Tue May 20 22:28:35 2025 initialize(): rnglib() has been initialized. ranlib_test(): python version: 3.10.12 numpy version: 1.26.4 Test ranlib() genbet_test(): genbet() generates Beta deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: 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 genchi_test(): genchi() generates Chi-square deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: DF = 6.4999 Sample data range: 0.346677 25.8286 Sample mean, variance: 6.53591 14.4705 Distribution mean, variance 6.4999 12.9998 genexp_test(): genexp() generates exponential deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: MU = 6.30545 Sample data range: 0.000758901 39.5328 Sample mean, variance: 6.2853 40.1683 Distribution mean, variance 6.30545 39.7587 genf_test(): genf() generates F deviates. initialize(): rnglib() has been initialized. N = 10000 Parameter values: 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 gengam_test(): gengam() generates Gamma deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: 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 genmn_test(): genmn() generates multivariate normal deviates. Warning! - No test code has been provided. genmn_test(): Normal end of execution. genmul_test(): genmul() generates a multinomial random deviate. initialize(): rnglib() 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 gennch_test(): gennch() generates noncentral Chi-square deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: 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 gennf_test(): gennf() generates noncentral F deviates. initialize(): rnglib() has been initialized. N = 10000 Parameter values: 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 gennor_test(): gennor() generates normal deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: 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 genprm_test(): genprm() generates a random permutation. initialize(): rnglib() 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 genunf_test(): genunf() generates uniform deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: 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 ignbin_test(): ignbin() generates binomial deviates. initialize(): rnglib() has been initialized. N = 10000 Parameter values: NN = 12 PP = 0.740434 Sample data range: 3 12 Sample mean, variance: 8.8692 2.26232 Distribution mean, variance 8.88521 2.3063 ignnbn_test(): ignnbn() generates negative binomial deviates. initialize(): rnglib() has been initialized. N = 10000 Parameter values: NN = 13 PP = 0.740434 Sample data range: 0 17 Sample mean, variance: 4.564 6.24793 Distribution mean, variance 4.55727 6.15486 ignpoi_test(): ignpoi() generates Poisson deviates. initialize(): rnglib() has been initialized. N = 1000 Parameter values: MU = 12.4164 Sample data range: 1 25 Sample mean, variance: 12.344 12.298 Distribution mean, variance 12.4164 12.4164 ignuin_test(): ignuin() generates uniformly distributed integers in a range. initialize(): rnglib() has been initialized. N = 10 Parameter values: LOW = 222 HIGH = 972 847 570 689 674 332 758 361 252 626 794 lennob_test(): lennob() returns the length of string to the last nonblank. LEN lennob ---------S--------- 8 8 "Hi, Bob!" 23 18 " How are you? " 4 0 " " low_level_test() 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(): rnglib() has been initialized. Testing generator 1 Testing generator 5 Testing generator 10 Testing generator 20 Testing generator 32 Number of disagreements found was 0 phrtsd_test(): phrtsd() converts a phrase into two numeric seeds. Phrase: "A1" Seeds: 297715297 395612060 Phrase: "shazam" Seeds: 810103054 104790073 Phrase: "Happy birthday" Seeds: 948353268 801147965 prcomp_test() prcomp() prints and compares covariance information. Warning! - No test code has been provided. prcomp_test: Normal end of execution. r4_exp_test(): 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_exponential_test(): 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 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 r8_exponential_test(): 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 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 setcov_test(): setcov() sets 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 sexpo_test(): 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 sgamma_test(): sgamma() 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 snorm_test(): 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 spofa_test(): spofa() LU factors a symmetric positive definite 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 stats_test(): 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 trstat_test(): trstat() returns the mean and variance for distributions. Distribution: "unf" Distribution parameter values: 10 20 Distribution mean, variance 15 8.33333 ranlib_test(): Normal end of execution. Tue May 20 22:28:36 2025