST Python version: 3.6.5 SEXPO generates exponentially distributed random values. 0.392897 1.957450 0.071320 0.120305 0.944687 1.995147 1.717409 0.284834 2.036888 1.688923 0.839825 0.857379 1.907682 0.698521 1.631880 1.580024 1.087684 6.030942 1.004441 0.283505 SEXPO_TEST Normal end of execution. SGAMMA_TEST Python version: 3.6.5 SGAMA generates gamma distributed random values. 0.083967 0.027781 0.243798 0.270589 0.074371 0.221302 0.188699 0.065395 0.229992 0.333197 0.023972 0.589673 0.064493 0.093439 1.354472 0.131442 0.517017 0.000483 0.028403 0.746629 SGAMMA_TEST Normal end of execution. SNORM_TEST Python version: 3.6.5 SNORM generates normally distributed random values. -0.276305 -0.070436 -0.798956 -0.471933 -0.067601 1.000538 -0.777514 1.448163 -0.256137 -0.455772 1.476813 -1.523165 -1.564552 -0.652906 -1.259176 0.310483 -0.685628 0.225481 1.118316 -0.826598 SNORM_TEST Normal end of execution. SPOFA_TEST SPOFA computes the LU factors of a positive definite symmetric matrix, Matrix A: 2 -1 0 0 0 -1 2 -1 0 0 0 -1 2 -1 0 0 0 -1 2 -1 0 0 0 -1 2 Call SPOFA to factor the matrix. Upper triangular factor U: 1.41421 -0.707107 0 0 0 0 1.22474 -0.816497 0 0 0 0 1.1547 -0.866025 0 0 0 0 1.11803 -0.894427 0 0 0 0 1.09545 Product Ut * U: 2 -1 0 0 0 -1 2 -1 0 0 0 -1 2 -1 0 0 0 -1 2 -1 0 0 0 -1 2 SPOFA_TEST Normal end of execution. STATS_TEST Python version: 3.6.5 STATS computes min, max, mean and variance for a vector. Vector X: 1 2 3 4 5 1 <= X <= 5 Mean = 3, Variance = 2.5 STATS_TEST: Normal end of execution. TRSTAT_TEST Python version: 3.6.5 TRSTAT returns the mean and variance for distributions. Distribution: "unf" Distribution parameters: 10 20 Distribution mean, variance 15 8.33333 TRSTAT_TEST Normal end of execution. RANLIB_TEST: Normal end of execution. Thu Sep 13 15:57:01 2018